%T Genetic programming: Proceedings of the first annual conference 1996 : Edited by John R. Koza, David E. Goldberg, David B. Fogel and Rick L. Riolo. MIT Press, Cambridge,
   MA. (1996). 568 pages. \$75.00
%J Computers \& Mathematics with Applications
%V 33
%N 5
%D 1997
%P 126--127
%I 
%U http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-D/2/23afe396341b39baf74fcd29db315b46
%Z No author given. Contents listing of \citekoza:gp96

%T Advances in genetic programming, volume 2 : Edited by Peter Angeline and Kenneth Kinnear, Jr. MIT Press, Cambridge, MA. (1996). 538 pages. \$50.00
%J Computers \& Mathematics with Applications
%V 33
%N 5
%D 1997
%P 129
%I 
%U http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-T/2/4d3bcc2dda31e9aca679eba60ff95a3a
%Z Contents listing of \citebook:1996:aigp2. No author given. To get, try other articles on page 129

%T Advances in genetic programming, volume III : Edited by Lee Spector, William B. Langdon, Una-May O'Reilly and Peter J. Angeline. MIT Press, Cambridge, MA. (1999). 476
   pages. \$55.00
%J Computers \& Mathematics with Applications
%V 38
%N 11-12
%D 1999
%P 291--291
%I 
%U http://www.sciencedirect.com/science/article/B6TYJ-48778B1-3H/2/1d6f4728f10e14a24f4f28189d15f818
%Z Contents listing of \citespector:1999:aigp3. No author given.

%T Genetic programming and data structures: Genetic programming + data STRUCTURES = automatic programming! : By W. B. Langdon. Kluwer Academic Publishers, Boston, MA. (1998).
   278 pages. \$125.00. NLG 285.00, GBP 85.00
%J Computers \& Mathematics with Applications
%V 37
%N 3
%D 1999
%P 132--132
%I 
%U http://www.sciencedirect.com/science/article/B6TYJ-489YTT5-2T/2/13179f12104abafe66b36e402ef358d9
%Z Contents listing of \citelangdon:book. No author given.

%T Genetic programming II: Automatic discovery of reusable programs : By John R. Koza. MIT Press, Cambridge, MA. (1994). 746 pages. \$45.00
%J Computers \& Mathematics with Applications
%V 29
%N 3
%D 1995
%P 115--115
%I 
%U http://www.sciencedirect.com/science/article/B6TYJ-48F4PJH-H/2/bd467ac24453cb0b3f9dbbf15075bedb
%Z Contents listing of \citekoza:gp2. No author given.

%T Evolutionary algorithms in engineering and computer science: Recent advances in genetic algorithms, evolution strategies, evolutionary programming, genetic programming and
   industrial applications : Edited by K. Miettinen, P. Neittaanmaki, M. M. Makela and J. Periaux. John Wiley \& Sons, Ltd., Chichester. (1999). pounds60.00
%J Computers \& Mathematics with Applications
%V 38
%N 11-12
%D 1999
%P 282--282
%I 
%U http://www.sciencedirect.com/science/article/B6TYJ-48778B1-24/2/ee28594e33abf3bd7c4a9fc997b98492

%T Automated generation of robust error recovery logic in assembly systems using genetic programming : Cem M. Baydar, Kazuhiro Saitou, v20, n1, 2001, pp55-68
%J Journal of Manufacturing Systems
%V 21
%N 6
%D 2002
%P 475--476
%I 
%U http://www.sciencedirect.com/science/article/B6VJD-4920DSC-1N/2/93bf79c7eb0d6ad94d169ed1b37ec77f
%Z Abstract of \citeBaydar200155

%A Amir Atapour Abarghouei
%A Afshin Ghanizadeh
%A Saman Sinaie
%A Siti Mariyam Shamsuddin
%T A Survey of Pattern Recognition Applications in Cancer Diagnosis
%B International Conference of Soft Computing and Pattern Recognition, SOCPAR '09
%D 2009
%P 448--453
%I 
%K genetic algorithms, genetic programming, artificial neural networks, cancer diagnosis, image processing, medical images, pattern recognition applications, wavelet analysis,
   cancer, medical image processing, pattern recognition
%X In this paper, some of the image processing and pattern recognition methods that have been used on medical images for cancer diagnosis are reviewed. Previous studies on
   Artificial Neural Networks, Genetic Programming, and Wavelet Analysis are described with their working process and advantages. The definition of each method is provided in
   this study, and the acknowledgment is granted for previous related research activities.
%8 Decemeber
%Z Also known as \cite5368648

%A H. Abbass
%A N. X. Hoai
%A R. I. (Bob) McKay
%T AntTAG: A New Method to Compose Computer Programs Using Colonies of Ants
%B Proceedings, 2002 World Congress on Computational Intelligence
%V 2
%D 2002
%P 1654--1666
%I IEEE Press Piscataway, NJ, USA
%K genetic algorithms, genetic programming
%U http://sc.snu.ac.kr/PAPERS/TAGACOcec02.pdf
%X Genetic Programming (GP) plays the primary role for the discovery of programs through evolving the program's set of parse trees. In this paper, we present a new technique
   for constructing programs through Ant Colony Optimisation (ACO) using the tree adjunct grammar (TAG) formalism. We call the method AntTAG and we show that the results are
   very promising.
%Z Refereed International Conference Papers

%A Fabio Abbattista
%A Valeria Carofiglio
%A Mario Koppen
%T Scout Algorithms and Genetic Algorithms: A Comparative Study
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 769
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-803.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A R. J. Abbott
%T Niches as a GA divide-and-conquer strategy
%B Proceedings of the Second Annual AI Symposium for the California State University
%E Art Chapman and Leonard Myers
%D 1991
%P 133--136
%I California State University
%K genetic algorithms, genetic programming

%A Russell J. Abbott
%T Object-Oriented Genetic Programming, An Initial Implementation
%B Procceedings of the Sixth International Conference on Computational Intelligence and Natural Computing
%D 2003
%I 
%C Embassy Suites Hotel and Conference Center, Cary, North Carolina USA
%K genetic algorithms, genetic programming, object-oriented, STGP
%U http://abbott.calstatela.edu/PapersAndTalks/OOGP.pdf
%X This paper describes oogp, an object-oriented genetic programming system. Oogp provides traditional genetic programming capabilities in an object-oriented framework. Among
   the advantages of object-oriented genetic programming are: (a) strong typing, (b) availability of existing class libraries for inclusion in generated programs, and (c)
   straightforward extensibility to include features such as iteration as object-oriented methods. Oogp is written in Java and makes extensive use of Java's reflection
   capabilities. Oogp includes a relatively straightforward but apparently innovative simplification capability.
%8 September  26-30
%Z http://axon.cs.byu.edu/CINC/ http://www.ee.duke.edu/JCIS/ parity Assignment-Stmt, Block-Stmt, If-Stmt, and While-Stmt Automatic simplification "A limit may be placed on the
   number of steps allowed. When exceeded, an ExcessiveStepsException is thrown" Iteration cites \citeHPL-2001-327.

%A Russ Abbott
%A Jiang Guo
%A Behzad Parviz
%T Guided Genetic Programming
%B The 2003 International Conference on Machine Learning; Models, Technologies and Applications (MLMTA'03)
%D 2003
%I CSREA Press
%C las Vegas
%K genetic algorithms, genetic programming, guided genetic programming
%U http://abbott.calstatela.edu/PapersAndTalks/Guided%20Genetic%20Programming.pdf
%X We argue that genetic programming has not made good on its promise to generate computer programs automatically. It then describes an approach that would allow that promise
   to be fulfilled by running a genetic programming engine under human guidance.
%8 23-26  June
%Z http://www.ashland.edu/~iajwa/conferences/2003/MLMTA/ Oogp Java. Sort (all ArrayList methods and iterate() loop and insertAsc()). Guided GP 3 populations (coevolution) "The
   user contributes...a top-down analysis". "Allowing a user to suggest building blocks may be a reasonable compromise". Cites \citelangdon:book \citeHPL-2001-327
   \citeppsn92:oReilly \citeicga93:kinnear

%A Russ Abbott
%A Behzad Parviz
%A Chengyu Sun
%T Genetic Programming Reconsidered
%B Proceedings of the International Conference on Artificial Intelligence, IC-AI '04, Volume 2 \& Proceedings of the International Conference on Machine Learning; Models,
   Technologies \& Applications, MLMTA '04
%E Hamid R. Arabnia and Youngsong Mun
%V 2
%D 2004
%P 1113--1116
%I CSREA Press
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, evolutionary pathway, fitness function, teleological evolution, adaptive evolution
%U http://abbott.calstatela.edu/PapersAndTalks/GeneticProgrammingReconsidered.pdf
%X Even though the Genetic Programming (GP) mechanism is capable of evolving any computable function, the means through which it does so is inherently flawed: the user must
   provide the GP engine with an evolutionary pathway toward a solution. Hence Genetic Programming is problematic as a mechanism for generating creative solutions to specific
   problems.
%8 June  21-24
%Z sort, fitness function cheat
%@ 1-932415-32-7

%A Ashraf M. Abdelbar
%A Sherif Ragab
%A Sara Mitri
%T Applying Co-Evolutionary Particle Swam Optimization to the Egyptian Board Game Seega
%B Proceedings of The First Asian-Pacific Workshop on Genetic Programming
%E Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan
%D 2003
%P 9--15
%I 
%C Rydges (lakeside) Hotel, Canberra, Australia
%K genetic algorithms, genetic programming
%8 8  Decemeber
%Z \citeaspgp03
%@ 0-9751724-0-9

%A Wafa Abdelmalek
%A Sana {Ben Hamida}
%A Fathi Abid
%T Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming
%J Journal of Applied Mathematics and Decision Sciences
%D 2009
%I Hindawi Publishing Corporation
%K genetic algorithms, genetic programming
%U http://www.hindawi.com/journals/ads/2009/179230.html
%X The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of
   genetic programming to volatility's forecasting. By using real data from S\&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied
   volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance
   measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results
   are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.
%Z Article ID 179230 1RU: MODESFI, Faculty of Economics and Business, Road of the Airport Km 4, 3018 Sfax, Tunisia 2Laboratory of Intelligent IT Engineering, Higher School of
   Technology and Computer Science, 2035 Charguia, Tunisia

%A I. Abd Latiff
%A M. O. Tokhi
%T Fast convergence strategy for Particle Swarm Optimization using spread factor
%B Evolutionary Computation, 2009. CEC '09. IEEE Congress on
%D 2009
%P 2693--2700
%I 
%K PSO velocity equation, fast convergence strategy, inertia weight, particle swarm optimization, spread factor, convergence, particle swarm optimisation
%8 May
%Z Not on GP

%A Hussein A. Abdou
%T Genetic programming for credit scoring: The case of Egyptian public sector banks
%J Expert Systems with Applications
%V 36
%N 9
%D 2009
%P 11402--11417
%I 
%K genetic algorithms, genetic programming, Credit scoring, Weight of evidence, Egyptian public sector banks
%U http://www.sciencedirect.com/science/article/B6V03-4VJSRWK-1/2/a3b8516f289c76c474c6a1eb9d26d7ec
%X Credit scoring has been widely investigated in the area of finance, in general, and banking sectors, in particular. Recently, genetic programming (GP) has attracted
   attention in both academic and empirical fields, especially for credit problems. The primary aim of this paper is to investigate the ability of GP, which was proposed as an
   extension of genetic algorithms and was inspired by the Darwinian evolution theory, in the analysis of credit scoring models in Egyptian public sector banks. The secondary
   aim is to compare GP with probit analysis (PA), a successful alternative to logistic regression, and weight of evidence (WOE) measure, the later a neglected technique in
   published research. Two evaluation criteria are used in this paper, namely, average correct classification (ACC) rate criterion and estimated misclassification cost (EMC)
   criterion with different misclassification cost (MC) ratios, in order to evaluate the capabilities of the credit scoring models. Results so far revealed that GP has the
   highest ACC rate and the lowest EMC. However, surprisingly, there is a clear rule for the WOE measure under EMC with higher MC ratios. In addition, an analysis of the
   dataset using Kohonen maps is undertaken to provide additional visual insights into cluster groupings.

%A A. B. {Abdul rahim}
%A J. Teo
%A A. Saudi
%T An Empirical Comparison of Code Size Limit in Auto-Constructive Artificial Life
%B 2006 IEEE Conference on Cybernetics and Intelligent Systems
%D 2006
%P 1--6
%I IEEE
%C Bangkok
%K genetic algorithms, genetic programming, Push, Breve, ALife, PushGP
%X This paper presents an evolving swarm system of flying agents simulated as a collective intelligence within the Breve auto-constructive artificial life environment. The
   behaviour of each agent is governed by genetically evolved program codes expressed in the Push programming language. There are two objectives in this paper, that is to
   investigate the effects of firstly code size limit and secondly two different versions of the Push genetic programming language on the auto-constructive evolution of
   artificial life. We investigated these genetic programming code elements on reproductive competence using a measure based on the self-sustainability of the population.
   Self-sustainability is the point in time when the current population's agents are able to reproduce enough offspring to maintain the minimum population size without any new
   agents being randomly injected from the system. From the results, we found that the Push2 implementation showed slightly better evolvability than Push3 in terms of
   achieving self-sufficiency. In terms of code size limit, the reproductive competence of the collective swarm was affected quite significantly at certain parameter settings
%8 June
%Z Sch. of Eng. & Inf. Technol., Univ. Malaysia Sabah
%@ 1-4244-0023-6

%A Neil Abernathy
%T Using a Genetic Algorithm to Select Beam Configurations for Radiosurgery of the Brain
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 1--7
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Ajith Abraham
%A Vitorino Ramos
%T Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 1384--1391
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming, Web Usage Mining, Ant Systems, Stigmergy, Data-Mining, Linear Genetic Programming.
%U http://arxiv.org/abs/cs/0412071
%X The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose
   from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to
   discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site
   management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and
   their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed
   adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others. In this paper, we propose an ant
   clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly shows
   that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that
   efficient when comparared to evolutionary-fuzzy clustering (i-miner) approach.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Ajith Abraham
%A Ravi Jain
%T Soft Computing Models for Network Intrusion Detection Systems
%R Technical Report
%D 2004
%I 
%I OSU
%K genetic algorithms, genetic programming, Cryptography and Security
%U http://arxiv.org/abs/cs/0405046
%X Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems
   against threats to confidentiality, integrity, and availability. There are two types of intruders: external intruders, who are unauthorised users of the machines they
   attack, and internal intruders, who have permission to access the system with some restrictions. This chapter presents a soft computing approach to detect intrusions in a
   network. Among the several soft computing paradigms, we investigated fuzzy rule-based classifiers, decision trees, support vector machines, linear genetic programming and
   an ensemble method to model fast and efficient intrusion detection systems. Empirical results clearly show that soft computing approach could play a major role for
   intrusion detection.
%O Journal-ref: Soft Computing in Knowledge Discovery: Methods and Applications, Saman Halgamuge and Lipo Wang (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag
   Germany, Chapter 16, 20 pages, 2004
%8 13  May  2004
%Z ACM-class: K.6.5 cs.CR/0405046

%A Ajith Abraham
%T Business Intelligence from Web Usage Mining
%J Journal of Information \& Knowledge Management
%V 2
%N 4
%D 2003
%P 375--390
%I 
%K genetic algorithms, genetic programming, Web mining, knowledge discovery, business intelligence, hybrid soft computing, neuro-fuzzy-genetic system
%U http://www.softcomputing.net/jikm.pdf
%X The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to
   choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining
   attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for
   effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. This paper presents the
   important concepts of Web usage mining and its various practical applications. Further a novel approach called "intelligent-miner" (i-Miner) is presented. i-Miner could
   optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A
   hybrid evolutionary fuzzy clustering algorithm is proposed to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a
   Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing
   maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi?Sugeno fuzzy inference system (to
   analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web
   usage-mining framework is efficient.
%Z see also \citeoai:arXiv.org:cs/0405030 http://www.worldscinet.com/jikm/jikm.shtml http://ajith.softcomputing.net Department of Computer Science, Oklahoma State University,
   700 N Greenwood Avenue, Tulsa, Oklahoma 74106-0700, USA

%A Ajith Abraham
%T Business Intelligence from Web Usage Mining
%D 2004
%I 
%U http://arXiv.org/abs/cs/0405030
%X The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose
   from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to
   discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site
   management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. In this paper, we present the important
   concepts of Web usage mining and its various practical applications. We further present a novel approach 'intelligent-miner' (i-Miner) to optimize the concurrent
   architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary
   fuzzy clustering algorithm is proposed in this paper to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a
   Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing
   maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi-Sugeno fuzzy inference system (to
   analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web
   usage-mining framework is efficient.
%8 May ~06
%Z see also \citeAbraham:2003:JIKM

%A Ajith Abraham
%T Evolutionary Computation in Intelligent Network Management
%B Evolutionary Computing in Data Mining
%S Studies in Fuzziness and Soft Computing
%E Ashish Ghosh and Lakhmi C. Jain
%V 163
%D 2004
%P 189--210
%I Springer
%K genetic algorithms, genetic programming, Linear Genetic Programming, LGP, intrusion detection, ANN, www, fuzzy clustering, fuzzy inference, computer security, RIPPER, demes
   (ring topology), steady state 32-bit FPU machine code GP, SVM, decision trees, i-miner
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980376-0,00.html
%X Data mining is an iterative and interactive process concerned with discovering patterns, associations and periodicity in real world data. This chapter presents two real
   world applications where evolutionary computation has been used to solve network management problems. First, we investigate the suitability of linear genetic programming
   (LGP) technique to model fast and efficient intrusion detection systems, while comparing its performance with artificial neural networks and classification and regression
   trees. Second, we use evolutionary algorithms for a Web usage-mining problem. Web usage mining attempts to discover useful knowledge from the secondary data obtained from
   the interactions of the users with the Web. Evolutionary algorithm is used to optimise the concurrent architecture of a fuzzy clustering algorithm (to discover data
   clusters) and a fuzzy inference system to analyse the trends. Empirical results clearly shows that evolutionary algorithm could play a major rule for the problems
   considered and hence an important data mining tool.
%O 9
%@ 3-540-22370-3

%A Ajith Abraham
%A Nadia Nedjah
%A Luiza {de Macedo Mourelle}
%T Evolutionary Computation: from Genetic Algorithms to Genetic Programming
%B Genetic Systems Programming: Theory and Experiences
%S Studies in Computational Intelligence
%E Nadia Nedjah and Ajith Abraham and Luiza de Macedo Mourelle
%V 13
%D 2006
%P 1--20
%I Springer
%C Germany
%K genetic algorithms, genetic programming, cartesian genetic programming
%U http://www.softcomputing.net/gpsystems.pdf
%X Evolutionary computation, offers practical advantages to the researcher facing difficult optimisation problems. These advantages are multi-fold, including the simplicity of
   the approach, its robust response to changing circumstance, its flexibility, and many other facets. The evolutionary approach can be applied to problems where heuristic
   solutions are not available or generally lead to unsatisfactory results. As a result, evolutionary computation have received increased interest, particularly with regards
   to the manner in which they may be applied for practical problem solving. we review the development of the field of evolutionary computations from standard genetic
   algorithms to genetic programming, passing by evolution strategies and evolutionary programming. For each of these orientations, we identify the main differences from the
   others. We also, describe the most popular variants of genetic programming. These include linear genetic programming (LGP), gene expression programming (GEP),
   multi-expression programming (MEP), Cartesian genetic programming (CGP), traceless genetic programming (TGP), gramatical evolution (GE) and genetic algorithm for deriving
   software (GADS).
%Z http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html
%@ 3-540-29849-5

%A Ajith Abraham
%A Crina Grosan
%T Evolving Intrusion Detection Systems
%B Genetic Systems Programming: Theory and Experiences
%S Studies in Computational Intelligence
%E Nadia Nedjah and Ajith Abraham and Luiza de Macedo Mourelle
%V 13
%D 2006
%P 57--80
%I Springer
%C Germany
%K genetic algorithms, genetic programming
%U http://falklands.globat.com/~softcomputing.net/ids-chapter.pdf
%X An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been
   misused. An IDS does not eliminate the use of preventive mechanism but it works as the last defensive mechanism in securing the system. We evaluate the performances of two
   Genetic Programming techniques for IDS namely Linear Genetic Programming (LGP) and Multi-Expression Programming (MEP). Results are then compared with some machine learning
   techniques like Support Vector Machines (SVM) and Decision Trees (DT). Empirical results clearly show that GP techniques could play an important role in designing real time
   intrusion detection systems.
%Z http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html
%@ 3-540-29849-5

%A Ajith Abraham
%A Crina Grosan
%T Genetic Programming Approach for Fault Modeling of Electronic Hardware
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 2
%D 2005
%P 1563--1569
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming, MEP, ANN, LGP
%U http://www.softcomputing.net/cec05.pdf
%X presents two variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modelling of
   electronic circuits can be best performed by the stressor - susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded
   the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after preprocessing and
   standardisation are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm. The performance of the
   proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for
   future fault monitoring systems.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Ajith Abraham
%A Crina Grosan
%T Decision Support Systems Using Ensemble Genetic Programming
%J Journal of Information \& Knowledge Management (JIKM)
%V 5
%N 4
%D 2006
%P 303--313
%I 
%K genetic algorithms, genetic programming, gene expression programming, Decision support systems, ensemble systems, evolutionary multi-objective optimisation
%X This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of
   three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic
   programming (LGP), Multi-Expression Programming (MEP) and Gene Expression Programming (GEP). The clustered data are used as the inputs to the genetic programming
   algorithms. Some simulation results demonstrating the difference of these techniques are also performed. Test results reveal that the proposed ensemble method performed
   better than the individual GP approaches and that the method is efficient.
%O Special topic: Knowledge Discovery Using Advanced Computational Intelligence Tools
%8 Decemeber

%A Ajith Abraham
%A Ravi Jain
%A Johnson Thomas
%A Sang Yong Han
%T D-SCIDS: Distributed soft computing intrusion detection system
%J Journal of Network and Computer Applications
%V 30
%N 1
%D 2007
%P 81--98
%I 
%K genetic algorithms, genetic programming
%X An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been
   misused. A Distributed IDS (DIDS) consists of several IDS over a large network (s), all of which communicate with each other, or with a central server that facilitates
   advanced network monitoring. In a distributed environment, DIDS are implemented using co-operative intelligent agents distributed across the network(s). This paper
   evaluates three fuzzy rule-based classifiers to detect intrusions in a network. Results are then compared with other machine learning techniques like decision trees,
   support vector machines and linear genetic programming. Further, we modelled Distributed Soft Computing-based IDS (D-SCIDS) as a combination of different classifiers to
   model lightweight and more accurate (heavy weight) IDS. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.
%8 January

%A Ajith Abraham
%T Real time intrusion prediction, detection and prevention programs
%B IEEE International Conference on Intelligence and Security Informatics, ISI 2008
%D 2008
%P xli--xlii
%I 
%K genetic algorithms, genetic programming, distributed intrusion detection systems, hidden Markov model, intrusion detection program, online risk assessment, real time
   intrusion detection, real time intrusion prediction, real time intrusion prevention, hidden Markov models, risk management, security of data
%X An intrusion detection program (IDP) analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. In this
   talk, we present some of the challenges in designing efficient intrusion detection systems (IDS) using nature inspired computation techniques, which could provide high
   accuracy, low false alarm rate and reduced number of features. Then we present some recent research results of developing distributed intrusion detection systems using
   genetic programming techniques. Further, we illustrate how intruder behavior could be captured using hidden Markov model and predict possible serious intrusions. Finally we
   illustrate the role of online risk assessment for intrusion prevention systems and some associated results.
%O IEEE ISI 2008 Invited Talk (VI)
%8 June
%Z Also known as \cite4565018

%A Ajith Abraham
%A Crina Grosan
%A Vaclav Snasel
%T Programming Risk Assessment Models for Online Security Evaluation Systems
%B 11th International Conference on Computer Modelling and Simulation, UKSIM '09
%D 2009
%P 41--46
%I 
%K genetic algorithms, genetic programming, genetic programming methods, human reasoning, online security evaluation systems, perception process, programming risk assessment
   models, risk management, security of data
%X Risk assessment is often done by human experts, because there is no exact and mathematical solution to the problem.Usually the human reasoning and perception process cannot
   be expressed precisely. This paper propose a genetic programming approach for risk assessment. Preliminary results indicate that genetic programming methods are robust and
   suitable for this problem when compared to other risk assessment models.
%8 25-27  March
%Z Also known as \cite4809735

%A Ajith Abraham
%A Crina Grosan
%A Hongbo Liu
%A Yuehui Chen
%T Hierarchical Takagi-Sugeno Models for Online Security Evaluation Systems
%B Fifth International Conference on Information Assurance and Security, IAS '09
%V 1
%D 2009
%P 687--692
%I 
%K genetic algorithms, genetic programming, hierarchical Takagi-Sugeno models, human perception, human reasoning, intrusion detection, neuro-fuzzy programming, online security
   evaluation systems, risk assessment, fuzzy reasoning, hierarchical systems, human factors, interactive programming, risk management, security of data
%X Risk assessment is often done by human experts, because there is no exact and mathematical solution to the problem. Usually the human reasoning and perception process
   cannot be expressed precisely. This paper propose a light weight risk assessment system based on an Hierarchical Takagi-Sugeno model designed using evolutionary algorithms.
   Performance comparison is done with neuro-fuzzy and genetic programming methods. Empirical results indicate that the techniques are robust and suitable for developing light
   weight risk assessment models, which could be integrated with intrusion detection and prevention systems.
%8 August
%Z Also known as \cite5283215

%A Zoe Abrams
%T Complimentary Selection as an Alternative Method for Population Reproduction
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 8--15
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Myriam Abramson
%A Lawrence Hunter
%T Classification using Cultural Co-Evolution and Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 249--254
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Abdel Latif {Abu Dalhoum}
%A Moh'd {Al Zoubi}
%A Marina {de la Cruz}
%A Alfonso Ortega
%A Manuel Alfonseca
%T A Genetic Algorithm for Solving the P-Median Problem
%B European Simulation and Modeling Conference ESM'2005
%E J. Manuel Feliz Teixeira and A. E.Carvalho Brito
%D 2005
%P 141--145
%I http://www.eurosis.org
%I Eurosim, The European Multidisciplinary Society for Modelling and Simulation Technology
%C Porto, Portugal
%K genetic algorithms, genetic programming, grammatical evolution, Christiansen grammar
%U http://arantxa.ii.uam.es/~alfonsec/docs/confint/pmedian.pdf
%8 October  24-26
%Z Title may be listed as "A Genetic Algorithm for solving the P-Medium Problem" http://www.eurosis.org/cms/files/proceedings/ESM/ESM2005contents.pdf
%@ 90-77381-22-8

%A Aybar C. Acar
%A Amihai Motro
%T Intensional Encapsulations of Database Subsets by Genetic Programming
%R Technical Report ISE-TR-05-01
%D 2005
%I 
%I Information and Software Engineering Department, The Volgenau School of Information Technology and Engineering, George Mason University
%K genetic algorithms, genetic programming
%U http://ise.gmu.edu/techrep/2005/05_01.pdf
%X Finding intensional encapsulations of database subsets is the inverse of query evaluation. Whereas query evaluation transforms an intensional expression (the query) to its
   extension (a set of data values), intensional encapsulation assigns an intensional expression to a given set of data values. We describe a method for deriving intensional
   representations of subsets of records in large database tables. Our method is based on the paradigm of genetic programming. It is shown to achieve high accuracy and
   maintain compact expression size, while requiring cost that is acceptable to all applications, but those that require instantaneous results. Intensional encapsulation has a
   broad range of applications including cooperative answering, information integration, security and data mining.
%8 February
%Z See \citeconf/dexa/AcarM05

%A Aybar C. Acar
%A Amihai Motro
%T Intensional Encapsulations of Database Subsets via Genetic Programming
%B Database and Expert Systems Applications, 16th International Conference, DEXA 2005, Proceedings
%S Lecture Notes in Computer Science
%E Kim Viborg Andersen and John K. Debenham and Roland Wagner
%V 3588
%D 2005
%P 365--374
%I Springer
%C Copenhagen, Denmark
%K genetic algorithms, genetic programming
%X Finding intensional encapsulations of database subsets is the inverse of query evaluation. Whereas query evaluation transforms an intensional expression (the query) to its
   extension (a set of data values), intensional encapsulation assigns an intensional expression to a given set of data values. We describe a method for deriving intensional
   representations of subsets of records in large database tables. Our method is based on the paradigm of genetic programming. It is shown to achieve high accuracy and
   maintain compact expression size, while requiring cost that is acceptable to all applications, but those that require instantaneous results. Intensional encapsulation has a
   broad range of applications including cooperative answering, information integration, security and data mining.
%8 August  22-26
%Z See also \citeAcarM05tr
%@ 3-540-28566-0

%A Aybar C. Acar
%T Query Consolidation: Interpreting Queries Sent to Independent Heterogenous Databases
%R Ph.D. Thesis
%D 2008
%I 
%I The Volgenau School of Information Technology and Engineering, George Mason University
%C Fairfax, VA, USA
%K genetic algorithms, genetic programming, Databases, Information Integration, Query Processing, Machine Learning
%U http://digilib.gmu.edu:8080/dspace/bitstream/1920/3223/1/Acar_Aybar.pdf
%X This dissertation introduces the problem of query consolidation, which seeks to interpret a set of disparate queries submitted to independent databases with a single global
   query. The problem has multiple applications, from improving virtual database design, to aiding users in information retrieval, to protecting against inference of sensitive
   data from a seemingly innocuous set of apparently unrelated queries. The problem exhibits attractive duality with the much-researched problem of query decomposition, which
   has been addressed intensively in the context of multidatabase environments: How to decompose a query submitted to a virtual database into a set of local queries that are
   evaluated in individual databases. The new problem is set in the architecture of a canonical multidatabase system, using it in the reverse direction. The reversal is built
   on the assumption of conjunctive queries and source descriptions. A rational and efficient query decomposition strategy is also assumed, and this decomposition is reversed
   to arrive at the original query by analyzing the decomposed components. The process incorporates several steps where a number of solutions must be considered, due to the
   fact that query decomposition is not injective. Initially, the problem of finding the most likely join plan between component queries is investigated. This is accomplished
   by leveraging the referential constraints available in the underlying multidatabase, or by approximating these constraints from the data when not available. This
   approximation is done using the information theoretic concept of conditional entropy. Furthermore, the most likely join plans are enhanced by the expansion of their
   projections and adding precision to their selection constraints by estimating the selection constraints that would be applied to these consolidations offline. Additionally,
   the extraction of a set of queries related to the same retrieval task from an ongoing sequence of incoming queries is investigated. A conditional random field model is
   trained to segment and label incoming query sequences. Finally, the candidate consolidations are re-encapsulated with a genetic programming approach to find simpler
   intentional descriptions that are extensionally equivalent to discover the original intent of the query. The dissertation explains and discusses all of the above operations
   and validates the methods developed with experimentation on synthesised and real-world data. The results are highly encouraging and verify that the accuracy, time
   performance, and scalability of the methods would make it possible to exploit query consolidation in production environments.
%8 23  July
%Z GP chapters 7, 8

%A Thomas Ackling
%A Bradley Alexander
%A Ian Grunert
%T Evolving patches for software repair
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1427--1434
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, SBSE, Python
%X Defects are a major concern in software systems. Unsurprisingly, there are many tools and techniques to facilitate the removal of defects through their detection and
   localisation. However, there are few tools that attempt to repair defects. To date, evolutionary tools for software repair have evolved changes directly in the program code
   being repaired. In this work we describe an implementation: pyEDB, that encodes changes as a series of code modifications or patches. These modifications are evolved as
   individuals. We show pyEDB to be effective in repairing some small errors, including variable naming errors in Python programs. We also demonstrate that evolving patches
   rather than whole programs simplifies the removal of spurious errors.
%8 12-16  July
%Z Sofware bugs cost 2 10**10 dollar pa. Repairs 5 relational operators and wrong name. AST. tracing program execution. mod-tables like gramatical evolution. Tarantula. 32 bit
   GA. Examples: middleFunc and Facebook smallworld. Also known as \cite2001768 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms
   (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)

%A Adam V. Adamopoulos
%A Efstratios F. Georgopoulos
%A Spiridon D. Likothanassis
%A Photios A. Anninos
%T Forecasting the MagnetoEncephaloGram (MEG) of Epileptic Patients Using Genetically Optimized Neural Networks
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1457--1462
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-767.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Thomas P. Adams
%T Creation of Simple, Deadline, and Priority Scheduling Algorithms using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 1--10
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2002/Adams.pdf
%8 June
%Z part of \citekoza:2002:gagp memory, SETMO..SETM4 ECJ8, iteration (over job queue) branch and selection branch, communicate via memory. Run time used throughout (ie by
   functions and terminals) to identify jobs (ie scheduling tasks). 32 tasks shortest job first

%A Giovanni Adorni
%A Federico Bergenti
%A Stefano Cagnoni
%T A cellular-programming approach to pattern classification
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 142--150
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%X In this paper we discuss the capability of the cellular programming approach to produce non-uniform cellular automata performing two-dimensional pattern classification.
   More precisely, after an introduction to the evolutionary cellular automata model, we describe a general approach suitable for designing cellular classifiers. The approach
   is based on a set of non-uniform cellular automata performing specific classification tasks, which have been designed by means of a cellular evolutionary algorithm. The
   proposed approach is discussed together with some preliminary results obtained on a benchmark data set consisting of car-plate digits.
%8 14-15  April
%Z EuroGP'98
%@ 3-540-64360-5

%A Giovanni Adorni
%A Stefano Cagnoni
%A Monica Mordonini
%T Genetic Programming of a Goal-Keeper Control Strategy for the RoboCup Middle Size Competition
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 109--119
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=109
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP Robot goalkeeper (.4m) controlled by twin cameras using GP. Able to intercept football sometimes.
%@ 3-540-65899-8

%A Giovanni Adorni
%A Stefano Cagnoni
%A Monica Mordonini
%T Efficient low-level vision program design using Sub-machine-code Genetic Programming
%B AIIA 2002, Workshop sulla Percezione e Visione nelle Macchine
%E Marco Gori
%D 2002
%I 
%C Siena, Italy
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/539182.html
%X Sub-machine-code Genetic Programming (SmcGP) is a variant of GP aimed at exploiting the intrinsic parallelism of sequential CPUs. The paper describes an approach to
   low-level vision algorithm design for real-time applications by means of Sub-machine-code Genetic Programming(SmcGP), a variant of GP aimed at exploiting the intrinsic
   parallelism of sequential CPUs. The SmcGPbased design of two processing modules of a license-plate recognition system is taken into consideration as a case study to show
   the potential of the approach. The paper reports results obtained in recognizing the very low-resolution binary patterns that have to be classified in such an application
   along with preliminary results obtained using SmcGP to design a license-plate extraction algorithm.
%O The Pennsylvania State University CiteSeer Archives
%8 10-13  September
%Z http://www-dii.ing.unisi.it/aiia2002/paper.htm

%A Giovanni Adorni
%A Stefano Cagnoni
%T Design of Explicitly or Implicitly Parallel Low-resolution Character Recognition Algorithms by Means of Genetic Programming
%B Soft Computing and Industry Recent Applications
%E Rajkumar Roy and Mario K\"oppen and Seppo Ovaska and Takeshi Furuhashi and Frank Hoffmann
%D 2001
%P 387--398
%I Springer-Verlag
%K genetic algorithms, genetic programming
%O Published 2002
%8 10--24  September
%Z WSC6 Out of print? http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394
%@ 1-85233-539-4

%A Michael Affenzeller
%A Stephan Winkler
%A Stefan Wagner
%A Andreas Beham
%T Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
%S Numerical Insights
%D 2009
%I CRC Press
%C Singapore
%K genetic algorithms, genetic programming
%U http://www.crcpress.com/product/isbn/9781584886297
%X Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and
   genetic programming (GP). It applies the algorithms to significant combinatorial optimisation problems and describes structure identification using HeuristicLab as a
   platform for algorithm development. The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties
   of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs
   to two combinatorial optimisation problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the
   field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems. Written by core members of the HeuristicLab team,
   this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By
   comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.
%Z Reviewed in \citePappa:2009:GPEM. My copy missing pages i to vi.
%@ 1-58488-629-3

%A Wasif Afzal
%A Richard Torkar
%A Robert Feldt
%T A Systematic Mapping Study on Non-Functional Search-based Software Testing
%B Proceedings of the 20th International Conference on Software Engineering and Knowledge Engineering (SEKE '08)
%D 2008
%P 488--493
%I Knowledge Systems Institute Graduate School
%C San Francisco, CA, USA
%K genetic algorithms, genetic programming
%X Automated software test generation has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional), grey-box
   (combination of structural and functional) and non-functional testing. In this paper, we undertake a systematic mapping study to present a broad review of primary studies
   on the application of search-based optimization techniques to non-functional testing. The motivation is to identify the evidence available on the topic and to identify gaps
   in the application of search-based optimization techniques to different types of non-functional testing. The study is based on a comprehensive set of 35 papers obtained
   after using a multi-stage selection criteria and are published in workshops, conferences and journals in the time span 1996--2007. We conclude that the search-based
   software testing community needs to do more and broader studies on non-functional search-based software testing (NFSBST) and the results from our systematic map can help
   direct such efforts.
%8 July  1-3
%@ 1-891706-22-5

%A Wasif Afzal
%A Richard Torkar
%T Suitability of Genetic Programming for Software Reliability Growth Modeling
%B The 2008 International Symposium on Computer Science and its Applications (CSA'08)
%D 2008
%P 114--117
%I IEEE Computer Society
%C Hobart, ACT
%K genetic algorithms, genetic programming, software reliability data points, software reliability growth modeling, SBSE
%X Genetic programming (GP) has been found to be effective in finding a model that fits the given data points without making any assumptions about the model structure. This
   makes GP a reasonable choice for software reliability growth modeling. This paper discusses the suitability of using GP for software reliability growth modeling and
   highlights the mechanisms that enable GP to progressively search for fitter solutions.
%8 13-15  October
%Z Also known as \cite4654071

%A Wasif Afzal
%A Richard Torkar
%T A comparative evaluation of using genetic programming for predicting fault count data
%B Proceedings of the Third International Conference on Software Engineering Advances (ICSEA'08)
%D 2008
%P 407--414
%I 
%C Sliema, Malta
%K genetic algorithms, genetic programming, prediction, software reliability growth modeling, SBSE
%X There have been a number of software reliability growth models (SRGMs) proposed in literature. Due to several reasons, such as violation of models' assumptions and
   complexity of models, the practitioners face difficulties in knowing which models to apply in practice. This paper presents a comparative evaluation of traditional models
   and use of genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The motivation of
   using a GP approach is its ability to evolve a model based entirely on prior data without the need of making underlying assumptions. The results show the strengths of using
   GP for predicting fault count data.
%8 26-31
%Z Also known as \cite4668139

%A Wasif Afzal
%A Richard Torkar
%A Robert Feldt
%T Prediction of fault count data using genetic programming
%B Proceedings of the 12th IEEE International Multitopic Conference (INMIC'08)
%D 2008
%P 349--356
%I IEEE
%C Karachi, Pakistan
%K genetic algorithms, genetic programming, SBSE, fault count data, prediction
%U http://drfeldt.googlepages.com/afzal_submitted0805icsea_prediction_.pdf
%X Software reliability growth modeling helps in deciding project release time and managing project resources. A large number of such models have been presented in the past.
   Due to the existence of many models, the models' inherent complexity, and their accompanying assumptions; the selection of suitable models becomes a challenging task. This
   paper presents empirical results of using genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial
   projects. The goodness of fit (adaptability) and predictive accuracy of the evolved model is measured using five different measures in an attempt to present a fair
   evaluation. The results show that the GP evolved model has statistically significant goodness of fit and predictive accuracy.
%8 23-24  Decemeber
%Z Also known as \cite4777762

%A Wasif Afzal
%A Richard Torkar
%A Robert Feldt
%T Search-Based Prediction of Fault Count Data
%B Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009
%E Massimiliano Di Penta and Simon Poulding
%D 2009
%P 35--38
%I IEEE
%C Windsor, UK
%K genetic algorithms, genetic programming, SBSE, search-based prediction, software fault count data, software reliability growth model, symbolic regression, regression
   analysis, software fault tolerance
%X Symbolic regression, an application domain of genetic programming (GP), aims to find a function whose output has some desired property, like matching target values of a
   particular data set. While typical regression involves finding the coefficients of a pre-defined function, symbolic regression finds a general function, with coefficients,
   fitting the given set of data points. The concepts of symbolic regression using genetic programming can be used to evolve a model for fault count predictions. Such a model
   has the advantages that the evolution is not dependent on a particular structure of the model and is also independent of any assumptions, which are common in traditional
   time-domain parametric software reliability growth models. This research aims at applying experiments targeting fault predictions using genetic programming and comparing
   the results with traditional approaches to compare efficiency gains.
%8 13-15  May
%Z order number P3675 http://www.ssbse.org/ Also known as \cite5033177

%A Wasif Afzal
%A Richard Torkar
%A Robert Feldt
%T A systematic review of search-based testing for non-functional system properties
%J Information and Software Technology
%V 51
%N 6
%D 2009
%P 957--976
%I 
%K genetic algorithms, genetic programming, Systematic review, Non-functional system properties, Search-based software testing
%U http://www.sciencedirect.com/science/article/B6V0B-4VHXDTD-1/2/9da989f9d874eb88d1f82d9a0878114b
%X Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness
   function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of
   metaheuristic search techniques for testing is promising due to the fact that exhaustive testing is infeasible considering the size and complexity of software under test.
   Search-based software testing has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional) and grey-box
   (combination of structural and functional) testing. In addition, metaheuristic search techniques have also been applied to test non-functional properties. The overall
   objective of undertaking this systematic review is to examine existing work into non-functional search-based software testing (NFSBST). We are interested in types of
   non-functional testing targeted using metaheuristic search techniques, different fitness functions used in different types of search-based non-functional testing and
   challenges in the application of these techniques. The systematic review is based on a comprehensive set of 35 articles obtained after a multi-stage selection process and
   have been published in the time span 1996-2007. The results of the review show that metaheuristic search techniques have been applied for non-functional testing of
   execution time, quality of service, security, usability and safety. A variety of metaheuristic search techniques are found to be applicable for non-functional testing
   including simulated annealing, tabu search, genetic algorithms, ant colony methods, grammatical evolution, genetic programming (and its variants including linear genetic
   programming) and swarm intelligence methods. The review reports on different fitness functions used to guide the search for each of the categories of execution time,
   safety, usability, quality of service and security; along with a discussion of possible challenges in the application of metaheuristic search techniques.
%8 June

%A Wasif Afzal
%T Search-Based Approaches to Software Fault Prediction and Software Testing
%R M.S. Thesis Licentiate Dissertation
%D 2009
%I 
%I School of Engineering, Dept. of Systems and Software Engineering, Blekinge Institute of Technology
%C Sweden
%K genetic algorithms, genetic programming, SBSE, Software Engineering, Computer Science, Artificial Intelligence
%U http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3?OpenDocument
%X Software verification and validation activities are essential for software quality but also constitute a large part of software development costs. Therefore efficient and
   cost-effective software verification and validation activities are both a priority and a necessity considering the pressure to decrease time-to-market and intense
   competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions related to software quality, when to stop testing, testing schedule
   and testing resource allocation needs to be as accurate as possible. This thesis investigates the application of search-based techniques within two activities of software
   verification and validation: Software fault prediction and software testing for non-functional system properties. Software fault prediction modeling can provide support for
   making important decisions as outlined above. In this thesis we empirically evaluate symbolic regression using genetic programming (a search-based technique) as a potential
   method for software fault predictions. Using data sets from both industrial and open-source software, the strengths and weaknesses of applying symbolic regression in
   genetic programming are evaluated against competitive techniques. In addition to software fault prediction this thesis also consolidates available research into predictive
   modeling of other attributes by applying symbolic regression in genetic programming, thus presenting a broader perspective. As an extension to the application of
   search-based techniques within software verification and validation this thesis further investigates the extent of application of search-based techniques for testing
   non-functional system properties. Based on the research findings in this thesis it can be concluded that applying symbolic regression in genetic programming may be a viable
   technique for software fault prediction. We additionally seek literature evidence where other search-based techniques are applied for testing of non-functional system
   properties, hence contributing towards the growing application of search-based techniques in diverse activities within software verification and validation.

%A Wasif Afzal
%A Richard Torkar
%A Robert Feldt
%A Greger Wikstrand
%T Search-based Prediction of Fault-slip-through in Large Software Projects
%B Second International Symposium on Search Based Software Engineering (SSBSE 2010)
%D 2010
%P 79--88
%I 
%C Benevento, Italy
%K genetic algorithms, genetic programming, gene expression programming, sbse, AIRS, GEP, GP, MR, PSO-ANN, artificial immune recognition system, artificial neural network,
   fault-slip-through, multiple regression, particle swarm optimisation, search-based prediction, software project, software testing process, artificial immune systems, fault
   tolerant computing, neural nets, particle swarm optimisation, program testing, regression analysis
%X A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing
   phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization
   based artificial neural networks (PSO-ANN), artificial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple
   regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement
   potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a
   telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative
   error measures. They indicate that the four search-based techniques (PSO-ANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through
   for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing
   phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing
   more consistent performance across two of the four test phases.
%8 7-9  September
%Z Also known as \cite5635180

%A Wasif Afzal
%T Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness
%B 17th Asia Pacific Software Engineering Conference (APSEC 2010)
%D 2010
%P 414--422
%I 
%K genetic algorithms, genetic programming, sbse, Bayesian technique, artificial immune recognition systems, back-propagation artificial neural networks, data mining,
   fault-proneness predictor, faults-slip-through metric, logistic regression, machine-learning techniques, receiver operating characteristic curve, search-based techniques,
   software faults, software quality, standard statistical technique, support vector machines, system test levels, tree-structured classifiers, backpropagation, data mining,
   neural nets, program testing, software quality, statistical analysis, support vector machines
%X Background: The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by
   focusing testing efforts on a subset of modules. Aims: This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules.
   Rather than predicting the fault-prone modules for the complete test phase, the prediction is done at the specific test levels of integration and system test. Method: We
   applied eight classification techniques, to the task of identifying fault prone modules, representing a variety of approaches, including a standard statistical technique
   for classification (logistic regression), tree-structured classifiers (C4.5 and random forests), a Bayesian technique (Naive Bayes), machine-learning techniques (support
   vector machines and back-propagation artificial neural networks) and search-based techniques (genetic programming and artificial immune recognition systems) on FST data
   collected from two large industrial projects from the telecommunication domain. Results: Using area under the receiver operating characteristic (ROC) curve and the location
   of (PF, PD) pairs in the ROC space, the faults slip-through metric showed impressive results with the majority of the techniques for predicting fault-prone modules at both
   integration and system test levels. There were, however, no statistically significant differences between the performance of different techniques based on AUC, even though
   certain techniques were more consistent in the classification performance at the two test levels. Conclusions: We can conclude that the faults-slip-through metric is a
   potentially strong predictor of fault-proneness at integration and system test levels. The faults-slip-through measurements interact in ways that is conveniently accounted
   for by majority of the data mining techniques.
%8 November  30-  Decemeber  3
%Z Blekinge Inst. of Technol., Ronneby, Sweden. Also known as \cite5693218

%A Wasif Afzal
%A Richard Torkar
%T On the application of genetic programming for software engineering predictive modeling: A systematic review
%J Expert Systems with Applications
%V 38
%N 9
%D 2011
%P 11984--11997
%I 
%K genetic algorithms, genetic programming, Systematic review, Symbolic regression, Modelling
%U http://www.sciencedirect.com/science/article/B6V03-52C8FT6-5/2/668361024e4b2bcf9a4a73195271591c
%X The objective of this paper is to investigate the evidence for symbolic regression using genetic programming (GP) being an effective method for prediction and estimation in
   software engineering, when compared with regression/machine learning models and other comparison groups (including comparisons with different improvements over the standard
   GP algorithm). We performed a systematic review of literature that compared genetic programming models with comparative techniques based on different independent project
   variables. A total of 23 primary studies were obtained after searching different information sources in the time span 1995-2008. The results of the review show that
   symbolic regression using genetic programming has been applied in three domains within software engineering predictive modeling: (i) Software quality classification (eight
   primary studies). (ii) Software cost/effort/size estimation (seven primary studies). (iii) Software fault prediction/software reliability growth modelling (eight primary
   studies). While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth
   modelling; the results are inconclusive for software cost/effort/size estimation.

%A Alexandru Agapie
%T Random Systems with Complete Connections
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 770
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-862.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Alexandros Agapitos
%A Simon M. Lucas
%T Learning Recursive Functions with Object Oriented Genetic Programming
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 166--177
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050166.pdf
%X This paper describes the evolution of recursive functions within an Object-Oriented Genetic Programming (OOGP) system. We evolved general solutions to factorial, Fibonacci,
   exponentiation, even-n-Parity, and nth-3. We report the computational effort required to evolve these methods and provide a comparison between crossover and mutation
   variation operators, and also undirected random search. We found that the evolutionary algorithms performed much better than undirected random search, and that mutation
   outperformed crossover on most problems.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006 Java reflection.
%@ 3-540-33143-3

%A Alexandros Agapitos
%A Simon M. Lucas
%T Evolving Efficient Recursive Sorting Algorithms
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 9227--9234
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, computational complexity, evolutionary computation, object-oriented languages, object-oriented programming, OOGP, evolutionary
   process, fitness function, language primitives, object oriented genetic programming, recursive sorting algorithms, time complexity
%U http://privatewww.essex.ac.uk/~aagapi/papers/AgapitosLucasEvolvingSort.pdf
%X Object Oriented Genetic Programming (OOGP) is applied to the task of evolving general recursive sorting algorithms. We studied the effects of language primitives and
   fitness functions on the success of the evolutionary process. For language primitives, these were the methods of a simple list processing package. Five different fitness
   functions based on sequence disorder were evaluated. The time complexity of the successfully evolved algorithms was measured experimentally in terms of the number of method
   invocations made, and for the best evolved individuals this was best approximated as O(n log(n)). This is the first time that sorting algorithms of this complexity have
   been evolved.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. Best in session. IEEE Xplore gives pages as 2677--2684
%@ 0-7803-9487-9

%A Alexandros Agapitos
%A Simon M. Lucas
%T Evolving a Statistics Class Using Object Oriented Evolutionary Programming
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 291--300
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X Object Oriented Evolutionary Programming is used to evolve programs that calculate some statistical measures on a set of numbers. We compared this technique with a more
   standard functional representation. We also studied the effects of scalar and Pareto-based multi-objective fitness functions to the induction of multi-task programs. We
   found that the induction of a program residing in an OO representation space is more efficient, yielding less fitness evaluations, and that scalar fitness performed better
   than Pareto-based fitness in this problem domain.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Alexandros Agapitos
%A Simon M. Lucas
%T Evolving Modular Recursive Sorting Algorithms
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 301--310
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X A fundamental issue in evolutionary learning is the definition of the solution representation language. We present the application of Object Oriented Genetic Programming to
   the task of coevolving general recursive sorting algorithms along with their primitive representation alphabet. We report the computational effort required to evolve target
   solutions and provide a comparison between crossover and mutation variation operators, and also undirected random search. We found that the induction of evolved method
   signatures (typed parameters and return type) can be realized through an evolutionary fitness-driven process. We also found that the evolutionary algorithm outperformed
   undirected random search, and that mutation performed better than crossover in this problem domain. The main result is that modular sorting algorithms can be evolved.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Alexandros Agapitos
%A Julian Togelius
%A Simon Mark Lucas
%T Evolving controllers for simulated car racing using object oriented genetic programming
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1543--1550
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, evolutionary computer games, evolutionary robotics, homologous uniform crossover, neural networks, object oriented, subtree
   macro-mutation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1543.pdf
%X The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively Co-evolves a population of adaptive mappings and associated
   genotypes is used to learn recursive solutions given a function set consisting of general (not implicitly recursive) machine-language instructions. PAM DGP using redundant
   encodings to model the evolution of the biological genetic code is found to more efficiently learn 2nd and 3rd order recursive Fibonacci functions than related
   developmental systems and traditional linear GP. PAM DGP using redundant encoding is also demonstrated to produce the semantically highest quality solutions for all three
   recursive functions considered (Factorial, 2nd and 3rd order Fibonacci). PAM DGP is then shown to have produced such solutions by evolving redundant mappings to select and
   emphasise appropriate subsets of the function set useful for producing the naturally recursive solutions.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Alexandros Agapitos
%A Julian Togelius
%A Simon M. Lucas
%T Multiobjective Techniques for the Use of State in Genetic Programming Applied to Simulated Car Racing
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 1562--1569
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Multi-objective optimisation is applied to encourage the effective use of state variables in car controlling programs evolved using Genetic Programming. Three different
   metrics for measuring the use of state within a program are introduced. Comparisons are performed among multi- and single-objective fitness functions with respect to
   learning speed and final fitness of evolved individuals, and attempts are made at understanding whether there is a trade-off between good performance and stateful
   controllers in this problem domain.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Alexandros Agapitos
%A Matthew Dyson
%A Simon M. Lucas
%A Francisco Sepulveda
%T Learning to recognise mental activities: genetic programming of stateful classifiers for brain-computer interfacing
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1155--1162
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, Brain computer interface, classification on Raw signal, stateful representation, statistical signal primitives
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1155.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389326

%A Alexandros Agapitos
%A Matthew Dyson
%A Jenya Kovalchuk
%A Simon Mark Lucas
%T On the genetic programming of time-series predictors for supply chain management
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1163--1170
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, Iterated single-step prediction, prediction/forecasting, single-step prediction, statistical time-series Features
%U http://privatewww.essex.ac.uk/~yvkova/Papers/GP_GECCO08.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389327

%A Alexandros Agapitos
%A Julian Togelius
%A Simon M. Lucas
%A Jurgen Schmidhuber
%A Andreas Konstantinidis
%T Generating Diverse Opponents with Multiobjective Evolution
%B Proceedings of the 2008 IEEE Symposium on Computational Intelligence and Games
%D 2008
%P 135--142
%I IEEE
%C Perth, Australia
%K genetic algorithms, genetic programming, Reinforcement Learning, Multiobjective Evolution, AI in Computer Games, EMOA, Car Racing, MOGA, AI game agent, computational
   intelligence, diverse opponent generation, game play learning, multiobjective evolutionary algorithm, nonplayer character, computer games, evolutionary computation,
   learning (artificial intelligence), multi-agent systems
%U http://julian.togelius.com/Agapitos2008Generating.pdf
%X For computational intelligence to be useful in creating game agent AI, we need to focus on creating interesting and believable agents rather than just learn to play the
   games well. To this end, we propose a way to use multiobjective evolutionary algorithms to automatically create populations of NPCs, such as opponents and collaborators,
   that are interestingly diverse in behaviour space. Experiments are presented where a number of partially conflicting objectives are defined for racing game competitors, and
   multiobjective evolution of GP-based controllers yield Pareto fronts of interesting controllers.
%8 Decemeber  15-18
%Z Also known as \cite5035632

%A Alexandros Agapitos
%A Michael O'Neill
%A Anthony Brabazon
%T Evolutionary Learning of Technical Trading Rules without Data-mining Bias
%B PPSN 2010 11th International Conference on Parallel Problem Solving From Nature
%S Lecture Notes in Computer Science
%E Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph
%V 6238
%D 2010
%P 294--303
%I Springer
%C Krakow, Poland
%K genetic algorithms, genetic programming
%X In this paper we investigate the profitability of evolved technical trading rules when controlling for data-mining bias. For the first time in the evolutionary computation
   literature, a comprehensive test for a rule's statistical significance using Hansen's Superior Predictive Ability is explicitly taken into account in the fitness function,
   and multi-objective evolutionary optimisation is employed to drive the search towards individual rules with better generalisation abilities. Empirical results on a spot
   foreign-exchange market index suggest that increased out-of-sample performance can be obtained after accounting for data-mining bias effects in a multi-objective fitness
   function, as compared to a single-criterion fitness measure that considers solely the average return.
%8 11-15  September

%A Alexandros Agapitos
%A Andreas Konstantinidis
%A Haris Haralambous
%A Harris Papadopoulos
%T Evolutionary Prediction of Total Electron Content over Cyprus
%B 6th IFIP Advances in Information and Communication Technology AIAI 2010
%S IFIP Advances in Information and Communication Technology
%E Harris Papadopoulos and Andreas Andreou and Max Bramer
%V 339
%D 2010
%P 387--394
%I Springer
%C Larnaca, Cyprus
%K genetic algorithms, genetic programming, Evolutionary Algorithms, Global Positioning System, Total Electron Content
%X Total Electron Content (TEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on trans-ionospheric links and subsequently
   overwhelm its negative impact in accurate position determination. In this paper, an Evolutionary Algorithm (EA), and particularly a Genetic Programming (GP) based model is
   designed. The proposed model is based on the main factors that influence the variability of the predicted parameter on a diurnal, seasonal and long-term time-scale.
   Experimental results show that the GP-model, which is based on TEC measurements obtained over a period of 11 years, has produced a good approximation of the modeled
   parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning. The GP-based approach performs better than the existing
   Neural Network-based approach in several cases.
%8 October  6-7
%Z http://www.cs.ucy.ac.cy/aiai2010/

%A Alexandros Agapitos
%A Michael O'Neill
%A Anthony Brabazon
%T Promoting the generalisation of genetically induced trading rules
%B Proceedings of the 4th International Conference on Computational and Financial Econometrics CFE'10
%E G. Kapetanios and O. Linton and M. McAleer and E. Ruiz
%D 2010
%P E678
%I ERCIM
%I CSDA, LSE, Queen Mary and Westerfield College
%C Senate House, University of London, UK
%K genetic algorithms, genetic programming
%U http://www.cfe-csda.org/cfe10/LondonBoA.pdf
%X The goal of Machine Learning is not to induce an exact representation of the training patterns themselves, but rather to build a model of the underlying pattern-generation
   process. One of the most important aspects of this computational process is how to obtain general models that are representative of the true concept, and as a result,
   perform efficiently when presented with novel patterns from that concept. A particular form of evolutionary machine learning, Genetic Programming, tackles learning problems
   by means of an evolutionary process of program discovery. In this paper we investigate the profitability of evolved technical trading rules when accounting for the problem
   of over-fitting. Out-of-sample rule performance deterioration is a well-known problem, and has been mainly attributed to the tendency of the evolved models to find
   meaningless regularities in the training dataset due to the high dimensionality of features and the rich hypothesis space. We present a review of the major established
   methods for promoting generalisation in conventional machine learning paradigms. Then, we report empirical results of adapting such techniques to the Genetic Programming
   methodology, and applying it to discover trading rules for various financial datasets.
%8 10-12  Decemeber
%Z http://www.cfe-csda.org/cfe10/

%A Alexandros Agapitos
%A Michael O'Neill
%A Anthony Brabazon
%A Theodoros Theodoridis
%T Maximum Margin Decision Surfaces for Increased Generalisation in Evolutionary Decision Tree Learning
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 61--72
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X Decision tree learning is one of the most widely used and practical methods for inductive inference. We present a novel method that increases the generalisation of
   genetically-induced classification trees, which employ linear discriminants as the partitioning function at each internal node. Genetic Programming is employed to search
   the space of oblique decision trees. At the end of the evolutionary run, a (1+1) Evolution Strategy is used to geometrically optimise the boundaries in the decision space,
   which are represented by the linear discriminant functions. The evolutionary optimisation concerns maximising the decision-surface margin that is defined to be the smallest
   distance between the decision-surface and any of the samples. Initial empirical results of the application of our method to a series of datasets from the UCI repository
   suggest that model generalisation benefits from the margin maximisation, and that the new method is a very competent approach to pattern classification as compared to other
   learning algorithms.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Alexandros Agapitos
%A Michael O'Neill
%A Anthony Brabazon
%T Stateful program representations for evolving technical trading rules
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 199--200
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming: Poster
%X A family of stateful program representations in grammar-based Genetic Programming are being compared against their stateless counterpart in the problem of binary
   classification of sequences of daily prices of a financial asset. Empirical results suggest that stateful classifiers learn as fast as stateless ones but generalise better
   to unseen data, rendering this form of program representation strongly appealing to the automatic programming of technical trading rules.
%8 12-16  July
%Z Also known as \cite2001969 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Alexandros Agapitos
%A Michael O'Neill
%A Anthony Brabazon
%A Theodoros Theodoridis
%T Learning Environment Models in Car Racing Using Stateful Genetic Programming
%B Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games
%D 2011
%P 219--226
%I IEEE
%C Seoul, South Korea
%K genetic algorithms, genetic programming, Reinforcement Learning, Multiobjective Evolution, AI in Computer Games, Car Racing, AI game agent, computational intelligence,
   diverse opponent generation, game play learning, nonplayer character, computer games, evolutionary computation, learning (artificial intelligence), multi-agent systems, 2D
   data structures, artificial agents, car racing games, learning environment models, model building behaviour, modular programs, non player characters, cognition, computer
   games, data structures, learning (artificial intelligence), multi-agent systems
%U http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper54.pdf
%X For computational intelligence to be useful in creating game agent AI we need to focus on methods that allow the creation and maintenance of models for the environment,
   which the artificial agents inhabit. Maintaining a model allows an agent to plan its actions more effectively by combining immediate sensory information along with a
   memories that have been acquired while operating in that environment. To this end, we propose a way to build environment models for non-player characters in car racing
   games using stateful Genetic Programming. A method is presented, where general-purpose 2-dimensional data-structures are used to build a model of the racing track. Results
   demonstrate that model-building behaviour can be cooperatively coevolved with car-controlling behaviour in modular programs that make use of these models in order to
   navigate successfully around a racing track.
%8 31  August  - 3  September
%Z Indexed memory. Also known as \cite6032010

%A Alexandros Agapitos
%A Abhinav Goyal
%A Cal Muckley
%T An Evolutionary Algorithmic Investigation of US Corporate Payout Policy
%B Natural Computing in Computational Finance (Volume 4)
%S Studies in Computational Intelligence
%E Anthony Brabazon and Michael O'Neill and Dietmar Maringer
%V 380
%D 2012
%P 123--139
%I Springer
%K genetic algorithms, genetic programming, US Corporate Payout Policy, Symbolic Regression
%U http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-23335-7
%O 7

%A Alexandros Agapitos
%A Michael O'Neill
%A Anthony Brabazon
%T Evolving Seasonal Forecasting Models with Genetic Programming for Pricing Weather-derivatives
%B Applications of Evolutionary Computing, EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC
%S LNCS
%E Cecilia Di Chio and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. Fernandez de Vega and Gianni A. Di Caro and Rolf Drechsler and Aniko Ekart and Anna I
   Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero
   and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis
%V 7248
%D 2011
%P 135--144
%I Springer Verlag Berlin
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming
%X In this study we evolve seasonal forecasting temperature models, using Genetic Programming (GP), in order to provide an accurate, localised, long-term forecast of a
   temperature profile as part of the broader process of determining appropriate pricing model for weather-derivatives, financial instruments that allow organisations to
   protect themselves against the commercial risks posed by weather fluctuations. Two different approaches for time-series modelling are adopted. The first is based on a
   simple system identification approach whereby the temporal index of the time-series is used as the sole regressor of the evolved model. The second is based on iterated
   single-step prediction that resembles autoregressive and moving average models in statistical time-series modelling. Empirical results suggest that GP is able to
   successfully induce seasonal forecasting models, and that autoregressive models compose a more stable unit of evolution in terms of generalisation performance for the three
   datasets investigated.
%8 11-13  April
%Z EvoFIN Part of \citeDiChio:2012:EvoApps EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012

%A Alexandros Agapitos
%A Michael O'Neill
%A Anthony Brabazon
%T Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather Derivatives
%B Financial Decision Making Using Computational Intelligence
%S Springer Optimization and Its Applications
%E Doumpos Michael and Zopounidis Constantin and Pardalos Panos
%V 70
%D 2012
%P 153--182
%I Springer
%K genetic algorithms, genetic programming, Weather derivatives pricing, Seasonal temperature forecasting, Autoregressive models, Supervised ensemble learning, Generalisation
%U http://www.springer.com/mathematics/applications/book/978-1-4614-3772-7
%O 6
%O Due: July 31, 2012

%A Paul-Michael Agapow
%T Computational Brittleness and the Evolution of Computer Viruses
%B Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation
%S LNCS
%E Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel
%V 1141
%D 1996
%P 2--11
%I Springer-Verlag Heidelberg, Germany
%C Berlin, Germany
%X In recent years computer viruses have grown to be of great concern. They have also been proposed as prototypical artificial life, but the possibility of their evolution has
   been dismissed due to modern computer programs being computationally brittle (i.e. a random change to a functional program will almost certainly render it non-functional)
   and the series of steps required for the evolution of a new virus being improbable. These allegations are examined by studying homology between functional program
   sequences. It is concluded that programs are far less brittle than expected. While the evolution of viruses de novo is still unlikely, evolution of pre-existing viruses and
   programs is feasible. This has significant implications for computer security and evolutionary computation.
%8 22-26  September
%Z http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 (UNIX Sun RISC) "programs are far less brittle than expected".
%@ 3-540-61723-X

%A Ashish Agarwal
%T Genetic Programming for Wafer Property Prediction After Plasma Enhanced
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 16--24
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Varun Aggarwal
%T Prediction of Protein Secondary Structure using Genetic Programming
%D 2003
%I 
%K genetic algorithms, genetic programming
%U http://web.mit.edu/varun_ag/www/psspreport.pdf
%X Project 1:Using SOM and Genetic Programming to predict Protein Secondary structure Project 2: Improving PSIPRED Predictions using Genetic Programming
%O Summer Internship Project Report During June-July 2003
%Z Under: Dr. Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden

%A Varun Aggarwal
%A Robert MacCallum
%T Evolved Matrix Operations for Post-Processing Protein Secondary Structure Predictions
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 220--229
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=220
%X Predicting the three-dimensional structure of proteins is a hard problem, so many have opted instead to predict the secondary structural state (usually helix, strand or
   coil) of each amino acid residue. This should be an easier task, but it now seems that a ceiling of around 76 percent per-residue three-state accuracy has been reached.
   Further improvements will require the correct processing of so-called "long-range information". We present a novel application of genetic programming to evolve high level
   matrix operations to post-process secondary structure prediction probabilities produced by the popular, state-of-the-art neural network based PSIPRED by David Jones. We
   show that global and long-range information may be used to increase three-state accuracy by at least 0.26 percentage points - a small but statistically significant
   difference. This is on top of the 0.14 percentage point increase already made by PSIPRED's built-in filters.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Varun Aggarwal
%A Una-May O'Reilly
%T Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms
%B Genetic Programming Theory and Practice IV
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%V 5
%D 2006
%P -
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, circuit sizing, symbolic regression, posynomial models, geometric programming
%U http://people.csail.mit.edu/unamay/publications-dir/gptp06.pdf
%X Starting from a broad description of analogue circuit design in terms of topology design and sizing, we discuss the difficulties of sizing and describe approaches that are
   manual or automatic. These approaches make use of blackbox optimisation techniques such as evolutionary algorithms or convex optimization techniques such as geometric
   programming. Geometric programming requires posynomial expressions for a circuit's performance measurements. We show how a genetic algorithm can be exploited to evolve a
   polynomial expression (i.e. model) of transistor (i.e. mosfet) behaviour more accurately than statistical techniques in the literature.
%O 7
%8 11-13  May
%Z part of \citeRiolo:2006:GPTP Published Jan 2007 after the workshop
%@ 0-387-33375-4

%A Davide Agnelli
%A Alessandro Bollini
%A Luca Lombardi
%T Image classification: an evolutionary approach
%J Pattern Recognition Letters
%V 23
%N 1-3
%D 2002
%P 303--309
%I 
%K genetic algorithms, genetic programming, Image classification, Supervised learning
%U http://www.sciencedirect.com/science/article/B6V15-443K10X-6/1/7af8206767ca79f9898fec720a84c656
%X Evolutionary algorithms are proving viable in solving complex optimization problems such as those typical of supervised learning approaches to image understanding. This
   paper presents an evolutionary approach to image classification and discusses some experimental results, suggesting that genetic programming could provide a convenient
   alternative to standard supervised learning methods.

%A Jose L. Aguilar
%A Mariela Cerrada
%T Reliability-Centered Maintenance Methodology-Based Fuzzy Classifier System Design for Fault Tolerance
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 621
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, classifiers
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Jose Aguilar
%A Pablo Miranda
%T Approaches Based on Genetic Algorithms for Multiobjective Optimization Problems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 3--10
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-873.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jesus Aguilar
%A Jose Riquelme
%A Miguel Toro
%T Three Geometric Approaches for representing Decision Rules in a Supervised Learning System
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 771
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-391.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99). See also
   \citeaguilar:1999:T
%@ 1-55860-611-4

%A Jesus Aguilar
%A Jose Riquelme
%A Miguel Toro
%T Three geometric approaches for representing decision rules in a supervised learning system
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 8--15
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms, data mining, supervised learning, hyper rectangles, rotated hyper rectangles, hyper ellipse
%X hyperrectangles, rotated hyperrectangles and hyperellipses
%8 13  July
%Z GECCO-99LB

%A Jose Aguilar
%A Mariela Cerrada
%T Fuzzy Classifier System and Genetic Programming on System Identification Problems
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 1245--1251
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d24.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Jose Aguilar
%A Mariela Cerrada
%T Genetic Programming-Based Approach for System Identification Applying Genetic Programming to obtain Separation
%B WSEAS NNA-FSFS-EC 2001
%D 2001
%P paper ID number 640
%I 
%I The World Scientific and Engineering Academy and Society (WSEAS)
%C Puerto De La Cruz, Tenerife, Spain
%K genetic algorithms, genetic programming, Genetic Programming, Evolutionary Computation, Identification Systems
%8 February ~11-15
%Z www.wseas.com/2001.xls

%A J. Aguilar
%A J. Altamiranda
%T A Data Mining Algorithm Based on the Genetic Programming
%D 2004
%I 
%K genetic algorithms, genetic programming, Data Mining, Clustering
%X Data Mining is composed by a set of methods to extract knowledgement from large database. One of these methods is Genetic Programming. In this work we use this method to
   build a Data Mining System that define a set of patterns in order to classify the data. We define a grammar, which is used by the Genetic Programming in order to define the
   rules that represent the patterns. In this way, we can group the data in class and simplify the information in the database according to the set of patterns.
%Z J. Aguilar Universidad de los Andes, Facultad de Ingenieria, Departamento de Computacion, CEMISID, Merida, Venezuela, 5101 J. Altamiranda Universidad de los Andes, Facultad
   de Ingenieria, Postgrado de Computacion, CEMISID, Merida, Venezuela, 5101

%A Jose Aguilar
%A Gilberto Gonzalez
%T Data Extrapolation Using Genetic Programming to Matrices Singular Values Estimation
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Simon M. Lucas and Gary Fogel and Graham Kendall and Ralf Salomon and Byoung-Tak Zhang and Carlos A. Coello Coello and Thomas Philip Runarsson
%D 2006
%P 3227--3230
%I IEEE Press
%C Vancouver, BC, Canada
%K genetic algorithms, genetic programming
%U http://ieeexplore.ieee.org/servlet/opac?punumber=11108
%X In mathematical models where the dimensions of the matrices are very large, the use of classical methods to compute the singular values is very time consuming and requires
   a lot of computational resources. In this way, it is necessary to find new faster methods to compute the singular values of a very large matrix. We present a method to
   estimate the singular values of a matrix based on Genetic Programming (GP). GP is an approach based on the evolutionary principles of the species. GP is used to make
   extrapolations of data out of sample data. The extrapolations of data are achieved by irregularity functions which approximate very well the trend of the sample data. GP
   produces from just simple's functions, operators and a fitness function, complex mathematical expressions that adjust smoothly to a group of points of the form (xi, yi). We
   obtain amazing mathematical formulas that follow the behaviour of the sample data. We compare our algorithm with two techniques: the linear regression and non linear
   regression approaches. Our results suggest that we can predict with some percentage of error the largest singular values of a matrix without computing the singular values
   of the whole matrix and using only some random selected columns of the matrix.
%8 16-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Arturo Hernandez Aguirre
%A Carlos A. Coello Coello
%A Bill P. Buckles
%T A Genetic Programming Approach to Logic Function Synthesis by Means of Multiplexers
%B Proceedings of the The First NASA/DOD Workshop on Evolvable Hardware
%E Adrian Stoica and Didier Keymeulen and Jason Lohn
%D 1999
%P 46--53
%I IEEE Computer Society
%I Jet Propulsion Laboratory, California Institute of Technology
%C Pasadena, California
%K genetic algorithms, genetic programming, evolvable hardware
%U http://computer.org/proceedings/eh/0256/02560046abs.htm
%X This paper presents an approach based on the use of genetic programming to synthesize logic functions. The proposed approach uses the 1-control line multiplexer as the only
   design unit, defining any logic function (defined by a truth table) through the replication of this single unit. Our fitness function first explores the search space trying
   to find a feasible design and then concentrates in the minimization of such (fully feasible) circuit. The proposed approach is illustrated using several sample Boolean
   functions.
%8 19-21  July
%Z EH-1999
%@ 0-7695-0256-3

%A Hernan E. Aguirre
%A Kiyoshi Tanaka
%A Tatsuo Sugimura
%T Cooperative Crossover and Mutation Operators in Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 772
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Arturo Hernandez-Aguirre
%A Carlos Coello-Coello
%T Mutual Information-based Fitness Functions for Evolutionary Circuit Synthesis
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%V 2
%D 2004
%P 1309--1316
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, EHW, Evolutionary Design Automation, Evolutionary design \& evolvable hardware
%U http://delta.cs.cinvestav.mx/~ccoello/conferences/cec04-muxmutual.pdf.gz
%X Mutual Information and Normalised Mutual Information measures are investigated. The goal is the analysis of some fitness functions based in mutual information and what
   problems prevent them from common use. We identify and find a clear explanation to them, thereafter, we propose new fitness functions and ran several experiments to
   investigate their effect on the search space, convergence time, and quality of solutions.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Dilip P. Ahalpara
%A Jitendra C. Parikh
%T Modeling Time Series of Real Systems using Genetic Programming
%D 2006
%I 
%K genetic algorithms, genetic programming
%U http://arxiv.org/PS_cache/nlin/pdf/0607/0607029v1.pdf
%X Analytic models of two computer generated time series (Logistic map and Rossler system) and two real time series (ion saturation current in Aditya Tokamak plasma and NASDAQ
   composite index) are constructed using Genetic Programming (GP) framework. In each case, the optimal map that results from fitting part of the data set also provides a very
   good description of rest of the data. Predictions made using the map iteratively range from being very good to fair.
%O ArXiv Nonlinear Sciences e-prints
%O Submitted to Physical Review E
%8 14  July
%Z nlin/0607029 See also \citeAhalpara:2008:IJMPC

%A Dilip P. Ahalpara
%A Jitendra C. Parikh
%T Genetic Programming based approach for Modeling Time Series data of real systems
%J International Journal of Modern Physics C, Computational Physics and Physical Computation
%V 19
%N 1
%D 2008
%P 63--91
%I 
%K genetic algorithms, genetic programming, Time series analysis, artificial neural networks
%X Analytic models of a computer generated time series (logistic map) and three real time series (ion saturation current in Aditya Tokamak plasma, NASDAQ composite index and
   Nifty index) are constructed using Genetic Programming (GP) framework. In each case, the optimal map that results from fitting part of the data set also provides a very
   good description of the rest of the data. Predictions made using the map iteratively are very good for computer generated time series but not for the data of real systems.
   For such cases, an extended GP model is proposed and illustrated. A comparison of these results with those obtained using Artificial Neural Network (ANN) is also carried
   out.
%Z IJMPC PACS numbers: 05.45.Tp, 02.30.NW Institute for Plasma Research, Near Indira Bridge, Bhat, Gandhinagar-382428, India Physical Research Laboratory, Navrangpura,
   Ahmedabad-380009, India

%A Dilip P. Ahalpara
%A Amit Verma
%A Jitendra C. Parikh
%A Prasanta K. Panigrahi
%T Characterizing and modelling cyclic behaviour in non-stationary time series through multi-resolution analysis
%J Pramana
%V 71
%D 2008
%P 459--485
%I Springer India, in co-publication with Indian Academy of Sciences
%K genetic algorithms, genetic programming, finance, Non-stationary time series, wavelet transform, Characterizing and modelling cyclic behaviour in non-stationary time series
   through multi-resolution analysis
%X A method based on wavelet transform is developed to characterise variations at multiple scales in non-stationary time series. We consider two different financial time
   series, S&P CNX Nifty closing index of the National Stock Exchange (India) and Dow Jones industrial average closing values. These time series are chosen since they are
   known to comprise of stochastic fluctuations as well as cyclic variations at different scales. The wavelet transform isolates cyclic variations at higher scales when random
   fluctuations are averaged out; this corroborates correlated behaviour observed earlier in financial time series through random matrix studies. Analysis is carried out
   through Haar, Daubechies-4 and continuous Morlet wavelets for studying the character of fluctuations at different scales and show that cyclic variations emerge at
   intermediate time scales. It is found that Daubechies family of wavelets can be effectively used to capture cyclic variations since these are local in nature. To get an
   insight into the occurrence of cyclic variations, we then proceed to model these wavelet coefficients using genetic programming (GP) approach and using the standard
   embedding technique in the reconstructed phase space. It is found that the standard methods (GP as well as artificial neural networks) fail to model these variations
   because of poor convergence. A novel interpolation approach is developed that overcomes this difficulty. The dynamical model equations have, primarily, linear terms with
   additive Padé-type terms. It is seen that the emergence of cyclic variations is due to an interplay of a few important terms in the model. Very interestingly GP model
   captures smooth variations as well as bursty behaviour quite nicely.
%8 November
%Z (1) Institute for Plasma Research, Near Indira Bridge, Bhat, Gandhinagar, 382 428, India (2) Physical Research Laboratory, Navrangpura, Ahmedabad, 380 009, India (3) Indian
   Institute of Science Education and Research, Salt Lake City, Kolkata, 700 106, India

%A Dilip Ahalpara
%A Siddharth Arora
%A M Santhanam
%T Genetic Programming Based Approach for Synchronization with Parameter Mismatches in EEG
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 13--24
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Dilip P. Ahalpara
%T Improved forecasting of time series data of real system using genetic programming
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 977--978
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, Poster
%X A study is made to improve short term forecasting of time series data of real system using Genetic Programming (GP) under the framework of time delayed embedding technique.
   GP based approach is used to make analytical model of time series data of real system using embedded vectors that help reconstruct the phase space. The map equations,
   involving non-linear symbolic expressions in the form of binary trees comprising of time delayed components in the immediate past, are first obtained by carrying out
   single-step GP fit for the training data set and usually they are found to give good fitness as well as single-step predictions. However while forecasting the time series
   based on multi-step predictions in the out-of-sample region in an iterative manner, these solutions often show rapid deterioration as we dynamically forward the solution in
   future time. With a view to improve on this limitation, it is shown that if the multi-step aspect is incorporated while making the GP fit itself, the corresponding GP
   solutions give multi-step predictions that are improved to a fairly good extent for around those many multi-steps as incorporated during the multi-step GP fit. Two
   different methods for multi-step fit are introduced, and the corresponding prediction results are presented. The modified method is shown to make better forecast for
   out-of-sample multi-step predictions for the time series of a real system, namely Electroencephelograph (EEG) signals.
%8 7-11  July
%Z Also known as \cite1830658 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Dilip Ahalpara
%A Abhijit Sen
%T A Sniffer Technique for an Efficient Deduction of Model Dynamical Equations using Genetic Programming
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 1--12
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming, local search, hill climbing
%X A novel heuristic technique that enhances the search facility of the standard genetic programming (GP) algorithm is presented. The method provides a dynamic sniffing
   facility to optimise the local search in the vicinity of the current best chromosomes that emerge during GP iterations. Such a hybrid approach, that combines the GP method
   with the sniffer technique, is found to be very effective in the solution of inverse problems where one is trying to construct model dynamical equations from either finite
   time series data or knowledge of an analytic solution function. As illustrative examples, some special function ordinary differential equations (ODEs) and integrable
   nonlinear partial differential equations (PDEs) are shown to be efficiently and exactly recovered from known solution data. The method can also be used effectively for
   solution of model equations (the direct problem) and as a tool for generating multiple dynamical systems that share the same solution space.
%8 27-29  April
%Z Mathematica. Order of partial or ordinary differential equation search in sequence starting with first order and increasing until satisfactory match found. Part of
   \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A John Ahlschwede
%T Using Genetic Programming to Play Mancala
%D 2000
%I 
%O http://www.corngolem.com/john/gp/index.html

%A Manu Ahluwalia
%A Terence C. Fogarty
%T Co-Evolving Classification Programs using Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 419
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Manu Ahluwalia
%A Larry Bull
%A Terence C. Fogarty
%T Co-evolving Functions in Genetic Programming: A Comparison in ADF Selection Strategies
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 3--8
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Ahluwalia_1997_.pdf
%8 13-16  July
%Z GP-97

%A Manu Ahluwalia
%A Larry Bull
%A Terence C. Fogarty
%T Co-evolving Functions in Genetic Programming: An Emergent Approach using ADFs and GLiB
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 1--6
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A M. Ahluwalia
%A L. Bull
%T Co-evolving Functions in Genetic Programming: Dynamic ADF Creation using GLiB
%B Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%E V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben
%V 1447
%D 1998
%P 809--818
%I Springer-Verlag Berlin
%C Mission Valley Marriott, San Diego, California, USA
%K genetic algorithms, genetic programming
%8 25-27  March
%Z EP-98. University of the West of England, UK
%@ 3-540-64891-7

%A Manu Ahluwalia
%A Larry Bull
%T A Genetic Programming-based Classifier System
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 11--18
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Manu Ahluwalia
%A Larry Bull
%T Coevolving Functions in Genetic Programming: Classification using K-nearest-neighbour
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 947--952
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-413.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Manu Ahluwalia
%A Larry Bull
%T Coevolving functions in genetic programming
%J Journal of Systems Architecture
%V 47
%N 7
%D 2001
%P 573--585
%I 
%K genetic algorithms, genetic programming, ADF, Classification, EDF, Feature selection/extraction, Hierarchical programs, Knn, Speciation
%U http://www.sciencedirect.com/science/article/B6V1F-43RV156-3/1/16dd3ab5502922479ef7bb1ca4f7b9c3
%X In this paper we introduce a new approach to the use of automatically defined functions (ADFs) within genetic programming. The technique consists of evolving a number of
   separate sub-populations of functions which can be used by a population of evolving main programs. We present and refine a set of mechanisms by which the number and
   constitution of the function sub-populations can be defined and compare their performance on two well-known classification tasks. A final version of the general approach,
   for use explicitly on classification tasks, is then presented. It is shown that in all cases the coevolutionary approach performs better than traditional genetic
   programming with and without ADFs.
%8 July

%A Ishfaq Ahmad
%T Cluster Computing: Genetic programming in clusters
%J IEEE Concurrency
%V 8
%N 3
%D 2000
%P 10--11, 13
%I 
%K genetic algorithms, genetic programming", acknowledgement = ack-nhfb
%8 July \slash  September
%Z http://csdl.computer.org/comp/mags/pd/2000/03/p3toc.htm

%A Hannu Ahonen
%A Paulo A. {de Souza Jr.}
%A Vijayendra Kumar Garg
%T A genetic algorithm for fitting Lorentzian line shapes in Mossbauer spectra
%J Nuclear Instruments and Methods in Physics Research B
%V 124
%D 1997
%P 633--638
%I 
%K genetic algorithms
%X A genetic algorithm was implemented for finding an approximative solution to the problem of fitting a combination of Lorentzian lines to a measured Mossbauer spectrum. This
   iterative algorithm exploits the idea of letting several solutions (individuals) compete with each other for the opportunity of being selected to create new solutions
   (reproduction). Each solution was represend as a string of binary digits (chromossome). In addition, the bits in the new solutions may be switched randomly from zero to one
   or conversely (mutation). The input of the program that implements the genetic algorithm consists of the measured spectrum, the maximum velocity, the peak positions and the
   expected number of Lorentzian lines in the spectrum. Each line is represented with the help of three variables, which correspond to its intensity, full line width at hald
   maxima and peak position. An additional parameter was associated to the background level in the spectrum. A chi-2 test was used for determining the quality of each
   parameter combination (fitness). The results obtained seem to be very promising and encourage to further development of the algorithm and its implementation.
%8 5  May

%A Malek Aichour
%A Evelyne Lutton
%T Cooperative Co-evolution Inspired Operators for Classical GP Schemes
%B Proceedings of International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO '07)
%S Studies in Computational Intelligence
%E Natalio Krasnogor and Giuseppe Nicosia and Mario Pavone and David Pelta
%V 129
%D 2007
%P 169--178
%I Springer
%C Acireale, Italy
%K genetic algorithms, genetic programming
%8 8-10  November

%A P. Aiyarak
%A A. S. Saket
%A M. C. Sinclair
%T Genetic Programming Approaches for Minimum Cost Topology Optimisation of Optical Telecommunication Networks
%B Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA
%D 1997
%I IEE Savoy Place, London, WC2R 0BL, UK
%C University of Strathclyde, Glasgow, UK
%K genetic algorithms, genetic programming, telecommunication networks, topology
%U http://uk.geocities.com/markcsinclair/ps/galesia97_aiy.ps.gz
%X This paper compares the relative efficiency of three approaches for the minimum-cost topology optimisation of the COST 239 European Optical Network (EON) using genetic
   programming. The GP was run for the central nine nodes using three approaches: relational function set, decision trees, and connected nodes. Only the best two, decision
   trees and connected nodes, were run for the full EON. The results are also compared with earlier genetic algorithm work on the EON.
%8 1-4  September
%Z GALESIA'97
%@ 0-85296-693-8

%A Frederick R. Akalin
%T Developing a Computer-Controller Opponent for a First-Person Simulation Game using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 11--20
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2002:gagp

%A Vahab Akbarzadeh
%A Alireza Sadeghian
%A Marcus V. {dos Santos}
%T Derivation of Relational Fuzzy Classification Rules Using Evolutionary Computation
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 1689--1693
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, constrained-syntax genetic programming, evolutionary computation, knowledge-based systems, mutation-based evolutionary algorithm,
   relational fuzzy classification rules, fuzzy set theory, knowledge based systems
%X An evolutionary system for derivation of fuzzy classification rules is presented. This system uses two populations: one of fuzzy classification rules, and one of membership
   function definitions. A constrained-syntax genetic programming evolves the first population and a mutation-based evolutionary algorithm evolves the second population. These
   two populations co-evolve to better classify the underlying dataset. Unlike other approaches that use fuzzification of continuous attributes of the dataset for discovering
   fuzzy classification rules, the system presented here fuzzifies the relational operators ``greater than'' and ``less than'' using evolutionary methods. For testing our
   system, the system is applied to the Iris dataset. Our experimental results show that our system outperforms previous evolutionary and non-evolutionary systems on accuracy
   of classification and derivation of interrelation between the attributes of the Iris dataset. The resulting fuzzy rules of the system can be directly used in
   knowledge-based systems.
%8 1-6  June
%Z Also known as \cite4630598 WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A M.-R. Akbarzadeh-T.
%A E. Tunstel
%A M. Jamshidi
%T Genetic Algorithms and Genetic Programming: Combining Strength in One Evolutionary Strategy
%B Proceedings of the 1997 WERC/HSRC Joint Conference on the Environment
%D 1997
%P 373--377
%I 
%I WERC Waste-management Education & Research Consortium New Mexico State University Box 30001, Department WERC Las Cruces, NM 88003-8001, USA HSRC Great Plains/Rocky Mountain
   Hazardous Substance Research Center Kansas State University 101 Ward Hall Manhattan, KS 66506-2502, USA
%C Albuquerque, NM, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Akbarzadeh_1997_jce.pdf
%X Genetic Algorithms (GA) and Genetic Programs (GP) are two of the most widely used evolution strategies for parameter optimisation of complex systems. GAs have shown a great
   deal of success where the representation space is a string of binary or real-valued numbers. At the same time, GP has demonstrated success with symbolic representation
   spaces and where structure among symbols is explored. This paper discusses weaknesses and strengths of GA and GP in search of a combined and more evolved optimization
   algorithm. This combination is especially attractive for problem domains with non-homogeneous parameters. In particular, a fuzzy logic membership function is represented by
   numerical strings, whereas rule-sets are represented by symbols and structural connectives. Two examples are provided which exhibit how GA and GP are best used in
   optimising robot performance in manipulating hazardous waste. The first example involves optimisation for a fuzzy controller for a flexible robot using GA and the second
   example illustrates usage of GP in optimizing an intelligent navigation algorithm for a mobile robot. A novel strategy for combining GA and GP is presented.
%8 26-29  April

%A M. R. Akbarzadeh-T.
%A E. Tunstel
%A K. Kumbla
%A M. Jamshidi
%T Soft computing paradigms for hybrid fuzzy controllers: experiments and applications
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%V 2
%D 1998
%P 1200--1205
%I IEEE Press
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming, neurocontrollers, fuzzy control, hierarchical systems, mobile robots, path planning, brushless DC motors, machine control,
   manipulators, soft computing paradigms, hybrid fuzzy controllers, neural networks, genetic algorithms, genetic programs, fuzzy logic-based schemes, added intelligence,
   adaptation, learning ability, direct drive motor, genetic algorithm-fuzzy hierarchical controller, flexible robot link, genetic programming-fuzzy behavior-based controller,
   mobile robot navigation task
%U http://ieeexplore.ieee.org/iel4/5612/15018/00686289.pdf?isNumber=15018
%X Neural networks (NN), genetic algorithms (GA), and genetic programs (GP) are often augmented with fuzzy logic-based schemes to enhance artificial intelligence of a given
   system. Such hybrid combinations are expected to exhibit added intelligence, adaptation, and learning ability. In the paper, implementation of three hybrid fuzzy
   controllers are discussed and verified by experimental results. These hybrid controllers consist of a hierarchical NN-fuzzy controller applied to a direct drive motor, a
   GA-fuzzy hierarchical controller applied to a flexible robot link, and a GP-fuzzy behavior-based controller applied to a mobile robot navigation task. It is experimentally
   shown that all three architectures are capable of significantly improving the system response.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
%@ 0-7803-4863-X

%A M.-R. Akbarzadeh-T.
%A K. Kumbla
%A E. Tunstel
%A M. Jamshidi
%T Soft computing for autonomous robotic systems
%J Computers and Electrical Engineering
%V 26
%N 1
%D 2000
%P 5--32
%I 
%K genetic algorithms, genetic programming, Soft computing, Neural networks, Fuzzy logic, Robotic control, Articial intelligence
%U http://citeseer.ist.psu.edu/373353.html
%X Neural networks (NN), genetic algorithms (GA), and genetic programming (GP) are augmented with fuzzy logic-based schemes to enhance artificial intelligence of automated
   systems. Such hybrid combinations exhibit added reasoning, adaptation, and learning ability. In this expository article, three dominant hybrid approaches to intelligent
   control are experimentally applied to address various robotic control issues which are currently under investigation at the NASA Center for Autonomous Control Engineering.
   The hybrid controllers consist of a hierarchical NN-fuzzy controller applied to a direct drive motor, a GA-fuzzy hierarchical controller applied to position control of a
   flexible robot link, and a GP-fuzzy behavior based controller applied to a mobile robot navigation task. Various strong characteristics of each of these hybrid combinations
   are discussed and used in these control architectures. The NN-fuzzy architecture takes advantage of NN for handling complex data patterns, the GA-fuzzy architecture uses
   the ability of GA to optimize parameters of membership functions for improved system response, and the GP-fuzzy architecture uses the symbolic manipulation capability of GP
   to evolve fuzzy rule-sets.
%Z citeseer 373353 version not identical to published version

%A M.-R. Akbarzadeh-T.
%A I. Mosavat
%A S. Abbasi
%T Friendship Modeling for Cooperative Co-Evolutionary Fuzzy Systems: A Hybrid GA-GP Algorithm
%B 2003 Proceedings of the 22nd International Conference of North American Fuzzy Information Processing Society
%D 2003
%I 
%K genetic algorithms, genetic programming

%A Yoshida Akira
%T Multiple-Organisms Learning and Evolution by Genetic Programming
%B Proceedings of The Third Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems
%E Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and Akira Namatame and Xin Yao and Mitsuo Gen
%D 1999
%I 
%C School of Computer Science Australian Defence Force Academy, Canberra, Australia
%K genetic algorithms, genetic programming
%8 22-25  November
%Z http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html Nara Advanced Institute of Science and Technology

%A Yoshida Akira
%T Intraspecific Evolution of Learning by Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 209--224
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=209
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Aydin Akyol
%A Yusuf Yaslan
%A Osman Kaan Erol
%T A Genetic Programming Classifier Design Approach for Cell Images
%B Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU
%S Lecture Notes in Computer Science
%E Khaled Mellouli
%V 4724
%D 2007
%P 878--888
%I Springer
%C Hammamet, Tunisia
%K genetic algorithms, genetic programming, cell classification, classifier design, pollen classification
%X This paper describes an approach for the use of genetic programming (GP) in classification problems and it is evaluated on the automatic classification problem of pollen
   cell images. In this work, a new reproduction scheme and a new fitness evaluation scheme are proposed as advanced techniques for GP classification applications. Also an
   effective set of pollen cell image features is defined for cell images. Experiments were performed on Bangor/Aberystwyth Pollen Image Database and the algorithm is
   evaluated on challenging test configurations. We reached at 96percent success rate on the average together with significant improvement in the speed of convergence.
%8 October  31 -  November  2
%Z cf email GP mailing list Mon, 24 Dec 2007 13:42:59 +0200 You may find MATLAB codes from my Web Page below: www3.itu.edu.tr/~okerol you may refer to: O.K. Erol, I.Eksin,
   2006. A New Optimization Method: Big-Bang Big-Crunch, Advences in Engineering Software, Elsevier, Vol 37, No.2, 106-111 doi:10.1016/j.advengsoft.2005.04.005

%A Ala' S. Al-Afeef
%T Image Reconstructing in Electrical Capacitance Tomography of Manufacturing Processes Using Genetic Programming
%R M.S. Thesis
%D 2010
%I 
%I Al-Balqa Applied University
%C Al-Salt, Jordan
%K genetic algorithms, genetic programming, Image Reconstructing, Electrical Capacitance Tomography
%U http://il.youtube.com/watch?v=ecN6JogE4hU
%X Electrical capacitance tomography is considered the most attractive technique for industrial process imaging because of its low construction cost, safety, fast data
   acquisition , non-invasiveness, non-intrusiveness, simple structure, wide application field and suitability for most kinds of flask and vessels, however, the low accuracy
   of the reconstructed images is the main limitation of implementing an ECT system. In order to improve the imaging accuracy, one may 1) increase the number of measurements
   by raising number of electrodes, 2) improve the reconstruction algorithm so that more information can be extracted from the captured data, however, increasing the number of
   electrodes has a limited impact on the imaging accuracy improvement. This means that, in order to improve the reconstructed image, more accurate reconstruction algorithms
   must be developed. In fact, ECT image reconstruction is still an inefficiently resolved problem because of many limitations, mainly the Soft-field and Ill-condition
   characteristic of ECT. Although there are many algorithms to solve the image reconstruction problem, these algorithms are not yet able to present a single model that can
   relate between image pixels and capacitance measurements in a mathematical relationship. The originality of this thesis lies in introducing a new technique for solving the
   non-linear inverse problem in ECT based on Genetic Programming (GP) to handle the ECT imaging for conductive materials. GP is a technique that has not been applied to ECT.
   GP found to be efficient in dealing with the Non-linear relation between the measured capacitance and permittivity distribution in ECT. This thesis provides new implemented
   software that can handle the ECT based GP problem with a user-friendly interface. The developed simulation results are promising.
%8 July

%A Alaa Al-Afeef
%A Alaa F. Sheta
%A Adnan Al-Rabea
%T Image reconstruction of a metal fill industrial process using Genetic Programming
%B 10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010
%D 2010
%P 12--17
%I 
%C Cairo
%K genetic algorithms, genetic programming, electrical capacitance tomography, ill-condition characteristic, image reconstruction, industrial process imaging, metal fill
   industrial process, soft-field characteristic, genetic algorithms, image reconstruction, industrial engineering, tomography, Process Tomography
%U http://sites.google.com/site/alaaalfeef/home/8.pdf
%X Electrical Capacitance Tomography (ECT) is one of the most attractive technique for industrial process imaging because of its low construction cost, safety,
   non-invasiveness, non-intrusiveness, fast data acquisition, simple structure, wide application field and suitability for most kinds of flask and vessels. However, image
   reconstruction based ECT suffers many limitations. They include the Soft-field and Ill-condition characteristic of ECT. The basic idea of the ECT for image reconstruction
   for a metal fill problem is to model the image pixels as a function of the capacitance measurements. Developing this relationship represents a challenge for systems
   engineering community. In this paper, we presents our innovative idea on solving the non-linear inverse problem for conductive materials of the ECT using Genetic
   Programming (GP). GP found to be a very efficient algorithm in producing a mathematical model of image pixels in the form of Lisp expression. The reported results are
   promising.
%8 29  November -1  Decemeber
%Z Also known as \cite5687299

%A Alaa Al-Afeef
%A Alaa Sheta
%A Adnan Rabea
%T Image Reconstruction of a Manufacturing Process: A Genetic Programming Approach
%D 2011
%I Lambert Academic Publishing
%K genetic algorithms, genetic programming
%U http://www.amazon.co.uk/Image-Reconstruction-Manufacturing-Process-Programming/dp/3844325697
%X Product Description Evolutionary Computation (EC) is one of the most attractive techniques in the area of Computer Science. EC includes Genetic Algorithms (GAs), Genetic
   Programming (GP), Evolutionary Strategy (ES) and Evolutionary Programming (EP). GP have been widely used to solve a variety of problems in image enhancement, analysis and
   segmentation. This book explores the use of GP as a powerful approach to solve the image reconstruction problem for Lost Foam Casting (LFC) manufacturing process. The data
   set was collected using the Electrical Capacitance Tomography (ECT) technique. ECT is one of the most attractive technique for industrial process imaging because of its low
   construction cost, safety, non-invasiveness, non-intrusiveness, fast data acquisition, simple structure, wide application field and suitability for most kinds of flask and
   vessels. GP found to be a very efficient algorithm in producing a mathematical model of image pixels in a form of Lisp expression. A Graphical User Interface (GUI) Toolbox
   based Matlab was developed to help analysing and visualising the reconstructed images based GP problem. The reported results are promising.
%8 April

%A Shri Vidhya Alagesan
%A Sruthi Kannan
%A G. Shanthi
%A A. P. Shanthi
%A Ranjani Parthasarathi
%T Intrinsic Evolution of Large Digital Circuits Using a Modular Approach
%B NASA/ESA Conference on Adaptive Hardware and Systems, AHS '08
%D 2008
%P 19--26
%I 
%K genetic algorithms, genetic programming, Cartesian genetic programming, Xilinx Virtex II Pro board, evolvable hardware, large digital circuits, modular approach, modular
   developmental Cartesian genetic programming, scalability problem, software platform, time consuming fitness evaluation, digital circuits
%X This work pioneers a generic and flexible approach to intrinsically evolve large digital circuits. One of the popular ways of handling the scalability problem prevalent in
   evolvable hardware (EHW) and evolve large circuits is to partition the circuit, evolve the individual partitions and then compact them. However, as the partition sizes
   become larger, this method also fails. This drawback is overcome by the modular developmental Cartesian genetic programming (MDCGP) technique, which still uses
   partitioning, but augments it further with horizontal and vertical reuse. The results obtained are promising and show that there is 100percent evolvability for 128-bit
   partitions, the largest partitions evolved so far. The fitness evaluation for the evolved partitions is done by downloading them onto Xilinx Virtex II Pro board. This work
   is the first step towards the development of a flexible evolvable framework which harnesses the power of hardware for the time consuming fitness evaluation and at the same
   time provides flexibility by carrying out the other parts using the easily modifiable software platform.
%8 June
%Z Also known as \cite4584250

%A Jarmo T. Alander
%T An Indexed Bibliography of Genetic Programming
%R Report Series no 94-1-GP
%D 1995
%I 
%I Department of Information Technology and Industrial Management, University of Vaasa
%C Finland
%K genetic algorithms, genetic programming
%U ftp://ftp.uwasa.fi/cs/report94-1/gaGPbib.ps.Z
%X 220 references. Indexed by subject, publication type and author
%Z http url reference not working Jan 95. ftp ok. Part of Alander's index of genetic algorithm publications (older versions, ie up to ~1993, are available via ftp, see ENCORE
   sites). New version dated May 18, 1995. See also Jarmo T. Alander. An indexed bibliography of genetic algorithms: Years 1957-1993. Art of CAD Ltd., Vaasa (Finland), 1994.
   (over 3000 GA references).

%A Jarmo T. Alander
%T An Indexed Bibliography of Genetic Algorithms: Years 1957--1993
%D 1994
%I Art of CAD ltd
%C Vaasa, Finland
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.53.4481&rep=rep1&type=pdf
%Z All GAs some 3000+ references

%A Jarmo T. Alander
%A Ghodrat Moghadampour
%A Jari Ylinen
%T 2nd order equation
%B Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA)
%S Proceedings of the University of Vaasa, Nro. 13
%E Jarmo T. Alander
%D 1996
%P 215--218
%I University of Vaasa
%I Finnish Artificial Intelligence Society
%C Vaasa (Finland)
%K genetic algorithms, genetic programming, mathematics, algebra
%U ftp://ftp.uwasa.fi/cs/2NWGA/Ghodrat2.ps.Z
%X In this work we have tried to use genetic programming to solve the simple second order equation
%O *on,*FIN,genetic programming,mathematics /algebra
%8 19.-23.~  August
%Z 2NWGA.bib gives title as 'Solving the second order equation using genetic programming' lil-gp evolution of formular for quadratic roots. lil-gp does not seem to be robust
   to find the solution formula of 2nd order equation

%A Amir Hossein Alavi
%A Amir Hossein Gandomi
%A Mohammad Ghasem Sahab
%A Mostafa Gandomi
%T Multi Expression Programming: A New Approach to Formulation of Soil Classification
%J Engineering with Computers
%V 26
%N 2
%D 2010
%P 111--118
%I 
%K genetic algorithms, genetic programming, Multi expression programming, Soil classification, Formulation
%U http://www.springerlink.com/content/q418u58024054r38/
%X This paper presents an alternative approach to formulation of soil classification by means of a promising variant of genetic programming (GP), namely multi expression
   programming (MEP). Properties of soil, namely plastic limit, liquid limit, colour of soil, percentages of gravel, sand, and fine-grained particles are used as input
   variables to predict the classification of soils. The models are developed using a reliable database obtained from the previously published literature. The results
   demonstrate that the MEP-based formulae are able to predict the target values to high degree of accuracy. The MEP-based formulation results are found to be more accurate
   compared with numerical and analytical results obtained by other researchers.
%8 April
%Z M. Gandomi School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

%A Amir Hossein Alavi
%A Amir Hossein Gandomi
%T A Robust Data Mining Approach for Formulation of Geotechnical Engineering Systems
%J International Journal of Computer Aided Methods in Engineering-Engineering Computations
%V 28
%N 3
%D 2011
%P 242--274
%I 
%K genetic algorithms, genetic programming, gene expression programming, multi expression programming, Data mining, Geotechnical engineering, Linear-based genetic programming,
   Formulation
%X Purpose- The complexity of analysis of geotechnical behaviour is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior,
   traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional models may lead
   to very large errors. In the present study, capabilities of promising variants of genetic programming (GP), namely linear genetic programming (LGP), gene expression
   programming (GEP) and multi expression programming (MEP) are illustrated by applying them to the formulation of several complex geotechnical engineering problems.
   Design/methodology/approach- LGP, GEP and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with
   the traditional GP, the LGP, GEP and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods
   have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This
   is one their major advantages over most of the traditional constitutive modeling methods. Findings- In order to demonstrate the simulation capabilities of LGP, GEP and MEP,
   they were applied to the prediction of (i) relative crest settlement of concrete-faced rockfill dams, (ii) slope stability, (iii) settlement around tunnels, and (iv) soil
   liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behaviour for
   the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable
   analysis tools accessible to practising engineers. Originality/value- The LGP, GEP and MEP approaches overcome the shortcomings of different methods previously presented in
   the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP and MEP provide
   prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the
   finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in-depth
   deterministic analyses.

%A Amir Hossein Alavi
%A Mahmoud Ameri
%A Amir Hossein Gandomi
%A Mohammad Reza Mirzahosseini
%T Formulation of Flow Number of Asphalt Mixes Using a Hybrid Computational Method
%J Construction and Building Materials
%V 25
%N 3
%D 2011
%P 1338--1355
%I 
%K genetic algorithms, genetic programming, Asphalt concrete mixture, Flow number, Simulated annealing, Marshall mix design, Regression analysis
%X A high-precision model was derived to predict the flow number of dense asphalt mixtures using a novel hybrid method coupling genetic programming and simulated annealing,
   called GP/SA. The proposed constitutive model correlates the flow number of Marshall specimens with the percentages of filler, bitumen, voids in mineral aggregate, Marshall
   stability and flow. The comprehensive experimental database used for the development of the model was established upon a series of uniaxial dynamic creep tests conducted in
   this study. Generalised regression neural network and multiple regression-based analyses were performed to benchmark the GP/SA model. The contributions of the variables
   affecting the flow number were evaluated through a sensitivity analysis. A subsequent parametric study was carried out and the trends of the results were confirmed with the
   results of the experimental study. The results indicate that the proposed GP/SA model is effectively capable of evaluating the flow number of asphalt mixtures. The derived
   model is remarkably straightforward and provides an analysis tool accessible to practising engineers.
%8 March
%Z a School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran b College of Civil Engineering, Tafresh University, Tafresh, Iran c Transportation
   Research Institute (TRI), Tehran, Iran

%A A. H. Alavi
%A A. H. Gandomi
%A A. A. R. Heshmati
%T Discussion on "Soft computing approach for real-time estimation of missing wave heights" by S.N. Londhe [Ocean Engineering 35 (2008) 1080-1089]
%J Ocean Engineering
%V 37
%N 13
%D 2010
%P 1239--1240
%I 
%K genetic algorithms, genetic programming, Tree structure, Wave forecasts
%U http://www.sciencedirect.com/science/article/B6V4F-50DXD90-1/2/b2489a1aebf49e771abca1b27d3b24b4
%X The paper studied by Londhe (2008) \citeLondhe20081080 uses genetic programming (GP) for estimation of missing wave heights. The paper includes some problems about the
   fundamental aspects and use of the GP approach. In this discussion, some controversial points of the paper are given.

%A Amir Hossein Alavi
%A Pejman Aminian
%A Amir Hossein Gandomi
%A Milad Arab Esmaeili
%T Genetic-based modeling of uplift capacity of suction caissons
%J Expert Systems with Applications
%V In Press, Uncorrected Proof
%D 2011
%I 
%K genetic algorithms, genetic programming, Gene expression programming, Suction caissons, Uplift capacity, Formulation
%U http://www.sciencedirect.com/science/article/B6V03-52P1KNK-4/2/f33267200d0fc51ad7a086befe3a361c
%X In this study, classical tree-based genetic programming (TGP) and its recent variants, namely linear genetic programming (LGP) and gene expression programming (GEP) are
   used to develop new prediction equations for the uplift capacity of suction caissons. The uplift capacity is formulated in terms of several inflecting variables. An
   experimental database obtained from the literature is employed to develop the models. Further, a conventional statistical analysis is performed to benchmark the proposed
   models. Sensitivity and parametric analyses are conducted to verify the results. TGP, LGP and GEP are found to be effective methods for evaluating the horizontal, vertical,
   and inclined uplift capacity of suction caissons. The TGP, LGP and GEP models reach a prediction performance better than or comparable with the models found in the
   literature.

%A Enrique Alba
%A Carlos Cotta
%A Jose M. Troya
%T Type-Constrained Genetic Programming for Rule-Base Definition in Fuzzy Logic Controllers
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 255--260
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Enrique Alba
%A Carlos Cotta
%A Jose M. Troya
%T Entropic and Real-Time Analysis of the Search with Panmictic, Structured, and Parallel Distributed Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 773
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Ga-808.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Enrique Alba
%A Jose M. Troya
%T Tackling epistasis with panmictic and structured genetic algorithms
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 1--7
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms, NK
%8 13  July
%Z GECCO-99LB

%A Enrique Alba
%A Carlos Cotta
%A Jose M. Troya
%T Evolutionary Design of Fuzzy Logic Controllers Using Strongly-Typed GP
%J Mathware \& Soft Computing
%V 6
%N 1
%D 1999
%P 109--124
%I 
%K genetic algorithms, genetic programming, Type System, Fuzzy Logic Controller, Cart-Centering Problem
%U http://docto-si.ugr.es/Mathware/v6n1/PS/7-alba.ps.gz
%X An evolutionary approach to the design of fuzzy logic controllers is presented in this paper. We propose the use of the genetic programming paradigm to evolve fuzzy
   rule-bases (internally represented as type-constrained syntactic trees). This model has been applied to the cart-centering problem, although it can be readily extended to
   other problems. The obtained results show that a good parameterization of the algorithm, and an appropriate evaluation function, can lead to near-optimal solutions.
%Z Mathware and softcomputing http://docto-si.ugr.es/Mathware/ENG/mathware.html

%A Enrique Alba
%T Parallel Metaheuristics: A New Class of Algorithms
%D 2005
%I John Wiley \& Sons
%C NJ, USA
%K genetic algorithms, genetic programming, book, text, general computer engineering
%U http://www.ebookmall.com/ebooks/parallel-metaheuristics-a-new-class-of-algorithms-alba-ebooks.htm
%X This single reference on parallel metaheuristic presents modern and ongoing research information on using, designing, and analysing efficient models of parallel algorithms.
   Table of Contents Author Information Introduction. PART I: INTRODUCTION TO METAHEURISTICS AND PARALLELISM. 1. An Introduction to Metaheuristic Techniques. 2. Measuring the
   Performance of Parallel Metaheuristics. 3. New Technologies in Parallelism. 4. Metaheuristics and Parallelism. PART II: PARALLEL METAHEURISTIC MODELS. 5. Parallel Genetic
   Algorithms. 6. Spatially Structured Genetic Programming. 7. Parallel Evolution Strategies. 8. Parallel Ant Colony Algorithms. 9. Parallel Estimation of Distribution
   Algorithms. 10. Parallel Scatter Search. 11. Parallel Variable Neighbourhood Search. 12. Parallel Simulated Annealing. 13. Parallel Tabu Search. 14. Parallel GRASP. 15.
   Parallel Hybrid Metaheuristics. 16. Parallel Multi Objective. 17. Parallel Heterogeneous Metaheuristics. PART III: THEORY AND APPLICATIONS. 18. Theory of Parallel Genetic
   Algorithms. 19. Parallel Metaheuristics. 20. Parallel Metaheuristics in Telecommunications. 21. Bioinformatics and Parallel Metaheuristics. Index.
%8 August
%Z US 95.
%@ 0-471-67806-6

%A Ana Claudia M. L. Albuquerque
%A Jorge D. Melo
%A Adriao D. {Doria Neto}
%T Evolutionary Computation and Parallel Processing Applied to the Design of Multilayer Perceptrons
%B Evolvable Machines: Theory \& Practice
%S Studies in Fuzziness and Soft Computing
%E Nadia Nedjah and Luiza de Macedo Mourelle
%V 161
%D 2004
%P 181--203
%I Springer
%C Berlin
%K genetic algorithms
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html
%O 8
%Z Springer says published in 2005 but available Nov 2004
%@ 3-540-22905-1

%A Paul Albuquerque
%A Bastien Chopard
%A Christian Mazza
%A Marco Tomassini
%T On the Impact of the Representation on Fitness Landscapes
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 1--15
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=1
%X In this paper we study the role of program representation on the properties of a type of Genetic Programming (GP) algorithm. In a specific case, which we believe to be
   generic of standard GP, we show that the way individuals are coded is an essential concept which impacts the fitness landscape. We give evidence that the ruggedness of the
   landscape affects the behavior of the algorithm and we find that, below a critical population, whose size is representation-dependent, premature convergence occurs.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A David Alderson
%T Toward a Technique for Cooperative Network Design Using Evolutionary Methods
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 1--10
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Ricardo Aler
%T Immediate transference of global improvements to all individuals in a population in Genetic Programming compared to Automatically Defined Functions for the EVEN-5 PARITY
   problem
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 60--70
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%X Koza has shown how automatically defined functions (ADFs) can reduce computational effort in the GP paradigm. In Koza's ADF, as well as in standard GP, an improvement in a
   part of a program (an ADF or a main body) can only be transferred via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements
   found by a single individual to the whole population. A system that implements this idea has been proposed and tested for the EVEN-5-PARITY and EVEN-6-PARITY problems.
   Results are very encouraging: computational effort is reduced (compared to Koza's ADFs) and the system seems to be less prone to early stagnation. Finally, our work
   suggests further research where less extreme approaches to our idea could be tested.
%8 14-15  April
%Z EuroGP'98
%@ 3-540-64360-5

%A Ricardo Aler
%A Daniel Borrajo
%A Pedro Isasi
%T Evolved Heuristics for Planning
%B Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%E V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben
%V 1447
%D 1998
%P 745--754
%I Springer-Verlag Berlin
%C Mission Valley Marriott, San Diego, California, USA
%K genetic algorithms, genetic programming
%8 25-27  March
%Z EP-98 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7 EvoCK compared with PRODIGY. HAMLET. Blocksworld domain.
%@ 3-540-64891-7

%A Ricardo Aler
%A Daniel Borrajo
%A Pedro Isasi
%T Genetic Programming and Deductive-Inductive Learning: A Multistrategy Approach
%B Proceedings of the Fifteenth International Conference on Machine Learning, ICML'98
%E Jude Shavlik
%D 1998
%P 10--18
%I Morgan Kaufmann
%C Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, Learning in Planning, Multistrategy learning
%U http://scalab.uc3m.es/~dborrajo/papers/icml98.ps.gz
%X Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP
   can benefit from domain knowledge obtained by other machine learning methods with more powerful heuristics. However, it is not obvious that a combination of GP and a
   knowledge intensive machine learning method can work better than the knowledge intensive method alone. In this paper we present a multistrategy approach where an already
   multistrategy approach (\sc hamlet combines analytical and inductive learning) and an evolutionary technique based on GP (EvoCK) are combined for the task of learning
   control rules for problem solving in planning. Results show that both methods complement each other, supplying to the other method what the other method lacks and obtaining
   better results than using each method alone.
%8 July
%Z ICML'98 http://www.cs.wisc.edu/icml98/ blocksworld many random problems generated in order to train system. No crossover, steady state population size = 2, tournament size
   = 2
%@ 1-55860-556-8

%A Ricardo Aler Mur
%T Programacion Genetica de Heuristicas para Planificacion
%R Ph.D. Thesis
%D 1999
%I 
%I Facultad de Informatica de la Universidad Politecnica de Madrid
%C Spain
%K genetic algorithms, genetic programming, Planning, Problem Solving, Rule Based System
%X The aim of this thesis is to use and extend the machine learning genetic programming (GP) paradigm to learn control knowledge for domain independent planning. GP will be
   used as a standalone technique and as part of a multi-strategy system. Planning is the problem of finding a sequence of steps to transform an initial state in a final
   state. Finding a correct plan is NP-hard. A solution proposed by Artificial Intelligence is to augment a domain independent planner with control knowledge, to improve its
   efficiency. Machine learning techniques are used for that purpose. However, although a lot has been achieved, the domain independent planning problem has not been solved
   completely, therefore there is still room for research. The reason for using GP to learn planning control knowledge is twofold. First, it is intended for exploring the
   control knowledge space in a less biased way than other techniques. Besides, learning search control knowledge with GP will consider the planning system, the domain theory,
   planning search and efficiency measures in a global manner, all at the same time. Second, GP flexibility will be used to add useful biases and characteristics to another
   learning method that lacks them (that is, a multi-strategy GP based system). In the present work, Prodigy will be used as the base planner and Hamlet will be used as the
   learning system to which useful characteristics will be added through GP. In other words, GP will be used to solve some of Hamlet limitations by adding new
   biases/characteristics to Hamlet. In addition to the main goal, this thesis will design and experiment with methods to add background knowledge to a GP system, without
   modifying its basic algorithm. The first method seeds the initial population with individuals obtained by another method (Hamlet). Actually, this is the multi-strategy
   system discussed in the later paragraph. The second method uses a new genetic operator (instance based crossover) that is able to use instances/examples to bias its search,
   like other machine learning techniques. To test the validity of the methods proposed, extensive empirical and statistical validation will be carried out.
%8 July
%Z In Spanish: Genetic Programming of Heuristics for Planning School of Computer Science at Polytechnic University of Madrid Author: Ricardo Aler Mur Supervisors: Daniel
   Borrajo Millan and Pedro Isasi Vinuela

%A Ricardo Aler
%A Daniel Borrajo
%A Pedro Isasi
%T GP fitness functions to evolve heuristics for planning
%B Evolutionary Methods for AI Planning
%E Martin Middendorf
%D 2000
%P 189--195
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://scalab.uc3m.es/~dborrajo/papers/gecco00.ps.gz
%X There are several ways of applying Genetic Programming (GP) to STRIPS-like planning in the literature. In this paper we emphasise the use of a new one, based on learning
   heuristics for planning. In particular, we focus on the design of fitness functions for this task. We explore two alternatives (black and white box fitness functions) and
   present some empirical results
%8 8  July
%Z GECCO-2000WKS Part of \citewu:2000:GECCOWKS

%A Ricardo Aler
%A Daniel Borrajo
%A Pedro Isasi
%T Knowledge Representation Issues in Control Knowledge Learning
%B Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000)
%E Pat Langley
%D 2000
%P 1--8
%I Morgan Kaufmann
%C Stanford University, Standord, CA, USA
%K genetic algorithms, genetic programming, EBL, HAMLET, EVOCK
%U http://citeseer.ist.psu.edu/341634.html
%X Knowledge representation is a key issue for any machine learning task. There have already been many comparative studies about knowledge representation with respect to
   machine learning in classification tasks. However, apart from some work done on reinforcement learning techniques in relation to state representation, very few studies have
   concentrated on the effect of knowledge representation for machine learning applied to problem solving, and more specifically, to planning. In this paper, we present an
   experimental comparative study of the effect of changing the input representation of planning domain knowledge on control knowledge learning. We show results in two
   classical domains using three different machine learning systems, that have previously shown their effectiveness on learning planning control knowledge: a pure EBL
   mechanism, a combination of EBL and induction (HAMLET), and a Genetic Programming based system (EVOCK).
%O The Pennsylvania State University CiteSeer Archives
%8 June  29 -  July  2
%Z http://www.informatik.uni-trier.de/~ley/db/conf/icml/icml2000.html
%@ 1-55860-707-2

%A Ricardo Aler
%A Daniel Borrajo
%A Pedro Isasi
%T Grammars for Learning Control Knowledge with GP
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 1220--1227
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, computational linguistics, grammars, learning (artificial intelligence), search problems, AI planning system, EVOCK, Evolution of
   Control Knowledge, GP based system, PRODIGY, ad-hoc mechanisms, blocksworld domain, control knowledge learning, control rule language, control rule syntax, control rules,
   grammar approach flexibility, grammar specific, grammars, language restrictions, search space, standard GP, standard select type
%U http://scalab.uc3m.es/~dborrajo/papers/cec01.ps.gz
%X In standard GP there are no constraints on the structure to evolve: any combination of functions and terminals is valid. However, sometimes GP is used to evolve structures
   that must respect some constraints. Instead of ad-hoc mechanisms, grammars can be used to guarantee that individuals comply with the language restrictions. In addition,
   grammars permit great flexibility to define the search space. EVOCK (Evolution of Control Knowledge) is a GP based system that learns control rules for PRODIGY, an AI
   planning system. EVOCK uses a grammar to constrain individuals to PRODIGY 4.0 control rule syntax. The authors describe the grammar specific details of EVOCK. Also, the
   grammar approach flexibility has been used to extend the control rule language used by EVOCK in earlier work. Using this flexibility, tests were performed to determine
   whether using combinations of several types of control rules for planning was better than using only the standard select type. Experiments have been carried out in the
   blocksworld domain that show that using the combination of types of control rules does not get better individuals, but it produces good individuals more frequently
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . EVOCK,
   PRODIGY 4.0, blocksworld
%@ 0-7803-6658-1

%A Ricardo Aler
%A Daniel Borrajo
%A Pedro Isasi
%T Learning to Solve Planning Problems Efficiently by Means of Genetic Programming
%J Evolutionary Computation
%V 9
%N 4
%D 2001
%P 387--420
%I 
%K genetic algorithms, genetic programming, genetic planning, evolving heuristics, planning, search. EvoCK, STGP, blocks world, logistics, Prodigy4.0, STRIPS, PDL40.
%U http://www.mitpressjournals.org/doi/pdf/10.1162/10636560152642841
%X Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit
   two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more
   efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It
   is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain,
   instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks
   world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator Instance-Based Crossover that is able to use
   traces of the base planner as raw genetic material to be injected into the evolving population.
%8 Winter

%A Ricardo Aler
%A Daniel Borrajo
%A Pedro Isasi
%T Using genetic programming to learn and improve control knowledge
%J Artificial Intelligence
%V 141
%N 1-2
%D 2002
%P 29--56
%I 
%K genetic algorithms, genetic programming, Speedup learning, Multi-strategy learning, Planning
%U http://citeseer.ist.psu.edu/511810.html
%X The purpose of this article is to present a multi-strategy approach to learn heuristics for planning. This multi-strategy system, called HAMLET-EVOCK, combines a learning
   algorithm specialised in planning () and a genetic programming (GP) based system (: Evolution of Control Knowledge). Both systems are able to learn heuristics for planning
   on their own, but both of them have weaknesses. Based on previous experience and some experiments performed in this article, it is hypothesised that handicaps are due to
   its example-driven operators and not having a way to evaluate the usefulness of its control knowledge. It is also hypothesized that even if control knowledge is sometimes
   incorrect, it might be easily correctable. For this purpose, a GP-based stage is added, because of its complementary biases: GP genetic operators are not example-driven and
   it can use a fitness function to evaluate control knowledge. and are combined by seeding initial population with control knowledge. It is also useful for to start from a
   knowledge-rich population instead of a random one. By adding the GP stage to , the number of solved problems increases from 58% to 85% in the blocks world and from 50% to
   87% in the logistics domain (0% to 38% and 0% to 42% for the hardest instances of problems considered).
%8 October
%Z Hamlet, EvoCK, PRODIGY 4.0

%A John Aleshunas
%A Cezary Janikow
%T Cost-benefit Analysis of Using Heuristics in ACGP
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 1177--1183
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming
%X Constrained Genetic Programming (CGP) is a method of searching the Genetic Programming search space non-uniformly, giving preferences to certain subspaces according to some
   heuristics. Adaptable CGP (ACGP) is a method for discovery of the heuristics. CGP and ACGP have previously demonstrated their capabilities using first-order heuristics:
   parent-child probabilities. Recently, the same advantage has been shown for second-order heuristics: parent- children probabilities. A natural question to ask is whether we
   can benefit from extending ACGP with deeper-order heuristics. This paper attempts to answer this question by performing cost-benefit analysis while simulating the higher-
   order heuristics environment. We show that this method cannot be extended beyond the current second or possibly third-order heuristics without a new method to deal with the
   sheer number of such deeper-order heuristics.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A B. J. Alexander
%A M. J. Gratton
%T Constructing an Optimisation Phase Using Grammatical Evolution
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 1209--1216
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, grammatical evolution, SBSE, evolutionary computation, functional languages, grammars, optimising compilers, search problems,
   atomic hand-written optimisation phases, heuristic search techniques, intractable design space
%X Optimising compilers present their authors with an intractable design space. A substantial body of work has used heuristic search techniques to search this space for the
   purposes of adapting optimisers to their environment. To date, most of this work has focused on sequencing, tuning and guiding the actions of atomic hand-written
   optimisation phases. In this paper we explore the adaption of optimisers at a deeper level by demonstrating that it is feasible to automatically build a non-trivial
   optimisation phase, for a simple functional language, using Grammatical Evolution. We show that the individuals evolved compare well in performance to a handwritten
   optimisation phase on a range of benchmarks. We conclude with proposals of how this work and its applications can be extended.
%8 18-21  May
%Z Adl, DMO, C-MPI, FPGA, Semantics of program preserved. libGE, GAlib, effective crossover, Python, SWIG Python/C++. Canonical code. Five second time limit. Haskell. Training
   examples changed to "provide traction for the evolutionary process" p1213. "Evolution of fittest individuals highly discontinuous" p1214. Some examples where GE is
   competitive with hand written compiler optimisation, others less so. Evolved code _not_ like human. Compiler output _is_ correct. proof-of-concept. CEC 2009 - A joint
   meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as \cite4983083

%A Maria Eva {Alfaro Cid}
%T Optimisation of Time Domain Controllers for Supply Ships Using Genetic Algorithms and Genetic Programming
%R Ph.D. Thesis
%D 2003
%I 
%I The University of Glasgow
%C Glasgow, UK
%K genetic algorithms, genetic programming
%U http://casnew.iti.es/papers/ThesisEva.pdf
%X The use of genetic methods for the optimisation of propulsion and heading controllers for marine vessels is presented in this thesis. The first part of this work is a study
   of the optimisation, using Genetic Algorithms, of controller designs based on a number of different time-domain control methodologies such as PID, Sliding Mode, H? and Pole
   Placement. These control methodologies are used to provide the structure for propulsion and navigation controllers for a ship. Given the variety in the number of parameters
   to optimise and the controller structures, the Genetic Algorithm is tested in different control optimisation problems with different search spaces. This study presents how
   the Genetic Algorithm solves this minimisation problem by evolving controller parameters solutions that satisfactorily perform control duties while keeping actuator usage
   to a minimum. A variety of genetic operators are introduced and a comparison study is conducted to find the Genetic Algorithm scheme best suited to the parameter controller
   optimisation problem. The performance of the four control methodologies is also compared. A variation of Genetic Algorithms, the Structured Genetic Algorithm, is also used
   for the optimisation of the H? controller. The H? controller optimisation presents the difficulty that the optimisation focus is not on parameters but on transfer
   functions. Structured Genetic Algorithm incorporates hierarchy in the representation of solutions making it very suitable for structural optimisation. The H? optimisation
   problem has been found to be very appropriate for comparing the performance of Genetic Algorithms versus Structured Genetic Algorithm. During the second part of this work,
   the use of Genetic Programming to optimise the controller structure is assessed. Genetic Programming is used to evolve control strategies that, given as inputs the current
   and desired state of the propulsion and heading dynamics, generate the commanded forces required to manoeuvre the ship. Two Genetic Programming algorithms are implemented.
   The only difference between them is how they generate the numerical constants needed for the solution of the problem. The first approach uses a random generation of
   constants while the second approach uses a combination of Genetic Programming with Genetic Algorithms. Finally, the controllers optimised using genetic methods are
   evaluated through computer simulations and real manoeuvrability tests in a laboratory water basin facility. The robustness of each controller is analysed through the
   simulation of environmental disturbances. Also, optimisations in presence of disturbances are carried out so that the different controllers obtained can be compared. The
   particular vessels used in this study are two scale models of a supply ship called CyberShip I and CyberShip II. The results obtained illustrate the benefits of using
   Genetic Algorithms and Genetic Programming to optimise propulsion and navigation controllers for surface ships.
%8 October

%A Eva Alfaro-Cid
%A Anna Esparcia-Alc\'{a}zar
%A Ken Sharman
%T Clasificaci\'on de Senales de Electroencefalograma Usando Programaci\'on Gen\'etica
%B Actas del IV Congreso Espanol sobre Metaheur\'isticas, Algoritmos Evolutivos y Bioinspirados (MAEB'05)
%D 2005
%I 
%C Granada, Spain
%K genetic algorithms, genetic programming
%U http://www.iti.upv.es/cas/nade/data/maeb05vfinal.pdf
%8 September
%Z in Spanish

%A Eva Alfaro-Cid
%A Euan William McGookin
%A David James Murray-Smith
%T Evolution of a Strategy for Ship Guidance Using Two Implementations of Genetic Programming
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 250--260
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=250
%X In this paper the implementation of Genetic Programming (GP) to optimise a controller structure for a supply ship is assessed. GP is used to evolve control strategies that,
   given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the commanded forces required to manoeuvre the ship. The
   optimised controllers are evaluated through computer simulations and real manoeuvrability tests in a water basin laboratory. In order to deal with the issue of the
   generation of numerical constants, two kinds of GP algorithms are implemented. The first one chooses the constants necessary to create the controller structure by random
   generation . The second algorithm includes a Genetic Algorithms (GAs) technique for the optimisation of such constants. The results obtained illustrate the benefits of
   using GP to optimise propulsion and navigation controllers for ships.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Eva Alfaro-Cid
%A Anna Esparcia-Alc{\'a}zar
%A Ken Sharman
%T Using distributed genetic programming to evolve classifiers for a brain computer interface
%B ESANN'2006 proceedings - European Symposium on Artificial Neural Networks
%E Michel Verleysen
%D 2006
%P 59--66
%I 
%C Bruges, Belgium
%K genetic algorithms, genetic programming
%U http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-44.pdf
%X The objective of this paper is to illustrate the application of genetic programming to evolve classifiers for multi-channel time series data. The paper shows how high
   performance distributed genetic programming (GP) has been implemented for evolving classifiers. The particular application discussed herein is the classification of human
   electroencephalographic (EEG) signals for a brain-computer interface (BCI). The resulting classifying structures provide classification rates comparable to those obtained
   using traditional, human-designed, classification
%8 26-28  April
%Z http://www.dice.ucl.ac.be/Proceedings/esann/
%@ 2-930307-06-4

%A Eva Alfaro-Cid
%A Ken Sharman
%A Anna I. Esparcia-Alcazar
%T Evolving a Learning Machine by Genetic Programming
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 958--962
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, simulated annealing, function set, learning machine, learning node, optimization algorithm, simulated annealing
%X We describe a novel technique for evolving a machine that can learn. The machine is evolved using a Genetic Programming (GP) algorithm that incorporates in its function set
   what we have called a learning node. Such a node is tuned by a second optimisation algorithm (in this case Simulated Annealing), mimicking a natural learning process and
   providing the GP tree with added flexibility and adaptability. The result of the evolution is a system with a fixed structure but with some variable parameters. The system
   can then learn new tasks in new environments without undergoing further evolution.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages as 254--258
%@ 0-7803-9487-9

%A Eva {Alfaro Cid}
%A Ken Sharman
%A Anna I. {Esparcia Alc\'{a}zar}
%T Predicci\'on de quiebra empresarial usando programaci\'on gen\'etica
%B Actas del V Congreso Espa\~nol sobre Metaheur\'isticas, Algoritmos Evolutivos y Bioinspirados (MAEB'07)
%D 2007
%P 703--710
%I 
%C Tenerife, Spain
%K genetic algorithms, genetic programming
%8 Febrero
%Z in Spanish

%A Eva {Alfaro Cid}
%A Ken Sharman
%A Anna I. {Esparcia Alc\'{a}zar}
%A Alberto {Cuesta Ca{\~n}ada}
%T Aprendizaje autom\'atico con programaci\'on gen\'etica
%B Actas del V Congreso Espa\~nol sobre Metaheur\'isticas, Algoritmos Evolutivos y Bioinspirados (MAEB'07)
%D 2007
%P 819--826
%I 
%C Tenerife, Spain
%K genetic algorithms, genetic programming
%8 Febrero
%Z in Spanish

%A Eva Alfaro-Cid
%A Ken Sharman
%A Anna I. Esparcia-Alc\`azar
%T A genetic programming approach for bankruptcy prediction using a highly unbalanced database
%B Applications of Evolutionary Computing, EvoWorkshops2007: EvoCOMNET, EvoFIN, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, EvoTransLog
%S LNCS
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal
   Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang
%V 4448
%D 2007
%P 169--178
%I Springer Verlag
%C Valencia, Spain
%K genetic algorithms, genetic programming, SVM
%X in this paper we present the application of a genetic programming algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database of
   Spanish companies. The database has two important drawbacks: the number of bankrupt companies is very small when compared with the number of healthy ones (unbalanced data)
   and a considerable number of companies have missing data. For comparison purposes we have solved the same problem using a support vector machine. Genetic programming has
   achieved very satisfactory results, improving those obtained with the support vector machine.
%8 11-13  April
%Z EvoWorkshops2007

%A Eva Alfaro-Cid
%A Antonio Miguel Mora
%A Juan Juli{\'a}n Merelo Guerv{\'o}s
%A Anna Esparcia-Alc{\'a}zar
%A Ken Sharman
%T A SOM and GP Tool for Reducing the Dimensionality of a Financial Distress Prediction Problem
%B Proceedings of EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops
%S Lecture Notes in Computer Science
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Rolf Drechsler and Anik\'o Ek\'art and Anna Esparcia-Alc\'azar and Muddassar Farooq and
   Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang
%V 4974
%D 2008
%P 123--132
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March

%A E. Alfaro-Cid
%A P. A. Castillo
%A A. Esparcia
%A K. Sharman
%A J. J. Merelo
%A A. Prieto
%A J. L. J. Laredo
%T Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 2902--2908
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to
   minimise one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimises the most important error
   type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes
   problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take
   into account only the global classification error. In this paper we propose and compare two methods to optimise both error types in classification: artificial neural
   networks and function trees ensembles created through multiobjective Optimization. Since the multiobjective Optimization process produces a set of equally optimal results
   (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the
   best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form
   of an analytical function.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Eva Alfaro-Cid
%A Euan W. McGookin
%A David J. Murray-Smith
%A Thor I. Fossen
%T Genetic Programming for the Automatic Design of Controllers for a Surface Ship
%J IEEE Transactions on Intelligent Transportation Systems
%V 9
%N 2
%D 2008
%P 311--321
%I 
%K genetic algorithms, genetic programming, control system synthesis, navigation, propulsion, ships CyberShip II, automatic design, controller structure, navigation
   controllers, propulsion controllers, supply ship, surface ship
%X In this paper, the implementation of genetic programming (GP) to design a controller structure is assessed. GP is used to evolve control strategies that, given the current
   and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the commanded forces required to maneuver the ship. The controllers created
   using GP are evaluated through computer simulations and real maneuverability tests in a laboratory water basin facility. The robustness of each controller is analyzed
   through the simulation of environmental disturbances. In addition, GP runs in the presence of disturbances are carried out so that the different controllers obtained can be
   compared. The particular vessel used in this paper is a scale model of a supply ship called CyberShip II. The results obtained illustrate the benefits of using GP for the
   automatic design of propulsion and navigation controllers for surface ships.
%8 June
%Z Also known as \cite4517335

%A Eva Alfaro-Cid
%A Anna Esparcia-Alcazar
%A Ken Sharman
%A Francisco {Fernandez de Vega}
%A J. J. Merelo
%T Prune and Plant: A New Bloat Control Method for Genetic Programming
%B Eighth International Conference on Hybrid Intelligent Systems, HIS '08
%D 2008
%P 31--35
%I 
%K genetic algorithms, genetic programming, bloat control method, genetic operator, prune and plant, time consumption, tree size reduction, mathematical operators, trees
   (mathematics)
%X This paper reports a comparison of several bloat control methods and also evaluates a new proposal for limiting the size of the individuals: a genetic operator called prune
   and plant. The aim of this work is to prove the adequacy of this new method. Since a preliminary study of the method has already shown promising results, we have performed
   a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of
   the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these
   problem domains prune and plant has demonstrated to be better in terms of fitness, size reduction and time consumption than any of the other bloat control techniques under
   comparison.
%8 September
%Z Also known as \cite4626601

%A Eva Alfaro-Cid
%A Alberto Cuesta-Canada
%A Ken Sharman
%A Anna Esparcia-Alcazar
%T Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming
%B Natural Computing in Computational Finance
%S Studies in Computational Intelligence
%E Anthony Brabazon and Michael O'Neill
%V 100
%D 2008
%P 161--185
%I Springer
%K genetic algorithms, genetic programming, STGP, SVM
%X In this chapter we present the application of a genetic programming (GP) algorithm to the problem of bankruptcy prediction. To carry out the research we have used a
   database that includes extensive information (not only economic) from the companies. In order to handle the different data types we have used Strongly Typed GP and variable
   reduction. Also, bloat control has been implemented to obtain comprehensible classification models. For comparison purposes we have solved the same problem using a support
   vector machine (SVM). GP has achieved very satisfactory results, improving those obtained with the SVM.
%O 9

%A Eva Alfaro-Cid
%A Anna I. Esparcia-Alc\'{a}zar
%A Pilar Moya
%A Beatriu Femenia-Ferrer
%A Ken Sharman
%A J. J. Merelo
%T Modeling Pheromone Dispensers Using Genetic Programming
%B Applications of Evolutionary Computing, EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC,
   EvoTRANSLOG
%S Lecture Notes in Computer Science
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Anik\'o Ek\'art and Anna Esparcia-Alc\'azar and Muddassar Farooq and Andreas Fink and
   Penousal Machado and Jon McCormack and Michael O'Neill and Ferrante Neri and Mike Preuss and Franz Rothlauf and Ernesto Tarantino and Shengxiang Yang
%V 5484
%D 2009
%P 635--644
%I Springer
%I EvoStar
%C Tubingen, Germany
%K genetic algorithms, genetic programming
%X Mating disruption is an agricultural technique that intends to substitute the use of insecticides for pest control. This technique consists of the diffusion of large
   amounts of sexual pheromone, so that the males are confused and mating is disrupted. Pheromones are released using devices called dispensers. The speed of release is,
   generally, a function of time and atmospheric conditions such as temperature and humidity. One of the objectives in the design of the dispensers is to minimise the effect
   of atmospheric conditions in the performance of the dispenser. With this objective, the Centro de Ecologia Quimica Agricola (CEQA) has designed an experimental dispenser
   that aims to compete with the dispensers already in the market. The hypothesis we want to validate (and which is based on experimental results) is that the performance of
   the CEQA dispenser is independent of the atmospheric conditions, as opposed to the most widely used commercial dispenser, Isomate CPlus. This was done using a genetic
   programming (GP) algorithm. GP evolved functions able to describe the performance of both dispensers and that support the initial hypothesis.
%8 April  15-17
%Z ECJ. EvoWorkshops2009 held in conjunction with EuroGP2009, EvoCOP2009, EvoBIO2009

%A Eva Alfaro-Cid
%A Anna Esparcia-Alcazar
%A Pilar Moya
%A J. J. Merelo
%A Beatriu Femenia-Ferrer
%A Ken Sharman
%A Jaime Primo
%T Multiobjective genetic programming approach for a smooth modeling of the release kinetics of a pheromone dispenser
%B GECCO-2009 Symbolic regression and modeling workshop (SRM)
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 2225--2230
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X The accurate modeling of the release kinetics of pheromone dispensers is a matter or great importance for ensuring that the dispenser field-life covers the flight period of
   the pest and for optimizing the layout of dispensers in the treated area. A new experimental dispenser has been recently designed by researchers at the Instituto
   Agroforestal del Mediterraneo - Centro de Ecologia Quimica Agricola (CEQA) of the Universidad Politecnica de Valencia (Spain). The most challenging problem for the modeling
   of the release kinetics of this dispensers is the difficulty in obtaining experimental measurements for building the model. The procedure for obtaining these data is very
   costly, both time and money wise, therefore the available data across the whole season are scarce. In prior work we demonstrated the utility of using Genetic Programming
   (GP) for this particular problem. However, the models evolved by the GP algorithm tend to have discontinuities in those time ranges where there are not available
   measurements. In this work we propose the use of a multiobjective Genetic Programming for modeling the performance of the CEQA dispenser. We take two approaches, involving
   two and nine objectives respectively. In the first one, one of the objectives of the GP algorithm deals with how well the model fits the experimental data, while the second
   objective measures how "smooth" the model behaviour is. In the second approach we have as many objectives as data points and the aim is to predict each point separately
   using the remaining ones. The results obtained endorse the utility of this approach for those modeling problems characterized by the lack of experimental data.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Eva Alfaro-Cid
%A J. J. Merelo
%A Francisco {Fernandez de Vega}
%A Anna I. Esparcia-Alcazar
%A and Ken Sharman
%T Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study
%J Evolutionary Computation
%V 18
%N 2
%D 2010
%P 305--332
%I 
%K genetic algorithms, genetic programming
%X This paper reports a comparison of several bloat control methods and also evaluates a recent proposal for limiting the size of the individuals: a genetic operator called
   prune and plant. The aim of this work is to test the adequacy of this method. Since a preliminary study of the method has already shown promising results, we have performed
   a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of
   the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these
   problem domains, prune and plant has demonstrated to be better in terms of fitness, size reduction, and time consumption than any of the other bloat control techniques
   under comparison. The experimental part of the study presents a comparison of performance in terms of phenotypic and genotypic diversity. This comparison study can provide
   the practitioner with some relevant clues as to which bloat control method is better suited to a particular problem and whether the advantage of a method does or does not
   derive from its influence on the genetic pool diversity.
%8 Summer

%A Manuel Alfonseca
%A Alfonso Ortega
%T Book Review: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
%J Genetic Programming and Evolvable Machines
%V 5
%N 4
%D 2004
%P 393
%I 
%K genetic algorithms, genetic programming, grammatical evolution
%8 Decemeber
%Z review of \citeoneill:book. Article ID: 5272973

%A Atif M. Alhejali
%A Simon M. Lucas
%T Evolving diverse Ms. Pac-Man playing agents using genetic programming
%B UK Workshop on Computational Intelligence (UKCI 2010)
%D 2010
%P 1--6
%I 
%K genetic algorithms, genetic programming, Ms PacMan game, reactive agents, computer games, learning (artificial intelligence), software agents
%X This paper uses genetic programming (GP) to evolve a variety of reactive agents for a simulated version of the classic arcade game Ms. Pac-Man. A diverse set of behaviours
   were evolved using the same GP setup in three different versions of the game. The results show that GP is able to evolve controllers that are well-matched to the game used
   for evolution and, in some cases, also generalise well to previously unseen mazes. For comparison purposes, we also designed a controller manually using the same function
   set as GP. GP was able to significantly outperform this hand-designed controller. The best evolved controllers are competitive with the best reactive controllers reported
   for this problem.
%8 8-10  September
%Z Also known as \cite5625586

%A Atif M. Alhejali
%A Simon M. Lucas
%T Using a Training Camp with Genetic Programming to Evolve Ms Pac-Man Agents
%B Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games
%D 2011
%P 118--125
%I IEEE
%C Seoul, South Korea
%K genetic algorithms, genetic programming, Pac-Man, Evolving Controllers, Decomposition learning, Training camp
%U http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper31.pdf
%X This paper investigates using a training camp in conjunction with Genetic Programming in the evolution of Ms Pac-Man playing agents. We measure the amount of effort, time
   and resources required to run the training camp successfully. The approach is compared with standard GP. The results indicate that better and more stable performance can be
   achieved using the training camp method at the expense of greater manual effort in the design of the training scenarios. However, in addition to the better results, the
   training camp also provides more detailed insight into the strengths and weaknesses of each controller.
%8 31  August  - 3  September

%A B. Ali
%A A. E. A. Almaini
%A T. Kalganova
%T Evolutionary Algorithms and Theirs Use in the Design of Sequential Logic Circuits
%J Genetic Programming and Evolvable Machines
%V 5
%N 1
%D 2004
%I 
%K genetic algorithms, evolvable hardware, sequential circuits, state assignment
%X design synchronous sequential logic circuits with minimum number of logic gates is suggested. The proposed method consists of four main stages. The first stage is concerned
   with the use of genetic algorithms (GA) for the state assignment problem to compute optimal binary codes for each symbolic state and construct the state transition table of
   finite state machine (FSM). The second stage defines the subcircuits required to achieve the desired functionality. The third stage evaluates the subcircuits using
   extrinsic Evolvable Hardware (EHW). During the fourth stage, the final circuit is assembled. The obtained results compare favourably against those produced by manual
   methods and other methods based on heuristic techniques.
%8 March
%Z Article ID: 5264733

%A Mostafa Z. Ali
%A Robert G. Reynolds
%A Xiangdong Che
%T Genetic Programming for Incentive-Based Design within a Cultural Algorithms Framework
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 249--269
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%O 16
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008

%A Shaukat Ali
%A Lionel C. Briand
%A Hadi Hemmati
%A Rajwinder K. Panesar-Walawege
%T A Systematic Review of the Application and Empirical Investigation of Search-Based Test-Case Generation
%J IEEE Transactions on Software Engineering
%V 36
%N 6
%D 2010
%P 742--762
%I 
%K genetic algorithms, genetic programming, SBSE
%U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5210118&isnumber=4359463
%X Metaheuristic search techniques have been extensively used to automate the process of generating test cases and thus providing solutions for a more cost-effective testing
   process. This approach to test automation, often coined as Search-based Software Testing (SBST), has been used for a wide variety of test case generation purposes. Since
   SBST techniques are heuristic by nature, they must be empirically investigated in terms of how costly and effective they are at reaching their test objectives and whether
   they scale up to realistic development artifacts. However, approaches to empirically study SBST techniques have shown wide variation in the literature. This paper presents
   the results of a systematic, comprehensive review that aims at characterising how empirical studies have been designed to investigate SBST cost-effectiveness and what
   empirical evidence is available in the literature regarding SBST cost-effectiveness and scalability. We also provide a framework that drives the data collection process of
   this systematic review and can be the starting point of guidelines on how SBST techniques can be empirically assessed. The intent is to aid future researchers doing
   empirical studies in SBST by providing an unbiased view of the body of empirical evidence and by guiding them in performing well designed empirical studies.
%8 November -  Decemeber
%Z cites one GP paper: \citeWappler:2007:ASE. TSESI-2008-09-0283

%A Mohammad Ali Ghorbani
%A Rahman Khatibi
%A Ali Aytek
%A Oleg Makarynskyy
%A Jalal Shiri
%T Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks
%J Computer \& Geosciences
%V 36
%N 5
%D 2010
%P 620--627
%I 
%K genetic algorithms, genetic programming, Sea-level variations, Forecasting, Artificial Neural Networks, Comparative studies
%U http://www.sciencedirect.com/science/article/B6V7D-4YCS020-1/2/514d629e145e62f37dbf599a1a7608a9
%X Water level forecasting at various time intervals using records of past time series is of importance in water resources engineering and management. In the last 20 years,
   emerging approaches over the conventional harmonic analysis techniques are based on using Genetic Programming (GP) and Artificial Neural Networks (ANNs). In the present
   study, the GP is used to forecast sea level variations, three time steps ahead, for a set of time intervals comprising 12 h, 24 h, 5 day and 10 day time intervals using
   observed sea levels. The measurements from a single tide gauge at Hillarys Boat Harbour, Western Australia, were used to train and validate the employed GP for the period
   from December 1991 to December 2002. Statistical parameters, namely, the root mean square error, correlation coefficient and scatter index, are used to measure their
   performances. These were compared with a corresponding set of published results using an Artificial Neural Network model. The results show that both these artificial
   intelligence methodologies perform satisfactorily and may be considered as alternatives to the harmonic analysis.

%A Sultan Aljahdali
%A Alaa F. Sheta
%T Software effort estimation by tuning COOCMO model parameters using differential evolution
%B 2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)
%D 2010
%I 
%C Hammamet, Tunisia
%K genetic algorithms, genetic programming, sbse, COOCMO model parameter tuning, NASA software project dataset, constructive cost model, differential evolution, mathematical
   model, optimisation algorithm, software effort estimation, software projects cost estimation, statistical model, optimisation, software cost estimation
%X Accurate estimation of software projects costs represents a challenge for many government organisations such as the Department of Defense (DOD) and NASA. Statistical models
   considerably used to assist in such a computation. There is still an urgent need on finding a mathematical model which can provide an accurate relationship between the
   software project effort/cost and the cost drivers. A powerful algorithm which can optimise such a relationship via tuning mathematical model parameters is urgently needed.
   In two new model structures to estimate the effort required for software projects using Genetic Algorithms (GAs) were proposed as a modification to the famous Constructive
   Cost Model (COCOMO). In this paper, we follow up on our previous work and present Differential Evolution (DE) as an alternative technique to estimate the COCOMO model
   parameters. The performance of the developed models were tested on NASA software project dataset provided in. The developed COCOMO-DE model was able to provide good
   estimation capabilities.
%8 16-19  May
%Z 'We suggest the use of Genetic Programming (GP) technique to build suitable model structure for the software effort estimation.' Also known as \cite5586985

%A Jess Allen
%A Hazel M. Davey
%A David Broadhurst
%A Jim K. Heald
%A Jem J. Rowland
%A Stephen G. Oliver
%A Douglas B. Kell
%T High-throughput classification of yeast mutants for functional genomics using metabolic footprinting
%J Nature Biotechnology
%V 21
%N 6
%D 2003
%P 692--696
%I 
%K genetic algorithms, genetic programming
%U http://dbkgroup.org/Papers/NatureBiotechnology21(692-696).pdf
%X Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies
   dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and
   thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype
   of silent mutations of yeast genes1. However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput
   (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is
   time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites
   from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium.
   Metabolic footprinting can distinguish between different physiological states of wild-type yeast and between yeast single-gene deletion mutants even from related areas of
   metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming2-8, we show that metabolic footprinting is an effective
   method to classify 'unknown' mutants by genetic defect.
%8 June

%A Jess Allen
%A Hazel M. Davey
%A David Broadhurst
%A Jem J. Rowland
%A Stephen G. Oliver
%A Douglas B. Kell
%T Discrimination of Modes of Action of Antifungal Substances by Use of Metabolic Footprinting
%J Applied and Environmental Microbiology
%V 70
%N 10
%D 2004
%P 6157--6165
%I 
%K genetic algorithms, genetic programming
%X Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of
   their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action
   (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their metabolic footprints by using
   direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified
   according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using
   just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.
%8 October
%Z PMID:

%A Sam Allen
%A Edmund K. Burke
%A Matthew R. Hyde
%A Graham Kendall
%T Evolving reusable 3D packing heuristics with genetic programming
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 931--938
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X This paper compares the quality of reusable heuristics designed by genetic programming (GP) to those designed by human programmers. The heuristics are designed for the
   three dimensional knapsack packing problem. Evolutionary computation has been employed many times to search for good quality solutions to such problems. However, actually
   designing heuristics with GP for this problem domain has never been investigated before. In contrast, the literature shows that it has taken years of experience by human
   analysts to design the very effective heuristic methods that currently exist. Hyper-heuristics search a space of heuristics, rather than directly searching a solution
   space. GP operates as a hyper-heuristic in this paper, because it searches the space of heuristics that can be constructed from a given set of components. We show that GP
   can design simple, yet effective, stand-alone constructive heuristics. While these heuristics do not represent the best in the literature, the fact that they are designed
   by evolutionary computation, and are human competitive, provides evidence that further improvements in this GP methodology could yield heuristics superior to those designed
   by humans.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A A. Almal
%A W. P. Worzel
%A E. A. Wollesen
%A C. D. MacLean
%T Content Diversity in Genetic Programming and its Correlation with Fitness
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 177--190
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, diversity, chaos game, fitness correlation, visualisation
%O 12
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Arpit A. Almal
%A Anirban P. Mitra
%A Ram H. Datar
%A Peter F. Lenehan
%A David W. Fry
%A Richard J. Cote
%A William P. Worzel
%T Using genetic programming to classify node positive patients in bladder cancer
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 239--246
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Biological Applications, algorithms and similarity measures, bladder cancer, classification rules, classifier design and
   evaluation, concept learning and induction, feature design and evaluation, feature selection, machine learning, Nodal staging, pattern analysis, program synthesis,
   synthesis
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p239.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A A. A. Almal
%A C. D. MacLean
%A W. P. Worzel
%T Program Structure-Fitness Disconnect and Its Impact On Evolution In GP
%B Genetic Programming Theory and Practice V
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2007
%P 143--158
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, phenotype, genotype, evolutionary dynamics, GP structure, GP content, speciation, population, fitness
%X Simple Genetic Programming (GP) is generally considered to lack the strong separation between genotype and phenotype found in natural evolution. In many cases, the genotype
   and the phenotype are considered identical in GP since the program representation does not undergo any modification prior to its encounter with ÈenvironmentÉ in the form of
   inputs and a fitness function. However, this view overlooks a key fact: fitness in GP is determined without reference to the makeup of the individual programs but
   evolutionary changes occur in the structure and content of the individual without reference to its fitness. This creates a disconnect between Ègenetic recombinationÉ and
   fitness similar to that in nature that can create unexpected effects during the evolution of a population and suggests an important dynamic that has not been thoroughly
   considered by the GP community. This paper describes some of the observed effects of this disconnect and studies some approaches for the estimating diversity of a
   population which could lead to a new way of modelling the dynamics of GP. We also speculate on the similarity of these effects and some recently studied aspects of natural
   evolution.
%O 9
%8 17-19  May
%Z part of \citeRiolo:2007:GPTP Published 2008

%A A. A. Almal
%A C. D. MacLean
%A W. P. Worzel
%T A Population Based Study of Evolutionary Dynamics in Genetic Programming
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 19--29
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%O 2
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008

%A Magnus Almgren
%T Communicating Agents Developed with Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 25--32
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A M. R. Al-Mulla
%A F. Sepulveda
%A M. Colley
%A A. Kattan
%T Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction
%B Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009
%D 2009
%P 2633--2638
%I 
%C Minneapolis, Minnesota, USA
%K genetic algorithms, genetic programming, GP training phase, K-means clustering, fuzzy classifier, isometric contraction, isometric sEMG signal filtering, localized muscle
   fatigue classification, nonfatigue classifier, rectified surface electromyography, statistical feature extraction, transition-to-fatigue classifier, two-dimensional
   Euclidean space, biomechanics, electromyography, fatigue, feature extraction, filtering theory, fuzzy logic, medical signal processing, neurophysiology, pattern clustering,
   signal classification, statistical analysis
%X Genetic programming is used to generate a solution that can classify localized muscle fatigue from filtered and rectified surface electromyography (sEMG). The GP has two
   classification phases, the GP training phase and a GP testing phase. In the training phase, the program evolved with multiple components. One component analyzes statistical
   features extracted from sEMG to chop the signal into blocks and label them using a fuzzy classifier into three classes: non-fatigue, transition-to-fatigue and fatigue. The
   blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is then applied to group similar data
   blocks. Each cluster is then labeled according to its dominant members. The programs that achieve good classification are evolved. In the testing phase, it tests the signal
   using the evolved components, however without the use of a fuzzy classifier. As the results show the evolved program achieves good classification and it can be used on any
   unseen isometric sEMG signals to classify fatigue without requiring any further evolution. The GP was able to classify the signal into a meaningful sequence of non-fatigue
   -> transition-to-fatiguer -> fatigue. By identifying a transition-to fatigue state the GP can give a prediction of an oncoming fatigue. The genetic classifier gave
   promising results 83.17percent correct classification on average of all signals in the test set, especially considering that the GP is classifying muscle fatigue for ten
   different individuals.
%8 2-6  September
%Z Also known as \cite5335368

%A Mohamed R. Al-Mulla
%A Francisco Sepulveda
%A M. Colley
%T Evolved pseudo-wavelet function to optimally decompose sEMG for automated classification of localized muscle fatigue
%J Medical Engineering and Physics
%V 33
%N 4
%D 2011
%P 411--417
%I 
%K genetic algorithms, Localized muscle fatigue, sEMG, Wavelet analysis, matlab
%X The purpose of this study was to develop an algorithm for automated muscle fatigue detection in sports related scenarios. Surface electromyography (sEMG) of the biceps
   muscle was recorded from ten subjects performing semi-isometric (i.e., attempted isometric) contraction until fatigue. For training and testing purposes, the signals were
   labelled in two classes (Non-Fatigue and Fatigue), with the labelling being determined by a fuzzy classifier using elbow angle and its standard deviation as inputs. A
   genetic algorithm was used for evolving a pseudo-wavelet function for optimising the detection of muscle fatigue on any unseen sEMG signals. Tuning of the generalised
   evolved pseudo-wavelet function was based on the decomposition of twenty sEMG trials. After completing twenty independent pseudo-wavelet evolution runs, the best run was
   selected and then tested on ten previously unseen sEMG trials to measure the classification performance. Results show that an evolved pseudo-wavelet improved the
   classification of muscle fatigue between 7.31percent and 13.15percent when compared to other wavelet functions, giving an average correct classification of 88.41percent
%8 May

%A Cesar L. Alonso
%A Jorge Puente
%A Jose Luis Montana
%T Straight Line Programs: A New Linear Genetic Programming Approach
%B 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '08
%V 2
%D 2008
%P 517--524
%I 
%K genetic algorithms, genetic programming, computer programs, data structure, linear genetic programming approach, program tree encoding, straight line programs, symbolic
   regression problems, linear programming, regression analysis, tree data structures
%X Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data
   structure, named straight line program (slp), to represent computer programs. The main features of this structure are described and new recombination operators for GP
   related to slp's are introduced. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's
   consistently outperforms conventional GP based on tree structured representations.
%8 November
%Z Also known as \cite4669818

%A Cesar L. Alonso
%A Jose Luis Montana
%A Jorge Puente
%A Cruz Enrique Borges
%T A new Linear Genetic Programming approach based on straight line programs: some Theoretical and Experimental Aspects
%J International Journal on Artificial Intelligence Tools
%V 18
%N 5
%D 2009
%P 757--781
%I 
%K genetic algorithms, genetic programming, slp, Vapnik-Chervonenkis dimension, VC
%X Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data
   structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related
   to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems.
   Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.
%Z IJAIT

%A Cesar L. Alonso
%A Jose Luis Montana
%A Cruz Enrique Borges
%T Evolution Strategies for Constants Optimization in Genetic Programming
%B 21st International Conference on Tools with Artificial Intelligence, ICTAI '09
%D 2009
%P 703--707
%I 
%K genetic algorithms, genetic programming, computer program, constants optimization, evolutionary computation methods, learning problems, linear genetic programming approach,
   symbolic regression problem, regression analysis
%X Evolutionary computation methods have been used to solve several optimization and learning problems. This paper describes an application of evolutionary computation methods
   to constants optimization in genetic programming. A general evolution strategy technique is proposed for approximating the optimal constants in a computer program
   representing the solution of a symbolic regression problem. The new algorithm has been compared with a recent linear genetic programming approach based on straight-line
   programs. The experimental results show that the proposed algorithm improves such technique.
%8 November
%Z Also known as \cite5366517

%A Fernando Alonso
%A Loic Martinez
%A Aurora Perez-Perez
%A Agustin Santamaria
%A Juan Pedro Valente
%T Modelling Medical Time Series Using Grammar-Guided Genetic Programming
%B 8th Industrial Conference in Data Mining, Medical Applications, E-Commerce, Marketing and Theoretical Aspects, ICDM 2008
%S Lecture Notes in Computer Science
%E Petra Perner
%V 5077
%D 2008
%P 32--46
%I Springer
%C Leipzig, Germany
%K genetic algorithms, genetic programming, Time series characterization, isokinetics, symbolic distance, information extraction, reference model, text mining
%X The analysis of time series is extremely important in the field of medicine, because this is the format of many medical data types. Most of the approaches that address this
   problem are based on numerical algorithms that calculate distances, clusters, reference models, etc. However, a symbolic rather than numerical analysis is sometimes needed
   to search for the characteristics of time series. Symbolic information helps users to efficiently analyse and compare time series in the same or in a similar way as a
   domain expert would. This paper describes the definition of the symbolic domain, the process of converting numerical into symbolic time series and a distance for comparing
   symbolic temporal sequences. Then, the paper focuses on a method to create the symbolic reference model for a certain population using grammar-guided genetic programming.
   The work is applied to the isokinetics domain within an application called I4.
%8 July  16-18
%Z Context Free Grammar

%A Fernando Alonso
%A Loic Martinez
%A Agustin Santamaria
%A Aurora Perez
%A Juan Pedro Valente
%T GGGP-based method for modeling time series: operator selection, parameter optimization and expert evaluation
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 989--990
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, grammar-guided genetic programming, Poster
%X This paper describes the theoretical and experimental analysis conducted to define the best values for the various operators and parameters of a grammar-guided genetic
   programming process for creating isokinetic reference models for top competition athletes. Isokinetics is a medical domain that studies the strength exerted by the patient
   joints (knee, ankle, etc.). We also present an evaluation of the resulting reference models comparing our results with the reference models output using other methods.
%8 7-11  July
%Z Also known as \cite1830664 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Anas N. Al-Rabadi
%T Book Review: Lee Spector $\bullet$ Automatic Quantum Computer Programming: A Genetic Programming Approach. Kluwer Academic Publishers (2004). ISBN 1-4020-7894-3. 100. 153
   pp.
%J The Computer Journal
%V 49
%N 1
%D 2006
%P 129--130
%I 
%K genetic algorithms, genetic programming
%U http://comjnl.oxfordjournals.org/cgi/content/full/49/1/129; http://comjnl.oxfordjournals.org/cgi/reprint/49/1/129
%8 January
%Z review of \citespector:book

%A Sameer H. Al-Sakran
%A John R. Koza
%A Lee W. Jones
%T Automated Re-invention of a Previously Patented Optical Lens System Using Genetic Programming
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 25--37
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=25
%X The three dozen or so known instances of human-competitive designs produced by genetic programming for antennas, mechanical systems, circuits, and controllers raise the
   question of whether the genetic programming can be extended to the design of complex structures from other fields. This paper discusses efforts to apply genetic programming
   to the automated design of optical lens systems. The paper can be read from two different perspectives. First, broadly, it chronicles the step-by-step process by which the
   authors approached the problem of applying genetic programming to a domain that was new to them. Second, more narrowly, it describes the use of genetic programming to
   re-create the complete design for the previously patented Tackaberry-Muller optical lens system. Genetic programming accomplished this "from scratch" without starting from
   a pre-specified number of lens and a pre-specified layout and without starting from a pre-existing good design. The genetically evolved design for the Tackaberry-Muller
   lens system is an example, in the field of optical design, of a human-competitive result produced by genetic programming.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Bjorn K. Alsberg
%A Nathalie Marchand-Geneste
%A Ross D. King
%T A new 3D molecular structure representation using quantum topology with application to structure-property relationships
%J Chemometrics and Intelligent Laboratory Systems
%V 54
%N 2
%D 2000
%P 75--91
%I 
%K genetic algorithms, genetic programming, Structure representation using quantum topology, StruQT, Quantitative structure-activity relationships, QSAR, Quantitative
   structure-property relationships, QSPR, Atoms in molecules, AIM, Quantum chemistry, Bader theory, Multivariate analysis, Partial least squares regression, 3D structure
   representation, Variable selection
%U http://www.sciencedirect.com/science/article/B6TFP-426XTF7-1/2/36265a259de8f80d4918ee6612612218
%X We present a new 3D molecular structure representation based on Richard F.W. Bader's quantum topological atoms in molecules (AIM) theory for use in quantitative
   structure-property/activity relationship (QSPR/QSAR) modelling. Central to this structure representation using quantum topology (StruQT) are critical points located on the
   electron density distribution of the molecules. Other gradient fields such as the Laplacian of the electron density distribution can also be used. The type of critical
   point of particular interest is the bond critical point (BCP) which is here characterised by using the following three parameters: electron density [rho], the Laplacian
   [nabla]2[rho] and the ellipticity [epsi]. This representation has the advantage that there is no need to probe a large number of lattice points in 3D space to capture the
   important parts of the 3D electronic structure as is necessary in, e.g. comparative field analysis (CoMFA). We tested the new structure representation by predicting the
   wavelength of the lowest UV transition for a system of 18 anthocyanidins. Different quantitative structure-property relationship (QSPR) models are constructed using several
   chemometric/machine learning methods such as standard partial least squares regression (PLS), truncated PLS variable selection, genetic algorithm-based variable selection
   and genetic programming (GP). These models identified bonds that either take part in decreasing or increasing the dominant excitation wavelength. The models also correctly
   emphasised on the involvement of the conjugated [pi] system for predicting the wavelength through flagging the BCP ellipticity parameters as important for this particular
   data set.

%A Riyad Alshammari
%A Peter Lichodzijewski
%A Malcolm I. Heywood
%A A. Nur Zincir-Heywood
%T Classifying SSH encrypted traffic with minimum packet header features using genetic programming
%B GECCO-2009 Defense applications of computational intelligence workshop
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 2539--2546
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X The classification of Encrypted Traffic, namely Secure Shell (SSH), on the fly from network TCP traffic represents a particularly challenging application domain for machine
   learning. Solutions should ideally be both simple - therefore efficient to deploy - and accurate. Recent advances to team based Genetic Programming provide the opportunity
   to decompose the original problem into a subset of classifiers with non-overlapping behaviors, in effect providing further insight into the problem domain and increasing
   the throughput of solutions. Thus, in this work we have investigated the identification of SSH encrypted traffic based on packet header features without using IP addresses,
   port numbers and payload data. Evaluation of C4.5 and AdaBoost - representing current best practice - against the Symbiotic Bid-based (SBB) paradigm of team-based Genetic
   Programming (GP) under data sets common and independent from the training condition indicates that SBB based GP solutions are capable of providing simpler solutions without
   sacrificing accuracy.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Riyad Alshammari
%A A. Nur Zincir-Heywood
%T Unveiling Skype encrypted tunnels using GP
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X The classification of Encrypted Traffic, namely Skype, from network traffic represents a particularly challenging problem. Solutions should ideally be both simple
   -therefore efficient to deploy -and accurate. Recent advances to team-based Genetic Programming provide the opportunity to decompose the original problem into a subset of
   classifiers with non-overlapping behaviours. Thus, in this work we have investigated the identification of Skype encrypted traffic using Symbiotic Bid-Based (SBB) paradigm
   of team based Genetic Programming (GP) found on flow features without using IP addresses, port numbers and payload data. Evaluation of SBB-GP against C4.5 and AdaBoost
   -representing current best practice -indicates that SBB-GP solutions are capable of providing simpler solutions in terms number of features used and the complexity of the
   solution/model without sacrificing accuracy.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586288

%A Riyad Alshammari
%A A. Nur Zincir-Heywood
%T An investigation on the identification of VoIP traffic: Case study on Gtalk and Skype
%B 2010 International Conference on Network and Service Management (CNSM)
%D 2010
%P 310--313
%I 
%K genetic algorithms, genetic programming, AdaBoost, C4.5, Gtalk, IP address, Skype, VoIP encrypted traffic, machine learning, source/destination port, Internet telephony,
   learning (artificial intelligence), telecommunication traffic
%X The classification of encrypted traffic on the fly from network traces represents a particularly challenging application domain. Recent advances in machine learning provide
   the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviours, in effect providing further insight into the problem domain.
   Thus, the objective of this work is to classify VoIP encrypted traffic, where Gtalk and Skype applications are taken as good representatives. To this end, three different
   machine learning based approaches, namely, C4.5, AdaBoost and Genetic Programming (GP), are evaluated under data sets common and independent from the training condition. In
   this case, flow based features are employed without using the IP addresses, source/destination ports and payload information. Results indicate that C4.5 based machine
   learning approach has the best performance.
%8 25-29  October
%Z Also known as \cite5691210

%A Riyad Alshammari
%A A. Nur Zincir-Heywood
%T Is Machine Learning losing the battle to produce transportable signatures against VoIP traffic?
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 1542--1549
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Related Areas and Applications:Testing Evolutionary Algorithms on Real-world Numerical Optimisation Problems
%X Traffic classification becomes more challenging since the traditional techniques such as port numbers or deep packet inspection are ineffective against voice over IP (VoIP)
   applications, which uses non-standard ports and encryption. Statistical information based on network layer with the use of machine learning (ML) can achieve high
   classification accuracy and produce transportable signatures. However, the ability of ML to find transportable signatures depends mainly on the training data sets. In this
   paper, we explore the importance of sampling training data sets for the ML algorithms, specifically Genetic Programming, C5.0, Naive Bayesian and AdaBoost, to find
   transportable signatures. To this end, we employed two techniques for sampling network training data sets, namely random sampling and consecutive sampling. Results show
   that random sampling and 90-minute consecutive sampling have the best performance in terms of accuracy using C5.0 and SBB, respectively. In terms of complexity, the size of
   C5.0 solutions increases as the training size increases, whereas SBB finds simpler solutions.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Fawaz A. Alsulaiman
%A Nizar Sakr
%A Julio J. Valdes
%A Abdulmotaleb {El Saddik}
%A Nicolas D. Georganas
%T Feature selection and classification in genetic programming: Application to haptic-based biometric data
%B IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009
%D 2009
%P 1--7
%I 
%K genetic algorithms, genetic programming, gene expression programming, analytic function, dimensionality reducers, feature selection, haptic dataset, haptic-based biometric
   data, haptic-based biometrics problem, high-dimensional haptic feature space, perfect classification model, feature extraction, haptic interfaces, pattern classification
%X In this paper, a study is conducted in order to explore the use of genetic programming, in particular gene expression programming (GEP), in finding analytic functions that
   can behave as classifiers in high-dimensional haptic feature spaces. More importantly, the determined explicit functions are used in discovering minimal
   knowledge-preserving subsets of features from very high dimensional haptic datasets, thus acting as general dimensionality reducers. This approach is applied to the
   haptic-based biometrics problem; namely, in user identity verification. GEP models are initially generated using the original haptic biometric datatset, which is imbalanced
   in terms of the number of representative instances of each class. This procedure was repeated while considering an under-sampled (balanced) version of the datasets. The
   results demonstrated that for all datasets, whether imbalanced or under-sampled, a certain number (on average) of perfect classification models were determined. In
   addition, using GEP, great feature reduction was achieved as the generated analytic functions (classifiers) exploited only a small fraction of the available features.
%8 July
%Z Also known as \cite5356540

%A Lee Altenberg
%T The Evolution of Evolvability in Genetic Programming
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 47--74
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%X The notion of ``evolvability'' --- the ability of a population to produce variants fitter than any yet existing --- is developed as it applies to genetic algorithms. A
   theoretical analysis of the dynamics of genetic programming predicts the existence of a novel, emergent selection phenomenon: the evolution of evolvability. This is
   produced by the proliferation, within programs, of blocks of code that have a higher chance of increasing fitness when added to programs. Selection can then come to mold
   the \em variational aspects of the way evolved programs are represented. A model of code proliferation within programs is analyzed to illustrate this effect. The
   mathematical and conceptual framework includes: the definition of evolvability as a measure of performance for genetic algorithms; application of Price's \em Covariance and
   Selection Theorem to show how the fitness function, representation, and genetic operators must interact to produce evolvability --- namely, that genetic operators produce
   offspring with fitnesses specifically correlated with their parent's fitnesses; how blocks of code emerge as a new level of replicator, proliferating as a function of their
   ``constructional fitness'', which is distinct from their schema fitness; and how programs may change from innovative code to conservative code as the populations mature.
   Several new selection techniques and genetic operators are proposed in order to give better control over the evolution of evolvability and improved evolutionary
   performance. Copyright 1996 Lee Altenberg
%O 3
%Z Price's Covariance and Selection Theorem 1970 Nature 227 pages 520-521 Fisher's Theorem 1930 "The Genetical Theory of Natural Selection, Clarendon Press, Oxford, UK pages
   30-37 Generally better theory for GP -> additional fitness (of blocks) Also known as \citeAltenberg:1994EEGP

%A Lee Altenberg
%T Evolving better representations through selective genome growth
%B Proceedings of the 1st IEEE Conference on Evolutionary Computation
%V 1
%D 1994
%P 182--187
%I IEEE Piscataway, NJ, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://dynamics.org/Altenberg/FILES/LeeEBR.pdf
%X The choice of how to represent the search space for a genetic algorithm (GA) is critical to the GA's performance. Representations are usually engineered by hand and fixed
   for the duration of the GA run. Here a new method is described in which the degrees of freedom of the representation --- i.e. the genes -- are increased incrementally. The
   phenotypic effects of the new genes are randomly drawn from a space of different functional effects. Only those genes that initially increase fitness are kept. The
   genotype-phenotype map that results from this selection during the constructional of the genome allows better adaptation. This effect is illustrated with the NK landscape
   model. The resulting genotype-phenotype maps are much less epistatic than generic maps would be. They have extremely low values of ``K'' --- the number of fitness
   components affected by each gene. Moreover, these maps are exquisitely tuned to the specifics of the random fitness functions, and achieve fitnesses many standard
   deviations above generic NK landscapes with the same \gp\ maps. The evolved maps create adaptive landscapes that are much smoother than generic NK landscapes ever are. Thus
   a caveat should be made when making arguments about the applicability of generic properties of complex systems to evolved systems. This method may help to solve the problem
   of choice of representations in genetic algorithms. Copyright 1996 Lee Altenberg
%8 27-29  June

%A Lee Altenberg
%T Emergent phenomena in genetic programming
%B Evolutionary Programming --- Proceedings of the Third Annual Conference
%E Anthony V. Sebald and Lawrence J. Fogel
%D 1994
%P 233--241
%I World Scientific Publishing
%C San Diego, CA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/398393.html
%X Evolutionary computation systems exhibit various emergent phenomena, primary of which is adaptation. In genetic programming, because of the indeterminate nature of the
   representation, the evolution of both recombination distributions and representations can emerge from the population dynamics. A review of ideas on these phenomena is
   presented, including theory on the evolution of evolvability through differential proliferation of subexpressions within programs. An analysis is given of a model of
   genetic programming dynamics that is supportive of the ``Soft Brood Selection'' conjecture, which was proposed as a means to counteract the emergence of highly conservative
   code, and instead favor highly evolvable code. Copyright 1996 Lee Altenberg
%8 24-26  February
%Z EP-94 http://www.wspc.com.sg/books/compsci/2401.html http://www.natural-selection.com/eps/EP94.html
%@ 981-02-1810-9

%A Lee Altenberg
%T The Schema Theorem and Price's Theorem
%B Foundations of Genetic Algorithms 3
%E L. Darrell Whitley and Michael D. Vose
%D 1994
%P 23--49
%I Morgan Kaufmann San Francisco, CA, USA
%I International Society for Genetic Algorithms
%C Estes Park, Colorado, USA
%K genetic algorithms, genetic programming
%U http://dynamics.org/Altenberg/FILES/LeeSTPT.pdf
%X Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its
   implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications for how well a GA is performing.
   Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in
   results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general.
   However, schemata re-emerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a ``missing''
   schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of ``adaptive landscape'' analysis is examined and
   counterexamples offered to the commonly used correlation statistic. Instead, an alternative statistic---the transmission function in the fitness domain--- is proposed as
   the optimal statistic for estimating GA performance from limited samples. Copyright 1996 Lee Altenberg
%O Published 1995
%8 31  July --2  August
%Z FOGA-3 Deals with GAs as a whole, not specifically GP.
%@ 1-55860-356-5

%A Lee Altenberg
%T Genome growth and the evolution of the genotype-phenotype map
%B Evolution as a Computational Process
%E Wolfgang Banzhaf and Frank H. Eeckman
%D 1995
%P 205--259
%I Springer-Verlag
%C Berlin, Germany
%K genetic algorithms, genetic programming
%U http://dynamics.org/Altenberg/FILES/LeeGGEGPM.pdf
%X The evolution of new genes is distinct from evolution through allelic substitution in that new genes bring with them new degrees of freedom for genetic variability.
   Selection in the evolution of new genes can therefore act to sculpt the dimensions of variability in the genome. This ``constructional'' selection effect is an evolutionary
   mechanism, in addition to genetic modification, that can affect the variational properties of the genome and its evolvability. One consequence is a form of genic selection:
   genes with large potential for generating new useful genes when duplicated ought to proliferate in the genome, rendering it ever more capable of generating adaptive
   variants. A second consequence is that alleles of new genes whose creation produced a selective advantage may be more likely to also produce a selective advantage, provided
   that gene creation and allelic variation have correlated phenotypic effects. A fitness distribution model is analyzed which demonstrates these two effects quantitatively.
   These are effects that select on the nature of the genotype-phenotype map. New genes that perturb numerous functions under stabilizing selection, i.e. with high pleiotropy,
   are unlikely to be advantageous. Therefore, genes coming into the genome ought to exhibit low pleiotropy during their creation. If subsequent offspring genes also have low
   pleiotropy, then genic selection can occur. If subsequent allelic variation also has low pleiotropy, then that too should have a higher chance of not being deleterious. The
   effects on pleiotropy are illustrated with two model genotype-phenotype maps: Wagner's linear quantitative-genetic model with Gaussian selection, and Kauffman's ``NK''
   adaptive landscape model. Constructional selection is compared with other processes and ideas about the evolution of constraints, evolvability, and the genotype-phenotype
   map. Empirical phenomena such as dissociability in development, morphological integration, and exon shuffling are discussed in the context of this evolutionary process.
   Copyright 1996 Lee Altenberg

%A Lee Altenberg
%A Marcus W. Feldman
%T Selection, generalized transmission, and the evolution of modifier genes. II. Modifier polymorphisms
%D 1995
%I 
%U ftp://ftp.mhpcc.edu/pub/incoming/altenberg/LeeSGTEMG2MP.ps.Z
%O In preparation

%A Lee Altenberg
%T Modularity in Evolution: Some Low-Level Questions
%B Modularity: Understanding the Development and Evolution of Complex Natural Systems
%E Diego Rasskin-Gutman and Werner Callebaut
%D 2005
%P 99--128
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://dynamics.org/Altenberg/FILES/LeeMESLLQ.pdf
%X Intuitive notions about the advantages of modularity for evolvability run into the problem of how we parse the organism into traits. In order to resolve the question of
   multiplicity, there needs to be a way to get the human observer out of the way, and define modularity in terms of physical processes. I will offer two candidate ideas
   towards this resolution: the dimensionality of phenotypic variation, and the causal screening off of phenotypic variables by other phenotypic variables. With this
   framework, the evolutionary advantages that have been attributed to modularity do not derive from modularity per se. Rather, they require that there be an 'alignment'
   between the spaces of phenotypic variation, and the selection gradients that are available to the organism. Modularity may facilitate such alignment, but it is not
   sufficient; the appropriate phenotype-fitness map in conjunction with the genotype-phenotype map is also necessary for evolvability. Conclusion I have endeavoured in this
   essay to delve into some of the low-level conceptual issues associated with the idea of modularity in the genotype-phenotype map. My main proposal is that the evolutionary
   advantages that have been attributed to modularity do not derive from modularity per se. Rather, they require that there be an 'alignment' between the spaces of phenotypic
   variation, and the selection gradients that are available to the organism. Modularity in the genotype-phenotype map may make such an alignment more readily attained, but it
   is not sufficient; the appropriate phenotype-fitness map in conjunction with the genotype-phenotype map is also necessary for evolvability.
%O 5
%8 June
%Z Quantitative mutational effects under the 'House of Cards' vs. ``random-walk'' assumptions. http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10484&mode=toc
%@ 0-262-03326-7

%A Lee Altenberg
%T Open Problems in the Spectral Analysis of Evolutionary Dynamics
%B Frontiers of Evolutionary Computation
%S Genetic Algorithms And Evolutionary Computation Series
%E Anil Menon
%V 11
%D 2004
%P 73--99
%I Kluwer Academic Publishers
%C Boston, MA, USA
%K genetic algorithms, genetic programming
%U http://dynamics.org/Altenberg/FILES/LeeOPSAED.pdf
%X For broad classes of selection and genetic operators, the dynamics of evolution can be completely characterised by the spectra of the operators that define the dynamics, in
   both infinite and finite populations. These classes include generalised mutation, frequency-independent selection, uniparental inheritance. Several open questions exist
   regarding these spectra: 1. For a given fitness function, what genetic operators and operator intensities are optimal for finding the fittest genotype? The concept of rapid
   first hitting time, an analog of Sinclair's rapidly mixing Markov chains, is examined. 2. What is the relationship between the spectra of deterministic infinite population
   models, and the spectra of the Markov processes derived from them in the case of finite populations? 3. Karlin proved a fundamental relationship between selection, rates of
   transformation under genetic operators, and the consequent asymptotic mean fitness of the population. Developed to analyse the stability of polymorphisms in subdivided
   populations, the theorem has been applied to unify the reduction principle for self-adaptation, and has other applications as well. Many other problems could be solved if
   it were generalised to account for the interaction of different genetic operators. Can Karlin's theorem on operator intensity be extended to account for mixed genetic
   operators?
%O 4
%Z Revised 2010
%@ 1-4020-7524-3

%A Lee Altenberg
%T Evolvability Suppression to Stabilize Far-Sighted Adaptations
%J Artificial Life
%V 11
%N 3
%D 2005
%P 427--443
%I 
%K genetic algorithms
%X The opportunistic character of adaptation through natural selection can lead to `evolutionary pathologies'---situations in which traits evolve that promote the extinction
   of the population. Such pathologies include imprudent predation and other forms of habitat over-exploitation or the `tragedy of the commons', adaptation to temporally
   unreliable resources, cheating and other antisocial behaviour, infectious pathogen carrier states, parthenogenesis, and cancer, an intra-organismal evolutionary pathology.
   It is known that hierarchical population dynamics can protect a population from invasion by pathological genes. Can it also alter the genotype so as to prevent the
   generation of such genes in the first place, i.e. suppress the evolvability of evolutionary pathologies? A model is constructed in which one locus controls the expression
   of the pathological trait, and a series of modifier loci exist which can prevent the expression of this trait. It is found that multiple `evolvability checkpoint' genes can
   evolve to prevent the generation of variants that cause evolutionary pathologies. The consequences of this finding are discussed.
%8 Fall

%A A. Alvarez
%A Alejandro Orfila
%A G. Basterretxea
%A J. Tintore
%A G. Vizoso
%A A. Fornes
%T Forecasting front displacements with a satellite based ocean forecasting (SOFT) system
%J Journal of Marine Systems
%V 65
%N 1-4
%D 2007
%P 299--313
%I 
%K genetic algorithms, genetic programming, Satellite data, Ocean prediction, Front evolution
%X Relatively long term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical
   models, called satellite based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. The forecast skill of SOFT systems predicting the sea
   surface temperature (SST) at sub-basin spatial scale (from hundreds to thousand kilometres), has been extensively explored in previous works. Thus, these works were mostly
   focused on predicting large scale patterns spatially stationary. At spatial scales smaller than sub-basin (from tens to hundred kilometres), spatiotemporal variability is
   more complex and propagating structures are frequently present. In this case, traditional SOFT systems based on Empirical Orthogonal Function (EOF) decompositions could not
   be optimal prediction systems. Instead, SOFT systems based on Complex Empirical Orthogonal Functions (CEOFs) are, a priori, better candidates to resolve these cases. In
   this work we study and compare the performance of an EOF and CEOF based SOFT systems forecasting the SST at weekly time scales of a propagating mesoscale structure. The
   SOFT system was implemented in an area of the Northern Balearic Sea (Western Mediterranean Sea) where a moving frontal structure is recurrently observed. Predictions from
   both SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the implemented SOFT systems are superior
   in terms of predictability to persistence. No substantial differences have been found between the EOF and CEOF-SOFT systems.
%O Marine Environmental Monitoring and Prediction - Selected papers from the 36th International Liege Colloquium on Ocean Dynamics
%8 March

%A Gabriel Alvarez
%T Standard Versus Micro-Genetic Algorithms for Seismic Trace Inversion
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 1--10
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Luis F. Alvarez
%A Vassili V. Toropov
%T Application of Genetic Programming to the Choice of a Structure of Global Approximations
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Luis F. Alvarez
%A Vassili V. Toropov
%A David C. Hughes
%A Ashraf F. Ashour
%T Approximation model building using genetic programming methodology: applications
%B Second ISSMO/AIAA Internet Conference on Approximations and Fast Reanalysis in Engineering Optimization
%E Thouraya Baranger and Fred van Keulen
%D 2000
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/512359.html
%X Genetic Programming methodology is used for the creation of approximation functions obtained by the response surface methodology. Two important aspects of the problems are
   addressed: the choice of the plan of experiment and the model tuning using the least-squares response surface fitting. Several examples show the applications of the
   technique to problems where the values of response functions are obtained either by numerical simulation or laboratory experimentation.
%O The Pennsylvania State University CiteSeer Archives
%8 25  May -2  June
%Z Multicriteria Optimization of the Manufacturing Process for Roman Cement

%A Marcos Alvarez-Diaz
%A Alberto Alvarez
%T Forecasting exchange rates using genetic algorithms
%J Applied Economics Letters
%V 10
%N 6
%D 2003
%P 319--322
%I 
%K genetic algorithms, genetic programming
%X A novel approach is employed to investigate the predictability of weekly data on the euro/dollar, British pound/dollar, Deutsch mark/dollar, Japanese yen/dollar, French
   franc/dollar and Canadian dollar/dollar exchange rates. A functional search procedure based on the Darwinian theories of natural evolution and survival, called genetic
   algorithms (hereinafter GA), was used to find an analytical function that best approximates the time variability of the studied exchange rates. In all cases, the
   mathematical models found by the GA predict slightly better than the random walk model. The models are heavily dominated by a linear relationship with the most recent past
   value, while contributions from nonlinear terms to the total forecasting performance are rather small. In consequence, the results agree with previous works establishing
   explicitly that nonlinear nature of exchange rates cannot be exploited to substantially improve forecasting.
%8 April

%A Marcos Alvarez-Diaz
%A Alberto Alvarez
%T Genetic multi-model composite forecast for non-linear prediction of exchange rates
%J Empirical Economics
%V 30
%N 3
%D 2005
%P 643--663
%I 
%K genetic algorithms, genetic programming, Composite-forecast or data-fusion, neural networks, exchange-rate forecasting
%X The existence of non-linear deterministic structures in the dynamics of exchange rates has already been amply demonstrated. In this paper, we attempt to exploit these
   non-linear structures employing forecasting techniques, such as Genetic Programming and Neural Networks, in the specific case of the Yen/US$ and Pound Sterling/US$ exchange
   rates. Forecasts obtained from genetic programming and neural networks are then genetically fused to verify whether synergy provides an improvement in the predictions. Our
   analysis considers both point predictions and the anticipating of either depreciations or appreciations.
%8 October

%A Marcos Alvarez-Diaz
%A Marcos Dominquez-Torreiro
%T Using Genetic Algorithms to Estimate and Validate Bioeconomic Models: The Case of the Ibero-atlantic Sardine Fishery
%J Journal of Bioeconomics
%V 8
%N 1
%D 2006
%P 55--65
%I 
%K genetic algorithms, genetic programming, bioeconomic modeling, linear and non-linear forecasting
%X The Neo-classical approach to fisheries management is based on designing and applying bioeconomic models. Traditionally, the basic bioeconomic models have used
   pre-established non-linear functional forms (logistic, Cobb-Douglas) in order to try to reflect the dynamics of the renewable resources under study. This assumption might
   cause misspecification problems and, in consequence, a loss of predictive ability. In this work we intend to verify if there is a bias motivated by employing the said
   non-linear parametric perspective. For this purpose, we employ a novel non-linear and non-parametric prediction method, called Genetic Algorithms, and we compare its
   results with those obtained from the traditional methods.
%8 April
%Z p 64 "Unlike a uni-variant analysis, DARWIN now allows us to look for functional relationships between two or more time-series."

%A Marcos Alvarez-Diaz
%A Gonzalo {Caballero Miguez}
%T The quality of institutions: A genetic programming approach
%J Economic Modelling
%V 25
%N 1
%D 2008
%P 161--169
%I 
%K genetic algorithms, genetic programming, Quality of institutions, Institutional determinants, Non-parametric perspective
%U http://www.sciencedirect.com/science/article/B6VB1-4P0VD80-1/2/c0bb8da3af64aa1ea6b0a4f90e4790b0
%X The new institutional economics has studied the determinants of the quality of institutions. Traditionally, the majority of the empirical literature has adopted a
   parametric and linear approach. These forms impose ad hoc functional structures, sometimes introducing relationships between variables that are forced and misleading. This
   paper analyses the determinants of the quality of institutions using a non-parametric and non-linear approach. Specifically, we employ a Genetic Program (GP) to study the
   functional relation between the quality of institutions and a set of historical, economical, geographical, religious and social variables. Besides this, we compare the
   obtained results with those employing a parametric perspective (Ordinary Least Square Regression). Following the empirical results of our application, we can conclude that
   the parametric perspective adopted in previous papers about institutional quality could be accurate.

%A Marcos Alvarez-Diaz
%A Gonzalo {Caballero Miguez}
%A Mario Solino
%T The institutional determinants of CO2 emissions: A computational modelling approach using Artificial Neural Networks and Genetic Programming
%R FUNCAS Working Paper 401
%D 2008
%I 
%I Fundacion de las Cajas de Ahorros
%C Madrid
%K genetic algorithms, genetic programming, ANN
%U http://www.funcas.es/Publicaciones/InformacionArticulos/Publicaciones.asp?ID=1411
%8 July

%A Marcos Alvarez-Diaz
%A Josep Mateu-Sbert
%A Jaume Rossello-Nadal
%T Forecasting tourist arrivals to Balearic Islands using genetic programming
%J International Journal of Computational Economics and Econometrics
%V 1
%N 1
%D 2009
%P 64--75
%I Inderscience Publishers
%K genetic algorithms, genetic programming, tourism forecasting, Diebold-Mariano test, tourist arrivals, Balearic Islands, UK, United Kingdom, Germany, Spain
%U http://www.inderscience.com/link.php?id=29153
%X Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a genetic program (GP) to
   predict monthly tourist arrivals from UK and Germany to Balearic Islands, Spain is explored. GP has already been employed satisfactorily in different scientific areas,
   including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the
   model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc
   interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, moving
   average and ARIMA), the empirical results reveal that GP can be a valuable tool in this field.
%8 November ~06

%A Marcos {Alvarez Diaz}
%A Manuel Gonzalez Gomez
%A Angeles Saavedra Gonzalez
%A Jacobo {De Una Alvarez}
%T On dichotomous choice contingent valuation data analysis: Semiparametric methods and Genetic Programming
%J Journal of Forest Economics
%V 16
%N 2
%D 2010
%P 145--156
%I 
%K genetic algorithms, genetic programming, Dichotomous choice contingent valuation, Genetic program, Parametric techniques, Proportional hazard model
%U http://www.sciencedirect.com/science/article/B7GJ5-4XY3F46-1/2/d98566d6ee97a4f7f2c2f1b9deb29bc1
%X The aim of this paper is twofold. Firstly, we introduce a novel semi-parametric technique called Genetic Programming to estimate and explain the willingness to pay to
   maintain environmental conditions of a specific natural park in Spain. To the authors' knowledge, this is the first time in which Genetic Programming is employed in
   contingent valuation. Secondly, we investigate the existence of bias due to the functional rigidity of the traditional parametric techniques commonly employed in a
   contingent valuation problem. We applied standard parametric methods (logit and probit) and compared with results obtained using semi parametric methods (a proportional
   hazard model and a genetic program). The parametric and semiparametric methods give similar results in terms of the variables finally chosen in the model. Therefore, the
   results confirm the internal validity of our contingent valuation exercise.
%8 April

%A Marcos Alvarez-Diaz
%A Alberto Alvarez
%T Forecasting exchange rates using local regression
%J Applied Economics Letters
%V 17
%N 5
%D 2010
%P 509--514
%I 
%K genetic algorithms, genetic programming, local search
%X In this article we use a generalisation of the standard nearest neighbours, called local regression (LR), to study the predictability of the yen/US dollar and pound
   sterling/US dollar exchange rates. We also compare our results with those previously obtained with global methods such as neural networks, genetic programming, data fusion
   and evolutionary neural networks. We want to verify if we can generalise to the exchange rate forecasting problem the belief that local methods beat global ones.
%8 March
%Z In this letter we have used LR to verify three aspects regarding to exchange rate forecasting for the Japanese yen and the British pound against US dollar. Firstly, we
   analyse their predictability discovering the existence of a short-term predictable structure in the temporal evolution of both currencies. Secondly, we confirm the
   homogeneity behaviour in terms of forecasting for weekly exchange rates and, finally, we also verify that local methods do not always beat to the global ones in an exchange
   rate forecasting exercise.

%A Marcos {Alvarez Diaz}
%T Speculative strategies in the foreign exchange market based on genetic programming predictions
%J Applied Financial Economics
%V 20
%N 6
%D 2010
%P 465--476
%I 
%K genetic algorithms, genetic programming
%X In this article, we investigate the out-of-sample forecasting ability of a Genetic Program (GP) to approach the dynamic evolution of the yen/US dollar and British pound/US
   dollar exchange rates, and verify whether the method can beat the random walk model. Later on, we use the predicted values to generate a trading rule and we check the
   possibility of obtaining extraordinary profits in the foreign exchange market. Our results reveal a slight forecasting ability for one-period-ahead, which is lost when more
   periods ahead are considered. On the other hand, our trading strategy obtains above-normal profits. However, when transaction costs are incorporated, the profits
   practically disappear or become negative.
%8 March
%Z Department of Economics, University of Vigo, Galicia, Spain

%A Saoirse Amarteifio
%A Michael O'Neill
%T An Evolutionary Approach to Complex System Regulation Using Grammatical Evolution
%B Artificial Life XI Ninth International Conference on the Simulation and Synthesis of Living Systems
%E Jordan Pollack and Mark Bedau and Phil Husbands and Takashi Ikegami and Richard A. Watson
%D 2004
%P 551--556
%I The MIT Press
%C Boston, Massachusetts
%K genetic algorithms, genetic programming, grammatical evolution
%8 12-15  September
%Z http://www.alife9.org/ ALIFE9
%@ 0-262-66183-7

%A Saoirse Amarteifio
%A Michael O'Neill
%T Coevolving Antibodies with a Rich Representation of Grammatical Evolution
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 1
%D 2005
%P 904--911
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming, grammatical evolution
%X A number of natural anticipatory systems employ dual processes of feature definition and feature exploitation. Presented here, a coevolutionary dual process model based on
   the immune system, considers the effect of coevolving complementary templates to bias feature selection and recombination. This work considers the issue of module
   exploitation in evolutionary algorithms. Our approach is characterised by the use of rich representations in grammatical evolution.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Saoirse Amarteifio
%T Interpreting a Genotype-Phenotype Map with Rich Representations in XMLGE
%R M.S. Thesis Master of Science in Computer Science
%D 2005
%I 
%I University of Limerick
%C University of Limerick, Ireland
%K genetic algorithms, genetic programming, grammatical evolution, xml
%U http://ncra.ucd.ie/downloads/pub/SaoirseMScThesis.pdf
%X A novel XML implementation of Grammatical Evolution is developed. This has a number of interesting features such as the use of XSLT for genetic operators and the use of
   reflection to build an object tree from an XML expression tree. This framework is designed to be used for remote or local evaluation of evolved program structures and
   provides a number of abstraction layers for program evaluation and evolution. A dynamical swarm system is evolved as a special-case function induction problem to illustrate
   the application of XMLGE. Particle behaviours are evolved to optimise colony performance. A dual process evolutionary algorithm based on the immune system using rich
   representations is developed. A dual process feature detection and feature integration model is described and the performance shown on benchmark GP problems. An adaptive
   feature detection method uses coevolving XPath antibodies to take selective interest in primary structures. Grammars are used to generate reciprocal binding structures
   (antibodies) given any primary domain grammar. A codon compression algorithm is developed which shows performance improvements on symbolic regression and multiplexer
   problems. The algorithm is based on questions about the information content of a genome. This also exploits information from the rich representation of XMLGE.

%A Yali Amit
%A Donald Geman
%T Shape Quantization and Recognition with Randomized Trees
%J Neural Computation
%V 9
%N 7
%D 1997
%P 1545--1588
%I 
%X We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior
   information about shape invariance and regularity. Each query corresponds to a spatial arrangement of several local topographic codes (or tags), which are in themselves too
   primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the
   queries are a natural partial ordering corresponding to increasing structure and complexity; semi-invariance, meaning that most shapes of a given class will answer the same
   way to two queries that are successive in the ordering; and stability, since the queries are not based on distinguished points and substructures. No classifier based on the
   full feature set can be evaluated, and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build
   tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features chosen depend on the branch
   that is traversed. Due to the number and nature of the queries, standard decision tree construction based on a fixed-length feature vector is not feasible. Instead we
   entertain only a small random sample of queries at each node, constrain their complexity to increase with tree depth, and grow multiple trees. The terminal nodes are
   labelled by estimates of the corresponding posterior distribution over shape classes. An image is classified by sending it down every tree and aggregating the resulting
   distributions. The method is applied to classifying handwritten digits and synthetic linear and nonlinear deformations of three hundred LATeX symbols. State-of-the-art
   error rates are achieved on the National Institute of Standards and Technology database of digits. The principal goal of the experiments on LATeX symbols is to analyse
   invariance, generalisation error and related issues, and a comparison with artificial neural networks methods is presented in this context.
%8 October
%Z MIT Press Cited by \citeMatthewGSmith:2005:GPEM.

%A Martyn Amos
%A Paul E. Dunne
%A Alan Gibbons
%T DNA Simulation of Boolean Circuits
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 679--683
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K DNA Computing
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Deepa Anand
%A K. K. Bharadwaj
%T Adaptive user similarity measures for recommender systems: A genetic programming approach
%B 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010)
%V 8
%D 2010
%P 121--125
%I 
%K genetic algorithms, genetic programming, adaptive user similarity measure, collaborative filtering, data environment, item discovery, item searching, optimal transformation
   function, preference value, raw ratings data, recommender system, similarity assessment, similarity estimation, user-item interaction, groupware, information filtering,
   recommender systems
%X Recommender systems signify the shift from the paradigm of searching for items to discovering items and have been employed by an increasing number of e-commerce sites for
   matching users to their preferences. Collaborative Filtering is a popular recommendation technique which exploits the past user-item interactions to determine user
   similarity. The preferences of such similar users are leveraged to offer suggestions to the active user. Even though several techniques for similarity assessment have been
   suggested in literature, no technique has been proven to be optimal under all contexts/data conditions. Hence, we propose a two-stage process to assess user similarity, the
   first is to learn the optimal transformation function to convert the raw ratings data to preference data by employing genetic programming, and the second is to use the
   preference values, so derived, to compute user similarity. The application of such learnt user bias gives rise to adaptive similarity measures, i.e. similarity estimates
   that are dataset dependent and hence expected to work best under any data environment. We demonstrate the superiority of our proposed technique by contrasting it to
   traditional similarity estimation techniques on four different datasets representing varied data environments.
%8 9-11  July
%Z Sch. of Comput. & Syst. Sci., Jawaharlal Nehru Univ., Delhi, India Also known as \cite5563737

%A Kenneth R. Anderson
%T Courage in Profiling
%R Technical Report
%D 1994
%I 
%I BBN
%K genetic algorithms, genetic programming, CASCOR1
%U http://openmap.bbn.com/~kanderso/performance/postscript/courage-in-profiles.ps
%8 28  July
%Z Compares speed of GP systems written in C (Tackett's SGPC which uses a tree representation) and Lisp (John Koza) on a symbolic regression problem, Optimised lisp performs
   better than expected. Koza lisp GP code performance improved 30 fold by use of profiling. Software: ftp://openmap.bbn.com/pub/kanderson/faster94/faster94/courage/koza3.lisp
   2. You can easily convert your eval into a closure compiler: Paper: http://www.iro.umontreal.ca/~feeley/papers/complang87.ps.gz

%A Eike Falk Anderson
%T Off-Line Evolution of Behaviour for Autonomous Agents in Real-Time Computer Games
%B Parallel Problem Solving from Nature - PPSN VII
%S Lecture Notes in Computer Science, LNCS
%E Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel
%N 2439
%D 2002
%P 689--699
%I Springer-Verlag
%C Granada, Spain
%K genetic algorithms, genetic programming, Games, Machine Learning, Fitness Evaluation
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=689
%O Available from http://link.springer.de/link/service/series/0558/papers/2439/243900689.pdf
%8 7-11  September
%@ 3-540-44139-5

%A Bjorn Andersson
%A Per Svensson
%A Peter Nordin
%A Mats Nordahl
%T Reactive and Memory-Based Genetic Programming for Robot Control
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 161--172
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=161
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP AIMGP machine code GP, memory, simulated robot
%@ 3-540-65899-8

%A Bjorn Andersson
%A Per Svensson
%A Peter Nordin
%A Mats Nordahl
%T On-line Evolution of Control for a Four-Legged Robot Using Genetic Programming
%B Real-World Applications of Evolutionary Computing
%S LNCS
%E Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter
   and Terence C. Fogarty
%V 1803
%D 2000
%P 319--326
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming, linear GP
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=319
%8 17  April
%Z "Galloping only appears after many hours of training" p323. EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April
   17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61
%@ 3-540-67353-9

%A Claes Andersson
%A Mats G. Nordahl
%T Evolving Coupled Map Lattices for Computation
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 151--162
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%X Genetic Programming is used to evolve coupled map lattices for density classification. The most successful evolved rules depending only on nearest neighbors (r=1) show
   better performance than existing r=3 cellular automaton rules on this task.
%8 14-15  April
%Z EuroGP'98
%@ 3-540-64360-5

%A Thord Andersson
%A Per-Erik Forssen
%T The Rolling Stones - Genetic Programming in AIP
%D 2000
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/491253.html
%X This report describes the design of a soccer playing agent developed in the scope of the AI Programming course. This agent uses a variant of the subsumption architecture
   [2]. The primitive behaviours that dene the intelligence of the agent are evolved using genetic programming [4]. We chose the genetic-programming approach instead of
   designs such as decision trees etc, since we wanted the intelligence in the agents to be truly articial, and not designed
%O The Pennsylvania State University CiteSeer Archives
%O student project
%8 March ~06

%A Daichi Ando
%A Palle Dahlsted
%A Mats Nordahl
%A Hitoshi Iba
%T Interactive GP with Tree Representation of Classical Music Pieces
%B Applications of Evolutionary Computing, EvoWorkshops2007: EvoCOMNET, EvoFIN, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, EvoTransLog
%S LNCS
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal
   Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang
%V 4448
%D 2007
%P 577--584
%I Springer Verlag
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X Research on the application of Interactive Evolutionary Computation(IEC) to the field of musical computation has been improved in recent years, marking an interesting
   parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not
   necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music
   composition. To solve this difficulties, we purpose a new IEC approach to music composition based on classical music theory. In this paper, we describe an established
   system according to the above idea, and detail of making success of composition a piece.
%8 11-13  April
%Z EvoWorkshops2007

%A Daichi Ando
%A Hitoshi Iba
%T Interactive Composition Aid System by Means of Tree Representation of Musical Phrase
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 4258--4265
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Research on the application of Interactive Evolutionary Computation (IEC) to the field of musical computation has been improved in recent years, marking an interesting
   parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not
   necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music
   composition. To solve this difficulties, the authors purpose a new IEC approach to music composition based on classical music theory. In this paper, the authors describe an
   established system according to the above idea, and detail of making success of composition a piece.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Jun Ando
%A Tomoharu Nagao
%T Image classification and processing using modified parallel-ACTIT
%B IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
%D 2009
%P 1787--1791
%I 
%K genetic algorithms, genetic programming, automatic construction of tree-structural image transformation, image classification, image recognition, modified parallel-ACTIT,
   training image sets, image classification, tree data structures
%X Image processing and recognition technologies are required to solve various problems. We have already proposed the system which automatically constructs image processing
   with Genetic Programming (GP), Automatic Construction of Tree-structural Image Transformation (ACTIT). However, it is necessary that training image sets are properly
   classified in advance if they have various characteristics. In this paper, we propose Modified Parallel-ACTIT which automatically classifies training image sets into
   several subpopulations. And it optimizes tree-structural image transformation for each training image sets in each subpopulations. We show experimentally that Modified
   Parallel-ACTIT is more effective in comparison with ordinary ACTIT.
%8 October
%Z Also known as \cite5346894

%A Shin Ando
%A Hitoshi Iba
%A Erina Sakamoto
%T Modeling Genetic Network by Hybrid GP
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 291--296
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%U http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/17336/http:zSzzSzwww.miv.t.u-tokyo.ac.jpzSz~ibazSztmpzSzando.pdf/modeling-genetic-network-by.pdf
%X We present an Evolutionary Modelling method for modeling genetic regulatory networks. The method features hybrid algorithm of Genetic Programming with statistical analysis
   to derive systems of differential equations. Genetic Programming and Least Mean Square method were combined to identify a concise form of regulation between the variables
   from a given set of time series. Also, results of multiple runs were statistically analysed to indicate the term with robust and significant influence. Our approach was
   evaluated in artificial data and real world data.
%8 12-17  May
%Z oai:CiteSeerPSU:520794
%@ 0-7803-7278-6

%A Shin Ando
%A Erina Sakamoto
%A Hitoshi Iba
%T Evolutionary modeling and inference of gene network
%J Information Sciences
%V 145
%N 3-4
%D 2002
%P 237--259
%I 
%K genetic algorithms, genetic programming, Gene network, Evolutionary modeling, Time series prediction
%U http://www.sciencedirect.com/science/article/B6V0C-46WWB37-3/2/963172f8c0faa12d700376b07bfc96a5
%X we describe an Evolutionary Modeling (EM) approach to building causal model of differential equation system from time series data. The main target of the modeling is the
   gene regulatory network. A hybrid method of Genetic Programming (GP) and statistical analysis is featured in our work. GP and Least Mean Square method (LMS) were combined
   to identify a concise form of regulation between the variables from a given set of time series. Our approach was evaluated in several real-world problems. Further, Monte
   Carlo analysis is applied to indicate the robust and significant influence from the results for gene network analysis purpose.
%8 September

%A Shin Ando
%A Hitoshi Iba
%T Classification of Gene Expression Profile Using Combinatory Method of Evolutionary Computation and Machine Learning
%J Genetic Programming and Evolvable Machines
%V 5
%N 2
%D 2004
%P 145--156
%I 
%K genetic algorithms, genetic programming, evolutionary computation, artificial immune system, wrapper approach, gene expression classification, cancer diagnosis
%X The analysis of large amount of gene expression profiles, which became available by rapidly developed monitoring tools, is an important task in Bioinformatics. The problem
   we address is the discrimination of gene expression profiles of different classes, such as cancerous/benign tissues. Two subtasks in such problem, feature subset selection
   and inductive learning has critical effect on each other. In the wrapper approach, combinatorial search of feature subset is done with performance of inductive learning as
   search criteria. This paper compares few combinations of supervised learning and combinatorial search when used in the wrapper approach. Also an extended GA implementation
   is introduced, which uses Clonal selection, a data-driven selection method. It compares very well to standard GA. The analysis of the obtained classifier reveals
   synergistic effect of genes in discrimination of the profiles.
%8 June
%Z Part of \citebanzhaf:2004:biogec Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster (1)
   Department of Electronics, School of Engineering, University of Tokyo, Yokohama, Japan (2) Department of Frontier Informatics, School of Frontier Science, University of
   Tokyo, Chiba, Japan

%A David Andre
%T Artificial Evolution of Intelligence: Lessons from natural evolution: An illustrative approach using Genetic Programming
%R BS Honors Thesis
%D 1994
%I 
%I Stanford University, Symbolic Systems Program
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.66.1367&rep=rep1&type=pdf

%A David Andre
%T Automatically Defined Features: The Simultaneous Evolution of 2-Dimensional Feature Detectors and an Algorithm for Using Them
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 477--494
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%O 23
%Z Mixture of GP and two dee GA

%A David Andre
%T Evolution of Mapmaking Ability: Strategies for the evolution of learning, planning, and memory using genetic programming
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 250--255
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, evolved representations, gold collection, information encoding, intelligent agent, learning, mapmaking evolution; memory,
   multi-phasic fitness environment, planning, brain models, cartography, cognitive systems, learning (artificial intelligence), planning (artificial intelligence)
%X An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to genetic programming
   have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning,
   planning, and memory using genetic programming. The approach uses a multi-phasic fitness environment that enforces the use of memory and allows fairly straightforward
   comprehension of the evolved representations. An illustrative problem of `gold' collection is used to demonstrate the usefulness of the approach. The results indicate that
   the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans
%8 27-29  June

%A David Andre
%T Learning and Upgrading Rules for an OCR System Using Genetic Programming
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%D 1994
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/cache/papers/cs/802/http:zSzzSzwww.cs.berkeley.eduzSz~dandrezSzpaperszSzAndre_WCCI_94_OCR_Boundary.pdf/learning-and-upgrading-rules.pdf
%8 27-29  June
%Z Uses GP both to recognise C in various fonts and to maintain manually produced extremely high level code when a new font is added

%A David Andre
%T The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming
%B Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)
%E Larry J. Eshelman
%D 1995
%P 248--255
%I Morgan Kaufmann San Francisco, CA, USA
%C Pittsburgh, PA, USA
%K genetic algorithms, genetic programming, memory
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Andre_1995_ammsp.pdf
%8 15-19  July
%Z Worlds 2x2, 4x4 and 8x8. Separate program trees for mapmaker and mapuser. ADFs used by mapusers only. Uses repeat, repeati (Repeat_Index), IncMem. Torrodial memory,
   isomophic to world. Steady state, Tournament selection (8) but with smaller (2) tournament group size for deletion. Evolved programs subjected to analysis and explanation.
   Evolved general solutions from limited test cases. Suggests simple strategies dominate more complex ones. GP better than random.
%@ 1-55860-370-0

%A David Andre
%A John R. Koza
%T Parallel Genetic Programming on a Network of Transputers
%B Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications
%E Justinian P. Rosca
%D 1995
%P 111--120
%I 
%C Tahoe City, California, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/andre_1995_parallel.pdf
%8 9  July
%Z like \citeKoza:1995:pGPnt part of \citerosca:1995:ml

%A David Andre
%T The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action
%B IJCAI-95 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence
%V 1
%D 1995
%P 741--747
%I Morgan Kaufmann San Francisco, CA, USA
%I IJCAII,AAAI,CSCSI
%C Montreal, Quebec, Canada
%K genetic algorithms, genetic programming, memory
%U http://ijcai.org/Past%20Proceedings/IJCAI-95-VOL%201/pdf/097.pdf
%X An essential component of an intelligent agent is the ability to notice, encode, store, and use information about its environment. Traditional approaches to program
   induction have focused on evolving functional or reactive programs. This paper presents MAPMAKER, a method for the automatic generation of agents that discover information
   about their environment, encode this information for later use, and create simple plans using the stored mental models. In this method, agents are multi-part computer
   programs that communicate through a shared memory. Both the programs and the representation scheme are evolved using genetic programming. An illustrative problem of 'gold'
   collection is used to demonstrate the method in which one part of a program makes a map of the world and stores it in memory, and the other part uses this map to find the
   gold The results indicate that the method can evolve programs that store simple representations of their environments and use these representations to produce simple plans.
%8 20-25  August
%Z MAPMAKER searches for gold
%@ 1-55860-363-8

%A David Andre
%A Forrest H {Bennett III}
%A John R. Koza
%T Evolution of Intricate Long-Distance Communication Signals in Cellular Automata using Genetic Programming
%B Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems
%V 1
%D 1996
%I MIT Press Cambridge, MA, USA
%C Nara, Japan
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/alife1996gkl.pdf
%X A cellular automata rule for the majority classification task was evolved using genetic programming with automatically defined functions. The genetically evolved rule has
   an accuracy of 82.326%. This level of accuracy exceeds that of the Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other rules produced by
   known previous automated approaches. Our genetically evolved rule is qualitatively different from other rules in that it uses a fine-grained internal representation of
   density information; it employs a large number of different domains and particles; and it uses an intricate set of signals for communicating information over large
   distances in time and space.
%8 16--18  May
%Z Alife-5 A longer version of this paper will be presented at the GP-96 conference. GP gets best solution to GKL problem "The population size used to evolve the current
   world's record for the GKL majority classification 1-dimensionall 2-sate 7-neighbor cellular authomata problem was 51,200. I believe Melanie Mitchell at the Santa Fe
   Institute has been doing continuing additional work on using GAs to evolve CA rules for various other problems."

%A David Andre
%A John R. Koza
%T Parallel Genetic Programming: A Scalable Implementation Using The Transputer Network Architecture
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 317--338
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/334
%X This chapter describes the parallel implementation of genetic programming in the C programming language using a PC type computer (running Windows) acting as a host and a
   network of processing nodes using the transputer architecture. Using this approach, researchers of genetic algorithms and genetic programming can acquire computing power
   that is intermediate between the power of currently available workstations and that of supercomputers at a cost that is intermediate between the two. This approach is
   illustrated by a comparison of the computational effort required to solve the problem of symbolic regression of the Boolean even-5-parity function with different migration
   rates. Genetic programming required the least computational effort with an 5% migration rate. Moreover, this computational effort was less than that required for solving
   the problem with a serial computer and a panmictic population of the same size. That is, apart from the nearly linear speed-up in executing a fixed amount of code inherent
   in the parallel implementation of genetic programming, the use of distributed sub-populations with only limited migration delivered more than linear speed-up in solving the
   problem.
%O 16
%@ 0-262-01158-1

%A David Andre
%A Forrest H {Bennett III}
%A John R. Koza
%T Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 3--11
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X It is difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information
   across great distances in the cellular space. Various human- written algorithms have appeared in the past two decades for the vexatious majority classification task for
   one-dimensional two-state cellular automata. This paper describes how genetic programming with automatically defined functions evolved a rule for this task with an accuracy
   of 82.326%. This level of accuracy exceeds that of the original 1978 Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other known rules
   produced by automated methods. The rule evolved by genetic programming is qualitatively different from all previous rules in that it employs a larger and more intricate
   repertoire of domains and particles to represent and communicate information across the cellular space.
%8 28--31  July
%Z GP-96

%A David Andre
%A Astro Teller
%T A Study in Program Response and the Negative Effects of Introns in Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 12--20
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/TellerGP96/TellerGP96.html
%X The standard method of obtaining a response in tree-based genetic programming is to take the value returned by the root node. In non-tree representations, alternate methods
   have been explored. One alternative is to treat a specific location in indexed memory as the response value when the program terminates. The purpose of this paper is to
   explore the applicability of this technique to tree-structured programs and to explore the intron effects that these studies bring to light. This paper's experimental
   results support the finding that this memory-based program response technique is an improvement for some, but not all, problems. In addition, this paper's experimental
   results support the finding that, contrary to past research and speculation, the addition or even facilitation of introns can seriously degrade the search performance of
   genetic programming.
%8 28--31  July
%Z GP-96 html version available from http://www.cs.cmu.edu/~astro/

%A David Andre
%A John R. Koza
%T A parallel implementation of genetic programming that achieves super-linear performance
%B Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications
%E Hamid R. Arabnia
%V III
%D 1996
%P 1163--1174
%I CSREA
%C Sunnyvale
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/pdpta1996.pdf
%X This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach,
   researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of
   supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort required to solve a benchmark problem. Because of the
   decoupled character of genetic programming, our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the
   use of subpopulations delivered a super linear speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the
   parallel genetic programming system evolved solutions that are competitive with human performance on the same problem.
%8 9-11  August
%Z Awarded Best Paper Award PDPTA'96

%A David Andre
%T Learning and Upgrading Rules for an Optical Character Recognition System Using Genetic Programming
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%I Oxford University Press
%K genetic algorithms, genetic programming
%O section G8.1
%Z invited chapter
%@ 0-7503-0392-1

%A David Andre
%T Multi-level parallelism in automatically synthesizing soccer-playing programs for Robocup using genetic programming
%D 1998
%I 
%K genetic algorithms, genetic programming, memory
%U http://citeseer.ist.psu.edu/245675.html
%X Many of the various proposals for tomorrow's supercomputers have included clusters of multiprocessors as an essential component. However, when designing the systems of the
   future, it is important to insure that the nature of the parallelism provided matches up with some relevant and important set of algorithms. This project presents empirical
   program synthesis as an algorithm that can successfully exploit the multiple levels of interconnect present in an multi-SMP cluster system. When applying program synthesis
   techniques to difficult problems, it is often the case that two distinct levels of parallelism will emerge. First, many example programs must be tested -- and can often be
   tested in parallel. This matches up with the "slow" interconnect on a clump-based system. Second, the execution of a particular program can often be parallelized,
   especially if the program is complicated or requires interactions with a complex simulation. This level of parallelism, in contrast to the first, often requires
   fine-grained communication. Thus, this matches up with the "fast" level of the clump-based system. In particular, this project presents a multi-level parallel system for
   the automatic program synthesis of soccer-playing agents for the Robocup simulator competition using genetic programming. The system uses both the fast shared-memory
   communication of the SMP system as well as a much slower mechanism for the inter-SMP communication. The system is benchmarked on a variety of configurations, and speedup
   curves are presented. Additionally, a simple LogP analysis comparing the performance of the designed system with a single-processor based NOW system is presented. Finally,
   the Robocup project is reviewed and the future work outlined.
%Z my ghostview (Jan 2002) barfs at cs267_final.ps but it prints

%A David Andre
%A John R. Koza
%T A parallel implementation of genetic programming that achieves super-linear performance
%J Information Sciences
%V 106
%N 3-4
%D 1998
%P 201--218
%I 
%K genetic algorithms, genetic programming
%U http://www.davidandre.com/papers/isj97.ps
%X This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach,
   researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of
   supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort required to solve a benchmark problem. Because of the
   decoupled character of genetic programming, our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the
   use of subpopulations delivered a <i>super-linear</i> speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the
   parallel genetic programming system evolved solutions that are competitive with human performance.
%Z Information Sciences http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt

%A David Andre
%A Forrest H {Bennett III}
%A John Koza
%A Martin A. Keane
%T On the Theory of Designing Circuits using Genetic Programming and a Minimum of Domain Knowledge
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%P 130--135
%I IEEE Press
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming
%X The problem of analog circuit design is a difficult problem that is generally viewed as requiring human intelligence to solve. Considerable progress has been made in
   automating the design of certain categories of purely digital circuits; however, the design of analog electrical circuits and mixed analog-digital circuits has not proved
   to be as amenable to automation. When critical analog circuits are required for a project, skilled and highly trained experts are necessary. Previous work on applying
   genetic programming to the design of analog circuits has proved to be successful at evolving a wide variety of circuits, including filters, amplifiers, and computational
   circuits; however, previous approaches have required the specification of an appropriate embryonic circuit. This paper explores a method to eliminate even this small amount
   of problem specific knowledge, and, in addition, proves that the representation used is capable of producing all circuits.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
%@ 0-7803-4869-9

%A D. Andre
%A A. Teller
%T Evolving Team Darwin United
%B RoboCup-98: Robot Soccer World Cup II
%S LNCS
%E M. Asada and H. Kitano
%V 1604
%D 1999
%P 346--351
%I Springer Verlag
%C Paris, France
%K genetic algorithms, genetic programming
%U http://206.210.94.135/work/pdfs/Teller_Astro.pdf
%X The RoboCup simulator competition is one of the most challenging international proving grounds for contemporary AI research. Exactly because of the high level of complexity
   and a lack of reliable strategic guidelines, the pervasive attitude has been that the problem can most successfully be attacked by human expertise, possibly assisted by
   some level of machine learning. This led, in RoboCup'97, to a field of simulator teams all of whose level and style of play were heavily influenced by the human designers
   of those teams. It is the thesis of our work that machine learning, if given the opportunity to design (learn) ``everything'' about how the simulator team operates, can
   develop a competitive simulator team that solves the problem using highly successful, if largely non- human, styles of play. To this end, Darwin United is a team of eleven
   players that have been evolved as a team of coordinated agents in the RoboCup simulator. Each agent is given a subset of the lowest level perceptual inputs and must learn
   to execute series of the most basic actions (turn, kick, dash) in order to participate as a member of the team. This paper presents our motivation, our approach, and the
   specific construction of our team that created itself from scratch.
%8 July  1998
%Z LNCS 1604 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66320-7 READ and WRITE functions, ie memory, 8 programs control the 11 players however these 8 can use 8
   shared ADFs
%@ 3-540-66320-7

%A Peter Andreae
%A Huayang Xie
%A Mengjie Zhang
%T Genetic Programming for detecting rhythmic stress in spoken English
%J International Journal of Knowledge-Based and Intelligent Engineering Systems
%V 12
%N 1
%D 2008
%P 15--28
%I IOS Press
%K genetic algorithms, genetic programming
%U http://iospress.metapress.com/content/k017m554023m5732/
%X Rhythmic stress detection is an important but difficult problem in speech recognition. This paper describes an approach to the automatic detection of rhythmic stress in New
   Zealand spoken English using a linear genetic programming system with speaker independent prosodic features and vowel quality features as terminals to classify each vowel
   segment as stressed or unstressed. In addition to the four standard arithmetic operators, this approach also uses other functions such as trigonometric and conditional
   functions in the function set to cope with the complexity of the task. The error rate on the training set is used as the fitness function. The approach is examined and
   compared to a decision tree approach and a support vector machine approach on a speech data set with 703 vowels segmented from 60 female adult utterances. The genetic
   programming approach achieved a maximum average accuracy of 92.6percent. The results suggest that the genetic programming approach developed in this paper outperforms the
   decision tree approach and the support vector machine approach for stress detection on this data set in terms of the detection accuracy, the ability of handling redundant
   features, and the automatic feature selection capability.
%Z KES, see also \citexie:evows06

%A Martin Andrews
%A Richard Prager
%T Genetic Programming for the Acquisition of Double Auction Market Strategies
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 355--368
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%O 16
%Z " a GP approach was very successful in learning strategies for playing a simple game with complex dynamics" Ref Knobeln Contest: Sanfrancisco.ira.uka.de [129.13.13.110]
   /pub/knobeln Generational GP pop=300, touranment selection? size=2? Comparison with Simulated Annealing:SA also good but GP better Best GP exceeded performance of handcode
   routines (on average?) 65% of time. Check details of what exctly this means. Set number of games played so could distinquish meadian from top quartile with 95% confidence.
   Claims it helps, but doesnt seem to have either speeded things at lot or made much better result.

%A J. H. Ang
%A E. J. Teoh
%A C. H. Tan
%A K. C. Goh
%A K. C. Tan
%T Dimension Reduction Using Evolutionary Support Vector Machines
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X This paper presents a novel approach of hybridising two conventional machine learning algorithms for dimension reduction. Genetic Algorithm (GA) and Support Vector Machines
   (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set using principles of evolutionary process,
   after which the reduced dataset is presented to the SVMs. Simulation results show that GA-SVM hybrid is able to produce good classification accuracy and a high level of
   consistency. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the
   population with newly formed chromosomes. This correlation measure injects greater diversity and increases the overall fitness of the population
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Peter John Angeline
%T Evolutionary Algorithms and Emergent Intelligence
%R Ph.D. Thesis
%D 1993
%I 
%I Ohio State University
%K genetic algorithms, genetic programming
%U http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/dissrefs.ps.Z
%Z http://citeseer.ist.psu.edu/114089.html has introduction

%A Peter John Angeline
%T Genetic Programming and Emergent Intelligence
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 75--98
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%O 4
%Z "Contrasts GP with other Weak/strong AI methods, credit assignment, USEFUL, diplodity=redundancy=good, hierarchical code/decode of subroutines better than Koza ADF Loads of
   references" I realized that inherent dynamics of genetic programming encouraged certain emergent properties. The most important of these is that introns emerge naturally
   from the process to protect the developing program from crossover. Others in the field think this extra stuff in the genetic program is a bad thing, reflected by their
   choice of the term "bloating" for the effect. This chapter is the first to take a positive view on GP introns and other emergent phenomena. I think this is the first paper
   to associate the "extra" code in genetic programs with the intron concept.

%A Peter J. Angeline
%A Jordan B. Pollack
%T Competitive Environments Evolve Better Solutions for Complex Tasks
%B Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93
%E Stephanie Forrest
%D 1993
%P 264--270
%I Morgan Kaufmann 2929 Campus Drive, Suite 260, San Mateo, CA 94403, USA
%C University of Illinois at Urbana-Champaign
%K genetic algorithms, genetic programming
%U http://www.natural-selection.com/Library/1993/icga93.ps.Z
%X In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective
   measure. Such objectivity entails significant knowledge about the environment which suggests either the problem has previously been solved or other non-evolutionary
   techniques may be more efficient. Furthermore, for many complex tasks an independent fitness function is either impractical or impossible to provide. In this paper, we
   demonstrate that competitive fitness functions, i.e. fitness functions that are dependent on the constituents of the population, can provide a more robust training
   environment than independent fitness functions. We describe three differing methods for competitive fitness, and discuss their respective advantages.
%8 17-21  July
%Z very like thesis One method I investigated was called competitive fitness functions which is a fitness function that compares performance between members of the population
   to determine a ranking of individuals for reproduction. THis obviates the need for a quantitative model of the quality of solutions and replaces it with a more simplistic
   measure of "x is better than y". The paper explores this concept using GLiB and appeared in ICGA93.
%@ 1-55860-299-2

%A P. J. Angeline
%T Genetic programming: A current snapshot
%B Proceedings of the Third Annual Conference on Evolutionary Programming
%E D. B. Fogel and W. Atmar
%D 1994
%I Evolutionary Programming Society
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/147407.html
%X Genetic programming is a promising variant of genetic algorithms that evolves dynamic, hierarchical structures often described as programs. Genetic programming's
   flexibility to tailor the representation language to the problem being solved, and its specially designed crossover operator provide a robust tool for evolving problem
   solutions. This paper provides an introduction to genetic programming, a short review of dynamic representations used in evolutionary systems and their relation to genetic
   programming, and a description of some of genetic programming's inherent properties. The paper concludes with a review of on going research and some potential future
   directions for the field.

%A Peter J. Angeline
%A Jordan B. Pollack
%T The evolutionary induction of subroutines
%B Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society
%D 1992
%P 236--241
%I Lawrence Erlbaum
%C Bloomington, Indiana, USA
%K genetic algorithms, genetic programming
%U http://www.natural-selection.com/Library/1992/cogsci92.ps.Z
%X we describe a genetic algorithm capable of evolving large programs by exploiting two new genetic operators which construct and deconstruct parameterized subroutines. These
   subroutines protect useful partial solutions and help to solve the scaling problem for a class of genetic problem solving methods. We demonstrate that our algorithm
   acquires useful subroutines by evolving a modular program from scratch to play and win at Tic-Tac-Toe against a flawed expert. This work also serves to amplify our previous
   note (Pollack, 1991) that a phase transition is the principle behind induction in dynamical cognitive models.
%Z GLiB is an emergent method for discovering task-specific modular decompositions in genetic programs. At least this is how I used to talk about it. I know consider this an
   individual-level self-adaptive method for forming decompositions in genetic programs.

%A P. J. Angeline
%A J. B. Pollack
%T Coevolving High-Level Representations
%R July Technical report 92-PA-COEVOLVE
%D 1993
%I 
%I Laboratory for Artificial Intelligence. The Ohio State University
%K genetic algorithms, genetic programming
%U http://www.demo.cs.brandeis.edu/papers/alife3.pdf
%X Several evolutionary simulations allow for a dynamic resizing of the genotype. This is an important alternative to constraining the genotype's maximum size and complexity.
   In this paper, we add an additional dynamic to simulated evolution with the description of a genetic algorithm that coevolves its representation language with the
   genotypes. We introduce two mutation operators that permit the acquisition of modules from the genotypes during evolution. These modules form an increasingly highlevel
   representation language specific to the developmental environment. Experimental results illustrating interesting properties of the acquired modules and the evolved
   languages are provided.

%A Peter J. Angeline
%A Jordan Pollack
%T Evolutionary Module Acquisition
%B Proceedings of the Second Annual Conference on Evolutionary Programming
%E D. Fogel and W. Atmar
%D 1993
%P 154--163
%I 
%I The Evolutionary Programming Society
%C La Jolla, CA, USA
%K genetic algorithms, genetic programming, FSM, GLiB
%U http://www.natural-selection.com/Library/1993/ep93.ps.Z
%X Evolutionary programming and genetic algorithms share many features, not the least of which is a reliance of an analogy to natural selection over a population as a means of
   implementing search. With their commonalities come shared problems whose solutions can be investigated at a higher level and applied to both. One such problem is the
   manipulation of solution parameters whose values encode a desirable sub-solution. In this paper, we define a superset of evolutionary programming and genetic algorithms,
   called evolutionary algorithms, and demonstrate a method of automatic modularization that protects promising partial solutions and speeds acquisition time.
%8 25-26  February
%Z Artificial Ant (John Muir). Finite State Machines. Genetic Library Builder

%A P. J. Angeline
%A J. B. Pollack
%T Coevolving high-level representations
%B Artificial Life III
%S SFI Studies in the Sciences of Complexity
%E Christopher G. Langton
%V XVII
%D 1994
%P 55--71
%I Addison-Wesley
%C Santa Fe, New Mexico
%K genetic algorithms, genetic programming
%U http://www.demo.cs.brandeis.edu/papers/alife3.ps.gz
%X Several evolutionary simulations allow for a dynamic resizing of the genotype. This is an important alternative to constraining the genotype's maximum size and complexity.
   In this paper, we add an additional dynamic to simulated evolution with the description of a genetic algorithm that coevolves its representation language with the
   genotypes. We introduce two mutation operators that permit the acquisition of modules from the genotypes during evolution. These modules form an increasingly high-level
   representation language specific to the developmental environment. Experimental results illustrating interesting properties of the acquired modules and the evolved
   languages are provided.
%8 15-19  June  1992
%Z ALife3 Held June 1992 in Santa Fe, New Mexico, USA GLiB, Tower of Hanoi, Tic Tac Toe. Also in thesis.

%A Peter J. Angeline
%T Genetic programming: On the programming of computers by means of natural selection,John R. Koza, A Bradford Book, MIT Press, Cambridge MA, 1992, ISBN 0-262-11170-5, xiv +
   819pp., US\$55.00
%J Biosystems
%V 33
%N 1
%D 1994
%P 69--73
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6T2K-49N8PP4-23/2/021e3e016b39a87da29046c37f423f73
%O Book review
%Z Review of \citekoza:book

%A Peter J. Angeline
%T Evolution Revolution: An Introduction to the Special Track on Genetic and Evolutionary Programming
%J IEEE Expert
%V 10
%N 3
%D 1995
%P 6--10
%I 
%K genetic algorithms, genetic programming
%O Guest editor's introduction
%8 June
%Z Dec 2011 NO PDF given with 10.1109/MIS.1995.10027 fab colour picture by Karl Sims 6 articles in special track; 2 use evolutionary programming, 2 use genetic programming
   (\citeTackett:1995:mGP and \citewong:1995:glp) and 2 use hybrids ((GA and GP \citehoward:1995:GA-P) and (Riziki and Zmuda, August 1995 GA and EP morphological pattern
   recognition))

%A P. J. Angeline
%T Morphogenic Evolutionary Computations: Introduction, Issues and Examples
%B Evolutionary Programming IV: The Fourth Annual Conference on Evolutionary Programming
%E John Robert McDonnell and Robert G. Reynolds and David B. Fogel
%D 1995
%P 387--401
%I MIT Press
%K genetic algorithms, genetic programming
%U http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=4397
%Z EP-95
%@ 0-262-13317-2

%A Peter J. Angeline
%T Adaptive and Self-Adaptive Evolutionary Computations
%B Computational Intelligence: A Dynamic Systems Perspective
%E Marimuthu Palaniswami and Yianni Attikiouzel
%D 1995
%P 152--163
%I IEEE Press
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/angeline95adaptive.html
%X This paper reviews the various studies that have introduced adaptive and selfadaptive parameters into Evolutionary Computations. A formal definition of an adaptive
   evolutionary computation is provided with an analysis of the types of adaptive and self-adaptive parameter update rules currently in use. Previous studies are reviewed and
   placed into a categorisation that helps to illustrate their similarities and differences

%T Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/umrjb/toc
%@ 0-262-01158-1

%A Peter J. Angeline
%T Genetic Programming's Continued Evolution
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 1--20
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/18
%O 1
%@ 0-262-01158-1

%A Peter J. Angeline
%T Two Self-Adaptive Crossover Operators for Genetic Programming
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 89--110
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/106
%O 5
%Z THese were called Selective Self-Adaptive Crossover and Self-adaptive Multi-Crossover.
%@ 0-262-01158-1

%A Peter J. Angeline
%T An Investigation into the Sensitivity of Genetic Programming to the Frequency of Leaf Selection During Subtree Crossover
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 21--29
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.natural-selection.com/Library/1996/gp96.zip
%8 28--31  July
%Z GP-96 multiple types of mutation Sunspot Numbers data from http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html

%A Peter J. Angeline
%T Evolving Fractal Movies
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 503--511
%I MIT Press
%C Stanford University, CA, USA
%K Evolutionary Programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 EP paper

%A Peter J. Angeline
%T Subtree Crossover: Building Block Engine or Macromutation?
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 9--17
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://ncra.ucd.ie/COMP41190/SubtreeXoverBuildingBlockorMacromutation_angeline_gp97.ps
%8 13-16  July
%Z GP-97

%A Peter J. Angeline
%T An Alternative to Indexed Memory for Evolving Programs with Explicit State Representations
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 423--430
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K evolutionary programming and evolution strategies
%8 13-16  July
%Z GP-97

%A Peter J. Angeline
%T Tracking Extrema in Dynamic Environments
%B Proceedings of the 6th International Conference on Evolutionary Programming
%S Lecture Notes in Computer Science
%E P. J. Angeline and R. G. Reynolds and J. R. McDonnell and R. Eberhart
%V 1213
%D 1997
%I Springer Verlag
%C Indianapolis, Indiana, USA
%K genetic algorithms, genetic programming
%U http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-62788-X
%X Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques
   to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain
   the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against
   an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some
   dynamic functions, self-adaptation is effective while for others it is detrimental.
%8 April  13-16
%Z EP-97
%@ 3-540-62788-X

%A Peter J. Angeline
%A David B. Fogel
%T An evolutionary program for the identification of dynamical systems
%B Application and Science of Artificial Neural Networks III
%E S. Rogers
%V 3077
%D 1997
%P 409--417
%I Bellingham, WA, USA
%I SPIE-The International Society for Optical Engineering
%K genetic algorithms, genetic programming, evolutionary computation, evolutionary programming, system identification, dynamical systems, optimization
%U http://www.natural-selection.com/Library/1997/spie97.pdf
%X Various forms of neural networks have been applied to the identification of non-linear dynamical systems. In most of these methods, the network architecture is set prior to
   training. In this paper, a method that evolves a symbolic solution for plant models is described. This method uses an evolutionary program to manipulate collections of
   parse trees expressed in a task specific language. Experiments performed on two unknown plants show this method is competitive with those that train neural networks for
   similar problems

%A Peter J. Angeline
%T Parse Trees
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%I Oxford University Press
%K genetic algorithms, genetic programming
%O section C1.6
%@ 0-7503-0392-1

%A Peter J. Angeline
%T Mutation: Parse Trees
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%I Oxford University Press
%K genetic algorithms, genetic programming
%O section C3.2.5
%Z grow, shrink,switch, cycle=point
%@ 0-7503-0392-1

%A Peter J. Angeline
%T Crossover: parse trees
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%I Oxford University Press
%K genetic algorithms, genetic programming
%O section C3.3.5
%@ 0-7503-0392-1

%A Peter J. Angeline
%T Subtree Crossover Causes Bloat
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 745--752
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, evolutionary programming
%8 22-25  July
%Z GP-98, Even-5 parity, intertwined spirals, sunspot prediction.
%@ 1-55860-548-7

%A Peter J. Angeline
%T A Historical Perspective on the Evolution of Executable Structures
%J Fundamenta Informaticae
%V 35
%N 1--4
%D 1998
%P 179--195
%I 
%K genetic algorithms, genetic programming
%U http://www.natural-selection.com/Library/1998/gphist.pdf
%X Genetic programming (Koza 1992) is a method of inducing behaviors represented as executable programs. The generality of the approach has spawned a proliferation of work in
   the evolution of executable structures that is unmatched in the history of the subject. This paper describes the standard approach to genetic programming, as defined in
   Koza (1992), and then presents the significant studies that preceded its inception as well as the diversification of techniques evolving executable structures that is
   currently underway in the field.
%8 August
%Z Special volume: Evolutionary Computation Also published in book form, see \citeangeline:1999:hpees

%A Peter J. Angeline
%T Multiple Interacting Programs: A Representation for Evolving Complex Behaviors
%J Cybernetics and Systems
%V 29
%N 8
%D 1998
%P 779--806
%I 
%K genetic algorithms, genetic programming, mips
%U http://www.tandf.co.uk/journals/frameloader.html?http://www.tandf.co.uk/journals/tf/01969722.html
%X This paper defines a representation for expressing complex behaviors, called multiple interacting programs (MIPs), and describes an evolutionary method for evolving
   solutions to difficult problems expressed as MIPs structures. The MIPs representation is a generalization of neural network architectures that can model any type of dynamic
   system. The evolutionary training method described is based on an evolutionary program originally used to evolve the architecture and weights of recurrent neural networks.
   Example experiments demonstrate the training method s ability to evolve appropriate MIPs solutions for difficult problems. An analysis of the evolved solutions shows their
   dynamics to be interesting and non-trivial.
%8 November
%Z Sun spots, Santa Fe trail Artifical Ant, 5-bit reverser, Tree, ANN

%A Peter J. Angeline
%T Evolving Predictors for Chaotic Time Series
%B Proceedings of SPIE: Application and Science of Computational Intelligence
%E S. Rogers and D. Fogel and J. Bezdek and B. Bosacchi
%V 3390
%D 1998
%P 170--80
%I Bellingham, WA, USA
%I SPIE
%K genetic algorithms, genetic programming, evolutionary computation, evolutionary programming, neural networks, chaotic time series prediction
%U http://www.natural-selection.com/Library/1998/spie98.pdf
%X Neural networks are a popular representation for inducing single-step predictors for chaotic times series. For complex time series it is often the case that a large number
   of hidden units must be used to reliably acquire appropriate predictors. This paper describes an evolutionary method that evolves a class of dynamic systems with a form
   similar to neural networks but requiring fewer computational units. Results for experiments on two popular chaotic times series are described and the current methods
   performance is shown to compare favorably with using larger neural networks.

%A Peter J. Angeline
%T A Historical Perspective on the Evolution of Executable Structures
%B Evolutionary Computation
%E A. E. Eiben and A. Michalewicz
%D 1999
%I Ohmsha
%C Tokyo
%K genetic algorithms, genetic programming
%U http://www.ohmsha.co.jp/data/books/e_contents/4-274-90269-2.htm
%Z This is the book edition of the journal, Fundamenta Informaticae, Volume 35, Nos. 1-4, 1998. See also \citeangeline:1998:hpees
%@ 4-274-90269-2

%A Peter J. Angeline
%T Parse trees
%B Evolutionary Computation 1 Basic Algorithms and Operators
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 2000
%P 155--159
%I Institute of Physics Publishing
%C Bristol
%K genetic algorithms, genetic programming
%O 19
%Z http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=IP274
%@ 0-7503-0664-5

%A Plamen Angelov
%A Arthur Kordon
%A Xiaowei Zhou
%T Evolving fuzzy inferential sensors for process industry
%B 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008
%D 2008
%P 41--46
%I 
%C Witten-Boommerholz, Germany
%K genetic algorithms, genetic programming, Dow Chemical Company, Takagi-Sugeno-fuzzy system, fuzzy inferential sensor, multi-objective genetic-programming-based optimization,
   on-line input selection techniques, on-line learning algorithm, process industry, self-tuning inferential soft sensor, chemical industry, fuzzy set theory, fuzzy systems,
   sensors
%X This paper describes an approach to design self-developing and self-tuning inferential soft sensors applicable to process industries. The proposal is for a
   Takagi-Sugeno-fuzzy system framework that has evolving (open structure) architecture, and an on-line (possibly real-time) learning algorithm. The proposed methodology is
   novel and it addresses the problems of self-development and self-calibration caused by drift in the data patterns due to changes in the operating regimes, catalysts aging,
   industrial equipment wearing, contamination etc. The proposed computational technique is data-driven and parameter-free (it only requires a couple of parameters with clear
   meaning and suggested values). In this paper a case study of four problems of estimation of chemical properties is considered, however, the methodology has a much wider
   validity. The optimal inputs to the proposed evolving inferential sensor are determined a priori and off-line using a multi-objective genetic-programming-based
   optimization. Different on-line input selection techniques are under development. The methodology is validated on real data provided by the Dow Chemical Company, USA.
%8 4-7  March
%Z Also known as \cite4484565

%A Mauro Annunziato
%A Carlo Bruni
%A Matteo Lucchetti
%A Stefano Pizzuti
%T Artificial Life Approach for Continuous Optimisation of Non Stationary Dynamical Systems
%J Integrated Computer-Aided Engineering
%V 10
%N 2
%D 2003
%P 111--125
%I 
%K genetic algorithms, genetic programming, artificial life
%U http://iospress.metapress.com/openurl.asp?genre=article&issn=1069-2509&volume=10&issue=2&spage=111
%X we develop an intelligent system to approach dynamical optimisation problems emerging in control of complex systems. In particular our proposal is to exploit the adaptivity
   of an artificial life (alife) environment in order to achieve "not control rules but autonomous structures able to dynamically adapt and to generate optimised-control
   rules". The basic features of the proposed approach are: no intensive modelling (continuous learning directly from measurements) and capability to follow the system
   evolution (adaptation to environmental changes). The suggested methodology has been tested on an energy regulation problem deriving from a classical testbed in dynamical
   systems experimentations: the Chua's circuit. We supposed not to know the system dynamics and to be able to act only on a subset of control parameters, letting the others
   vary in time in a random discrete way. We let the optimisation process searching for the new best value of performance, whenever a drop due to changes in fitness landscape
   occurred. We present the most important results showing the effectiveness of the proposed approach in adapting to environmental non-stationary changes by recovering the
   optimal value of process performance.

%A Jason Ansel
%A Maciej Pacula
%A Saman Amarasinghe
%A Una-May O'Reilly
%T An efficient evolutionary algorithm for solving incrementally structured problems
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1699--1706
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, SBSE, Real world applications
%X Many real world problems have a structure where small problem instances are embedded within large problem instances, or where solution quality for large problem instances
   is loosely correlated to that of small problem instances. This structure can be exploited because smaller problem instances typically have smaller search spaces and are
   cheaper to evaluate. We present an evolutionary algorithm, INCREA, which is designed to incrementally solve a large, noisy, computationally expensive problem by deriving
   its initial population through recursively running itself on problem instances of smaller sizes. The INCREA algorithm also expands and shrinks its population each
   generation and cuts off work that doesn't appear to promise a fruitful result. For further efficiency, it addresses noisy solution quality efficiently by focusing on
   resolving it for small, potentially reusable solutions which have a much lower cost of evaluation. We compare INCREA to a general purpose evolutionary algorithm and find
   that in most cases INCREA arrives at the same solution in significantly less time.
%8 12-16  July
%Z Research compiler petabricks. GPEA. Aim: autotuning for computer when program when is actually installed on that computer. Looks at recursive sort and which chooses one of
   4 types of sort (Insertion sort, quick sort, radix sort and a dummy) to use at each level of recursion. Noisy fitness evaluation (run for real, not simulation). uses T-test
   (trying to be too fair?). Examples: sort, matrix multiply (matmult) and eig (symmetric eigen problem). Also known as \cite2001805 GECCO-2011 A joint meeting of the
   twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)

%A Gary Anthes
%T Deep Data Dives Discover Natural Laws
%J Communications of the ACM
%V 52
%N 11
%D 2009
%P 13--14
%I 
%K genetic algorithms, genetic programming
%U http://cacm.acm.org/magazines/2009/11/48443-deep-data-dives-discover-natural-laws/pdf
%X Computer scientists have found a way to bootstrap science, using evolutionary computation to find fundamental meaning in massive amounts of raw data. Mining scientific data
   for patterns and relationships has been a common practice for decades, and the use of self-mutating genetic algorithms is nothing new, either. But now a pair of computer
   scientists at Cornell University have pushed these techniques into an entirely new realm, one that could fundamentally transform the methods of science at the frontiers of
   research.
%O News
%8 November
%Z Report on \citeScience09:Schmidt

%A Lisa Patricia Anthony
%T Evolving board evaluation fuctions for a complex strategy game
%R M.S. Thesis
%D 2002
%I 
%I Drexel University
%K genetic algorithms, genetic programming
%U http://dspace.library.drexel.edu/bitstream/1860/18/1/anthony_thesis.pdf
%X The development of board evaluation functions for complex strategy games has been approached in a variety of ways. The analysis of game interactions is recognized as a
   valid analogy to common real-world problems, which often present difficulty in designing algorithms to solve them. Genetic programming, as a branch of evolutionary
   computation, provides advantages over traditional algorithms in solving these complex real-world problems in speed, robustness and flexibility. This thesis attempts to
   address the problem of applying genetic programming techniques to the evolution of a strategy for evaluating potential moves in a one-step lookahead intelligent agent
   heuristic for a complex strategybased game. This is meant to continue the work in artificial intelligence which seeks to provide computer systems with the tools they need
   to learn how to operate within a domain, given only the basic building blocks. The issues surrounding this problem are formulated and techniques are presented within the
   realm of genetic programming which aim to contribute to the solution of this problem. The domain chosen is the strategy game known as Acquire, whose object is to amass
   wealth while investing stock in hotel chains and effecting mergers of these chains as they grow. The evolution of the board evaluation functions to be used by agent players
   of the game is accomplished via genetic programming. Implementation details are discussed, empirical results are presented, and the strategies of some of the best players
   are analyzed. Future improvements on these techniques within this domain are outlined, as well as implications for artificial intelligence and genetic programming.
%8 Decemeber ~30
%Z format = 318461

%A Jan Antolik
%T Evolutionary Tree Genetic Programming
%R M.S. Thesis Master of Science
%D 2004
%I 
%I Department of Computing and Information Sciences, College of Arts and Sciences, Kansan State University
%C Manhattan, Kansas, USA
%K genetic algorithms, genetic programming
%U http://www.ms.mff.cuni.cz/~antoj9am/thesis.pdf
%X We introduce an extension of a genetic programming (GP) algorithm we call Evolutionary Tree Genetic Programming (ETGP). The biological motivation behind this work is the
   observation that the natural evolution follows a tree like pattern. We want to simulate similar behaviour in artificial evolutionary systems such as GP. In this thesis we
   provide multiple reasons why we believe simulation of this phenomenon can be beneficial for GP systems. We present various empirical results from test runs. As the test bed
   for our experiments two standard benchmark problems for GP systems are used, particularly the Artificial Ant problem and the Multiplexer problem. The performance of the
   ETGP algorithm is compared to the performance of GP system. Unfortunately no significant speedup is found. Some unexpected behaviors of our system are also identified, and
   a hypothesis is formulated that addresses the question of why we observe this strange behaviour and the lack of speedup. Suggestions on how to extend the ETGP system to
   overcome the problems identified by this hypothesis are then presented in the end of our concluding chapter.
%Z Approved by: Major Professor William Hsu

%A Jan Antolik
%A William H. Hsu
%T Evolutionary tree genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1789--1790
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1789.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Maria A. Antoniou
%A Efstratios F. Georgopoulos
%A Konstantinos A. Theofilatos
%A Anastasios P. Vassilopoulos
%A Spiridon D. Likothanassis
%T A Gene Expression Programming Environment for Fatigue Modeling of Composite Materials
%B 6th Hellenic Conference on Artificial Intelligence: Theories, Models and Applications (SETN 2010)
%S Lecture Notes in Computer Science
%E Stasinos Konstantopoulos and Stavros J. Perantonis and Vangelis Karkaletsis and Constantine D. Spyropoulos and George A. Vouros
%V 6040
%D 2010
%P 297--302
%I Springer
%C Athens, Greece
%K genetic algorithms, genetic programming
%X In the current paper is presented the application of a Gene Expression Programming Environment in modeling the fatigue behavior of composite materials. The environment was
   developed using the JAVA programming language, and is an implementation of a variation of Gene Expression Programming. Gene Expression Programming (GEP) is a new
   evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical
   expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or
   translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities
   (both structurally and functionally). This is the main difference between GEP and classical tree based Genetic Programming techniques. In order to evaluate the performance
   of the presented environment, we tested it in fatigue modeling of composite materials.
%8 May  4-7

%A Maria Antoniou
%A Efstratios Georgopoulos
%A Konstantinos Theofilatos
%A Spiridon Likothanassis
%T Forecasting Euro - United States Dollar Exchange Rate with Gene Expression Programming
%B 6th IFIP Advances in Information and Communication Technology AIAI 2010
%S IFIP Advances in Information and Communication Technology
%E Harris Papadopoulos and Andreas Andreou and Max Bramer
%V 339
%D 2010
%P 78--85
%I Springer
%C Larnaca, Cyprus
%K genetic algorithms, genetic programming, Gene Expression Programming
%X In the current paper we present the application of our Gene Expression Programming Environment in forecasting Euro-United States Dollar exchange rate. Specifically, using
   the GEP Environment we tried to forecast the value of the exchange rate using its previous values. The data for the EURO-USD exchange rate are online available from the
   European Central Bank (ECB). The environment was developed using the JAVA programming language, and is an implementation of a variation of Gene Expression Programming. Gene
   Expression Programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision
   trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then
   the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the
   expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based Genetic Programming
   techniques.
%8 October  6-7
%Z http://www.cs.ucy.ac.cy/aiai2010/

%A Hendrik James Antonisse
%T A Grammar-Based Genetic Algorithm
%B Foundations of Genetic Algorithms
%E Gregory J. E. Rawlins
%D 1991
%P 193--204
%I Morgan Kaufmann San Mateo
%C Indiana University, Bloomington, USA
%K genetic algorithms, genetic programming, inductive bias, high-level representations, crossover
%8 15--18  July  1990
%Z FOGA-90 Published in 1991. cited by \citebruhn:2002:ECJ grammar-based crossover, parity. K-armed bandit
%@ 1-55860-170-8

%A Shinya Aoki
%A Tomoharu Nagao
%T Automatic construction of tree-structural image transformation using genetic programming
%B Proceedings of the 1999 International Conference on Image Processing (ICIP-99)
%V 1
%D 1999
%P 529--533
%I IEEE Los Alamitos, CA, USA
%C Kobe
%K genetic algorithms, genetic programming
%X We previously proposed an automatic construction method of image transformations. In this method, we approximated an unknown image transformation by a series of several
   known image filters, and a genetic algorithm optimizes their combination to meet the processing purpose presented by sets of original and target images. In this paper, we
   propose an extended method named "Automatic Construction of Tree-structural Image Transformations (ACTIT)". In this new method, a tree whose interior nodes are image
   filters and leaf ones are input images approximates the transformation. The structures of the trees are optimized using genetic programming. ACTIT finds practical filter
   combinations that are too complicated to be designed by hand. It can be applied to various kinds of image processing tasks. We show examples of its applications to document
   and medical image processing
%8 October  ~24--28

%A Joaquim N. Aparicio
%A Luis Correia
%A Fernando Moura-Pires
%T Populations are Multisets-PLATO
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1845--1850
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K methodology, pedagogy and philosophy
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/MP-603.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Masamoto Arakawa
%A Kiyoshi Hasegawa
%A Kimito Funatsu
%T QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network
%J Chemometrics and Intelligent Laboratory Systems
%V 83
%N 2
%D 2006
%P 91--98
%I 
%K genetic algorithms, genetic programming, Multi-objective optimisation, Variable selection, HEPT, quantitative structure activity relationship
%X Quantitative structure-activity relationship (QSAR) has been developed for a set of inhibitors of the human immunodeficiency virus 1 (HIV-1) reverse transcriptase,
   derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT). Structural descriptors used in this study are Hansch constants for each substituent and topological
   descriptors. We have applied the variable selection method based on multi-objective genetic programming (GP) to the HEPT data and constructed the nonlinear QSAR model using
   counter-propagation (CP) neural network with the selected variables. The obtained network is accurate and interpretable. Moreover in order to confirm a predictive ability
   of the model, a validation test was performed.
%8 15  September

%A Claus Aranha
%A Hitoshi Iba
%T The effect of using evolutionary algorithms on ant clustering techniques
%B Proceedings of the Third Asian-Pacific workshop on Genetic Programming
%E The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen
%D 2006
%P 24--34
%I 
%C Military Technical Academy, Hanoi, VietNam
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/Aranha_2006_ASPGP.pdf
%X Ant-based clustering is a biologically inspired data clustering technique. In this technique, multiple agents carry the information to be clustered, and make local
   comparisons. In this work we use genetic algorithms to improve the implementation and use of ant-clustering techniques.
%Z http://www.aspgp.org

%A Dieferson L. A. Araujo
%A Heitor S. Lopes
%A Alex A. Freitas
%T A parallel genetic algorithm for rule discovery in large databases
%B Proceedings of IEEE Systems, Man and Cybernetics Conference
%V III
%D 1999
%P 940--945
%I 
%K genetic algorithms, data mining, parallel
%U http://www.cpgei.cefetpr.br/publicacoes/1999/ieeesmc99.zip
%O Tokyo, Japan, 12-15/october/1999
%Z http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=823354&isnumber=17812&punumber=6569&k2dockey=823354@ieeecnfs&query=(freitas%20a.%20%20a.%3Cin%3Eau%20)&pos=7

%A Dieferson L. A. Araujo
%A Heitor S. Lopes
%A Alex A. Freitas
%T Rule discovery with a parallel genetic algorithm
%B Data Mining with Evolutionary Algorithms
%E Alex A. Freitas and William Hart and Natalio Krasnogor and Jim Smith
%D 2000
%P 89--94
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, data mining, parallel
%U http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/gecco2000b.zip
%8 8  July
%Z GECCO-2000WKS Part of \citewu:2000:GECCOWKS

%A Sergio G. Araujo
%A A. Mesquita
%A Aloysio C. P. Pedroza
%T Using Genetic Programming and High Level Synthesis to Design Optimized Datapath
%B Evolvable Systems: From Biology to Hardware, Fifth International Conference, ICES 2003
%S LNCS
%E Andy M. Tyrrell and Pauline C. Haddow and Jim Torresen
%V 2606
%D 2003
%P 434--445
%I Springer-Verlag
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X a methodology to design optimised electronic digital systems from high abstraction level descriptions. The methodology uses Genetic Programming in addition to high-level
   synthesis tools to automatically improve design structural quality (area measure). A two-stage, multiobjective optimization algorithm is used to search for circuits with
   the desired functionality subjected additionally to chip area constraints. Experiment with a square-root approximation datapath design targeted to FPGA exemplifies the
   proposed methodology.
%8 17-20  March
%Z ICES-2003
%@ 3-540-00730-X

%A Sergio Granato {de Araujo}
%A Antonio C. Mesquita
%A Aloysio C. P. Pedroza
%T S\'intese de Circuitos Digitais Otimizados via Programa\c c\~ao Gen\'etica
%B XXX Semin\'ario Integrado de Software e Hardware
%D 2003
%I 
%C Unicamp, Campinas, SP, Brasil
%K genetic algorithms, genetic programming
%U http://www.sbc.org.br/sbc2003_cd/pdf/arq0088.pdf broken
%X This paper presents a methodology for the design of optimized electronic digital systems from high abstraction level descriptions. The methodology uses Genetic Programming
   in addition to high-level synthesis tools to improve the design quality (area optimization). A two-stage, multiobjective optimization algorithm was used to search for
   circuits with the desired functionality subjected additionally to chip area constraints. Experiment with a square-root approximation function design targeted to FPGA
   illustrates the methodology.
%8 2-8  August
%Z XXIII Brazilian Symposium on Computation (SBC'03) http://www.ic.unicamp.br/sbc2003/enia.html url broken 27 Sep 2004 In Portuguese

%A Lourdes Araujo
%T Genetic Programming for Natural Language Parsing
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 230--239
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=230
%X Our aim is to prove the effectiveness of the genetic programming approach in automatic parsing of sentences of real texts. Classical parsing methods are based on complete
   search techniques to find the different interpretations of a sentence. However, the size of the search space increases exponentially with the length of the sentence or text
   to be parsed and the size of the grammar, so that exhaustive search methods can fail to reach a solution in a reasonable time. This paper presents the implementation of a
   probabilistic bottom-up parser based on genetic programming which works with a population of partial parses, i.e. parses of sentence segments. The quality of the
   individuals is computed as a measure of its probability, which is obtained from the probability of the grammar rules and lexical tags involved in the parse. In the approach
   adopted herein, the size of the trees generated is limited by the length of the sentence. In this way, the size of the search space, determined by the size of the sentence
   to parse, the number of valid lexical tags for each words and specially by the size of the grammar, is also limited.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A L. Araujo
%T Multiobjective Genetic Programming for Natural Language Parsing and Tagging
%B Parallel Problem Solving from Nature - PPSN IX
%S LNCS
%E Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao
%V 4193
%D 2006
%P 433--442
%I Springer-Verlag Berlin
%C Reykjavik, Iceland
%K genetic algorithms, genetic programming
%U http://ppsn2006.raunvis.hi.is/proceedings/055.pdf
%X Parsing and Tagging are very important tasks in Natural Language Processing. Parsing amounts to searching the correct combination of grammatical rules among those
   compatible with a given sentence. Tagging amounts to labelling each word in a sentence with its lexical category and, because many words belong to more than one lexical
   class, it turns out to be a disambiguation task. Because parsing and tagging are related tasks, its simultaneous resolution can improve the results of both of them. This
   work aims developing a multiobjective genetic program to perform simultaneously statistical parsing and tagging. It combines the statistical data about grammar rules and
   about tag sequences to guide the search of the best structure. Results show that any of the implemented multiobjective optimisation models improve on the results obtained
   in the resolution of each problem separately.
%8 9-13  September
%Z PPSN-IX
%@ 3-540-38990-3

%A Lourdes Araujo
%A Jesus Santamaria
%T Evolving natural language grammars without supervision
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Unsupervised grammar induction is one of the most difficult works of language processing. Its goal is to extract a grammar representing the language structure using texts
   without annotations of this structure. We have devised an evolutionary algorithm which for each sentence evolves a population of trees that represent different parse trees
   of that sentence. Each of these trees represent a part of a grammar. The evaluation function takes into account the contexts in which each sequence of Part-Of-Speech tags
   (POSseq) appears in the training corpus, as well as the frequencies of those POSseqs and contexts. The grammar for the whole training corpus is constructed in an
   incremental manner. The algorithm has been evaluated using a well known Annotated English corpus, though the annotation have only been used for evaluation purposes. Results
   indicate that the proposed algorithm is able to improve the results of a classical optimisation algorithm, such as EM (Expectation Maximisation), for short grammar
   constituents (right side of the grammar rules), and its precision is better in general.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586291

%A Filipe {de Lima Arcanjo}
%A Gisele Lobo Pappa
%A Paulo Viana Bicalho
%A {Wagner Meira, Jr.}
%A Altigran Soares {da Silva}
%T Semi-supervised genetic programming for classification
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1259--1266
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, Genetics based machine learning
%X Learning from unlabeled data provides innumerable advantages to a wide range of applications where there is a huge amount of unlabeled data freely available.
   Semi-supervised learning, which builds models from a small set of labeled examples and a potential large set of unlabeled examples, is a paradigm that may effectively use
   those unlabeled data. Here we propose KGP, a semi-supervised transductive genetic programming algorithm for classification. Apart from being one of the first
   semi-supervised algorithms, it is transductive (instead of inductive), i.e., it requires only a training dataset with labeled and unlabeled examples, which should represent
   the complete data domain. The algorithm relies on the three main assumptions on which semi-supervised algorithms are built, and performs both global search on labeled
   instances and local search on unlabeled instances. Periodically, unlabeled examples are moved to the labeled set after a weighted voting process performed by a committee.
   Results on eight UCI datasets were compared with Self-Training and KNN, and showed KGP as a promising method for semi-supervised learning.
%8 12-16  July
%Z Also known as \cite2001746 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Francesco Archetti
%A Stefano Lanzeni
%A Enza Messina
%A Leonardo Vanneschi
%T Genetic programming for human oral bioavailability of drugs
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 255--262
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Biological Applications, bioavailability, bioinformatics, complexity measures, molecular descriptors, performance measures, SVM,
   ANN, LLSR, CFS, PCA, AIC, feature selection, SMILES
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p255.pdf
%X Automatically assessing the value of bioavailability from the chemical structure of a molecule is a very important issue in biomedicine and pharmacology. In this paper, we
   present an empirical study of some well known Machine Learning techniques, including various versions of Genetic Programming, which have been trained to this aim using a
   dataset of molecules with known bioavailability. Genetic Programming has proven the most promising technique among the ones that have been considered both from the point of
   view of the accurateness of the solutions proposed, of the generalisation capabilities and of the correlation between predicted data and correct ones. Our work represents a
   first answer to the demand for quantitative bioavailability estimation methods proposed in literature, since the previous contributions focus on the classification of
   molecules into classes with similar bioavailability. Categories and Subject Descriptors
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060 Winner best paper.
%@ 1-59593-186-4

%A Francesco Archetti
%A Stefano Lanzeni
%A Enza Messina
%A Leonardo Vanneschi
%T Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs
%B EvoBIO 2007, Proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
%S Lecture Notes in Computer Science
%E Elena Marchiori and Jason H. Moore and Jagath C. Rajapakse
%V 4447
%D 2007
%P 11--23
%I Springer Berlin Heidelberg NewYork
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X Computational methods allowing reliable pharmacokinetics predictions for newly synthesised compounds are critically relevant for drug discovery and development. Here we
   present an empirical study focusing on various versions of Genetic Programming and other well known Machine Learning techniques to predict Median Oral Lethal Dose (LD50)
   and Plasma Protein Binding (%PPB) levels. Since these two parameters respectively characterise the harmful effects and the distribution into human body of a drug, their
   accurate prediction is essential for the selection of effective molecules. The obtained results confirm that Genetic Programming is a promising technique for predicting
   pharmacokinetics parameters, both from the point of view of the accurateness and of the generalisation ability.
%8 April  11-13
%Z EvoBIO2007
%@ 3-540-71782-X

%A Francesco Archetti
%A Stefano Lanzeni
%A Enza Messina
%A Leonardo Vanneschi
%T Genetic programming for computational pharmacokinetics in drug discovery and development
%J Genetic Programming and Evolvable Machines
%V 8
%N 4
%D 2007
%P 413--432
%I 
%K genetic algorithms, genetic programming, Computational pharmacokinetics, Drug discovery, QSAR
%X The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient's organism without inducing toxic effects. Moreover
   the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods
   allowing reliable predictions of newly synthesised compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive
   pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient's
   organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral
   lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterise the percentage of initial drug dose that effectively reaches
   the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules.
   Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the
   generalisation ability.
%O special issue on medical applications of Genetic and Evolutionary Computation
%8 Decemeber
%Z GP, LS2-GP, LS2-C-GP, DF-GP, AIC, Weka ANN, SVM, Linear regression

%A Francesco Archetti
%A Ilaria Giordani
%A Leonardo Vanneschi
%T Genetic programming for QSAR investigation of docking energy
%J Applied Soft Computing
%V 10
%N 1
%D 2010
%P 170--182
%I 
%K genetic algorithms, genetic programming, Machine learning, Regression, Docking energy, Computational biology, Drug design, QSAR
%U http://www.sciencedirect.com/science/article/B6W86-4WP47KG-3/2/20419bfc47761543f509e96265d88e5d
%X Statistical methods, and in particular Machine Learning, have been increasingly used in the drug development workflow to accelerate the discovery phase and to eliminate
   possible failures early during clinical developments. In the past, the authors of this paper have been working specifically on two problems: (i) prediction of drug induced
   toxicity and (ii) evaluation of the target drug chemical interaction based on chemical descriptors. Among the numerous existing Machine Learning methods and their
   application to drug development (see for instance [F. Yoshida, J.G. Topliss, QSAR model for drug human oral bioavailability, Journal of Medicinal Chemistry 43 (2000)
   2575-2585; Frohlich, J. Wegner, F. Sieker, A. Zell, Kernel functions for attributed molecular graphs - a new similarity based approach to ADME prediction in classification
   and regression, QSAR and Combinatorial Science, 38(4) (2003) 427-431; C.W. Andrews, L. Bennett, L.X. Yu, Predicting human oral bioavailability of a compound: development of
   a novel quantitative structure-bioavailability relationship, Pharmacological Research 17 (2000) 639-644; J Feng, L. Lurati, H. Ouyang, T. Robinson, Y. Wang, S. Yuan, S.S.
   Young, Predictive toxicology: benchmarking molecular descriptors and statistical methods, Journal of Chemical Information Computer Science 43 (2003) 1463-1470; T.M. Martin,
   D.M. Young, Prediction of the acute toxicity (96-h LC50) of organic compounds to the fat head minnow (Pimephales promelas) using a group contribution method, Chemical
   Research in Toxicology 14(10) (2001) 1378-1385; G. Colmenarejo, A. Alvarez-Pedraglio, J.L. Lavandera, Chemoinformatic models to predict binding affinities to human serum
   albumin, Journal of Medicinal Chemistry 44 (2001) 4370-4378; J. Zupan, P. Gasteiger, Neural Networks in Chemistry and Drug Design: An Introduction, 2nd edition, Wiley,
   1999]), we have been specifically concerned with Genetic Programming. A first paper [F. Archetti, E. Messina, S. Lanzeni, L. Vanneschi, Genetic programming for
   computational pharmacokinetics in drug discovery and development, Genetic Programming and Evolvable Machines 8(4) (2007) 17-26 \citeArchetti:2007:GPEM] has been devoted to
   problem (i). The present contribution aims at developing a Genetic Programming based framework on which to build specific strategies which are then shown to be a valuable
   tool for problem (ii). In this paper, we use target estrogen receptor molecules and genistein based drug compounds. Being able to precisely and efficiently predict their
   mutual interaction energy is a very important task: for example, it may have an immediate relationship with the efficacy of genistein based drugs in menopause therapy and
   also as a natural prevention of some tumours. We compare the experimental results obtained by Genetic Programming with the ones of a set of non-evolutionary Machine
   Learning methods, including Support Vector Machines, Artificial Neural Networks, Linear and Least Square Regression. Experimental results confirm that Genetic Programming
   is a promising technique from the viewpoint of the accuracy of the proposed solutions, of the generalization ability and of the correlation between predicted data and
   correct ones.
%8 January

%A Francesco Archetti
%A Ilaria Giordani
%A Leonardo Vanneschi
%T Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset
%J Computers \& Operations Research
%V 37
%N 8
%D 2010
%P 1395--1405
%I 
%K genetic algorithms, genetic programming, Machine learning, Regression, Microarray data, Anticancer therapy, NCI-60
%U http://www.sciencedirect.com/science/article/B6VC5-4VS40CF-4/2/a55e5b35bc3d30ac9057d5fb8cdcd2d0
%X Statistical methods, and in particular machine learning, have been increasingly used in the drug development workflow. Among the existing machine learning methods, we have
   been specifically concerned with genetic programming. We present a genetic programming-based framework for predicting anticancer therapeutic response. We use the NCI-60
   microarray dataset and we look for a relationship between gene expressions and responses to oncology drugs Fluorouracil, Fludarabine, Floxuridine and Cytarabine. We aim at
   identifying, from genomic measurements of biopsies, the likelihood to develop drug resistance. Experimental results, and their comparison with the ones obtained by Linear
   Regression and Least Square Regression, hint that genetic programming is a promising technique for this kind of application. Moreover, genetic programming output may
   potentially highlight some relations between genes which could support the identification of biological meaningful pathways. The structures that appear more frequently in
   the 'best' solutions found by genetic programming are presented.
%O Operations Research and Data Mining in Biological Systems

%A Andrea Arcuri
%A Xin Yao
%T Coevolving Programs and Unit Tests from their Specification
%B IEEE International Conference on Automated Software Engineering (ASE)
%D 2007
%I 
%I IEEE
%C Atlanta, Georgia, USA
%K genetic algorithms, genetic programming, Automatic Programming, Coevolution, Software Testing, Formal Specification, Sorting, SBSE
%X Writing a formal specification before implementing a program helps to find problems with the system requirements. The requirements might be for example incomplete and
   ambiguous. Fixing these types of errors is very difficult and expensive during the implementation phase of the software development cycle. Although writing a formal
   specification is usually easier than implementing the actual code, writing a specification requires time, and often it is preferred, instead, to use this time on the
   implementation. In this paper we introduce for the first time a framework that might evolve any possible generic program from its specification. We use the Genetic
   Programming to evolve the programs, and at the same time we exploit the specifications to coevolve sets of unit tests. Programs are rewarded on how many tests they do not
   fail, whereas the unit tests are rewarded on how many programs they make fail. We present and analyse four different problems on which this novel technique is successfully
   applied.
%8 November  5-9
%Z http://www.cse.msu.edu/ase2007/

%A Andrea Arcuri
%T On the automation of fixing software bugs
%B ICSE Companion '08: Companion of the 30th international conference on Software engineering
%D 2008
%P 1003--1006
%I ACM New York, NY, USA
%C Leipzig, Germany
%K genetic algorithms, genetic programming, co-evolution, SuA, SBSE
%U http://delivery.acm.org/10.1145/1380000/1370223/p1003-arcuri.pdf
%X Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a
   bug after its presence has been discovered is still a duty of the programmers. Techniques to help the software developers for locating bugs exist though, and they take name
   of Automated Debugging. However, to our best knowledge, there has been only little attempt in the past to completely automate the actual changing of the software for fixing
   the bugs. Therefore, in this paper we propose an evolutionary approach to automate the task of fixing bugs. The basic idea is to evolve the programs (e.g., by using Genetic
   Programming) with a fitness function that is based on how many unit tests they are able to pass. If a formal specification of the buggy software is given, more
   sophisticated fitness functions can be designed. Moreover, by using the formal specification as an oracle, we can generate as many unit tests as we want. Hence, a
   co-evolution between programs and unit tests might take place to give even better results. It is important to know that, to fix the bugs in a program with this novel
   approach, a user needs only to provide either a formal specification or a set of unit tests. No other information is required.
%O Doctoral symposium session
%Z p1006 "We are building our prototype on top of our previous system for AP" \citeArcuri:2007:ASE also known as \cite1370223 Doctoral Symposium of the IEEE International
   Conference in Software Engineering

%A Andrea Arcuri
%A Xin Yao
%T A Novel Co-Evolutionary Approach to Automatic Software Bug Fixing
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X Many tasks in Software Engineering are very expensive, and that has led the investigation to how to automate them. In particular, Software Testing can take up to half of
   the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered
   is still a duty of the programmers. In this paper we propose an evolutionary approach to automate the task of fixing bugs. This novel evolutionary approach is based on
   Co-evolution, in which programs and test cases co-evolve, influencing each other with the aim of fixing the bugs of the programs. This competitive co-evolution is similar
   to what happens in nature for predators and prey. The user needs only to provide a buggy program and a formal specification of it. No other information is required. Hence,
   the approach may work for any implementable software. We show some preliminary experiments in which bugs in an implementation of a sorting algorithm are automatically
   fixed.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Andrea Arcuri
%A David Robert White
%A John Clark
%A Xin Yao
%T Multi-Objective Improvement of Software using Co-evolution and Smart Seeding
%B Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)
%S Lecture Notes in Computer Science
%E Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb
   and Kay Chen Tan and J\"urgen Branke and Yuhui Shi
%V 5361
%D 2008
%P 61--70
%I Springer
%C Melbourne, Australia
%K genetic algorithms, genetic programming, SBSE
%X Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction
   in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve
   execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for
   optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming
   to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population
   of test cases to encourage the preservation of the program's semantics, and exploit the original program through seeding of the population in order to focus the search. We
   carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional
   criteria could be optimised in a similar manner.
%8 Decemeber  7-10
%Z Also known as \citeDBLP:conf/seal/ArcuriWCY08

%A Andrea Arcuri
%T Evolutionary Repair of Faulty Software
%R Technical Report CSR-09-02
%D 2009
%I 
%I University of Birmingham, School of Computer Science
%C B15 2TT, UK
%K genetic algorithms, genetic programming, SBSE
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2009/CSR-09-02.pdf
%X Testing and fault localization are very expensive software engineering tasks that have been tried to be automated. Although many successful techniques have been designed,
   the actual change of the code for fixing the discovered faults is still a human-only task. Even in the ideal case in which automated tools could tell us exactly where the
   location of a fault is, it is not always trivial how to fix the code. In this paper we analyse the possibility of automating the complex task of fixing faults. We propose
   to model this task as a search problem, and hence to use for example evolutionary algorithms to solve it. We then discuss the potential of this approach and how its current
   limits can be addressed in the future. This task is extremely challenging and mainly unexplored in literature. Hence, this paper only covers an initial investigation and
   gives directions for future work. A research prototype called JAFF and a case study are presented to give first validation of this approach.
%8 April
%Z cited by \citeAckling:2011:GECCO

%A Andrea Arcuri
%T On Search Based Software Evolution
%B Proceedings 1st International Symposium on Search Based Software Engineering SSBSE 2009
%E Massimiliano Di Penta and Simon Poulding
%D 2009
%P 39--42
%I IEEE
%C Windsor, UK
%K genetic algorithms, genetic programming, SBSE, program coevolution, program test case, search algorithm, software engineering problem, software evolution, program testing,
   search problems, software engineering
%X Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering
   problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a
   novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems.This framework can be used to tackle software engineering
   tasks such as automatic refinement, fault correction,improving non-functional criteria and reverse engineering.While the programs evolve to accomplish one of these tasks,
   test cases are co-evolved at the the same time to find new faults in the evolving programs.
%8 13-15  May
%Z order number P3675 http://www.ssbse.org/ Also known as \cite5033178

%A Andrea Arcuri
%T Automatic software generation and improvement through search based techniques
%R Ph.D. Thesis
%D 2009
%I 
%I School of Computer Science, University of Birmingham
%C UK
%K genetic algorithms, genetic programming, SBSE
%U http://etheses.bham.ac.uk/400/
%X Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering
   problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a
   novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems. This framework can be used to tackle software engineering
   tasks such as Automatic Refinement, Fault Correction and Improving Non-functional Criteria. These tasks are very difficult, and their automation in literature has been
   limited. To get a better understanding of how search algorithms work, there is the need of a theoretical foundation. That would help to get better insight of search based
   software engineering. We provide first theoretical analyses for search based software testing, which is one of the main components of our co-evolutionary framework. This
   thesis gives the important contribution of presenting a novel framework, and we then study its application to three difficult software engineering problems. In this thesis
   we also give the important contribution of defining a first theoretical foundation.
%8 August

%A Andrea Arcuri
%A Xin Yao
%T Co-evolutionary automatic programming for software development
%J Information Sciences
%D 2010
%I 
%K genetic algorithms, genetic programming, SBSE, STGP, Automatic programming, Automatic refinement, Co-evolution, Software testing
%U http://www.sciencedirect.com/science/article/B6V0C-4Y34WFM-2/2/6700572128cf209a061759f28c5b7020
%X Since the 1970s the goal of generating programs in an automatic way (i.e., Automatic Programming) has been sought. A user would just define what he expects from the program
   (i.e., the requirements), and it should be automatically generated by the computer without the help of any programmer. Unfortunately, this task is much harder than
   expected. Although transformation methods are usually employed to address this problem, they cannot be employed if the gap between the specification and the actual
   implementation is too wide. In this paper we introduce a novel conceptual framework for evolving programs from their specification. We use genetic programming to evolve the
   programs, and at the same time we exploit the specification to co-evolve sets of unit tests. Programs are rewarded by how many tests they do not fail, whereas the unit
   tests are rewarded by how many programs they make to fail. We present and analyse seven different problems on which this novel technique is successfully applied.
%O In Press, Corrected Proof
%Z competitive co-evolution. MaxValue, AllEqual, Triangle Classification, Swap, Order, Sorting, Median. One variable called result write_result read_result. Simple first order
   logic specification. Java, ECJ. Random sampling SSP, ensemble N-version programming.

%A Andrea Arcuri
%T Evolutionary repair of faulty software
%J Applied Soft Computing
%V 11
%N 4
%D 2011
%P 3494--3514
%I 
%K genetic algorithms, genetic programming, Repair, Fault localisation, Automated debugging, Search Based Software Engineering, Coevolution
%U http://www.sciencedirect.com/science/article/B6W86-5223XWX-1/2/5d81be4fc12644887723df167e134516
%X Testing and fault localization are very expensive software engineering tasks that have been tried to be automated. Although many successful techniques have been designed,
   the actual change of the code for fixing the discovered faults is still a human-only task. Even in the ideal case in which automated tools could tell us exactly where the
   location of a fault is, it is not always trivial how to fix the code. In this paper we analyse the possibility of automating the complex task of fixing faults. We propose
   to model this task as a search problem, and hence to use for example evolutionary algorithms to solve it. We then discuss the potential of this approach and how its current
   limitations can be addressed in the future. This task is extremely challenging and mainly unexplored in the literature. Hence, this paper only covers an initial
   investigation and gives directions for future work. A research prototype called JAFF and a case study are presented to give first validation of this approach.

%A David H. Ardell
%T TOPE and Magic Squares: A Simple GA Approach to Combinatorial Optimization
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 1--6
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 Decemeber
%Z Uses Genesis This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming"
   (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-187263-3

%A Maritza Arganis
%A Rafael Val
%A Jordi Prats
%A Katya Rodriguez
%A Ramon Dominguez
%A Josep Dolz
%T Genetic Programming and Standardization in Water Temperature Modelling
%J Advances in Civil Engineering
%V 2009
%D 2009
%I Hindawi Publishing Corporation
%K genetic algorithms, genetic programming
%U http://downloads.hindawi.com/journals/ace/2009/353960.pdf
%X An application of Genetic Programming (an evolutionary computational tool) without and with standardization data is presented with the aim of modeling the behavior of the
   water temperature in a river in terms of meteorological variables that are easily measured, to explore their explanatory power and to emphasize the utility of the
   standardization of variables in order to reduce the effect of those with large variance. Recorded data corresponding to the water temperature behavior at the Ebro River,
   Spain, are used as analysis case, showing a performance improvement on the developed model when data are standardized. This improvement is reflected in a reduction of the
   mean square error. Finally, the models obtained in this document were applied to estimate the water temperature in 2004, in order to provide evidence about their
   applicability to forecasting purposes.
%Z Article ID 353960

%A Masanori Arita
%A Akira Suyama
%A Masami Hagiya
%T A Heuristic Approach for Hamiltonian Path Problem with Molecules
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 457--462
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K DNA Computing
%8 13-16  July
%Z GP-97

%A Konstantine Arkoudas
%T Automatically Discovering Euler's Identity via Genetic Programming
%B AAAI Fall Symposium
%E Selmer Bringsjord and Andrew Shilliday
%D 2008
%P 1--7
%I AAAI Menlo Park, California, USA
%C Arlington, Virginia, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Papers/Symposia/Fall/2008/FS-08-03/FS08-03-001.pdf
%X We show that by using machine learning techniques (genetic programming, in particular), Euler's famous identity (V - E + F = 2) can be automatically discovered from a
   limited amount of data indicating the values of V , E, and F for a small number of polyhedra the five platonic solids. This result suggests that mechanized inductive
   techniques have an important role to play in the process of doing creative mathematics, and that large amounts of data are not necessary for the extraction of important
   regularities. Genetic programming was implemented from scratch in SML-NJ.
%8 November  7-9
%Z Technical Report FS-08-03. Published by The AAAI Press http://www.aaai.org/Press/Reports/Symposia/Fall/fs-08-03.php Cube, triangular prism, pentagonal prism, square
   pyramid, triangular pyramid, pentagonal pyramid, octahedron, tower, truncated cube. SML. The source code can be downloaded from www.rpi.edu/~arkouk/euler/

%A V. Arkov
%A C. Evans
%A P. J. Fleming
%A D. C. Hill
%A J. P. Norton
%A I. Pratt
%A D. Rees
%A K. Rodriguez-Vazquez
%T System Identification Strategies Applied to Aircraft Gas Turbine Engines
%J Annual Reviews in Control
%V 24
%N 1
%D 2000
%P 67--81
%I 
%K genetic algorithms, genetic programming, gas turbines, system identification, frequency domain, multisine signals least-squares estimation, time-varying systems, structure
   selection
%U http://www.sciencedirect.com/science/article/B6V0H-482MDPD-8/2/dd470648e2228c84efe7e14ca3841b7e
%X A variety of system identification techniques are applied to the derivation of models of aircraft gas turbine dynamics. The motivation behind the study is to improve the
   efficiency and cost-effectiveness of system identification techniques currently used in the industry. Four system identification approaches are outlined in this paper. They
   are based upon: identification using ambient noise only data, multisine testing and frequency-domain identification, time-varying models estimated using extended least
   squares with optimal smoothing, and multiobjective genetic programming to select model structure.
%Z Also known as \citeArkov200067

%A Pasquale Arpaia
%A Fabrizio Clemente
%A Carlo Manna
%A Giuseppe Montenero
%T Automatic modeling based on cultural programming for osseointegration diagnosis
%B IEEE Instrumentation and Measurement Technology Conference, I2MTC '09
%D 2009
%P 1274--1277
%I 
%C Singapore
%K genetic algorithms, genetic programming, gene expression programming, EIS data, artificial intelligence, automatic modeling, bone implant, cultural programming, electrical
   impedance spectroscopy, evolutionary programming approach, metallic implant, osseointegration diagnosis, prosthesis, artificial intelligence, biomedical measurement, bone,
   electric impedance measurement, equivalent circuits, evolutionary computation, genetics, medical computing, orthopaedics, prosthetics
%X The problem of modelling equivalent circuits for interpreting Electrical Impedance Spectroscopy (EIS) data in monitoring osseointegration level of metallic implants in bone
   is faced by means of an evolutionary programming approach based on cultural algorithms. With respect to state-of-the-art gene expression programming, the information on
   search advance acquired by most promising individuals during the evolution is shared with the entire population of potential solutions and stored also for next generations.
   Experimental results of the application such cultural programming-based analytical modelling to in-vitro EIS measurements of bone in-growth around metallic implants during
   prosthesis osseointegration are presented.
%8 5-7  May
%Z Also known as \cite5168651

%A Tughrul Arslan
%T Book Review: Evolvable Components--From Theory to Hardware Implementations
%J Genetic Programming and Evolvable Machines
%V 6
%N 4
%D 2005
%P 461--462
%I 
%K genetic algorithms, evolvable hardware
%X Book Review: Evolvable Components--From Theory to Hardware Implementations by Lukas Sekanina Springer, 2003, ISBN 3-540-40377-9
%8 Decemeber
%Z review of \citesekanina:2003:book

%A M. Arvaneh
%A H. Ahmadi
%A A. Azemi
%A M. Shajiee
%A Z. S. Dastgheib
%T Prediction of Paroxysmal Atrial Fibrillation by dynamic modeling of the PR interval of ECG
%B International Conference on Biomedical and Pharmaceutical Engineering, ICBPE '09
%D 2009
%P 1--5
%I 
%K genetic algorithms, genetic programming, ECG signal, PR interval, Paroxysmal Atrial Fibrillation, electrocardiography, neural networks, electrocardiography, neural nets
%X In this work, we propose a new method for prediction of Paroxysmal Atrial Fibrillation (PAF) by only using the PR interval of ECG signal. We first obtain a nonlinear
   structure and parameters of PR interval by a Genetic Programming (GP) based algorithm. Next, we use the neural networks for prediction of PAF. The inputs of the neural
   networks are the parameters of nonlinear model of the PR intervals. For the modeling and prediction we have limited ourselves to only 30 seconds of an ECG signal, which is
   one of the advantages of our proposed approach. For comparison purposes, we have modeled 30 seconds of ECG signals by time based modeling method and have compared
   prediction results of them.
%8 2-4  Decemeber
%Z Also known as \cite5384063

%A Mojtaba Asadi
%A Mehdi Eftekhari
%A Mohammad Hossein Bagheripour
%T Evaluating the strength of intact rocks through genetic programming
%J Applied Soft Computing
%V 11
%N 2
%D 2011
%P 1932--1937
%I 
%K genetic algorithms, genetic programming, Information criterion, Intact rock, Failure criteria
%U http://www.sciencedirect.com/science/article/B6W86-50CVPW4-2/2/863c13a5a1c7be6da7b1ea6592b11bd3
%X Good prediction of the strength of rocks has many theoretical and practical applications. Analysis, design and construction of underground openings and tunnels, open pit
   mines and rock-based foundations are some examples of applications in which prediction of the strength of rocks is of great importance. The prediction might be done using
   mathematical expressions called failure criteria. In most cases, failure criteria of jointed rocks contain the value of strength of intact rock, i.e. the rock without
   joints and cracks. Therefore, the strength of intact rock can be used directly in applications and indirectly to predict the strength of jointed rock masses. On the other
   part, genetic programming method is one of the most powerful methods in machine learning field and could be used for non-linear regression problems. The derivation of an
   appropriate equation for evaluating the strength of intact rock is the common objective of many researchers in civil and mining engineering; therefore, mathematical
   expressions were derived in this paper to predict the strength of the rock using a genetic programming approach. The data of 51 rock types were used and the efficiency of
   equations obtained was illustrated graphically through figures.
%8 March
%Z a Sirjan engineering college, Department of Civil Engineering, Iran b Shahid Bahonar University of Kerman, Department of Computer Engineering, Iran c Shahid Bahonar
   University of Kerman, Department of Civil Engineering, Iran

%A Kenneth Ashcraft
%T Nark: Evolving Bug-Finding Compiler Extensions with Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 11--20
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2003/Ashcraft.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A I. Ashiru
%A C. A. Czarnecki
%T Evolving communicating controllers for multiple mobile robot systems
%J Microprocessors and Microsystems
%V 21
%N 6
%D 1998
%P 393--402
%I 
%K genetic algorithms, genetic programming, Mobile robots, Communication
%U http://www.sciencedirect.com/science/article/B6V0X-3TB0788-6/2/445577f1e7cd0c0d531457835edf327e
%X Multiple mobile robot systems working together to achieve a task have many advantages over single robot systems. However, the planning and execution of a task which is to
   be undertaken by multiple robots is extremely difficult. To date no tools exist which allow such systems to be engineered. One of the key questions that arises when
   developing such systems is: does communication between the robots aid the completion of the task, and if so what information should be communicated? This paper presents the
   results of an investigation undertaken to address the above question. The approach adopted is to use genetic programming (GP) with the aim of evolving a controller, and
   letting the evolution process determine what information should be communicated and how best to use this information. A number of experiments were performed with the aim of
   determining the communication requirements. The results of these experiments are presented in this paper. It is shown that the GP system evolved controllers whose
   performance benefitted as a result of the communication process.

%A Dan Ashlock
%T GP-Automata for Dividing the Dollar
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 18--26
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/ashlock_1997_GPdd.pdf
%8 13-16  July
%Z GP-97

%A Dan Ashlock
%A Charles Richter
%T The Effect of Splitting Populations on Bidding Strategies
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 27--34
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/ashlock_1997_spbs.pdf
%X In this paper we explore the effects of splitting a single population of artificial agents engaging in a simple double auction game into two competing populations by
   modifying experiments reported in [Ashlock, 1997]. The original paper used a new genetic programming tool, termed GP-Automata, to induce bidding strategies with a genetic
   algorithm for Nash's game divide the dollar. The motivation for performing the research is the biological notion of inclusive fitness and kinship theory. The a priori
   hypothesis of the authors was that behaviour of the agents in the simulated market would change substantially when they were no longer forced to be similar to one another
   by the genetic mechanism used to induce new bidding strategies. While breeding takes place only within each population, all bidding is between agents from different
   populations. The agents in the original (single population) paper strongly favoured "fair" Nash equilibria of the divide the dollar game, at odds with the economic theory
   for egoistic agents. When controls for kinship effects are implemented by splitting the population a substantial effect is observed. When agents doing the bidding are not
   close genetic kin to one another the 'unfair' Nash equilbria regain a great deal of their former prominence. This result is of importance to any sort of evolutionary
   algorithm creating artificial agents, as kinship theory can confound game-theoretic predictions that assume egoistic agents. The current research also arguably increases
   the level of realism in the simulation of a double auction market.
%8 13-16  July
%Z GP-97

%A Dan Ashlock
%A James I. Lathrop
%T A Fully Characterized Test Suite for Genetic Programming
%B Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%E V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben
%V 1447
%D 1998
%P 537--546
%I Springer-Verlag Berlin
%C Mission Valley Marriott, San Diego, California, USA
%K genetic algorithms, genetic programming
%8 25-27  March
%Z EP-98. Iowa State University.
%@ 3-540-64891-7

%A Dan Ashlock
%A Mark Joenks
%T ISAc Lists, A Different Representation for Program Induction
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 3--10
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Dan Ashlock
%A Kenneth M. Bryden
%T Thermal agents: An application of genetic programming to virtual engineering
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 1340--1347
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%X The temperature profile across an object is easy to compute by iterative methods. The time spent waiting for iterative solutions to converge for multiple objects in a
   complex configuration is an impediment to exploratory analysis of engineering systems. A rapidly computed initial guess can speed convergence for an iterative thermal
   solver. We describe and test a system for creating thermal agents that supply such initial guesses. Thermal agents are specific to an object geometry but general across
   different thermal boundary conditions. During an off-line training phase, genetic programming is used to locate a thermal agent by training on one or more sets of boundary
   conditions. In use, thermal agents transform boundary conditions into a rapidly converged set of initial values on a cellular decomposition of an object.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Daniel Ashlock
%A Kenneth Bryden
%A Steven Corns
%T On Taxonomy of Evolutionary Computation Problems
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%V 2
%D 2004
%P 1713--1719
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, data visualisation, evolutionary computation, graph theory, pattern classification, pattern clustering, tree data structures, tree
   searching cladogram, classification technique, evolutionary computation problems, graph based evolutionary algorithms, hierarchical clustering, standard taxonomic
   technique, taxonomy, Theory of evolutionary algorithms, Combinatorial \& numerical optimization
%X Taxonomy is the practice of classifying members of a group based on their measurable characteristics. In evolutionary computation the problem of telling when two problems
   are similar is both challenging and important. An accurate classification technique would yield large benefits by permitting a researcher to rationally chose algorithm and
   parameter setting based on past experience. This study uses a standard taxonomic technique, hierarchical clustering, on a set of taxonomic characters derived from a
   comparative study using graph based evolutionary algorithms.
%8 20-23  June
%Z Also known as \cite1331102. CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Daniel Ashlock
%A Stephen Willson
%A Nicole Leahy
%T Coevolution and Tartarus
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 1618--1624
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Coevolution \& collective behavior, Evolutionary intelligent agents
%U http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01331089
%X This study applies coevolution to the Tartarus task. If the coevolving test cases are viewed as a form of parasite the question of virulence becomes an important feature of
   the algorithm. This study compares two types of parasites. The impact of coevolution in this study is at odds with intuition and statistically significant. Analysis
   suggests that disruptive crossover has a key effect. In the presence of disruptive crossover, coevolution may need to be modified to be effective. The key method of dealing
   with disruptive crossover is tracking the age of the Tartarus agents. Using only older agents to drive coevolution of test cases substantially enhances the performance of
   one of the two type of coevolution studied.
%8 20-23  June
%Z GP-automata. Disruptive effect of crossover and mutation important in co-evolution studies. CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Daniel A. Ashlock
%A Kenneth M. Bryden
%A Wendy Ashlock
%A Stephen P. Gent
%T Rapid Training of Thermal Agents with Single Parent Genetic Programming
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2122--2129
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X The temperature profile across an object can be computed by iterative methods. The time spent waiting for iterative solutions to converge for multiple objects in a complex
   configuration is an impediment to exploratory analysis of engineering systems. A high-quality rapidly computed initial guess can speed convergence for an iterative
   algorithm. A system is described and tested for creating thermal agents that supply such initial guesses. Thermal agents are specific to an object but general across
   different thermal boundary conditions. During an off-line training phase, genetic programming is used to locate a thermal agent by training on several sets of boundary
   conditions. In use, thermal agents transform boundary conditions into rapidly-converged initial values on a cellular decomposition of an object. the impact of using single
   parent genetic programming on thermal agents is tested. Single parent genetic programming replaces the usual sub-tree crossover in genetic programming with crossover with
   members of an unchanging ancestor set. The use of this ancestor set permits the incorporation of expert knowledge into the system as well as permitting the re-use of
   solutions derived on one object to speed training of thermal agents for another object. For three types of experiments, incorporating expert knowledge; re-using evolved
   solutions; and transferring knowledge between distinct configurations statistically significant improvements are obtained with single parent techniques.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Daniel A. Ashlock
%A Kenneth M. Bryden
%A Steven Corns
%A Justin Schonfeld
%T An Updated Taxonomy of Evolutionary Computation Problems using Graph-based Evolutionary Algorithms
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 403--410
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X Graph based evolutionary algorithms use combinatorial graphs to impose a topology or geographic structure on an evolving population. It has been demonstrated that, for a
   fixed problem, time to solution varies substantially with the choice of graph. This variation is not simple with very different graphs yielding faster solution times for
   different problems. Normalised time to solution for many graphs thus forms an objective character that can be used for classifying the type of a problem, separate from its
   hardness measured with average time to solution. This study uses fifteen combinatorial graphs to classify 40 evolutionary computation problems. The resulting classification
   is done using neighbour joining, and the results are also displayed using non-linear projection. The different methods of grouping evolutionary computation problems into
   similar types exhibit substantial agreement. Numerical optimisation problems form a close grouping while some other groups of problems scatter across the taxonomy. This
   paper updates an earlier taxonomy of 23 problems and introduces new classification techniques.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages as 96--103
%@ 0-7803-9487-9

%A Daniel Ashlock
%T Evolutionary Computation for Modeling and Optimization
%D 2006
%I Springer
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/content/978-0-387-22196-0
%X Evolutionary Computation for Optimisation and Modelling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming,
   evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of
   selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on
   efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization,
   evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to
   biology and bioinformatics and introduces a number of tools that can be used in biological modelling, including evolutionary game theory. Advanced techniques such as
   cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered. This book presents a large number of homework problems, projects, and
   experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or
   first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and
   applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool. Written for: Undergraduate and graduate students
%Z GP in Chapter 12 ISAc, Chapter 13 graph based EA, Chapter 14 cellular encoding

%A Daniel Ashlock
%A Kenneth M. Bryden
%A Nathan G. Johnson
%T Evolvable Threaded Controllers for a Multi-Agent Grid Robot Task
%B ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks
%E Cihan H. Dagli and Anna L. Buczak and David L. Enke and Mark Embrechts and Okan Ersoy
%V 16
%D 2006
%I 
%C St. Louis, MO, USA
%K genetic algorithms, genetic programming
%X If skip action (ISAc) lists are a linear genetic programming data structure that can be used as an evolvable grid robot controller. In this study ISAc lists are modified to
   run multiple control threads so that a single ISAc list can control multiple grid robots. The threaded ISAc lists are tested by evolving them to control 20--25 grid robots
   that all must exit a virtual room through a single door. The evolutionary algorithm used rapidly locates a variety of controllers that permit the room to be cleared
   efficiently.
%8 November  5-8

%A Daniel Ashlock
%A Kenneth M. Bryden
%T Function Stacks, GBEAs, and Crossover for the Parity Problem
%B ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks
%E Cihan H. Dagli and Anna L. Buczak and David L. Enke and Mark Embrechts and Okan Ersoy
%V 16
%D 2006
%I 
%C St. Louis, MO, USA
%K genetic algorithms, genetic programming
%X Function stacks are a directed acyclic graph representation for genetic programming that subsumes the need for automatically defined functions, substantially reduces the
   number of operations required to solve a problem, and permits the use of a conservative crossover operator. Function stacks are a generalisation of Cartesian genetic
   programming. Graph based evolutionary algorithms are a method for improving evolutionary algorithm performance by imposing a connection topology on an evolutionary
   population to strike an efficient balance between exploration and exploration. In this study the parity problems using function stacks for parity on 3, 4, 5, and 6
   variables are tested on fifteen graphical connection topologies with and without crossover. Choosing the correct graph is found to have a statistically significant impact
   on time to solution. The conservative crossover operator for function stacks, new in this study, is found to improve time to solution by 4 to 9 fold with more improvement
   in harder instances of the parity problem.
%O Part I: Evolutionary Computation
%8 November  5-8

%A Daniel Ashlock
%A Taika {von Konigslow}
%T Evolution of Artificial Ring Species
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X Biological ring species are a population surrounding a geographic obstruction such as a large lake or a mountain range. Adjacent sub-populations are mutually fertile, but
   fertility drops with distance. This study attempts to create examples of artificial ring species using evolutionary algorithms. ISAc lists, a representation with
   self-organised and potentially complex genetics, are used to evolve controllers for the Tartarus task. The breeding population of Tartarus controllers are arranged in a
   ring-shaped configuration with strictly local gene flow. Fertility is defined to be the probability that a child will have fitness at least that of its least fit parent.
   Fertility is found to drop steadily and significantly with distance around the ring in each of twelve replicates of the experiment. Comparison of fertility at various
   distances within a ring-shaped population is compared with sampled intra-population fertility. Some populations are found to have significantly higher than background
   fertility with other populations. This phenomena suggests the presence of aggressive genetics or dominant phenotype in which a creature has an enhanced probability of
   simply cloning its own phenotype during crossover. In addition to creating examples of artificial ring species this study also achieved a very high level of fitness with
   the Tartarus task. A comparison is made with another study that uses hybridisation to achieve record breaking Tartarus fitness.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Daniel Ashlock
%A Elizabeth Warner
%T The Geometry of Tartarus Fitness Cases
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X Tartarus is a standard AI task for grid robots in which boxes must be moved to the walls of a virtual world. There are 320,320 fitness cases for the standard Tartarus task
   of which 297,040 are valid according to the original statement of the problem. This paper studies different schemes for allocating fitness trials for Tartarus using an
   agent-based metric on the fitness cases to aid in the design process. This agent-based metric is a tool that permits exploration of the geometry of the space of fitness
   cases. The information gained from this exploration demonstrates why a scheme designed to yield a superior set of training cases in fact yielded an inferior one. The
   information gained also suggests a new scheme for allocating fitness trials that decreases the number of trials required to achieve a given fitness of the best agent. This
   scheme achieves similar fitness to a standard evolutionary algorithm using fewer fitness cases. The space of fitness cases for Tartarus is found, relative to the
   agent-based metric, to form a hollow sphere with a nonuniform distribution of the fitness cases within the space. The tools developed in this study include a generalisable
   technique for placing an agent-based metric space structure on the fitness cases of any problem that has multiple fitness cases. This metric space structure can be used to
   better understand the distribution of fitness cases and so design more effective evolutionary algorithms.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Daniel A. Ashlock
%A Kenneth M. Bryden
%A Steven Corns
%T Small Population Effects and Hybridization
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X This paper examines the confluence of two lines of research that seek to improve the performance of evolutionary computation systems through management of information flow.
   The first is hybridisation; the second is using small population effects. Hybridisation consists of restarting evolutionary algorithms with copies of bestof- population
   individuals drawn from many populations. Small population effects occur when an evolutionary algorithm's performance, either speed or probability of premature convergence,
   is improved by use of a very small population. This paper presents a structure for evolutionary computation called a blender which performs hybridisation of many small
   populations. The blender algorithm is tested on the PORS and Tartarus tasks. Substantial and significant effects result from varying the size of the small populations used
   and from varying the frequency with which hybridisation is performed. The major effect results from changing the frequency of hybridization; the impact of population size
   is more modest. The parameter settings which yield best performance of the blender algorithm are remarkably consistent across all seven sets of experiments performed.
   Blender performance is found to be superior to other algorithms for six cases of the PORS problem. For Tartarus, blender performs well, but not as well as the previous
   hybridization experiments that motivated its development.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Daniel Ashlock
%A Adam J. Shuttleworth
%A Kenneth M. Bryden
%T Induction of Virtual Sensors with Function Stacks
%B ANNIE 2009, Intelligent Engineering Systems through Artificial Neural Networks
%E Cihan H. Dagli and K. Mark Bryden and Steven M. Corns and Mitsuo Gen and Kagan Tumer and Gursel Suer
%V 19
%D 2009
%I 
%C St. Louis, MO, USA
%K genetic algorithms, genetic programming
%X Virtual sensors are mathematical models that predict the readings of a sensor in a location currently without an operational sensor. Virtual sensors can be used to
   compensate for a failed sensor or as a framework for supporting mathematical decomposition of a model of a complex system. This study applies a novel genetic programming
   representation called a function stack to the problem of virtual sensor induction in a simple thermal system. Real-valued function stacks are introduced in this study. The
   thermal system modelled is a heat exchanger. Function stacks are found to be able to efficiently find compact and accurate models for each often sensors using the data from
   the other sensors. This study serves as proof-of-concept for using function stacks as a modeling technology for virtual sensors.
%O Part I

%A Daniel Ashlock
%A Douglas McCorkle
%A Kenneth M. Bryden
%T Logic Function Induction with the Blender Algorithm Using Function Stacks
%B ANNIE 2009, Intelligent Engineering Systems through Artificial Neural Networks
%E Cihan H. Dagli and K. Mark Bryden and Steven M. Corns and Mitsuo Gen and Kagan Tumer and Gursel Suer
%V 19
%D 2009
%P 189--196
%I 
%C St. Louis, MO, USA
%K genetic algorithms, genetic programming
%X This paper applies two techniques, hybridisation and small population effects, to the problem of logic function induction. It also uses an efficient representation for
   genetic programming called a function stack. Function stacks are a directed acyclic graph representation used in place of the more common tree-structured representation.
   This study is the second exploring an algorithm for evolutionary computation called the blender algorithm which performs hybridization of many small populations. The
   blender algorithm is tested on the 3 and 4 variable parity problems. Confirming and sharpening earlier results on the use of small population sizes for the parity problem,
   it is demonstrated that subpopulation size and intervals between population mixing steps are critical parameters. The blender algorithm is found to perform well on the
   parity problem.
%O Part III Evolutionary Computation

%A Daniel Ashlock
%A Justin Schonfeld
%T Evolution for automatic assessment of the difficulty of sokoban boards
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Many games have a collection of boards with the difficulty of an instance of the game determined by the starting configuration of the board. Correctly rating the difficulty
   of the boards is somewhat haphazard and required either a remarkable level of understanding of the game or a good deal of play-testing. In this study we explore
   evolutionary algorithms as a tool to automatically grade the difficulty of boards for a version of the game sokoban. Mean time-to-solution by an evolutionary algorithm and
   number of failures to solve a board are used as a surrogate for the difficulty of a board. Initial testing with a simple string-based representation, giving a sequence of
   moves for the Sokoban agent, provided very little signal; it usually failed. Two other representations, based on a reactive linear genetic programming structure called an
   ISAc list, generated useful hardness-classification information for both hardness surrogates. These two representations differ in that one uses a randomly initialised
   population of ISAc lists while the other initialises populations with competent agents pre-trained on random collections of sokoban boards. The study encompasses four
   hardness surrogates: probability-of-failure and mean time-to-solution for each of these two representations. All four are found to generate similar information about board
   hardness, but probability-of-failure with pre-evolved agents is found to be faster to compute and to have a clearer meaning than the other three board-hardness surrogates.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586239

%A Wendy Ashlock
%A Dan Ashlock
%T Single Parent Genetic Programming
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 2
%D 2005
%P 1172--1179
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X The most controversial part of genetic programming is its highly disruptive and potentially innovative subtree crossover operator. The clearest problem with the crossover
   operator is its potential to induce defensive metaselection for large parse trees, a process usually termed "bloat." Single parent genetic programming is a form of genetic
   programming in which bloat is reduced by doing subtree crossover with a fixed population of ancestor trees. Analysis of mean tree size growth demonstrates that this fixed
   and limited set of crossover partners provides implicit, automatic control on tree size in the evolving population, reducing the need for additionally disruptive trimming
   of large trees. The choice of ancestor trees can also incorporate expert knowledge into the genetic programming system. The system is tested on four problems:
   plus-one-recall-store (PORS), odd parity, plus-times-half (PTH) and a bioinformatic model fitting problem (NIPs). The effectiveness of the technique varies with the problem
   and choice of ancestor set. At the extremes, improvements in time to solution in excess of 4700-fold were observed for the PORS problem, and no significant improvements for
   the PTH problem were observed.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Wendy Ashlock
%T Using Very Small Population Sizes in Genetic Programming
%B 2006 IEEE World Congress on Computational Intelligence, 2006 IEEE Congress on Evolutionary Computation
%D 2006
%P 1023--1030
%I 
%C Vancouver
%K genetic algorithms, genetic programming
%X very small (4-7) population sizes in genetic programming. When using exploitive operators, this results in hill climbing; when using exploratory operators this results in
   genetic drift. The end result is a different way of searching the space which gives insight into the fitness landscape and the nature of the variation operators used. the
   use of very small population sizes is compared with the use of population sizes up to 1000 for three genetic programming problems: 4-parity using parse trees, Tartarus
   using ISAc lists, and several versions of plus-onerecall-store (PORS) using parse trees. For 4-parity and Tartarus with 60 ISAc nodes, algorithms with very small population
   sizes found more solutions faster. For PORS, the effect was less pronounced: more solutions were found, but the algorithm was faster only than when using slightly larger
   populations. For Tartarus with 30 ISAc nodes, no effect was detected.
%8 16-21  July

%A Wendy Ashlock
%T Mutation vs. Crossover with Genetic Programming
%B ANNIE 2006, Intelligent Engineering Systems through Artificial Neural Networks
%E Cihan H. Dagli and Anna L. Buczak and David L. Enke and Mark Embrechts and Okan Ersoy
%V 16
%D 2006
%I 
%C St. Louis, MO, USA
%K genetic algorithms, genetic programming
%X Understanding how variation operators work leads to a better understanding both of the search space and of the problem being solved. This study examines the behaviour of
   mutation and crossover operators in genetic programming using parse trees to find solutions to 3-parity and 4-parity. The standard subtree crossover and subtree mutation
   operators are studied along with two new operators, fold mutation and fusion crossover. They are studied in terms of how often and how fast they solve the problem; how much
   they change the fitness on average; and what proportion of variations are neutral, harmful, and helpful. It is found that operators behave differently when used alone than
   when used together with another operator and that some operators behave differently when solving 3-parity and when solving 4-parity.
%O Part I: Evolutionary Computation
%8 November  5-8

%A A. F. Ashour
%A L. F. Alvarez
%A V. V. Toropov
%T Empirical modelling of shear strength of RC deep beams by genetic programming
%J Computers and Structures
%V 81
%N 5
%D 2003
%P 331--338
%I 
%K genetic algorithms, genetic programming, Reinforced concrete deep beams, Empirical model building
%U http://www.sciencedirect.com/science/article/B6V28-47S6J5M-5/2/03211d57903fd1d7c48ac56fb32d1d36
%X This paper investigates the feasibility of using previous termgeneticnext term programming (GP) to create an empirical model for the complicated non-linear relationship
   between various input parameters associated with reinforced concrete (RC) deep beams and their ultimate shear strength. GP is a relatively new form of artificial
   intelligence, and is based on the ideas of Darwinian theory of evolution and previous termgenetics.next term The size and structural complexity of the empirical model are
   not specified in advance, but these characteristics evolve as part of the prediction. The engineering knowledge on RC deep beams is also included in the search process
   through the use of appropriate mathematical functions. The model produced by GP is constructed directly from a set of experimental results available in the literature. The
   validity of the obtained model is examined by comparing its response with the shear strength of the training and other additional datasets. The developed model is then used
   to study the relationships between the shear strength and different influencing parameters. The predictions obtained from GP agree well with experimental observations.
%8 March

%A Muhammad Waqar Aslam
%A Zhechen Zhu
%A Asoke K. Nandi
%T Automatic digital modulation classification using Genetic Programming with K-Nearest Neighbor
%B MILCOM 2010
%D 2010
%P 1731--1736
%I 
%K genetic algorithms, genetic programming, 16QAM, 64QAM, BPSK, K-nearest neighbour, QPSK, automatic digital modulation classification, civil application, computer
   simulations, military application, quadrature amplitude modulation, quadrature phase shift keying, signal classification
%X Automatic modulation classification is an intrinsically interesting problem with various civil and military applications. A generalised digital modulation classification
   algorithm has been developed and presented in this paper. The proposed algorithm uses Genetic Programming (GP) with K-Nearest Neighbour (K-NN). The algorithm is used to
   identify BPSK, QPSK, 16QAM and 64QAM modulations. Higher order cumulants have been used as input features for the algorithm. A two-stage classification approach has been
   used to improve the classification accuracy. The high performance of the method is demonstrated using computer simulations and in comparisons with existing methods.
%8 October  31-  November  3
%Z Also known as \cite5680232

%A V. G. Asouti
%A I. C. Kampolis
%A K. C. Giannakoglou
%T A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization
%J Genetic Programming and Evolvable Machines
%V 10
%N 4
%D 2009
%P 373--389
%I 
%K genetic algorithms, Asynchronous evolutionary algorithms, Metamodels, Grid computing, Aerodynamic shape optimization
%X A Grid-enabled asynchronous metamodel-assisted evolutionary algorithm is presented and assessed on a number of aerodynamic shape optimization problems. An efficient way of
   implementing surrogate evaluation models or metamodels (artificial neural networks) in the context of an asynchronous evolutionary algorithm is proposed. The use of
   metamodels relies on the inexact pre-evaluation technique already successfully applied to synchronous (i.e. generation-based) evolutionary algorithms, which needs to be
   revisited so as to efficiently cooperate with the asynchronous search method. The so-created asynchronous metamodel-assisted evolutionary algorithm is further enabled for
   Grid Computing. The Grid deployment of the algorithm relies on three middleware layers: GridWay, Globus Toolkit and Condor. Single- and multi-objective CFD-based designs of
   isolated airfoils and compressor cascades are handled using the proposed algorithm and the gain in CPU cost is demonstrated.
%8 Decemeber
%Z Parallel CFD & Optimization Unit, Lab. of Thermal Turbomachines, School of Mechanical Engineering, National Technical University of Athens, P.O. Box 64069, Athens, 15710,
   Greece

%A M. Atkin
%A P. R. Cohen
%T Genetic programming to learn an agent's monitoring strategy
%B Proceedings of the AAAI-93 Workshop on Learning Action Models
%E Wei-Min Shen
%D 1993
%P 36--41
%I AAAI Press
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Library/Workshops/ws93-06.php
%X Many tasks require an agent to monitor its environment, but little is known about appropriate monitoring strategies to use in particular situations. Our approach is to
   learn good monitoring strategies with a genetic programming algorithm. To this end, we have developed a simple agent programming language in which we represent monitoring
   strategies as programs that control a simulated robot, and a simulator in which the programs can be evaluated. The effect of different environments and tasks is determined
   experimentally; changing features of the environment will change which strategies are learnt. The correspondence can then be analysed.
%Z Also available as \citeAtkin:1993:GPLAMSa?

%A M. Atkin
%A P. R. Cohen
%T Genetic programming to learn an agent's monitoring strategy
%R Technical report TR-93-26
%D 1993
%I 
%I Computer Science Department, University of Massachusetts
%C Amherst, MA, USA
%K genetic algorithms, genetic programming
%U http://www-eksl.cs.umass.edu/papers/93-26.ps
%Z Also available as \citeAtkin:1993:GPLAMS?

%A Marc S. Atkin
%A Paul R. Cohen
%T Learning monitoring strategies: A difficult genetic programming application
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 328--332a
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, cupcake problem, agent control language, genetic programming application, monitoring strategy learning, optimal strategies,
   possible behaviour, learning (artificial intelligence), monitoring; optimisation
%U http://citeseer.ist.psu.edu/94049.html
%X Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed
   results. Since the agent control language was kept purposefully general, the set of monitoring strategies constitutes only a small part of the overall space of possible
   behaviours. Because of this, it was often difficult for the genetic algorithm to evolve them, even though their performance was superior. These results raise questions as
   to how easy it will be for genetic programming to scale up as the areas it is applied to become more complex.
%8 27-29  June
%Z Novel? chrome/program structure linear, close to assembly language, used GOTOs and interrupt handlers. Did _not_ get performance improvement on changing to parse trees. Did
   evolve progs to control agents which moved to the goal without colliding with an obstacle. Finally cautions about problems with GP scaling up. "Also tried local mating
   (also known as fine grain parallelism)" Also available as Technical Report 94-52, Dept. of Computer Science, University of Massachusetts/Amherst, USA?

%A Marc S. Atkin
%A Paul R. Cohen
%T Monitoring in Embedded Agents
%R Computer Science Technical Report 95-66
%D 1995
%I 
%I Experimental Knowledge Systems Laboratory, Computer Science Department, University of Massachusetts
%C Box 34610, Lederle Graduate Research Center, Amherst. MA 01003-4610, USA
%K genetic algorithms, genetic programming
%U http://www-eksl.cs.umass.edu/papers/ijcai95-msa_95-66.pdf
%X Finding good monitoring strategies is an important process in the design of any embedded agent. We describe the nature of the monitoring problem, point out what makes it
   difficult, and show that while periodic monitoring strategies are often the easiest to derive, they are not always the most appropriate. We demonstrate mathematically and
   empirically that for a wide class of problems, the so-called 'cupcake problems', there exists a simple strategy, interval reduction, that outperforms periodic monitoring.
   We also show how features of the environment may influence the choice of the optimal strategy. The paper concludes with some thoughts about a monitoring strategy taxonomy,
   and what its defining features might be.
%Z refs to Atkin's Masters Thesis. Simulated robot in 2 dee world, sensors, conditionals, loop. LTB, explains what the cupcake problem is. interrupt handlers. Theoretical
   justification for cupcake result.

%A Marc S. Atkin
%A Paul R. Cohen
%T Monitoring Strategies for Embedded Agents: Experiments and Analysis
%J Adaptive Behavior
%V 4
%N 2
%D 1995
%P 125--172
%I 
%K genetic algorithms, genetic programming, Monitoring, embedded agents, planning
%U http://www-eksl.cs.umass.edu/papers/atkin96.pdf
%X Monitoring is an important activity for any embedded agent. To operate effectively, agents must gather information about their environment. The policy by which they do this
   is called a monitoring strategy. Our work has focused on classifying different types of monitoring strategies and understanding how strategies depend on features of the
   task and environment. We have discovered only a few general monitoring strategies, in particular periodic and interval reduction, and speculate that there are no more. The
   relative advantages and generality of each strategy will be discussed in detail. The wide applicability of interval reduction will be demonstrated both empirically and
   analytically. We conclude with a number of general laws that state when a strategy is most appropriate.
%8 Fall

%A Daniel L Atkins
%A Roman Klapaukh
%A Will N Browne
%A Mengjie Zhang
%T Evolution of aesthetically pleasing images without human-in-the-loop
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Evolutionary Art is a sub-field of Evolutionary Computing that involves creating interesting images using Evolutionary Techniques. Previously Genetic Programming has been
   used to create such images autonomously -that is, without a human in the loop. However, this work did not explore alternative fitness measures, consider colour in fitness
   or provide independent validation of results. Four fitness functions based on the concept that the pleasingness of an image is based on the ratio of image complexity to
   processing complexity are explored. We introduce the use of Shannon Entropy as a measure of image complexity to compare with Jpeg Compression. Similarly, we introduce Run
   Length Encoding to compare with Fractal Compression as a measure of processing complexity. A survey of 100 participants showed that it is possible to generate aesthetically
   pleasing graphics using each fitness function. Importantly, it was the introduction of colour that separated the aesthetic effects of the fitness measures.
%8 18-23  July
%Z Three 'separate' GP programs: one for each colour, per image. RMIT-GP package. Web based trial with Likert scale (1..5). Nice pictures. WCCI 2010. Also known as
   \cite5586283

%A Daniel Atkins
%A Kourosh Neshatian
%A Mengjie Zhang
%T A Domain Independent Genetic Programming Approach to Automatic Feature Extraction for Image Classification
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 238--245
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming
%X In this paper we explore the application of Genetic Programming (GP) to the problem of domain-independent image feature extraction and classification. We propose a new
   GP-based image classification system that extracts image features autonomously, and compare its performance against a baseline GP-based classifier system that uses
   human-extracted features. We found that the proposed system has a similar performance to the baseline system, and that GP is capable of evolving a single program that can
   both extract useful features and use those features to classify an image.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A John A. Atkinson-Abutridy
%A Julio R. Carrasco-Leon
%T An evolutionary model for dynamically controlling a behavior-based autonomous agent
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 16--24
%I 
%C Orlando, Florida, USA
%8 13  July
%Z GECCO-99LB

%A Laurent Atlan
%A Jerome Bonnet
%A Martine Naillon
%T Learning Distributed Reactive Strategies by Genetic Programming for the General Job Shop Problem
%B Proceedings of the 7th annual Florida Artificial Intelligence Research Symposium
%D 1994
%I IEEE Press
%I Dassault-Aviation, Artificial Intelligence Department
%C Pensacola, Florida, USA
%K genetic algorithms, genetic programming
%U ftp://ftp.ens.fr/pub/reports/biologie/disgajsp.ps.Z
%X proposed is a general system to infer symbolic policy functions for distributed reactive scheduling in non-stationary environments. The job shop problem is only used as a
   validating case study. Our system is based both on an original distributed scheduling model and on genetic programming for the inference of symbolic policy functions. The
   purpose is to determine heuristic policies that are local in time, long term near-optimal, and robust with respect to perturbations. Furthermore, the policies are local in
   state space: the global decision problem is split into as many decision problems as there are agents, i.e. machines in the job shop problem. If desired, the genetic
   algorithm can use expert knowledge as a priori knowledge, via implementation of the symbolic representation of the policy functions.
%8 May
%Z "To be published in the proceedings of the Seventh Annual Florida Artificial Intelligence Research Symposium" DGT/DEA/IA2 December 1993 Combination of GP and Giffler and
   Thompson algorithm

%A Indriyati Atmosukarto
%A Linda G. Shapiro
%A Carrie Heike
%T The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications
%B Proceedings of the 2010 20th International Conference on Pattern Recognition
%E Aytul Ercil
%D 2010
%P 2444--2447
%I IEEE
%I International Association for Pattern Recognition (IAPR)
%C Istanbul, Turkey
%K genetic algorithms, genetic programming, 3D Shape quantification
%U http://grail.cs.washington.edu/pub/papers/atmosukarto2010uog.pdf
%X Craniofacial disorders commonly result in various head shape dysmorphologies. The goal of this work is to quantify the various 3D shape variations that manifest in the
   different facial abnormalities in individuals with a craniofacial disorder called 22q11.2 Deletion Syndrome. Genetic programming (GP) is used to learn the different 3D
   shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1],
   [2].
%8 23-26  August
%Z ICPR '10

%A Indriyati Atmosukarto
%T 3D Shape Analysis for Quantification, Classification, and Retrieval
%R Ph.D. Thesis
%D 2010
%I 
%I Computer Science and Engineering, University of Washington
%C USA
%K genetic algorithms, genetic programming
%U http://grail.cs.washington.edu/theses/AtmosukartoPhd.pdf
%X Three-dimensional objects are now commonly used in a large number of applications including games, mechanical engineering, archaeology, culture, and even medicine. As a
   result, researchers have started to investigate the use of 3D shape descriptors that aim to encapsulate the important shape properties of the 3D objects. This thesis
   presents new 3D shape representation methodologies for quantification, classification and retrieval tasks that are flexible enough to be used in general applications, yet
   detailed enough to be useful in medical craniofacial dysmorphology studies. The methodologies begin by computing low-level features at each point of the 3D mesh and
   aggregating the features into histograms over mesh neighbourhoods. Two different methodologies are defined. The first methodology begins by learning the characteristics of
   salient point histograms for each particular application, and represents the points in a 2D spatial map based on longitude-latitude transformation. The second methodology
   represents the 3D objects by using the global 2D histogram of the azimuth-elevation angles of the surface normals of the points on the 3D objects. Four datasets, two
   craniofacial datasets and two general 3D object datasets, were obtained to develop and test the different shape analysis methods developed in this thesis. Each dataset has
   different shape characteristics that help explore the different properties of the methodologies. Experimental results on classifying the craniofacial datasets show that our
   methodologies achieve higher classification accuracy than medical experts and existing state-of-the-art 3D descriptors. Retrieval and classification results using the
   general 3D objects show that our methodologies are comparable to existing view-based and feature-based descriptors and outperform these descriptors in some cases. Our
   methodology can also be used to speed up the most powerful general 3D object descriptor to date.
%Z GPLAB, Matlab

%A Indriyati Atmosukarto
%T GPLAB: software review
%J Genetic Programming and Evolvable Machines
%V 12
%N 4
%D 2012
%P 457--459
%I 
%K genetic algorithms, genetic programming
%O Software Review
%8 Decemeber
%Z Matlab

%A Douglas A. Augusto
%A Helio J. C. Barbosa
%T Symbolic Regression via Genetic Programming
%B VI Brazilian Symposium on Neural Networks (SBRN'00)
%D 2000
%P 173
%I 
%C Rio de Janeiro, RJ, Brazil
%K genetic algorithms, genetic programming
%U http://csdl.computer.org/comp/proceedings/sbrn/2000/0856/00/08560173abs.htm
%X In this work, we present an implementation of symbolic regression, which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled
   languages are not usually the most efficient ones. The present approach employs a simple representation for tree-like structures by making use of Read's linear code,
   leading to more simplicity and better performance when compared with traditional GP implementations. Creation, crossover and mutation of individuals are formalized. An
   extension allowing for the creation of random coefficients is presented. The efficiency of the proposed implementation was confirmed in computational experiments, which are
   summarized in this paper.
%O VI Simposio Brasileiro de Redes Neurais
%8 January  22-25

%A Douglas A. Augusto
%A Helio J. C. Barbosa
%A Nelson F. F. Ebecken
%T Coevolution of data samples and classifiers integrated with grammatically-based genetic programming for data classification
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1171--1178
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, competitive coevolution, context-free grammar, data classification
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1171.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389328

%A Douglas Adriano Augusto
%A Helio Jose Correa Barbosa
%A Nelson Francisco Favilla Ebecken
%T Coevolutionary multi-population genetic programming for data classification
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 933--940
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, distributed genetic programming
%8 7-11  July
%Z Also known as \cite1830650 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Douglas A. Augusto
%A Helio J. C. Barbosa
%A Andre M. S. Barreto
%A Heder S. Bernardino
%T A new approach for generating numerical constants in grammatical evolution
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 193--194
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution: Poster
%X A new approach for numerical-constant generation in Grammatical Evolution is presented. Experiments comparing our method with the three most popular methods for constant
   creation are performed. By varying the number of bits to represent a constant, we can increase our method's precision to the desired level of accuracy, overcoming by a
   large margin the other approaches.
%8 12-16  July
%Z Also known as \cite2001966 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Peter Augustsson
%A Krister Wolff
%A Peter Nordin
%T Creation Of A Learning, Flying Robot By Means Of Evolution
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 1279--1285
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, evolutionary robotics, evolutionary algorithm, flying
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/ROB196.pdf
%X We demonstrate the first instance of a real on-line robot learning to develop feasible flying (flapping) behavior, using evolution. Here we present the experiments and
   results of the first use of evolutionary methods for a flying robot. With nature's own method, evolution, we address the highly non-linear fluid dynamics of flying. The
   flying robot is constrained in a test bench where timing and movement of wing flapping is evolved to give maximal lifting force. The robot is assembled with standard
   off-the-shelf R/C servomotors as actuators. The implementation is a conventional steady-state linear evolutionary algorithm.
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
   Winner of the best-paper award at GECCO-2002, in Evolutionary Robotics.
%@ 1-55860-878-8

%A Melanie Aurnhammer
%T Evolving Texture Features by Genetic Programming
%B Applications of Evolutionary Computing, EvoWorkshops2007: EvoCOMNET, EvoFIN, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, EvoTransLog
%S LNCS
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal
   Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang
%V 4448
%D 2007
%P 351--358
%I Springer Verlag
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X Feature extraction is a crucial step for Computer Vision applications. Finding appropriate features for an application often means hand-crafting task specific features with
   many parameters to tune. A generalisation to other applications or scenarios is in many cases not possible. Instead of engineering features, we describe an approach which
   uses Genetic Programming to generate features automatically. In addition, we do not predefine the dimension of the feature vector but pursue an iterative approach to
   generate an appropriate number of features. We present this approach on the problem of texture classification based on co-occurrence matrices. Our results are compared to
   those obtained by using seven Haralick texture features, as well as results reported in the literature on the same database. Our approach yielded a classification
   performance of up to 87percent which is an improvement of 30percent over the Haralick features. We achieved an improvement of 12percent over previously reported results
   while reducing the dimension of the feature vector from 78 to four.
%8 11-13  April
%Z EvoWorkshops2007

%A M. P. Austin
%A R. G. Bates
%A M. A. H. Dempster
%A S. N. Williams
%T Adaptive systems for foreign exchange trading
%R Working paper WP 15/2003
%D 2003
%I 
%I Judge Institute of Management, University of Cambridge
%C UK
%K genetic algorithms, genetic programming
%U http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/WP1503.pdf
%Z Research Papers in Management Studies. To appear in Eclectic \citeAustin:2004:E See \citeAustin:2004:QF

%A Mark Austin
%A Graham Bates
%A Michael Dempster
%A Stacy Williams
%T Adaptive systems for foreign exchange trading
%J Eclectic
%V 18
%D 2004
%P 21--26
%I 
%K genetic algorithms, genetic programming
%U http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/adaptive.pdf
%X A joint project between academics and bankers has shown how banks can improve the forecasting performance of their technical trading systems in foreign exchange markets.
   Professor Michael Dempster and Graham Bates, both of the Centre for Financial Research, Cambridge, and Dr Mark Austin and Dr Stacy Williams, both of HSBC Global Markets,
   outline the results of their research. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest it can be
   done. Automated trading systems are being used successfully to predict intraday and daily exchange rates. Trading systems using only publicly available technical indicators
   can be profitable ? but those that also use proprietary information can be more accurate and therefore more profitable. A joint project by the Centre for Financial Research
   (at the Judge Institute of Management, Cambridge University) and HSBC used the bank's customer order information to show that using proprietary information in trading
   systems can improve their forecasting performance and profitability. The research findings also intuitively make sense. Successful traders in the FX markets apply human
   judgement to a range of information and techniques. In this project the researchers mimicked these traders by combining the techniques of technical analysis with the stream
   of public and non-public information available to them.
%8 Autumn

%A Mark P. Austin
%A Graham Bates
%A Michael A. H. Dempster
%A Vasco Leemans
%A Stacy N. Williams
%T Adaptive systems for foreign exchange trading
%J Quantitative Finance
%V 4
%N 4
%D 2004
%P 37--45
%I Routledge, part of the Taylor \& Francis Group
%K genetic algorithms, genetic programming, fx trading
%U http://www-cfr.jbs.cam.ac.uk/archive/PRESENTATIONS/seminars/2006/dempster2.pdf
%X Foreign exchange markets are notoriously difficult to predict. For many years academics and practitioners alike have tried to build trading models, but history has not been
   kind to their efforts. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest otherwise. With newly
   developed computational techniques and newly available data, the development of successful trading models is looking possible. The Centre for Financial Research (CFR) at
   Cambridge University's Judge Institute of Management has been researching trading techniques in foreign exchange markets for a number of years. Over the last 18 months a
   joint project with HSBC Global Markets has looked at how the bank's proprietary information on customer order flow and on the customer limit order book can be used to
   enhance the profitability of technical trading systems in FX markets. Here we give an overview of that research and report our results.
%8 August
%Z Also in Eclectic 18 Autumn (2004) pp21-26 \citeAustin:2004:E www.eclectic.co.uk and technical report WP15/2003 \citeaustin:2003:WP

%A Mathieu Autones
%A Aryel Beck
%A Phillippe Camacho
%A Nicolas Lassabe
%A Herve Luga
%A Franccois Scharffe
%T Evaluation of chess position by modular neural network generated by genetic algorithm
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 1--10
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=1
%X Chess engine Tempo. One of the major difficulties for this type of program lies in the function for evaluating game positions. This function is composed of a large number
   of parameters which have to be determined and then adjusted. We propose an alternative which consists in replacing this function by an artificial neuron network (ANN).
   Without topological knowledge of this complex network, we use the evolutionist methods for its inception, thus enabling us to obtain, among other things, a modular network.
   Finally, we present our results: (i) reproduction of the XOR function which validates the method used and (ii) generation of an evaluation function
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Lerina Aversano
%A Massimiliano {Di Penta}
%A Kunal Taneja
%T A genetic programming approach to support the design of service compositions
%B Proceedings of the first International Workshop of Engineering Service Compositions, WESC'05
%S IBM Research Reports
%E Christian Zirpins and Guadalupe Ortiz and Winfried Lamersdorf and Wolfgang Emmerich
%N RC23821 (W0512-008)
%D 2005
%P 17--24
%I 
%C Amsterdam, The Netherlands
%K genetic algorithms, genetic programming
%U http://domino.research.ibm.com/library/cyberdig.nsf/papers/DE71563B7B69D362852570D000548D0D/$File/rc23821.pdf
%8 Decemeber
%Z Slides http://www.rcost.unisannio.it/mdipenta/papers/wesc05.pdf parts of \citeWESC05

%A Lerina Aversano
%A Massimiliano {Di Penta}
%A Kunal Taneja
%T A genetic programming approach to support the design of service compositions
%J International Journal of Computer Systems Science \& Engineering
%V 21
%N 4
%D 2006
%P 247--254
%I CRL Publishing, admin@crlpublishing.co.uk
%I Curtin University of Technology, Australia
%K genetic algorithms, genetic programming, SBSE, service compositions, distributed software, workflow
%U http://www.rcost.unisannio.it/mdipenta/papers/csse06.pdf
%X The design of service composition is one of the most challenging research problems in service-oriented software engineering. Building composite services is concerned with
   identifying a suitable set of services that orchestrated in some way is able to solve a business goal which cannot be resolved using a single service amongst those
   available. Despite the literature reports several approaches for (semi) automatic service composition, several problems, such as the capability of determining the
   composition's topology, still remain open. This paper proposes a search-based approach to semi-automatically support the design of service compositions. In particular, the
   approach uses genetic programming to automatically generate workflows that accomplish a business goal and exhibit a given QoS level, with the aim of supporting the service
   integrator activities in the finalization of the workflow.
%8 July
%Z WSDL, BPEL4WS. GP tree made of sequence, switch flow, loop nodes. Pop=100, Generations=1000, initioal pop<= 5 nodes. Fitness based on precision and recall. GP compared with
   exhuastive search. Cited by \citeRodriguez-Mier:2010:EI, cites \1068189 GECCO 2005. SeCSEP

%A J. L. Avila
%A Eva Lucrecia {Gibaja Galindo}
%A Sebastian Ventura
%T Multi-label Classification with Gene Expression Programming
%B Hybrid Artificial Intelligence Systems, 4th International Conference, HAIS 2009, Salamanca, Spain, June 10-12, 2009. Proceedings
%S Lecture Notes in Computer Science
%E Emilio Corchado and Xindong Wu and Erkki Oja and \'Alvaro Herrero and Bruno Baruque
%V 5572
%D 2009
%P 629--637
%I Springer
%K genetic algorithms, genetic programming, gene expression programming
%U http://dx.doi.org/10.1007/978-3-642-02319-4

%A Jose Luis Avila-Jimenez
%A Eva Gibaja
%A Sebastian Ventura
%T Evolving Multi-label Classification Rules with Gene Expression Programming: A Preliminary Study
%B Hybrid Artificial Intelligence Systems
%S Lecture Notes in Computer Science
%E Emilio Corchado and Manuel Grana Romay and Alexandre Manhaes Savio
%V 6077
%D 2010
%P 9--16
%I Springer
%C San Sebastian, Spain
%K genetic algorithms, genetic programming, gene expression programming
%X The present work expounds a preliminary work of a genetic programming algorithm to deal with multi-label classification problems. The algorithm uses Gene Expression
   Programming and codifies a classification rule into each individual. A niching technique assures diversity in the population. The final classifier is made up by a set of
   rules for each label that determines if a pattern belongs or not to the label. The proposal have been tested over several domains and compared with other multi-label
   algorithms and the results shows that it is specially suitable to handle with nominal data sets.
%8 June  23-25

%A Ali Aytek
%A Ozgur Kisi
%T A genetic programming approach to suspended sediment modelling
%J Journal of Hydrology
%V 351
%N 3-4
%D 2008
%P 288--298
%I 
%K genetic algorithms, genetic programming, Suspended sediment load, Rating curves, Soft computing
%X This study proposes genetic programming (GP) as a new approach for the explicit formulation of daily suspended sediment-discharge relationship. Empirical relations such as
   sediment rating curves are often applied to determine the average relationship between discharge and suspended sediment load. This type of models generally underestimates
   or overestimates the amount of sediment. During recent decades, some black box models based on artificial neural networks have been developed to overcome this problem. But
   these type of models are implicit that can not be simply used by other investigators. Therefore it is still necessary to develop an explicit model for the
   discharge-sediment relationship. It is aimed in this study, to develop an explicit model based on genetic programming. Explicit models obtained using the GP are compared
   with rating curves and multi-linear regression techniques in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on
   Tongue River in Montana are used as case studies. The results indicate that the proposed GP formulation performs quite well compared to sediment rating curves and
   multi-linear regression models and is quite practical for use.
%8 15  April
%Z Gaziantep University, Civil Engineering Department, Hydraulics Division, 27310 Gaziantep, Turkey Erciyes University, Civil Engineering Department, Hydraulics Division,
   38039 Kayseri, Turkey

%A Ali Aytek
%A M Asce
%A Murat Alp
%T An application of artificial intelligence for rainfall-runoff modeling
%J Journal of Earth System Science
%V 117
%N 2
%D 2008
%P 145--155
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.ias.ac.in/jess/apr2008/d093.pdf
%X This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modelling: the artificial neural networks (ANN) and the
   evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalised regression neural network (GRNN) methods are compared
   with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three
   rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical
   parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and
   determination coefficient (R2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite
   well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.
%8 April

%A Tevfik Aytekin
%A Emin Erkan Korkmaz
%A Halil Altay G{\"{u}}vennir
%T An application of genetic programming to the 4-OP problem using map-trees
%B Progress in Evolutionary Computation
%S Lecture Notes in Artificial Intelligence
%E Xin Yao
%V 956
%D 1995
%P 28--40
%I Springer-Verlag
%C Heidelberg, Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/16240.html
%X In Genetic programming (GP) applications the programs are expressed as parse trees. A node of a parse tree is an element either from the function-set or terminal-set, and
   an element of a terminal set can be used in a parse tree more than once. However, when we attempt to use the elements in the terminal set at most once, we encounter
   problems in creating the initial random population and in crossover and mutation operations. 4-Op problem is an example for such a situation. We developed a technique
   called map-trees to overcome these anomalies. Experimental results on 4-Op using map-trees are presented.
%Z Also technical report BU-CEIS-9441 Bilkent University Department of Computer Engineering

%A R. Muhammad Atif Azad
%A Conor Ryan
%A Mark E. Burke
%A Ali R. Ansari
%T A Re-examination Of The Cart Centering Problem Using The Chorus System
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 707--715
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
   Nominated for best at GECCO award
%@ 1-55860-878-8

%A R. Muhammad Atif Azad
%T A Position Independent Evolutionary Automatic Programming Algorithm - The Chorus System
%B Graduate Student Workshop
%E Sean Luke and Conor Ryan and Una-May O'Reilly
%D 2002
%P 260--263
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York
%K genetic algorithms, genetic programming, grammatical evolution
%8 8  July
%Z Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic
   Programming Conference (GP-2002) part of barry:2002:GECCO:workshop

%A R. Muhammad Atif Azad
%A Conor Ryan
%T Structural Emergence with Order Independent Representations
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1626--1638
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, grammatical evolution
%X This paper compares two grammar based Evolutionary Automatic Programming methods, Grammatical Evolution (GE) and Chorus. Both systems evolve sequences of derivation rules
   which can be used to produce computer programs, however, Chorus employs a position independent representation, while GE uses polymorphic codons, the meaning of which
   depends on the context in which they are used. We consider issues such as the order in which rules appear in individuals, and demonstrate that an order always emerges with
   Chorus, which is similar to that of GE, but more flexible. The paper also examines the final step of evolution, that is, how perfect individuals are produced, and how they
   differ from their immediate neighbours. We demonstrate that, although Chorus appears to be more flexible structure-wise, GE tends to produce individuals with a higher
   neutrality, suggesting that its representation can, in some cases, make finding the perfect solution easier.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Raja Muhammad Atif Azad
%T A Position Independent Representation for Evolutionary Automatic Programming Algorithms - The Chorus System
%R Ph.D. Thesis
%D 2003
%I 
%I University of Limerick
%C Ireland
%K genetic algorithms, genetic programming, Chorus System, Grammatical Evolution
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/azad_thesis.ps.gz
%X We describe a new position independent encoding system, Chorus, for grammar based Evolutionary Algorithms. This scheme is coarsely based on the manner in nature in which
   genes produce proteins that regulate the metabolic pathways of the cell. The phenotype is the behaviour of the cells metabolism, which corresponds to the development of the
   computer program in our case. In this procedure, the actual protein encoded by a gene is the same regardless of the position of the gene within the genome. We show that the
   Chorus system has a very convenient Regular Expression type schema notation that can be used to describe the presence of various phenotypic traits. This notation is used to
   demonstrate that massive areas of neutrality can exist in the search landscape, and the system is also shown to be able to dispense with large areas of the search space
   that are unlikely to contain useful solutions. The searching capability of the system is exemplified by its application on a number of proof of concept problems, where the
   system has shown comparable performance to Genetic Programming and Grammatical Evolution and, in certain cases, it has produced superior results. We also analyse the role
   of the crossover in the Chorus System and conclude by showing its application on a real world problem from the blood flow domain.
%8 Decemeber

%A R. Muhammad Atif Azad
%A Ali R. Ansari
%A Conor Ryan
%A Michael Walsh
%A Tim McGloughlin
%T An evolutionary approach to Wall Sheer Stress prediction in a grafted artery
%J Applied Soft Computing
%V 4
%N 2
%D 2004
%P 139--148
%I Elsevier
%K genetic algorithms, genetic programming, grammatical evolution, chorus system, Wall Shear Stress, Laser Doppler anemometry, Mathematical modeling, Computational Fluid
   Dynamics
%X Restoring the blood supply to a diseased artery is achieved by using a vascular bypass graft. The surgical procedure is a well documented and successful technique. The most
   commonly cited hemodynamic factor implicated in the disease initiation and proliferation processes at graft/artery junctions is Wall Shear Stress (WSS). WSS distributions
   are predicted using numerical simulations as they can provide quick and precise results to assess the effects that alternative graft/artery junction geometries have on the
   WSS distributions in bypass grafts. Validation of the numerical model is required and in vitro studies, using laser Doppler anemometry (LDA), have been employed to achieve
   this. Numerically, the Wall Shear Stress is predicted using velocity values stored in the computational cell near the wall and assuming zero velocity at the wall.
   Experimentally obtained velocities require a mathematical model to describe their behavior. This study employs a grammar based evolutionary algorithm termed Chorus for this
   purpose and demonstrates that Chorus successfully attains this objective. It is shown that even with the lack of domain knowledge, the results produced by this automated
   system are comparable to the results in the literature.
%8 May
%Z http://www.elsevier.com/wps/find/journaldescription.cws_home/621920/description#description

%A R. Muhammad Atif Azad
%A Conor Ryan
%T An Examination of Simultaneous Evolution of Grammars and Solutions
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 141--158
%I Kluwer
%C Ann Arbor
%K genetic algorithms, genetic programming, Grammatical Evolution, Evolving Grammars, Grammatical ADFs, Generative Representations
%X This chapter examines the notion of co-evolving grammars with a population of individuals. This idea has great promise because it is possible to dynamically reshape the
   solution space while evolving individuals. We compare such a system with a more standard system with fixed grammars and demonstrate that, on a selection of benchmark
   problems, the standard approach appears to be better. Several different context free grammars, including one inspired by Koza's GPPS system are examined, and a number of
   surprising results appear, which indicate that several representative GP benchmark problems are best tackled by a standard GP approach.
%O 10
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A R. Muhammad Atif Azad
%A Conor Ryan
%T Gecco 2008 grammatical evolution tutorial
%B GECCO-2008 tutorials
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 2339--2366
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, chorus, GAuGE, genetic algorithms (GA), grammars, linear strings
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2339.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1389058

%A R. Muhammad Atif Azad
%A Conor Ryan
%T Abstract functions and lifetime learning in genetic programming for symbolic regression
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 893--900
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%X Typically, an individual in Genetic Programming (GP) can not make the most of its genetic inheritance. Once it is mapped, its fitness is immediately evaluated and it
   survives only until the genetic operators and its competitors eliminate it. Thus, the key to survival is to be born strong. This paper proposes a simple alternative to this
   powerlessness by allowing an individual to tune its internal nodes and going through several evaluations before it has to compete with other individuals. We demonstrate
   that this system, Chameleon, outperforms standard GP over a selection of symbolic regression type problems on both training and test sets; that the system works
   harmoniously with two other well known extensions to GP, that is, linear scaling and a diversity promoting tournament selection method; that it can benefit dramatically
   from a simple cache; that adding to functions set does not always add to the tuning expense; and that tuning alone can be enough to promote smaller trees in the population.
   Finally, we touch upon the consequences of ignoring the effects of complexity when focusing on just the tree sizes to induce parsimony pressure in GP populations.
%8 7-11  July
%Z Also known as \cite1830645 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A R. Muhammad Atif Azad
%A Conor Ryan
%T Variance based selection to improve test set performance in genetic programming
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1315--1322
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X This paper proposes to improve the performance of Genetic Programming (GP) over unseen data by minimizing the variance of the output values of evolving models alongwith
   reducing error on the training data. Variance is a well understood, simple and inexpensive statistical measure; it is easy to integrate into a GP implementation and can be
   computed over arbitrary input values even when the target output is not known. Moreover, we propose a simple variance based selection scheme to decide between two models
   (individuals). The scheme is simple because, although it uses bi-objective criteria to differentiate between two competing models, it does not rely on a multi-objective
   optimisation algorithm. In fact, standard multi-objective algorithms can also employ this scheme to identify good trade-offs such as those located around the knee of the
   Pareto Front. The results indicate that, despite some limitations, these proposals significantly improve the performance of GP over a selection of high dimensional
   (multi-variate) problems from the domain of symbolic regression. This improvement is manifested by superior results over test sets in three out of four problems, and by the
   fact that performance over the test sets does not degrade as often witnessed with standard GP; neither is this performance ever inferior to that on the training set. As
   with some earlier studies, these results do not find a link between expressions of small sizes and their ability to generalise to unseen data.
%8 12-16  July
%Z Also known as \cite2001754 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Farooq Azam
%A H. F. VanLandingham
%T Dynamic Systems Identification: A Comparitive Study
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Farooq Azam
%A H. F. VanLandingham
%T Dynamic Systems Identification using Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A H. Md Azamathulla
%A A. Ab. Ghani
%A N. A. Zakaria
%A S. H. Lai
%A C. K. Chang
%A C. S. Leow
%A Z. Abuhasan
%T Genetic programming to predict ski-jump bucket spill-way scour
%J Journal of Hydrodynamics, Ser. B
%V 20
%N 4
%D 2008
%P 477--484
%I 
%K genetic algorithms, genetic programming, neural networks, spillway scour, ski-jump bucket
%U http://www.sciencedirect.com/science/article/B8CX5-4TCY8GV-B/2/f3004ab0cd7ed153a22b7f5d637afc89
%X Researchers in the past had noticed that application of Artificial Neural Networks (ANN) in place of conventional statistics on the basis of data mining techniques predicts
   more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming
   (GP) has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour
   depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be
   equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.
%8 August

%A H. Md. Azamathulla
%A Aminuddin {Ab Ghani}
%A Nor Azazi Zakaria
%A Aytac Guven
%T Genetic Programming to Predict Bridge Pier Scour
%J Journal of Hydraulic Engineering
%V 136
%N 3
%D 2010
%P 165--169
%I 
%K genetic algorithms, genetic programming, Local scour, Bridge pier, Artificial neural networks, Radial basis function
%X Bridge pier scouring is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant
   variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by authors), in the form of artificial
   neural networks (ANNs) and genetic programming (GP). 398 data sets of field measurements were collected from published literature and used to train the network or evolve
   the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The performance of GP was found more
   effective when compared to regression equations and ANNs in predicting the scour depth of bridge piers.

%A H. Md. Azamathulla
%A Aytac Guven
%A Yusuf Kagan Demir
%T Linear genetic programming to scour below submerged pipeline
%J Ocean Engineering
%V 38
%N 8-9
%D 2011
%P 995--1000
%I 
%K genetic algorithms, genetic programming, Local scour, Neuro-fuzzy, Pipelines
%U http://www.sciencedirect.com/science/article/B6V4F-52M3TGW-1/2/279184e6554e6b6977d8b9f0180c9f53
%X Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents Linear Genetic Programming (LGP),
   which is an extension to GP, as an alternative tool in the prediction of scour depth below a pipeline. The data sets of laboratory measurements were collected from
   published literature and were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The
   predictions of LGP were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at submerged pipeline.
%8 June

%A H. Md. Azamathulla
%A Z. Ahmad
%T GP approach for critical submergence of intakes in open channel flows
%J Journal of Hydroinformatics
%I 
%K genetic algorithms, genetic programming, critical submergence, intakes, open channel
%U http://www.iwaponline.com/jh/up/pdf/jh2012089.pdf
%X This technical note presents the genetic programming (GP) approach to predict the critical submergence for horizontal intakes in open channel flow for different bottom
   clearances. Laboratory data from the literature for the critical submergence for a wide range of flow conditions were used for the development and testing of the proposed
   method. Froude number, Reynolds number, Weber number and ratio of intake velocity and channel velocity were considered dominant parameters affecting the critical
   submergence. The proposed GP approach produced satisfactory results compared to the existing predictors.
%O In Press, Uncorrected Proof
%Z Vortex, water dam

%A H. Md. Azamathulla
%T Gene-expression programming to predict scour at a bridge abutment
%J Journal of Hydroinformatics
%V 14
%N 2
%D 2012
%P 324--331
%I 
%K genetic algorithms, genetic programming, gene expression programming, artificial neural networks, bridge abutments, local scour, radial basis function
%U http://www.iwaponline.com/jh/014/0324/0140324.pdf
%X The process involved in the local scour at an abutment is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for
   scour. This study presents the use of gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to estimate the scour
   depth. The datasets of laboratory measurements were collected from the published literature and used to train the network or evolve the program. The developed network and
   evolved programs were validated by using the observations that were not involved in training. The proposed GEP approach gives satisfactory results compared with existing
   predictors and artificial neural network (ANN) modelling in predicting the scour depth at an abutment.
%Z ANN, RBF. 'The overall performance of the GEP model is superior to the ANN model.' p330

%A Yaniv Azaria
%A Moshe Sipper
%T Using GP-Gammon: Using Genetic Programming to Evolve Backgammon Players
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 132--142
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=132
%X We apply genetic programming to the evolution of strategies for playing the game of backgammon. Pitted in a 1000-game tournament against a standard benchmark
   player---Pubeval---our best evolved program wins 58\% of the games, the highest verifiable result to date. Moreover, several other evolved programs attain win percentages
   not far behind the champion, evidencing the repeatability of our approach.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Yaniv Azaria
%A Moshe Sipper
%T GP-Gammon: Genetically Programming Backgammon Players
%J Genetic Programming and Evolvable Machines
%V 6
%N 3
%D 2005
%P 283--300
%I 
%K genetic algorithms, genetic programming, backgammon, self-learning, STGP, demes, coevolution
%U http://www.cs.bgu.ac.il/~sipper/papabs/gpgammon.pdf
%X We apply genetic programming to the evolution of strategies for playing the game of backgammon. We explore two different strategies of learning: using a fixed external
   opponent as teacher, and letting the individuals play against each other. We conclude that the second approach is better and leads to excellent results: Pitted in a
   1000-game tournament against a standard benchmark player Pubeval our best evolved program wins 62.4 percent of the games, the highest result to date. Moreover, several
   other evolved programs attain win percentages not far behind the champion, evidencing the repeatability of our approach.
%O Published online: 12 August 2005
%8 September
%Z ECJ

%A Vinaysheel Baber
%A Rema Ananthanarayanan
%A Krishna Kummamuru
%T Evolutionary Algorithm Approach to Bilateral Negotiations
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 202--211
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%X The Internet is quickly changing the way business-to-consumer and business-to-business commerce is conducted. The technology has created an opportunity to get beyond
   single-issue negotiation by determining sellers' and buyers' preferences across multiple issues, thereby creating possible joint gains for all parties. We develop simple
   multiple issue algorithms and heuristics that could be used in electronic auctions and electronic markets. In this study, we show how a genetic algorithm based technique,
   coupled with a simple heuristic can achieve good results in business negotiations. The negotiations' outcomes are evaluated on two dimensions: joint utility and number of
   ex-changes of offers to reach a deal. The results are promising and indicate possible use of such approaches in actual electronic commerce systems.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Vladan Babovic
%A A. W. Minns
%T Use of computational adaptive methodologies in hydroinformatics
%B Proceedings of the first international conference on hydroinformatics, Delft, Netherlands
%E A. Verwey and A. W. Minns and V. Babovic and C. Maksimovic
%D 1994
%P 201--210
%I A. A. Balkema P. O. Box 1675, Rotterdam, Netherlands
%K genetic algorithms, genetic programming
%U http://www.amazon.co.uk/Hydroinformatics-Proceedings-International-Conference-Netherlands/dp/9054105127
%X Summaries a study of the performance of artificial neural networks and GP compared to an empirically-based method using a problem of salt intrusion as an example.
%8 19--23  September
%Z Does not present clear winner (ANN, GP or traditional) upto reader to choose approriate to their problem. IHE-Delft, The Netherlands
%@ 90-5410-512-7

%A Vladan Babovic
%T Genetic Model Induction Based on Experimental Data
%B Proceedings of the XXVIth Congress of International Association for Hydraulics Research
%E J. Gardiner
%D 1995
%I Thomas Telford Ltd
%I International Association of Hydraulic Research
%C London, UK
%K genetic algorithms, genetic programming
%U http://www.iahr.net/e-shop/store/viewItem.asp?idProduct=91
%X GP used to perform an analysis of sediment transport data and to induce relationshop between bed concentration of suspended sediment and the hydraulic conditions. GP
   results similar accuracy to traditional techniques. IHE-Delft, The Netherlands
%8 11--15  September
%Z http://www.amazon.co.uk/Hydra-2000-Development-Proceedings-International/dp/0727720597/ref=sr_1_4?s=books&ie=UTF8&qid=1324144161&sr=1-4
%@ 0-7277-2059-7

%A Vladan Babovic
%T Emergence, Evolution, Intelligence: Hydroinformatics
%R Ph.D. Thesis
%D 1996
%I 
%I International Institute for Infrastructural, Hydraulic and Environmental Engineering and Technical University Delft
%C The Netherlands
%K genetic algorithms, genetic programming
%U http://repository.tudelft.nl/assets/uuid:58c50efe-4a6a-40b4-8c60-2b81d629b49c/EMERGENCE__EVOLUTION__INTELLIGENCE_HYDROINFORMATICS.PDF
%X The computer-controlled operating environments of such facilities as automated factories, nuclear power plants, telecommunication centres and space stations are continually
   becoming more complex.The situation is similar, if not even more apparent and urgent, in the case of water. Water is not only mankind's most valuable natural resource, but
   one which is in increasingly limited supply. The fresh water is the vita! natural resource which supports all environmental activities, that is, natura! economy, and all
   human socio-economic activities, that is, the artificial economy. The pressure for a sustainable control and exploration of water and thus for the peaceful co-existence of
   human- and hydro-economies, is not only a human, socio-economic pressure, but it is the question of life and death! Hydroinformatics - the nascent technology concerned with
   the flow of information related to the flow of fluids and all that they convey - is probably the best possible answer yet proposed to the problem of the control of the
   waters, the very arteries and veins of the biosphere. This work addresses some of the central issues within hydroinformatics paradigm. It focuses on ttie analysis of
   decentralised and distributed computation, as well as the issues of design of individual computatiorial agents using evolutionary algorithms.
%O Published by A. A. Balkema Publishers
%8 20  March
%Z Promotor: Abbott, M.B. See also \citebabovic:book
%@ 90-5410-404-X

%A Vladan Babovic
%T Emergence, evolution, intelligence; Hydroinformatics - A study of distributed and decentralised computing using intelligent agents
%D 1996
%I A. A. Balkema Publishers
%C Rotterdam, Holland
%K genetic algorithms, genetic programming
%X The computer controlled operating environments of such facilities as automated factories, nuclear power plants, telecommunication centres and space stations are continually
   becoming more complex. The situation is similar, if not even more apparent and urgent, in the case of water. Water is not only mankind's most valuable natural resource, but
   one which is in increasingly limited supply. The fresh water is the vital natural resource which supports all environmental activities, that is, natural economy, and all
   human socio-economic activities, that is, the artificial economy. The pressure for a sustainable control and exploration of water and thus for the peaceful co-existence of
   human- & hydro-economies is not only a human, socio-economic pressure, but it is the question of life and death. Hydroinformatics - the nascent technology concerned with
   the flow of information related to the flow of fluids and all that they convey - is probably the best possible answer yet proposed to the problem of the control of the
   waters, the very arteries and veins of the biosphere.
%Z publication of \citebabovic:thesis
%@ 90-5410-404-X

%A V. Babovic
%T Can water resources management benefit from artificial intelligence?
%B Computation Fluid Dynamics: Bunte Bilder in der Praxis
%E J. Kongeter
%D 1996
%P 337--358
%I Meinz Verlag
%C Aachen, Germany
%K genetic algorithms, genetic programming
%Z 26. IWASA International Wasserbau-Symposium Aachen 1995/96

%A Vladan Babovic
%A Michael B. Abbott
%T The evolution of equation from hydraulic data, Part I: Theory
%J Journal of Hydraulic Research
%V 35
%N 3
%D 1997
%P 397--410
%I 
%K genetic algorithms, genetic programming
%X Even as hydroinformatics continues to elaborate more advanced operational tools, languages and environments for engineering and management practice, it necessarily also
   promotes a number of concepts and methodologies that are eminently applicable within the more traditional areas of hydraulic research. Among the many new possibilities
   thereby introduced, that of evolving equations from hydraulic data using evolutionary algorithms has a particularly wide range of applications. The present paper is in two
   parts, the first of which introduces the subject and outlines its theory, while the second is given over to four representative applications and to some of the most
   immediate lessons that may be drawn from these. The first of the applications is derived from a hydrologie model but provides equations with purely hydraulic
   interpretations. The second, taken from sediment transport studies, raises the question of ambiguity in the identification of 'thresholds' in physical processes. It also
   provides a means for analysing the significance of variables and indicates the need, or otherwise, for introducing further variables. A third example, based upon physical
   observations of salt water intrusion in estuaries, introduces the application of the present methods to accelerating prediction processes, while the fourth example extends
   this kind of application to cover numerically generated data, in this case appertaining to the case of flow resistance in the presence of vegetation.
%Z See also \citebabovic:1997:eehd2

%A Vladan Babovic
%A Michael B. Abbott
%T The evolution of equation from hydraulic data, Part II: Applications
%J Journal of Hydraulic Research
%V 35
%N 3
%D 1997
%P 411--430
%I 
%K genetic algorithms, genetic programming
%X This second part of the paper \citebabovic:1997:eehd1 is given over to describing four representative applications and to some of the most immediate lessons that may be
   drawn from these. The first of the applications is derived from a hydrologic model but provides equations with purely hydraulic interpretations. The second, taken from
   sediment transport studies, raises the question of ambiguity in the identification of 'thresholds' in physical processes. It also provides a means for analysing the
   significance of variables and indicates the need, or otherwise, for introducing further variables. A third example, based upon physical observations of salt water intrusion
   in estuaries, introduces the application of the present methods to accelerating prediction processes, while the fourth example extends this kind of application to cover
   numerically-generated data, in this case appertaining to the case of flow resistance in the presence of vegetation. In conclusion, this work is set within the context of
   other developments, such as those of data mining and knowledge discovery generally

%A Vladan Babovic
%T On the Modelling and Forecasting of Non-linear Systems
%B Operational Water Management: Proceedings of the European Water Resources Association Conference, Copenhagen, Denmark, 3-6 September 1997
%E J. C. Refsgaard and E. A. Karalis
%D 1997
%P 195--202
%I Balkema
%C Rotterdam
%K genetic algorithms, genetic programming
%U http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=9054108975
%@ 90-5410-897-5

%A V. Babovic
%T Sediment transport data - Large knowledge mine
%B Proceedings of the Third International Conference on Hydroscience and Engineering
%D 1998
%I 
%C Cottbus, Germany
%K genetic algorithms, genetic programming

%A V. Babovic
%T A data mining approach to time series modelling and forecasting
%B Proceeding of the Third International Conference on Hydroinformatics
%E Babovic and Larsen
%D 1998
%P 847--856
%I Balkema Rotterdam
%C Copenhagen, Denmark
%K genetic algorithms, genetic programming, Vltava River system, flood control and protection of Prague, artificial neural networks
%Z Hydroinformatics'98
%@ 90-5410-983-1

%A Vladan Babovic
%T Mining sediment transport data with genetic programming
%B Proceedings of the First International Conference on New Information Technologies for Decision Making in Civil Engineering
%D 1998
%P 875--886
%I 
%C Montreal, Canada
%K genetic algorithms, genetic programming
%8 11-13  October

%A Vladan Babovic
%A Maarten Keijzer
%T Computer supported knowledge discovery - A case study in flow resistance induced by vegetation
%B Proceedings of the XXVIII Congress of International Association for Hydraulic Research
%D 1999
%I 
%C Graz, Austria
%K genetic algorithms, genetic programming
%8 22-27  August

%A V. Babovic
%A M. Keijzer
%T Data to knowledge - The new scientific paradigm
%B Water Industry Systems
%E D. Savic and G. Walters
%D 1999
%P 3--14
%I 
%C Exeter, United Kingdom
%K genetic algorithms, genetic programming

%A Vladan Babovic
%A Maarten Keijzer
%T Evolutionary algorithms approach to induction of differential equations
%B Proceedings of the Fourth International Conference on Hydroinformatics
%D 2000
%I 
%C Iowa City, USA
%K genetic algorithms, genetic programming

%A Vladan Babovic
%T Data Mining and Knowledge Discovery in Sediment Transport
%J Computer-Aided Civil and Infrastructure Engineering
%V 15
%N 5
%D 2000
%P 383--389
%I 
%K genetic algorithms, genetic programming
%X The means for data collection have never been as advanced as they are today. Moreover, the numerical models we use today have never been so advanced. Feeding and
   calibrating models against collected measurements, however, represents only a one-way flow: from measurements to the model. The observations of the system can be analyzed
   further in the search for the information they encode. Such automated search for models accurately describing data constitutes a new direction that can be identified as
   that of data mining. It can be expected that in the years to come we shall concentrate our efforts more and more on the analysis of the data we acquire from natural or
   artificial sources and that we shall mine for knowledge from the data so acquired. Data mining and knowledge discovery aim at providing tools to facilitate the conversion
   of data into a number of forms, such as equations, that provide a better understanding of the process generating or producing these data. These new models combined with the
   already available understanding of the physical processes -- the theory -- result in an improved understanding and novel formulations of physical laws and improved
   predictive capability. This article describes the data mining process in general, as well as an application of a data mining technique in the domain of sediment transport.
   Data related to the concentration of suspended sediment near a bed are analyzed by the means of genetic programming. Machine-induced relationships are compared against
   formulations proposed by human experts and are discussed in terms of accuracy and physical interpretability.
%8 September
%Z Article first published online: 17 DEC 2002

%A Vladan Babovic
%A Maarten Keijzer
%T Genetic programming as a model induction engine
%J Journal of Hydroinformatics
%V 1
%N 1
%D 2000
%P 35--60
%I 
%K genetic algorithms, genetic programming, data mining, knowledge discovery
%U http://www.iwaponline.com/jh/002/jh0020035.htm
%X Present day instrumentation networks already provide immense quantities of data, very little of which provides any insights into the basic physical processes that are
   occurring in the measured medium. This is to say that the data by itself contributes little to the knowledge of such processes. Data mining and knowledge discovery aim to
   change this situation by providing technologies that will greatly facilitate the mining of data for knowledge. In this new setting the role of a human expert is to provide
   domain knowledge, interpret models suggested by the computer and devise further experiments that will provide even better data coverage. Clearly, there is an enormous
   amount of knowledge and understanding of physical processes that should not be just thrown away. Consequently, we strongly believe that the most appropriate way forward is
   to combine the best of the two approaches: theory-driven, understanding-rich with data-driven discovery process. This paper describes a particular knowledge discovery
   algorithm Genetic Programming (GP). Additionally, an augmented version of GP - dimensionally aware GP - which is arguably more useful in the process of scientific discovery
   is described in great detail. Finally, the paper concludes with an application of dimensionally aware GP to a problem of induction of an empirical relationship describing
   the additional resistance to flow induced by flexible vegetation.
%8 January
%Z dimensionally aware GP. Additional river water flow resistance caused by flexible vegetation closure and strong typing (STGP). dimensionally aware brood selection.
   Kutija-Hong model.

%A Vladan Babovic
%A H. Bergmann
%T On Computer-Aided Discovery of Knowledge in Hydraulic Engineering
%B Advances in Hydraulic Research and Engineering
%E H. Bergmann
%D 2000
%I Technical University Graz
%C Graz
%K genetic algorithms, genetic programming

%A Vladan Babovic
%A Maarten Keijzer
%T On the introduction of declarative bias in knowledge discovery computer systems
%B New paradigms in river and estuarine management
%E Peter Goodwin
%D 2001
%I Kluwer
%K genetic algorithms, genetic programming
%Z Goodwin gives a short description of the workshop J. Hydraul. Eng. 127, 792 (2001); http://dx.doi.org/10.1061/(ASCE)0733-9429(2001)127:10(792) (2 pages)

%A Vladan Babovic
%A Maarten Keijzer
%A David Rodriguez Aquilera
%A Joe Harrington
%T An evolutionary approach to knowledge induction: Genetic Programming in Hydraulic Engineering
%B Proceedings of the World Water and Environmental Resources Congress
%E Don Phelps and Gerald Sehlke
%V 111
%D 2001
%P 64--64
%I ASCE
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://link.aip.org/link/?ASC/111/64/1
%X The process of scientific discovery has long been viewed as the pinnacle of creative thought. Thus, to many people, including some scientists themselves is seems unlikely
   candidate for automation by computer. However, over the past two decades researchers in AI have repeatedly questioned this attitude. The paper describes a specific
   evolutionary algorithm technique ? genetic programming ? within a scientific discovery framework, as well as its application on real world data.
%8 20-24  May
%Z World Water Congress 2001 number = 40569

%A Vladan Babovic
%A Jean-Philippe Drecourt
%A Maarten Keijzer
%A Peter Friis Hansen
%T Modelling of water supply assets: a data mining approach
%J Urban Water
%V 4
%N 4
%D 2002
%P 401--414
%I Elsevier
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6VR2-4718F0J-1/2/e361659261f99d438f8f2207f67eedf8

%A Vladan Babovic
%A Maarten Keijzer
%T Rainfall Runoff Modelling based on Genetic Programming
%J Nordic Hydrology
%V 33
%N 5
%D 2002
%P 331--346
%I 
%K genetic algorithms, genetic programming
%U http://www.iwaponline.com/nh/033/0331/0330331.pdf
%X The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and
   effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts
   of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases it
   is difficult to collect all the data necessary for such a model. By using data driven models such as genetic programming (GP), one can attempt to model runoff on the basis
   of available hydrometeorological data. This work addresses use of genetic programming for creating rainfall-runoff models on the basis of data alone, as well as in
   combination with conceptual models (i.e taking advantage of knowledge about the problem domain).

%A Vladan Babovic
%T Data mining in hydrology
%J Hydrological Processes
%V 19
%N 7
%D 2005
%P 1511--1515
%I 
%K genetic algorithms, genetic programming
%X Present-day instrumentation networks already provide immense quantities of data, very little of which provide any insight into the basic physical phenomena that are
   occurring in the medium measured. In order to exploit fully the information contained in the data, scientists are developing a suite of techniques to 'mine the knowledge'
   from data.
%8 30  April
%Z Invited Commentary

%A Vladan Babovic
%A Maarten Keijzer
%T Rainfall-Runoff Modeling Based on Genetic Programming
%B Encyclopedia of Hydrological Sciences
%E Malcolm G. Anderson and Keith Beven and et al.
%D 2006
%I Wiley
%K genetic algorithms, genetic programming, Hydroinformatics, symbolic regression, empirical equations, rainfall-runoff
%U http://onlinelibrary.wiley.com/doi/10.1002/0470848944.hsa017/abstract
%X The runoff formation process is believed to be highly nonlinear, time varying, spatially distributed, and not easily described by simple models. Considerable time and
   effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts
   of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases, it
   is difficult to collect all the data necessary for such a model. By using data-driven models such as genetic programming (GP), one can attempt to model runoff on the basis
   of available hydrometeorological data. This work addresses the use of GP for creating rainfall-runoff (R-R) models both on the basis of data alone, as well as in
   combination with conceptual models (i.e taking advantage of knowledge about the problem domain).
%8 15  April

%A Vladan Babovic
%T Data-Driven Knowledge Discovery: Four Roads to Vegetation-Induced Roughness Formulae
%B Numerical Modelling of Hydrodynamics for Water Resources: Proceedings of the International Workshop on Numerical Modelling of Hydrodynamic Systems
%E Pilar Garcia Navarro and Enrique Playan
%D 2007
%P 67--76
%I Taylor \& Franics, Balkema
%C Zaragoza, Spain
%K genetic algorithms, genetic programming
%U http://www.amazon.com/Numerical-Modelling-Hydrodynamics-Water-Resources/dp/0415440564/ref=cm_cr_pr_pb_t
%8 18-21  June
%Z http://www.unizar.es/nmhs/programme/programme.htm published 2008?
%@ 0-415-44056-4

%A Vladan Babovic
%T Introducing knowledge into learning based on genetic programming
%J Journal of Hydroinformatics
%V 11
%N 3-4
%D 2009
%P 181--193
%I 
%K genetic algorithms, genetic programming, empirical equations, hydraulics, sediment transport, strong typing, symbolic regression, units of measurement
%U http://www.iwaponline.com/jh/011/0181/0110181.pdf
%X This work examines various methods for creating empirical equations on the basis of data while taking advantage of knowledge about the problem domain. It is demonstrated
   that the use of high level concepts aid in evolving equations that are easier to interpret by domain specialists. The application of the approach to real-world problems
   reveals that the use of such concepts results in equations with performance equal or superior to that of human experts. Finally, it is argued that the algorithm is best
   used as a hypothesis generator assisting scientists in the discovery process.

%A Vladan Babovic
%A Raghuraj Rao
%T Evolutionary Computing in Hydrology
%B Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting
%E Bellie Sivakumar and Ronny Berndtsson
%D 2010
%P 347--369
%I World Scientific Publishing Co.
%C Singapore
%K genetic algorithms, genetic programming
%U http://ebooks.worldscinet.com/ISBN/9789814307987/9789814307987_0007.html
%X Many hydrologic processes are believed to be highly complex, nonlinear, time-varying, and spatially distributed. Hence, the governing mechanisms are not easily described by
   simple models. With unprecedented growth in instrumentation technology, recent investigations in hydrology are supported with immense quantities of data. In order to take
   full advantage of the information contained in such data, scientists are increasingly relying on a suite of data-driven techniques to understand the complex hydrologic
   processes. Evolutionary computing (EC) techniques, with a host of optimisation and modelling tools, can contribute significantly to achieve the objectives of this
   knowledge-discovery exercise in hydrology. This chapter discusses the utility of these EC techniques in attempting data analysis and modeling problems associated with
   hydrologic systems. It introduces the concept and working principle of EC techniques in general and reviews their applications to different domains of hydrology. The study
   also illustrates different case studies of genetic programming (GP) technique as a modelling, data assimilation, and model emulation tool
%O 7
%Z http://www.worldscibooks.com/environsci/7783.html
%@ 981-4307-97-1

%A B. V. Babu
%A S. Karthik
%T Genetic Programming for Symbolic Regression of Chemical Process Systems
%J Engineering Letters
%V 14
%N 2
%D 2007
%P 42--55
%I International Association of Engineers
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.8378
%X The novel evolutionary artificial intelligence formalism namely, genetic programming (GP) a branch of genetic algorithms is used to develop mathematical models based on
   input-output data, instead of conventional regression and neural network modeling techniques which are commonly used for this purpose. This paper summarizes the available
   MATLAB toolboxes and their features. Glucose to gluconic acid batch bioprocess has been modeled using both GPLAB and hybrid approach of GP and Orthogonal Least Square
   method (GP OLS). GP OLS which is capable of pruning of trees has generated parsimonious expressions simpler to GPLAB, with high fitness values and low mean square error
   which is an indicative of the good prediction accuracy. The capability of GP OLS to generate non-linear input-output dynamic systems has been tested using an example of
   fed-batch bioreactor. The simulation and GP model prediction results indicate GP OLS is an efficient and fast method for predicting the order and structure for non-linear
   input and output model.
%8 June
%Z http://www.engineeringletters.com/

%T GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Jaume Bacardit and Ivan Tanev and Joern Mehnen and Thomas Bartz-Beielstein and David Davis and Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen and Daniele
   Loiacono and Albert Orriols-Puig and Ryan Urbanowicz and Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis and Stephen L. Smith and
   Stefano Cagnoni and Robert Patton and William Rand and Forrest Stonedahl and Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward and Maria J. Blesa and
   Christian Blum and Steven Gustafson and Ekaterina Vladislavleva and Mark Hauschild and Martin Pelikan and Ender Ozcan and Andrew J. Parkes and Jonathan Rowe and Pascal
   Bouvry and Samee U. Khan and Gregoire Danoy and Alexandru-Adrian Tantar and Emilia Tantar and Bernabe Dorronsoro and Miguel Nicolau and Darrell Whitley
%D 2011
%I New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Bioinformatics, computational,
   systems, and synthetic biology, Digital entertainment technologies and arts, Evolutionary combinatorial optimization and metaheuristics, Estimation of distribution
   algorithms, Evolutionary multiobjective optimization, Evolution strategies and evolutionary programming, Genetics based machine learning, Generative and developmental
   systems, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory, Evolutionary computation in practice,
   Evolutionary computation techniques for constraint handling, Fourteenth international workshop on learning classifier systems, Computational intelligence on consumer games
   and graphics hardware (CIGPU), Medical applications of genetic and evolutionary computation (MedGEC), Evolutionary computation and multi-agent systems and simulation
   (ECoMASS) - fifth annual workshop, 1st workshop on evolutionary computation for designing generic algorithms, Bio-inspired solutions for wireless sensor networks (GECCO
   BIS-WSN 2011), 3rd symbolic regression and modeling workshop for GECCO 2011, Optimization by building and using probabilistic models (OBUPM-2011), Scaling behaviours of
   landscapes, parameters and algorithms, GreenIT evolutionary computation, Graduate students workshop, Late breaking abstracts, Specialized techniques and applications,
   Tutorials
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Brandon M. Bachman
%T Using the Genetic Algorithm with a Variable Length Genome for Architectural
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 33--39
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Thomas Back
%A U. Hammel
%A H.-P. Schwefel
%T Evolutionary computation: comments on the history and current state
%J IEEE Transactions on Evolutionary Computation
%V 1
%N 1
%D 1997
%P 3--17
%I 
%K genetic algorithms, genetic programming, EA, CS, evolutionstrategies, EP
%U http://ls11-www.cs.uni-dortmund.de/people/schwefel/publications/BHS97.ps.gz
%X Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article
   surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different
   approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming
   (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a
   brief overview on the manifold of application domains, although this necessarily must remain incomplete
%8 April
%Z Reference Cited: 220 CODEN: ITEVF5

%A Thomas Back
%A David B. Fogel
%A Darrell Whitley
%A Peter J. Angeline
%T Mutation operators
%B Evolutionary Computation 1 Basic Algorithms and Operators
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 2000
%P 237--255
%I Institute of Physics Publishing
%C Bristol
%K genetic algorithms, genetic programming
%O 32
%Z http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=IP274 Section 32.5 Parse trees p248--250. Grow, shrink, switch, cycle, Gaussian mutation of numbers,
   enforce size limit, enforce type match (STGP \citemontana:stgpEC)
%@ 0-7503-0664-5

%A Gerriet Backer
%T Learning with missing data using Genetic Programming
%B The 1st Online Workshop on Soft Computing (WSC1)
%D 1996
%I Nagoya University, Japan
%I Research Group on ECOmp of the Society of Fuzzy Theory and Systems (SOFT)
%C http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/
%K genetic algorithms, genetic programming, Machine learning, Missing data, Strongly Typed Genetic Programming STGP
%U http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/files/backer.ps.gz
%X Learning with imprecise or missing data has been a major challenge for machine learning. A new approach using Strongly Typed Genetic Programming is proposed here, which
   uses extra computations based on other input data to approximate the missing values. It eliminates the need for pre-processing and makes use of correlations between the
   input data. The decision process itself and the handling of unknown data can be extracted from the resulting program for an analysis afterwards. Comparing it to an
   alternative approach on a simple example shows the usefulness of this approach.
%8 19--30  August
%Z Adds "unknown" data type to STGP. demo on iris classification problem (see discussion on WSC1 pages) email WSC1 organisers wsc@bioele.nuee.nagoya-u.ac.jp

%A Kjell Backman
%A Palle Dahlstedt
%T A Generative Representation for the Evolution of Jazz Solos
%B Proceedings of EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops
%S Lecture Notes in Computer Science
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Rolf Drechsler and Anik\'o Ek\'art and Anna Esparcia-Alc\'azar and Muddassar Farooq and
   Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang
%V 4974
%D 2008
%P 371--380
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%X This paper describes a system developed to create computer based jazz improvisation solos. The generation of the improvisation material uses interactive evolution, based on
   a dual genetic representation: a basic melody line representation, with energy constraints ("rubber band") and a hierarchic structure of operators that processes the
   various parts of this basic melody. To be able to listen to and evaluate the result in a fair way, the computer generated solos have been imported into a musical
   environment to form a complete jazz composition. The focus of this paper is on the data representations developed for this specific type of music. This is the first
   published part of an ongoing research project in generative jazz, based on probabilistic and evolutionary strategies.
%8 26-28  March

%A Mohamed Bahy Bader-El-Den
%A Riccardo Poli
%T A GP-based hyper-heuristic framework for evolving 3-SAT heuristics
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1749--1749
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming: Poster, heuristics, hyper heuristic, SAT
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1749.pdf
%X We present, GP-HH, a framework for evolving local search 3-SAT heuristics based on GP. Evolved heuristics are compared against well-known SAT solvers with very encouraging
   results.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Mohamed Bader-El-Den
%A Riccardo Poli
%T Generating SAT Local-Search Heuristics using a GP Hyper-Heuristic Framework
%B Evolution Artificielle, 8th International Conference
%S Lecture Notes in Computer Science
%E Nicolas Monmarch\'e and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton
%V 4926
%D 2007
%P 37--49
%I Springer
%C Tours, France
%K genetic algorithms, genetic programming
%X We present GP-HH, a framework for evolving local-search 3-SAT heuristics based on GP. The aim is to obtain disposable heuristics which are evolved and used for a specific
   subset of instances of a problem. We test the heuristics evolved by GP-HH against well-known local-search heuristics on a variety of benchmark SAT problems. Results are
   very encouraging.
%8 29-31  October
%Z EA'07

%A Mohamed Bahy Bader-El-Den
%A Riccardo Poli
%T Inc*: An Incremental Approach for Improving Local Search Heuristics
%B Proceedings of the 8th European Conference, Evolutionary Computation in Combinatorial Optimization, EvoCOP
%S Lecture Notes in Computer Science
%E Jano I. van Hemert and Carlos Cotta
%V 4972
%D 2008
%P 194--205
%I Springer
%C Naples, Italy
%K genetic algorithms, genetic programming
%8 March  26-28
%Z also known as \citeconf/evoW/Bader-El-DenP08

%A Mohamed Bader-El-Den
%A Riccardo Poli
%T Analysis and Extension of the Inc* on the Satisfiability Testing Problem
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 3342--3349
%I IEEE
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, SAT
%X Inc (star) is a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall
   performance of the heuristic. The general idea of the algorithm is the following. Rather than attempting to directly solve a difficult problem, the algorithm dynamically
   chooses a smaller instance of the problem, and then increases the size of the instance only after the previous simplified instances have been solved, until the full size of
   the problem is reached. Genetic programming is used to discover new strategies for Inc<sup>*</sup>. Preliminary experiments on the satisfiability problem (SAT) problem have
   shown that Inc<sup>*</sup> is a competitive approach. In this paper we enhance Inc<sup>*</sup> and we experimentally test it on larger set of benchmarks, including big
   instances of SAT. Furthermore, we provide an analysis of the algorithm's behaviour.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Mohamed Bader-El-Den
%A Riccardo Poli
%T Evolving Effective Incremental Solvers for SAT with a Hyper-Heuristic Framework Based on Genetic Programming
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 163--179
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%O 11
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008. Also known as \citeEl-den:2008:GPTP

%A Mohamed Bader-El-Den
%A Riccardo Poli
%T Evolving Heuristics with Genetic Programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 601--602
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, heuristics, hyperheuristics, Inc*, SAT, Evolutionary combinatorial optimisation: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p601.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389212

%A Mohamed {Bader El Den}
%A Riccardo Poli
%T Grammar-Based Genetic Programming for Timetabling
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 2532--2539
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, hyperheuristics
%X We present a grammar-based genetic programming framework for the solving the timetabling problem via the evolution of constructive heuristics. The grammar used for
   producing new generations is based on graph colouring heuristics that have previously proved to be effective in constructing timetables as well as different slot allocation
   heuristics. The framework is tested on a widely used benchmarks in the field of exam time-tabling and compared with highly-tuned state-of-the- art approaches. Results shows
   that the framework is very competitive with other constructive techniques.
%8 18-21  May
%Z graph colouring, exam timetabling. Grammar used to control mixing of existing well established heuristics by GP to evolve a population of hyperheuristic. To cope with
   randomness in existing low level heuristics, each GP individual is run several times. CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number:
   CFP09ICE-CDR

%A Mohamed {Bader El Den}
%T Investigation of the role of Genetic Programming in a Hyper-Heuristic Framework for Combinatorial Optimization Problems
%R Ph.D. Thesis
%D 2009
%I 
%I School of Computer Science and Electronic Engineering, University of Essex
%C UK
%K genetic algorithms, genetic programming
%Z http://www.essex.ac.uk/csee/department/news/newsletter/28_09_09.aspx

%A Mohamed Bahy Bader-El-Den
%A Shaheen Fatima
%T Evolving Effective Bidding Functions for Auction based Resource Allocation Framework
%B International Conference on Evolutionary Computation (ICEC 2009)
%E Agostinho Rosa
%D 2009
%I 
%C Madeira, Portugal
%K genetic algorithms, genetic programming
%X In this paper, we present an auction based resource allocation framework. This framework, called GPAuc, uses genetic programming for evolving bidding functions. We describe
   GPAuc in the context of the exam timetabling problem (ETTP). In the ETTP, there is a set of exams, which must be assigned to a predefined set of slots. Here, the exam time
   tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot
   for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders'
   optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam
   time-tabling.
%8 5-7  October
%Z http://www.icec.ijcci.org/Abstracts/2009/ICEC_2009_Abstracts.htm

%A Mohamed Bahy Bader-El-Den
%A Riccardo Poli
%A Shaheen Fatima
%T Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework
%J Memetic Computing
%V 1
%N 3
%D 2009
%P 205--219
%I 
%K genetic algorithms, genetic programming, timetabling, Hyper-heuristics, Heuristics
%X This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is
   used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a
   good solution is found. The framework is tested on some of the most widely used benchmarks in the field of exam timetabling and compared with the best state-of-the-art
   approaches. Results show that the framework is very competitive with other constructive techniques, and did outperform other hyper-heuristic frameworks on many occasions.

%A Mohamed Bader-El-Den
%A Shaheen Fatima
%T Genetic Programming for Auction Based Scheduling
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 256--267
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X In this paper, we present a genetic programming (GP) framework for evolving agent's binding function (GPAuc) in a resource allocation problem. The framework is tested on
   the exam timetabling problem (ETP). There is a set of exams, which have to be assigned to a predefined set of slots and rooms. Here, the exam time tabling system is the
   seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such
   that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders' optimal bids. This is done
   using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam timetabling.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Khaled M. S. Badran
%A Peter I. Rockett
%T The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programming
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1551--1558
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, bloat, diversity preservation, multiobjective optimisation, population collapse
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1551.pdf
%X It has been observed previously that genetic programming populations can collapse to all single node trees when a parsimony measure (tree node count) is used in a
   multiobjective setting. We have investigated the circumstances under which this can occur for both the 6-parity boolean learning task and a range of benchmark machine
   learning problems. We conclude that mutation is an important and we believe a hitherto unrecognised factor in preventing population collapse in multiobjective genetic
   programming; without mutation we routinely observe population collapse. From systematic variation of the mutation operator, we conclude that a necessary condition to avoid
   collapse is that mutation produces, on average, an increase in tree sizes (bloating) at each generation which is then counterbalanced by the parsimony pressure applied
   during selection. Finally, we conclude that the use of a genotype diversity preserving mechanism is ineffective at preventing population collapse.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Khaled M. S. Badran
%A Peter Rockett
%T Integrating Categorical Variables with Multiobjective Genetic Programming for Classifier Construction
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 301--311
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Khaled Badran
%A Peter I. Rockett
%T The influence of mutation on population dynamics in multiobjective genetic programming
%J Genetic Programming and Evolvable Machines
%V 11
%N 1
%D 2010
%P 5--33
%I 
%K genetic algorithms, genetic programming, Multiobjective genetic programming, Population collapse, Mutation, Population dynamics, MOGP, bloat
%X Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but can sometimes lead to undesirable collapse of the
   population to all single-node trees. In this paper we report a detailed examination of why and when collapse occurs. We have used different types of crossover and mutation
   operators (depth-fair and sub-tree), different evolutionary approaches (generational and steady-state), and different datasets (6-parity Boolean and a range of benchmark
   machine learning problems) to strengthen our conclusion. We conclude that mutation has a vital role in preventing population collapse by counterbalancing parsimony pressure
   and preserving population diversity. Also, mutation controls the size of the generated individuals which tends to dominate the time needed for fitness evaluation and
   therefore the whole evolutionary process. Further, the average size of the individuals in a GP population depends on the evolutionary approach employed. We also demonstrate
   that mutation has a wider role than merely culling single-node individuals from the population; even within a diversity-preserving algorithm such as SPEA2 mutation has a
   role in preserving diversity.
%8 March
%Z Steady-state algorithm depth-fair crossover/depth-fair mutation

%A Khaled Badran
%A Peter Rockett
%T Multi-class pattern classification using single, multi-dimensional feature-space feature extraction evolved by multi-objective genetic programming and its application to
   network intrusion detection
%J Genetic Programming and Evolvable Machines
%V 13
%N 1
%D 2012
%P 33--63
%I 
%K genetic algorithms, genetic programming, Multi-class pattern classification, Feature extraction, Feature selection, Multi-objective genetic programming
%X In this paper we investigate using multi-objective genetic programming to evolve a feature extraction stage for multiple-class classifiers. We find mappings which transform
   the input space into a new, multi-dimensional decision space to increase the discrimination between all classes; the number of dimensions of this decision space is
   optimised as part of the evolutionary process. A simple and fast multi-class classifier is then implemented in this multi-dimensional decision space. Mapping to a single
   decision space has significant computational advantages compared to k -class-to-2-class decompositions; a key design requirement in this work has been the ability to
   incorporate changing priors and/or costs associated with mislabelling without retraining. We have employed multi-objective optimization in a Pareto framework incorporating
   solution complexity as an independent objective to be minimised in addition to the main objective of the misclassification error. We thus give preference to simpler
   solutions which tend to generalise well on unseen data, in accordance with Occam's Razor. We obtain classification results on a series of benchmark problems which are
   essentially identical to previous, more complex decomposition approaches. Our solutions are much simpler and computationally attractive as well as able to readily
   incorporate changing priors/costs. In addition, we have also applied our approach to the KDD-99 intrusion detection dataset and obtained results which are highly
   competitive with the KDD-99 Cup winner but with a significantly simpler classification framework.
%O Special Section on Evolutionary Algorithms for Data Mining
%8 March

%A Hyeon Bae
%A Tae-Ryong Jeon
%A Sungshin Kim
%A Hyun-Soo Kim
%A DongSeop Kim
%A Seung Soo Han
%A Gary S. May
%T Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling
%J Soft Computing - A Fusion of Foundations, Methodologies and Applications
%V 14
%N 2
%D 2010
%P 161--169
%I 
%K genetic algorithms, genetic programming, Neural network, Particle swarm optimization, Silicon solar cell fabrication
%X This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell
   fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in
   solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter
   diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet
   resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to
   search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the
   fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the
   second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five
   variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of
   nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated
   applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.

%A Patrick Van Bael
%A Dirk Devogelaere
%A M. Rijckaert
%T The Job Shop Problem Solved with Simple, Basic Evolutionary Search Elements
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 665--669
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Guy Baele
%A Nicolas Bredeche
%A Evert Haasdijk
%A Steven Maere
%A Nico Michiels
%A Yves {Van de Peer}
%A Christopher Schwarzer
%A Ronald Thenius
%T Open-Ended On-Board Evolutionary Robotics for Robot Swarms
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P -
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X The SYMBRION project stands at the crossroads of Artificial Life and Evolutionary Robotics: a swarm of real robots undergoes online evolution by exchanging information in a
   decentralized Evolutionary Robotics Scheme: the diffusion of each individual's genotype depends both on its ability to survive in an unknown environment as well as its
   ability to maximize mating opportunities during its lifetime, which suggests an implicit fitness. This paper presents early research and prospective ideas in the context of
   large-scale swarm robotics projects, focusing on the open-ended evolutionary approach in the SYMBRION project. One key issue of this work is to perform on-board evolution
   in a spatially distributed population of robots. A real-world experiment is also described which yields important considerations regarding open-ended evolution with real
   autonomous robots.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Ebrahim Bagheri
%A Hossein Deldari
%T Dejong Function Optimization by Means of a Parallel Approach to Fuzzified Genetic Algorithm
%B Proceedings of the 11th IEEE Symposium on Computers and Communications (ISCC 2006)
%E Paolo Bellavista and Chi-Ming Chen and Antonio Corradi and Mahmoud Daneshmand
%D 2006
%P 675--680
%I IEEE Computer Society
%C Cagliari, Sardinia, Italy
%K genetic algorithms
%X Genetic Algorithms are very powerful search methods that are used in different optimisation problems. Parallel versions of genetic algorithms are easily implemented and
   usually increase algorithm performance [4]. Fuzzy control as another optimisation solution along with genetic algorithms can significantly increase algorithm performance.
   Two variations for genetic algorithm and fuzzy system composition exist. In the first approach Genetic algorithms are used to optimise and model the structure of fuzzy
   systems through knowledge base or membership function design while the second approach exploits fuzzy to dynamically supervise genetic algorithm performance by speedily
   reaching an optimal solution. In this paper we propose a new method for fuzzy parallel genetic algorithms, in which a parallel client-server single population fuzzy genetic
   algorithm is configured to optimise the performance of the first three Dejong functions in order to reach a global solution in the least possible iterations. Simulations
   show much improvement in genetic algorithm performance evaluation.
%8 26-29  June
%@ 0-7695-2588-1

%A Stefania Baglioni
%A Celia da Costa Pereira
%A Dario Sorbello
%A Andrea G. B. Tettamanzi
%T An Evolutionary Approach to Multiperiod Asset Allocation
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 225--236
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=225
%X Portfolio construction can become a very complicated problem, as regulatory constraints, individual investor's requirements, non-trivial indices of risk and subjective
   quality measures are taken into account, together with multiple investment horizons and cash-flow planning. This problem is approached using a tree of possible scenarios
   for the future, and an evolutionary algorithm is used to optimize an investment plan against the desired criteria and the possible scenarios. An application to a real
   defined benefit pension fund case is discussed.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A A. J. Bagnall
%A G. D. Smith
%T Using an Adaptive Agent to Bid in a Simplified Model of the UK Market in Electricity
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 774
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bris.ac.uk/~kovacs/lcs.archive/Bagnall1999b.ps.gz
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Antoine B. Bagula
%A Hong F. Wang
%T On the Relevance of Using Gene Expression Programming in Destination-Based Traffic Engineering
%B Computational Intelligence and Security
%S Lecture Notes in Computer Science
%V 3801
%D 2005
%P 224--229
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming
%X This paper revisits the problem of Traffic Engineering (TE) to assess the relevance of using Gene Expression Programming (GEP) as a new fine-tuning algorithm in
   destination-based TE. We present a new TE scheme where link weights are computed using GEP and used as fine-tuning parameters in destination-based path selection. We apply
   the newly proposed TE scheme to compute the routing paths for the traffic offered to a 23- and 30-node test networks under different traffic conditions and differentiated
   services situations. We evaluate the performance achieved by the GEP algorithm compared to a memetic and the Open Shortest Path First (OSPF) algorithms in a simulated
   routing environment using the NS packet level simulator. Preliminary results reveal the relative efficiency of GEP compared to the memetic algorithm and OSPF routing.

%A Antoine B. Bagula
%T Traffic Engineering Next Generation IP Networks Using Gene Expression Programming
%B 10th IEEE/IFIP Network Operations and Management Symposium, NOMS 2006
%D 2006
%P 230--239
%I IEEE
%I IFIP
%C Vancouver
%K genetic algorithms, genetic programming, Gene Expression Programming
%X This paper addresses the problem of Traffic Engineering (TE) to evaluate the performance of evolutionary algorithms when used as IP routing optimisers and assess the
   relevance of using "Gene Expression Programming (GEP)" as a new fine-tuning algorithm in destination- and flow-based TE. We consider a TE scheme where link weights are
   computed using GEP and used as either fine-tuning parameters in Open Shortest Path First (OSPF) routing or static routing cost in Constraint Based Rouiigg((CRR. Thh
   reeuutligg SPFa nd CBR algorithms are referred to as OSPFgepand CBRgep. The GEP algorithm is based on a hybrid optimisation model where local search complements the global
   search implemented by classical evolutionary algorithms to improve the genetic individuals fitness through hill-climbing. We apply the newly proposed TE scheme to compute
   the routing paths for the traffic offered to a 23-, 28- and 30-node test networks under different traffic conditions and differentiated services situations. We evaluate the
   performance achieved by the OSPFgep, CBRgepalgorithms and OSPFmal, a destination-based routing algorithm where OSPF path selection is driven by the link weights computed by
   a Memetic Algorithm (MA). We compare the performance achieved by the OSPFgepalgorithm to the performance of the OSPFmaand OSPF algorithms in a simulated routing environment
   using NS. We also compare the quality of the paths found by the CBRgepalgorithm to the quality of the paths computed by the Constraint Shortest Path First (CSPF) algorithm
   when routing bandwidth-guaranteed tunnels using connection-level simulation.

%A Antoine B. Bagula
%T Hybrid Routing in Next Generation IP Networks: QoS Routing Mechanisms and Network Control Strategies
%R Ph.D. Thesis Doctoral Thesis
%D 2006
%I 
%I Royal Institute of Technology (KTH)
%C Stockholm, Sweden
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.diva-portal.org/diva/getDocument?urn_nbn_se_kth_diva-4213-2__fulltext.pdf
%8 Decemeber

%A Alireza Bahiraie
%A Noor {Akma bt Ibrahim}
%A A. K. M. Azhar
%T On the Predictability of Risk Box Approach by Genetic Programming Method for Bankruptcy Prediction
%J American Journal of Applied Sciences
%V 6
%N 9
%D 2009
%P 1748--1757
%I 
%K genetic algorithms, genetic programming, ratios analysis, risk box, bankruptcy prediction
%U http://www.scipub.org/fulltext/ajas/ajas691748-1757.pdf
%X \bf Problem statement: Theoretical based data representation is an important tool for model selection and interpretations in bankruptcy analysis since the numerical
   representation are much less transparent. Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-scalicity are solved
   in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. \bf Approach: This study presented new geometric
   technique for empirical analysis of bankruptcy risk using financial ratios. Within this framework, we proposed the use of a new ratio representation which named Risk Box
   measure (RB). We demonstrated the application of this geometric approach for variable representation, data visualization and financial ratios at different stages of
   corporate bankruptcy prediction models based on financial balance sheet ratios. These stages were the selection of variables (predictors), accuracy of each estimation model
   and the representation of each model for transformed and common ratios. \bf Results: We provided evidence of extent to which changes in values of this index were associated
   with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and direction of risk components. Results of Genetic
   Programming (GP) models were compared as different classification models and results showed the classifiers outperform by modified ratios.\bf Conclusion/Recommendations: In
   this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR) was proposed. Genetic programming method is
   substantially superior to the traditional methods such as MDA or Logistic method. It was strongly suggested the use of SR methodology for ratio analysis, which provided a
   conceptual and complimentary methodological solution to many problems associated with the use of ratios. Respectively, GP will provide heuristic non linear regression as a
   tool in providing forecasting regression for studies associated with financial data. Genetic programming as one of the modern classification method out performs by the use
   of modified ratios. Our new method would be a general methodological guideline associated with financial data analysis.

%A Haiying Bai
%A Noriko Yata
%A Tomoharu Nagao
%T Efficient evolutionary image processing using genetic programming: Reducing computation time for generating feature images of the Automatically Construction of
   Tree-Structural Image Transformation (ACTIT)
%B 10th International Conference on Intelligent Systems Design and Applications (ISDA 2010)
%D 2010
%P 302--307
%I 
%K genetic algorithms, genetic programming, ACTIT, automatically construction of tree-structural image transformation, evolutionary image processing, image feature filters,
   transformation images, image processing
%X Using well-established techniques of Genetic Programming (GP), we automatically optimise image feature filters over several inputs and within transformation images,
   improving the Automatic Construction of Tree-Structural Image Transformation (ACTIT) system. Our objective is to also produce optimal solutions in substantially less
   computation time than require for generating features of ACTIT. We improved the algorithm feature filters in the process through GP, which are expressed by trees in
   Automatic Construction of Tree-Structural Image Transformation, to reduce computation time. Through our experimentation, we show that our new approach is accurate and
   requires less computation time by maintaining the feature images in conjunction with the original images.
%8 November  29-  Decemeber  1
%Z Grad. Sch. of Environ. & Inf. Sci., Yokohama Nat. Univ., Yokohama, Japan. Also known as \cite5687249

%A Linge Bai
%A Manolya Eyiyurekli
%A David E. Breen
%T Self-organizing primitives for automated shape composition
%B IEEE International Conference on Shape Modeling and Applications, SMI 2008
%D 2008
%P 147--154
%I 
%K genetic algorithms, genetic programming, automated shape composition, cell behavior, chemical-field-driven aggregation, chemotaxis-driven aggregation behavior, cumulative
   chemical field, evolutionary computing process, fitness measure, macroscopic shape, mathematical function, morphogenic primitives, self-organizing primitive, shape
   formation, shape modeling, structure formation, computational geometry
%X Motivated by the ability of living cells to form into specific shapes and structures, we present a new approach to shape modeling based on self-organizing primitives whose
   behaviors are derived via genetic programming. The key concept of our approach is that local interactions between the primitives direct them to come together into a
   macroscopic shape. The interactions of the primitives, called morphogenic primitives (MP), are based on the chemotaxis-driven aggregation behaviors exhibited by actual
   living cells. Here, cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical
   field detected at its surface. MPs, though, do not attempt to completely mimic the behavior of real cells. The chemical fields are explicitly defined as mathematical
   functions and are not necessarily physically accurate. The explicit mathematical form of the chemical field functions are derived via genetic programming (GP), an
   evolutionary computing process that evolves a population of functions. A fitness measure, based on the shape that emerges from the chemical-field-driven aggregation,
   determines which functions will be passed along to later generations. This paper describes the cell interactions of MPs and the GP-based method used to define the chemical
   field functions needed to produce user- specified shapes from simple aggregating primitives.
%8 June
%Z Also known as \cite4547962

%A Linge Bai
%A Manolya Eyiyurekli
%A David E. Breen
%T Automated shape composition based on cell biology and distributed genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1179--1186
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, chemotaxis, distributed genetic programming, morphogenesis, self-organisation, shape composition
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1179.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389329

%A Linge Bai
%A Manolya Eyiyurekli
%A David E. Breen
%T An Emergent System for Self-Aligning and Self-Organizing Shape Primitives
%B Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO '08
%D 2008
%P 445--454
%I 
%K genetic algorithms, genetic programming, direct morphogenetic primitives, emergent behavior, emergent system, evolutionary computing, living cells, local interaction rules,
   natural phenomenon, self-aligning shape primitives, self-organizing shape primitives, simulation system, user-defined shape, computational geometry
%X Motivated by the natural phenomenon of living cells self-organizing into specific shapes and structures, we present an emergent system that uses evolutionary computing
   methods for designing and simulating self-aligning and self-organizing shape primitives.Given the complexity of the emergent behavior, genetic programming is employed to
   control the evolution of our emergent system. The system has two levels of description. At the macroscopic level, a user-specified, pre-defined shape is given as input to
   the system. The system outputs local interaction rules that direct morphogenetic primitives (MP) to aggregate into the shape. At the microscopic level, MPs follow
   interaction rules based only on local interactions. All MPs are identical and do not know the final shape to be formed. The aggregate is then evaluated at the macroscopic
   level for its similarity to the user-defined shape. In this paper, we present (1) an emergent system that discovers local interaction rules that direct MPs to form
   user-defined shapes, (2) the simulation system that implements these rules and causes MPs to self-align and self-organize into a user-defined shape, and (3) the robustness
   and scalability qualities of the overall approach.
%8 October
%Z Also known as \cite4663447

%A Stuart Bain
%A John Thornton
%A Abdul Sattar
%T Evolving Algorithms for Constraint Satisfaction
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 265--272
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Combinatorial \& numerical optimization
%U http://stuart.multics.org/publications/CEC2004.pdf
%X This paper proposes a framework for automatically evolving constraint satisfaction algorithms using genetic programming. The aim is to overcome the difficulties associated
   with matching algorithms to specific constraint satisfaction problems. A representation is introduced that is suitable for genetic programming and that can handle both
   complete and local search heuristics. In addition, the representation is shown to have considerably more flexibility than existing alternatives, being able to discover
   entirely new heuristics and to exploit synergies between heuristics. In a preliminary empirical study it is shown that the new framework is capable of evolving algorithms
   for solving the well-studied problem of boolean satisfiability testing.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A William Bains
%A Richard Gilbert
%A Lilya Sviridenko
%A Jose-Miguel Gascon
%A Robert Scoffin
%A Kris Birchall
%A Inman Harvey
%A John Caldwell
%T Evolutionary computational methods to predict oral bioavailability QSPRs
%J Current Opinion in Drug Discovery and Development
%V 5
%N 1
%D 2002
%P 44--51
%I 
%K genetic algorithms, genetic programming
%X This review discusses evolutionary and adaptive methods for predicting oral bioavailability (OB) from chemical structure. Genetic Programming (GP), a specific form of
   evolutionary computing, is compared with some other advanced computational methods for OB prediction. The results show that classifying drugs into 'high' and 'low' OB
   classes on the basis of their structure alone is solvable, and initial models are already producing output that would be useful for pharmaceutical research. The results
   also suggest that quantitative prediction of OB will be tractable. Critical aspects of the solution will involve the use of techniques that can: (i) handle problems with a
   very large number of variables (high dimensionality); (ii) cope with 'noisy' data; and (iii) implement binary choices to sub-classify molecules with behavior that are
   qualitatively different. Detailed quantitative predictions will emerge from more refined models that are hybrids derived from mechanistic models of the biology of oral
   absorption and the power of advanced computing techniques to predict the behavior of the components of those models in silico.
%8 January
%Z Amedis Pharmaceuticals. Review, Tutorial

%A William Bains
%A Antranig Basman
%A Cat White
%T HERG binding specificity and binding site structure: Evidence from a fragment-based evolutionary computing SAR study
%J Progress in Biophysics and Molecular Biology
%V 86
%N 2
%D 2004
%P 205--233
%I 
%K genetic algorithms, genetic programming, HERG, IKr, QSAR, Molecular descriptors, Prediction
%U http://www.sciencedirect.com/science/article/B6TBN-4BS4DJM-1/2/2bd8833742e401378469ee988d571705
%X We describe the application of genetic programming, an evolutionary computing method, to predicting whether small molecules will block the HERG cardiac potassium channel.
   Models based on a molecular fragment-based descriptor set achieve an accuracy of 85-90% in predicting whether the IC50 of a 'blind' set of compounds is <1 [mu]M. Analysis
   of the models provides a 'meta-SAR', which predicts a pharmacophore of two hydrophobic features, one preferably aromatic and one preferably nitrogen-containing, with a
   protonatable nitrogen asymmetrically situated between them. Our experience of the approach suggests that it is robust, and requires limited scientist input to generate
   valuable predictive results and structural understanding of the target.
%8 October
%Z Amedis, lilgp, Fixed weighted sum of ROC and Akaike fitness criterion (AIC) Many descriptors including not only description of compound but who and how measurements were
   made. GP run many times (1028+). http://www.elsevier.com/wps/find/journaldescription.cws_home/408/description#description Amedis Pharmaceuticals, Unit 162 Cambridge Science
   Park, Milton Road, Cambridge, UK p206 "...Fatal cardiac arrhythmias..." "HERG binds to many compounds." "Testing for HERG...effectively mandatory..." p207/p221 empirically
   derived penalty for "over-complex models, in order to prevent over-fitting" p211 "GP can select efficiently from a large number of input variables". p215 big difference
   between Training and validation ROC. Extracting chemically meaningful reasoning from evolved solutions. Relationship with possible mechanisms in HERG gap in cell wall. p226
   "we belive it is efficient use of the machine" [ie computer time].

%A Stephen D. Baird
%A Marcel Turcotte
%A Robert G. Korneluk
%A Martin Holcik
%T Searching for IRES
%J RNA
%V 12
%N 10
%D 2006
%P 1755--1785
%I RNA Society
%K genetic algorithms, genetic programming, IRES, RNA, secondary structure, prediction software
%X The cell has many ways to regulate the production of proteins. One mechanism is through the changes to the machinery of translation initiation. These alterations favor the
   translation of one subset of mRNAs over another. It was first shown that internal ribosome entry sites (IRESes) within viral RNA genomes allowed the production of viral
   proteins more efficiently than most of the host proteins. The RNA secondary structure of viral IRESes has sometimes been conserved between viral species even though the
   primary sequences differ. These structures are important for IRES function, but no similar structure conservation has yet to be shown in cellular IRES. With the advances in
   mathematical modeling and computational approaches to complex biological problems, is there a way to predict an IRES in a data set of unknown sequences? This review
   examines what is known about cellular IRES structures, as well as the data sets and tools available to examine this question. We find that the lengths, number of upstream
   AUGs, and %GC content of 5'-UTRs of the human transcriptome have a similar distribution to those of published IRES-containing UTRs. Although the UTRs containing IRESes are
   on the average longer, almost half of all 5'-UTRs are long enough to contain an IRES. Examination of the available RNA structure prediction software and RNA motif searching
   programs indicates that while these programs are useful tools to fine tune the empirically determined RNA secondary structure, the accuracy of de novo secondary structure
   prediction of large RNA molecules and subsequent identification of new IRES elements by computational approaches, is still not possible.
%8 October
%Z Paragraph on \citeYuh-JyhHu:2003:NAR PMCID: PMC1581980

%A Andrei Bajurnow
%A Vic Ciesielski
%T Function and terminal Set Selection for Evolving Goal Scoring Behaviour in Soccer Players
%B Proceedings of The First Asian-Pacific Workshop on Genetic Programming
%E Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan
%D 2003
%P 38--44
%I 
%C Rydges (lakeside) Hotel, Canberra, Australia
%K genetic algorithms, genetic programming
%8 8  Decemeber
%Z \citeaspgp03
%@ 0-9751724-0-9

%A Andrei Bajurnow
%A Vic Ciesielski
%T Layered Learning for Evolving Goal Scoring Behavior in Soccer Players
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 1828--1835
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Evolutionary intelligent agents, Evolutionary Computation and Games
%U http://goanna.cs.rmit.edu.au/~vc/papers/cec2004-bajurnow.pdf
%X Layered learning allows decomposition of the stages of learning in a problem domain. We apply this technique to the evolution of goal scoring behavior in soccer players and
   show that layered learning is able to find solutions comparable to standard genetic programs more reliably. The solutions evolved with layers have a higher accuracy but do
   not make as many goal attempts. We compared three variations of layered learning and find that maintaining the population between layers as the encapsulated learnt layer is
   introduced to be the most computationally efficient. The quality of solutions found by layered learning did not exceed those of standard genetic programming in terms of
   goal scoring ability.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A James Baker
%A Nuri Celik
%A Nobutaka Omaki
%A Jill Kobashigawa
%A Hyoung-Sun Youn
%A Magdy F. Iskander
%T On the design of integrated HF radar systems for Homeland Security applications
%B 2010 IEEE International Conference on Wireless Information Technology and Systems (ICWITS)
%D 2010
%I 
%K genetic algorithms, genetic programming, DSP algorithm, broadcast transmitter, clutter, coastal surveillance, electrically small antenna, homeland security, integrated HF
   radar system, optimum frequency channel, propagation modeling, terrain effect, military radar, national security, radar antennas, radar clutter, signal processing
%X In this paper, HCAC's research and development efforts on the development of integrated and low cost HF radar for coastal surveillance and other Homeland Security
   applications are summarised. The proposed design incorporates electrically small antenna for rapid deployment, supports operation on floating platforms by using enhanced
   DSP algorithms to mitigate clutter, incorporates improved propagation modelling to more accurately select optimum frequency channels based on atmospheric conditionxs and
   overcome the errors due to terrain effects, uses Genetic Programming for automatic target recognition and classification, and provides for passive radar operation using
   existing broadcast transmitters to enable covert operation.
%8 28  October -  September  3
%Z Also known as \cite5611859

%A Zohra Bakkoury
%T Feasibility Assessement and Optimal Scheduling of Water Supply Projects
%R Ph.D. Thesis
%D 2002
%I 
%I School of Engineering and Computer Science, Exeter University
%K genetic algorithms

%A Karthik Balakrishnan
%A Vasant Honavar
%T On Sensor Evolution in Robotics
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 455--460
%I MIT Press
%C Stanford University, CA, USA
%K Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 GA paper

%A Karthik Balakrishnan
%A Vasant Honavar
%T Spatial Learning for Robot Localization
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 389--397
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Artifical life and evolutionary robotics
%8 13-16  July
%Z GP-97

%A P. Balasubramaniam
%A A. Vincent Antony Kumar
%T Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming
%J Genetic Programming and Evolvable Machines
%V 10
%N 1
%D 2009
%P 71--89
%I 
%K genetic algorithms, genetic programming, Matrix Riccati differential equation, Nonlinear, Optimal control, Runge Kutta method, Singular system
%X In this paper, we propose a novel approach to find the solution of the matrix Riccati differential equation (MRDE) for nonlinear singular systems using genetic programming
   (GP). The goal is to provide optimal control with reduced calculation effort by comparing the solutions of the MRDE obtained from the well known traditional Runge Kutta
   (RK) method to those obtained from the GP method. We show that the GP approach to the problem is qualitatively better in terms of accuracy. Numerical examples are provided
   to illustrate the proposed method.
%8 March

%A Krisztian Balazs
%A Janos Botzheim
%A Laszlo T. Koczy
%T Hierarchical fuzzy system modeling by Genetic and Bacterial Programming approaches
%B IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010)
%D 2010
%P 1866--1871
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/Balazs_2010_ieee-fuzz.pdf
%X In this paper a method is proposed for constructing hierarchical fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve
   supervised machine learning problems. The resultant hierarchical rule base is the knowledge base, which is constructed by using structure constructing evolutionary
   techniques, namely, Genetic and Bacterial Programming Algorithms. Applying hierarchical fuzzy rule bases is a way of reducing the complexity of the knowledge base, whereas
   evolutionary methods ensure a relatively efficient learning process. This is the reason of the investigation of this combination.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5584220

%A Krisztian Balazs
%A Janos Botzheim
%A Laszlo T. Koczy
%T Hierarchical fuzzy system construction applying genetic and bacterial programming algorithms with expression tree building restrictions
%B World Automation Congress (WAC 2010)
%D 2010
%I 
%C Kobe, Japan
%K genetic algorithms, genetic programming, bacterial programming algorithm, black box system, genetic programming algorithm, hierarchical fuzzy rule system construction,
   input-output pairs, supervised machine learning problem, tree building restriction, fuzzy set theory, learning (artificial intelligence)
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5665326
%X In this paper various restrictions are proposed in the construction of hierarchical fuzzy rule bases by using Genetic and Bacterial Programming algorithms in order to model
   black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The properties (learning speed, accuracy) of the established systems
   are observed based on simulation results and they are compared to each other.
%8 19-23  September
%Z Also known as \cite5665326

%A Marton E. Balazs
%A Daniel L. Richter
%T A genetic algorithm with dynamic population: Experimental results
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 25--30
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms
%8 13  July
%Z GECCO-99LB

%A Robert Baldock
%A Kristina Shea
%T Structural Topology Optimization of Braced Steel Frameworks Using Genetic Programming
%B Intelligent Computing in Engineering and Architecture, 13th EG-ICE Workshop
%S Lecture Notes in Computer Science
%E Ian F. C. Smith
%V 4200
%D 2006
%P 54--61
%I Springer
%C Ascona, Switzerland
%K genetic algorithms, genetic programming
%X This paper presents a genetic programming method for the topological optimisation of bracing systems for steel frameworks. The method aims to create novel, but practical,
   optimally-directed design solutions, the derivation of which can be readily understood. Designs are represented as trees with one-bay, one-story cellular bracing units,
   operated on by design modification functions. Genetic operators (reproduction, crossover, mutation) are applied to trees in the development of subsequent populations. The
   bracing design for a three-bay, 12-story steel framework provides a preliminary test problem, giving promising initial results that reduce the structural mass of the
   bracing in comparison to previous published benchmarks for a displacement constraint based on design codes. Further method development and investigations are discussed.
%O Revised Selected Papers
%8 June  25-30
%Z (1) Engineering Design Centre, University of Cambridge, Cambridge, CB2 1PZ, UK (2) Product Development, Technical University of Munich, Boltzmannstrasse 15, D-85748
   Garching, Germany
%@ 3-540-46246-5

%A James F. Baldwin
%A Trevor P. Martin
%A James G. Shanahan
%T System Identification of Fuzzy Cartesian Granules Feature Models Using Genetic Programming
%B Fuzzy Logic in Artificial Intelligence, IJCAI'97 Workshop, Selected and Invited Papers
%S Lecture Notes in Artificial Intelligence
%E Anca L. Ralescu and James G. Shanahan
%V 1566
%D 1997
%P 91--116
%I Springer
%C Nagoya, Japan
%K genetic algorithms, genetic programming, artificial intelligence, fuzzy logic, IJCAI
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-164-22-1637718-0,00.html
%O Published 1999
%8 August  23-24
%Z DBLP, http://dblp.uni-trier.de DBLP:conf/ijcai/1997fl
%@ 3-540-66374-6

%A James F. Baldwin
%A Trevor P. Martin
%A James G. Shanahan
%T Controlling with words using automatically identified fuzzy Cartesian granule feature models
%J International Journal of Approximate Reasoning
%V 22
%N 1-2
%D 1999
%P 109--148
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V07-3XWJVTP-K/1/fca9fc7ee54707e1f2ed9847e29c1b7e
%X We present a new approach to representing and acquiring controllers based upon Cartesian granule features - multidimensional features formed over the cross product of words
   drawn from the linguistic partitions of the constituent input features - incorporated into additive models. Controllers expressed in terms of Cartesian granule features
   enable the paradigm "controlling with words" by translating process data into words that are subsequently used to interrogate a rule base, which ultimately results in a
   control action. The system identification of good, parsimonious additive Cartesian granule feature models is an exponential search problem. In this paper we present the
   G_DACG constructive induction algorithm as a means of automatically identifying additive Cartesian granule feature models from example data. G_DACG combines the powerful
   optimisation capabilities of genetic programming with a novel and cheap fitness function, which relies on the semantic separation of concepts expressed in terms of
   Cartesian granule fuzzy sets, in identifying these additive models. We illustrate the approach on a variety of problems including the modelling of a dynamical process and a
   chemical plant controller.

%A Joze Balic
%T Flexible Manufacturing Systems; Development - Structure - Operation - Handling - Tooling
%S Manufacturing technology
%D 1999
%I DAAAM International
%C Vienna
%K genetic algorithms, genetic programming
%U http://www.amazon.com/Contribution-integrated-manufacturing-Publishing-Manufacturing/dp/3901509038/ref=sr_1_1?ie=UTF8&s=books&qid=1254069037&sr=1-1
%Z Chapter 9.4 Using genetic algorithms for modelling of manufacturing processes. Described are: - overview of GP - modelling of forming process by GP - GA approach for
   optimisation of cutting conditions copies can be obtained from publication@daaam.com
%@ 3-901509-03-8

%A Joze Balic
%A Miran Brezocnik
%A Franci Cus
%T Modeling Of Mechanical Parts Compositions Using Genetic Programming
%D 2000
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/cache/papers/cs/13061/http:zSzzSzwww.faim2000.isr.umd.eduzSzfaimzSzexportzSz27e8am-b.pdf/modeling-of-mechanical-parts.pdf
%X The paper is a contribution to introducing biologically oriented ideas in conceiving a new and innovative idea in modern factories of the future. The intelligent system is
   treated as an autonomous organization structure efficiently adapting itself to the dynamic changes in the micro and macro environment. Simulation of self-organizing
   (genetic) composition of elementary (basic) mechanical parts into the product is presented as an example of the intelligent system. The genetic programming method is used.
   For conceiving the genetically based composition of parts, the parallels from the living systems are used. Composition takes place on the basis of the genetic content in
   the basic components and the influence of the environment. The genetically based composition takes place in a distributed way, non-deterministically, buttom-up, and in a
   self-organizing manner. The paper is also a contribution to the international research and development program Intelligent Manufacturing Systems which is one of the biggest
   projects ever introduced.
%O The Pennsylvania State University CiteSeer Archives
%Z not verified University of Maribor, Faculty of Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems, SI-2000 Maribor, Slovenia; University of Maribor,
   Faculty of Mechanical Engineering, Laboratory for Researches in Cutting, SI-2000 Maribor, Slovenia

%A J. Balic
%A M. Nastran
%T An on-line predictive system for steel wire straightening using genetic programming
%J Engineering Applications of Artificial Intelligence
%V 15
%N 6
%D 2002
%P 559--565
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V2M-48BKR53-2/2/4a53f22927ad32b0580540322d7c8868
%X Dimensional stability of forming processes is becoming more and more important in the modern production world. Especially when mass production is concerned, the
   technological system has to be reliable and accurate. Growing market demands are forcing production engineers towards process optimisation in order to achieve high
   machinery efficiency and reduce the production costs. An important precondition for improving the process chain is the prediction of process behaviour in advance. The paper
   is presenting the use of genetic programming to predict the wire geometry after forming. The results can be used as the basis for later optimisation of forming processes.

%A Joze Balic
%A Marjan Korosec
%T Intelligent tool path generation for milling of free surfaces using neural networks
%J International Journal of Machine Tools and Manufacture
%V 42
%N 10
%D 2002
%P 1171--1179
%I 
%K Neural network, CAD/CAM system, CAPP, ICAM, Milling strategy
%U http://www.sciencedirect.com/science/article/B6V4B-45YG41B-6/2/09eff48a04f9b22be6b2ed2dd0e6d3b1
%X The presented paper has an intention to show how with the help of Artificial Neural Network (ANN), the prediction of milling tool-path strategy could be made in order to
   establish which milling path strategy or their sequence will show the best results (will be the most appropriate) at free surface machining, according to set technological
   aim. In our case the best possible surface quality of machined surface was taken as the primary technological aim. Configuration of used Neural Network (NN) is presented,
   and the whole procedure is shown on an example of mould, for producing light switches. The verification of machined surface quality, according to average mean roughness,
   Ra, is also being done, and compared with the NN predicted results [COBISS.SI-ID 7318550]
%Z Not on GP

%A Joze Balic
%A Miha Kovacic
%A Bostjan Vaupotic
%T Intelligent Programming of CNC Turning Operations using Genetic Algorithm
%J Journal of intelligent manufacturing
%V 17
%N 3
%D 2006
%P 331--340
%I 
%K genetic algorithms, genetic programming, CNC programming, GA, Intelligent CAM, Turning, Tool path generation
%X CAD/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and generation of CNC programs for machine tools. The
   aim of our research is the design of a computer-aided, intelligent and genetic algorithm(GA) based programming system for CNC cutting tools selection, tool sequences
   planning and optimisation of cutting conditions. The first step is geometrical feature recognition and classification. On the basis of recognised features the module for
   GA-based determination of technological data determine cutting tools, cutting parameters (according to work piece material and cutting tool material) and detailed tool
   sequence planning. Material, which will be removed, is split into several cuts, each consisting of a number of basic tool movements. In the next step, GA operations such as
   reproduction, crossover and mutation are applied. The process of GA-based optimisation runs in cycles in which new generations of individuals are created with increased
   average fitness of a population. During the evaluation of calculated results (generated NC programmes) several rules and constraints like rapid and cutting tool movement,
   collision, clamping and minimum machining time, which represent the fitness function, were taken into account. A case study was made for the turning operation of a
   rotational part. The results show that the GA-based programming has a higher efficiency. The total machining time was reduced by 16percent. The demand for a high skilled
   worker on CAD/CAM systems and CNC machine tools was also reduced.
%8 June

%A Jerzy Marian Balicki
%T Multi-Criterion Genetic Programming With Negative Selection for Finding Pareto Solutions
%B Proceedings of the Second International Conference on Software and Data Technologies, ICSOFT 2007
%E Joaquim Filipe and Boris Shishkov and Markus Helfert
%D 2007
%P 120--127
%I INSTICC Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Multi-criterion genetic programming (MGP) is a relatively new approach for a decision making aid and it can be applied to determine the Pareto solutions. This purpose can
   be obtained by formulation of a multi-criterion optimization problem that can be solved by genetic programming. An improved negative selection procedure to handle
   constraints in the MGP has been proposed. In the test instance, both a workload of a bottleneck computer and the cost of system are minimized; in contrast, a reliability of
   the distributed system is maximized.
%8 22-25  July
%Z http://www.icsoft.org/ICSOFT2007/Area3.htm Distributed and Parallel Systems ICSOFT 2007 was held in conjunction with ENASE 2007 Others www.lania.mx/~ccoello/EMOObib.html ?

%A Shumeet Baluja
%A Dean Pomerleau
%A Todd Jochem
%T Towards Automated Artificial Evolution for Computer-generated Images
%J Connection Science
%V 6
%N 2 and 3
%D 1994
%P 325--354
%I 
%K genetic algorithms, genetic programming, artificial neural networks (ANN), simulated evolution, computer graphics
%U http://www.ri.cmu.edu/pub_files/pub3/baluja_shumeet_1994_1/baluja_shumeet_1994_1.pdf
%X In 1991, Karl Sims presented work on artificial evolution in which he used genetic algorithms to evolve complex structures for use in computer generated images and
   animations. The evolution of the computer generated images progressed from simple, randomly generated shapes to interesting images which the users interactively created.
   The evolution advanced under the constant guidance and supervision of the user. This paper describes attempts to automate the process of image evolution through the use of
   artificial neural networks. The central objective of this study is to learn the user's preferences, and to apply this knowledge to evolve aesthetically pleasing images
   which are similar to those evolved through interactive sessions with the user. This paper presents a detailed analysis of both the shortcomings and successes encountered in
   the use of five artificial neural network architectures. Further possibilities for improving the performance of a fully automated system are also discussed.
%Z also CMU techical report CMU//CS-93-198

%A Armand {Bankhead III}
%A Robert B. Heckendorn
%T Using evolvable genetic cellular automata to model breast cancer
%J Genetic Programming and Evolvable Machines
%V 8
%N 4
%D 2007
%P 381--393
%I 
%K genetic algorithms, Genetic cellular automata, DCIS, Progenitor hierarchy, Ductal simulation, Hereditary genetic predisposition, Hereditary breast cancer, CA
%X Cancer is an evolutionary process. Mutated cells undergo selection for abnormal growth and survival creating a tumour. We model this process with cellular automata that use
   a simplified genetic regulatory network simulation to control cell behaviour and predict cancer etiology. Our genetic model gives us the ability to relate genetic mutation
   to cancerous outcomes. The simulation uses known histological morphology, cell types, and stochastic behavior to specifically model ductal carcinoma in situ (DCIS), a
   common form of non-invasive breast cancer. Using this model we examine the effects of hereditary predisposition on DCIS incidence and aggressiveness. Results show that we
   are able to reproduce in vivo pathological features to hereditary forms of breast cancer: earlier incidence and increased aggressiveness. We also show that a contributing
   factor to the different pathology of hereditary breast cancer results from the ability of progenitor cells to pass cancerous mutations on to offspring.
%O special issue on medical applications of Genetic and Evolutionary Computation
%8 Decemeber
%Z 155 node beowulf cluster

%A Edwin Roger Banks
%A James Hayes
%A Edwin Nunez
%T Parametric Regression Through Genetic Programming
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP001.pdf
%X Parametric regression in genetic programming can substantially speed up the search for solutions. Paradoxically, the same technique has difficulty finding a true optimum
   solution. The parametric formulation of a problem results in a fitness landscape that looks like an inverted brush with many bristles of almost equal length (individuals of
   high fitness), but with only one bristle that is very slightly longer than the rest, the optimum solution. As such it is easy to find very good, even outstanding solutions,
   but very difficult to locate the optimum solution. In this paper parametric regression is applied to a minimum-time-to-target problem. The solution is equivalent to the
   classical brachistochrone. Two formulations were tried: a parametric regression and the classical symbolic regression formulation. The parametric approach was superior
   without exception. We speculate the parametric approach is more generally applicable to other problems and suggest areas for more research.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A E. R. Banks
%A J. C. Hayes
%A E. Nunez
%T Parametric Regression Through Genetic Programming
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WMSA003.pdf
%8 26-30  June
%Z GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A Edwin Roger Banks
%A Edwin Nunez
%A Paul Agarwal
%A Claudette Owens
%A Marshall McBride
%A Ron Liedel
%T Genetic Programming for Discrimination of Buried Unexploded Ordnance (UXO)
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2005)
%E Franz Rothlauf
%D 2005
%I 
%C Washington, D.C., USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/66-banks.pdf
%X According to the Department of Defense, over 10 million acres of land in the US need to be cleared of buried unexploded ordnance (UXO). Worldwide, UXO injures thousands
   each year. Cleanup costs are prohibitively expensive due to the difficulties in discriminating buried UXO from other inert non-UXO objects. Government agencies are actively
   searching for improved sensor methodologies to detect and discriminate buried UXO from other objects. We describe the results of work performed on data gathered by the
   GeoPhex GEM-3 electromagnetic sensor during their attempts to discriminate buried UXO at the U.S. Army Jefferson Proving Ground (JPG). We used a variety of evolutionary
   computing (EC) approaches that included genetic programming, genetic algorithms, and decision-tree methods. All approaches were essentially formulated as regression
   problems whereby the EC algorithms used sensor data to evolve buried UXO discrimination chromosomes. Predictions were then compared with a ground-truth file and the number
   of false positives and negatives determined
%8 25-29  June
%Z Distributed on CD-ROM at GECCO-2005

%A Edwin Roger Banks
%A Edwin Nunez
%A Paul Agarwal
%A Marshall McBride
%A Ronald Liedel
%A Claudette Owens
%T A Comparison of Evolutionary Computing Techniques Used to Model Bi-Directional Reflectance Distribution Functions
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006)
%E J\"orn Grahl
%D 2006
%I 
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp128.pdf
%X Bi-Directional Reflectance Distribution Functions are used in many fields including computer animation modelling, military defence (radar, lidar, etc.), and others. This
   paper explores a variety of approaches to modelling BRDFs using different evolutionary computing (EC) techniques. We concentrate on genetic programming (GP) and in hybrid
   GP approaches, obtaining very close correspondence between models and data. The problem of obtaining parameters that make particular BRDF models fit to laboratory-measured
   reflectance data is a classic symbolic regression problem. The goal of this approach is to discover the equations that model laboratory-measured data according to several
   criteria of fitness. These criteria involve closeness of fit, simplicity or complexity of the model (parsimony), form of the result, and speed of discovery. As expected,
   free form, unconstrained GP gave the best results in terms of minimising measurement errors. However, it also yielded the most complex model forms. Certain constrained
   approaches proved to be far superior in terms of speed of discovery. Furthermore, application of mild parsimony pressure resulted in not only simpler expressions, but also
   improved results by yielding small differences between the models and the corresponding laboratory measurements.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2006

%A Edwin Roger Banks
%A Paul Agarwal
%A Marshall McBride
%A Claudette Owens
%T A comparison of selection, recombination, and mutation parameter importance over a set of fifteen optimization tasks
%B GECCO-2009 Late-Breaking Papers
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 1971--1976
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X How does one choose an initial set of parameters for an evolutionary computing algorithm? Clearly some choices are dictated by the problem itself, such as the encoding of a
   problem solution, or how much time is available for running the evolution. Others, however, are frequently found by trial-and-error. These may include population sizes,
   number of populations, type of selection, recombination and mutation rates, and a variety of other parameters. Sometimes these parameters are allowed to co-evolve along
   with the solutions rather than by trial-and-error. But in both cases, an initial setting is needed for each parameter. When there are hundreds of parameters to be adjusted,
   as in some evolutionary computation tools, one would like to just spend time adjusting those that are believed to be most important, or sensitive, and leave the rest to
   start with an initial default value. Thus the primary goal of this paper is to establish the relative importance of each parameter. Establishing general guidance to assist
   in the determination of these initial default values is another primary goal of this paper. We propose to develop this guidance by studying the solutions resulting from
   variations around the default starting parameters applied across fifteen different application types.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Edwin Roger Banks
%A Paul Agarwal
%A Marshall McBride
%A Claudette Owens
%T Lessons learned in application of evolutionary computation to a set of optimization tasks
%B GECCO-2009 Late-Breaking Papers
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 1977--1982
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X Many GECCO papers discuss lessons learned in a particular application, but few papers discuss lessons learned over an ensemble of problem areas. A scan of the tables of
   contents of the Proceedings from GECCO 2005 and 2006 showed no paper title stressing lessons learned although the term "pitfall" appeared occasionally in abstracts,
   typically applying to a particular practice. We present in this paper a set of broadly applicable "lessons learned" in the application of evolutionary computing (EC)
   techniques to a variety of problem areas and present advice related to encoding, running, monitoring, and managing an evolutionary computing task.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Edwin Roger Banks
%A Paul Agarwal
%A Marshall McBride
%A Claudette Owens
%T Evolving Image Noise Filters through Genetic Programming
%B DoD High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC), 2009
%D 2009
%P 307--312
%I 
%K genetic algorithms, genetic programming, Linux cluster, communication interruptions, communications noise, evolutionary computation, grayscale noise, image filtering, image
   noise filters, remotely sensed imagery, salt-and-pepper noise, sensor noise, sequential uncompressed image information, Linux, filtering theory, image denoising, image
   resolution, image segmentation, image sequences
%X A form of Evolutionary Computation (EC) called Genetic Programming (GP) was used to automatically discover sequences of image noise filters to remove two types of image
   noise and a type of communications noise associated with a remotely sensed imagery. Sensor noise was modelled by the addition of salt-and-pepper and grayscale noise to the
   image. Communication noise was modeled by inserting a series of blank pixels in selected image rows to replicate dropped pixel segments occurring during communication
   interruptions of sequential uncompressed image information. A known image was used for training the evolver. Heavy amounts of noise were added to the known image, and a
   filter was evolved. (The filtered image was compared to the original with the average image-to-image pixel error establishing the fitness function.). The evolved filter
   derived for the noisy image was then applied to never-before-seen imagery affected by similar noise conditions to judge the universal applicability of the evolved GP
   filter. Examples of all described images are included in the presentation. A variety of image filter primitives were used in this experiment. The evolved sequences of
   primitives were each then sequentially applied to produce the final filtered image. These filters were evolved over a typical run length of one week each on a small Linux
   cluster. Once evolved, the filters were then transported to a PC for application to the never-before-seen images, using an evolve-once, apply-many-times approach. The
   results of this image filtering experiment were quite dramatic.
%8 15-18  June
%Z COLSA Corp., Huntsville, AL, USA Also known as \cite5729481

%A Edwin Roger Banks
%A Paul Agarwal
%A Marshall McBride
%A Claudette Owens
%T Toward a universal operator encoding for genetic programming
%B GECCO-2009 Late-Breaking Papers
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 1983--1986
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X The 2002 CEC paper entitled "Genetic Programming with Smooth Operators for Arithmetic Expressions: Diviplication and Subdition" by Ursem and Krink
   \citeursem:2002:gpwsofaedas proposed to blend certain arithmetic operators by interpolation to smooth the transition from one operator to another in the fitness landscape.
   Inspired by their idea, herein it is shown how to generalise further by using combinations of more than two operators, requiring log(N) additional parameters for each N
   operators so combined. Comparative results are reported for the application of this methodology to a variety of optimisation tasks including symbolic regression, an
   aspherical lens system design, a UAV path optimization, and a remote sensor image noise filter design.
%8 8-12  July
%Z cites \citepage:1999:smuxspmGP. Parsimony via fitness penalty, non-spherical lens design. Worse except for image noise filter where universal function set does best.
   Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Wolfgang Banzhaf
%T Genetic Programming for Pedestrians
%R MERL Technical Report 93-03
%D 1993
%I 
%I Mitsubishi Electric Research Labs
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/pedes93.ps.gz
%X We propose an extension to the Genetic Programming paradigm which allows users of traditional Genetic Algorithms to evolve computer programs. To this end, we have to
   introduce mechanisms like transscription, editing and repairing into Genetic Programming. We demonstrate the feasibility of the approach by using it to develop programs for
   the prediction of sequences of integer numbers.
%Z As \citebanzhaf:mrl

%A Wolfgang Banzhaf
%T Genetic Programming for Pedestrians
%B Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93
%E Stephanie Forrest
%D 1993
%P 628
%I Morgan Kaufmann
%I Mitsubishi Electrical Research Laboratories, Cambridge Research Center
%C University of Illinois at Urbana-Champaign
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GenProg_forPed.ps.Z
%X We propose an extension to the Genetic Programming paradigm which allows users of traditional Genetic Algorithms to evolve computer programs. To this end, we have to
   introduce mechanisms like transcription, editing and repairing into Genetic Programming. We demonstrate the feasibility of the approach by using it to develop programs for
   the prediction of sequences of integer numbers.
%8 17-21  July
%Z Also available as MRL Technical Report 93-03 11 pages. (\citebanzhaf:mrl:tech) 225 bit GA, 5 bit grouping encode terminal or two arg function, clean up by "editing" and
   "repair" to produce variable length tree shaped prog. No looping, recursion or memory. Demonstrated on learning sequences of small integers, fails on primes.

%A Wolfgang Banzhaf
%T Genotype-Phenotype-Mapping and Neutral Variation -- A case study in Genetic Programming
%B Parallel Problem Solving from Nature III
%S LNCS
%E Yuval Davidor and Hans-Paul Schwefel and Reinhard M\"anner
%V 866
%D 1994
%P 322--332
%I Springer-Verlag Berlin, Germany
%C Jerusalem
%K genetic algorithms, genetic programming
%U http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6
%X We propose the application of a genotype-phenotype mapping to the solution of constrained optimization problems. The method consists of strictly separating the search space
   of genotypes from the solution space of phenotypes. A mapping from genotypes into phenotypes provides for the appropriate expression of information represented by the
   genotypes. The mapping is constructed as to guarantee feasibility of phenotypic solutions for the problem under study. This enforcing of constraints causes multiple
   genotypes to result in one and the same phenotype. Neutral variants are therefore frequent and play an important role in maintaining genetic diversity. As a specific
   example, we discuss Binary Genetic Programming (BGP), a variant of Genetic Programming that uses binary strings as genotypes and program trees as phenotypes.
%8 9-14  October
%Z PPSN3 Tested on symbolic regression of 0.5x**2 and exp(-3.0*x**2) Produces high level code (FORTRAN, C?) which is compiled, claims this gives huge speedup.
%@ 3-540-58484-6

%A Wolfgang Banzhaf
%A Peter Nordin
%A Markus Olmer
%T Generating Adaptive Behavior for a Real Robot using Function Regression within Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 35--43
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.mun.ca/~banzhaf/papers/robot_over.pdf
%X We discuss the generation of adaptive behaviour for an autonomous robot within the framework of a special kind of function regression used in compiling Genetic Programming
   (GP). The control strategy for the robot is derived, using an evolutionary algorithm, from a continuous improvement of machine language programs which are varied and
   selected against each other. We give an overview of our recent work on several fundamental behaviors like obstacle avoidance and object following adapted from programs that
   were originally random sequences of commands. It is argued that the method is generally applicable where there is a need for quick adaptation within real-time problem
   domains
%8 13-16  July
%Z GP-97

%A Wolfgang Banzhaf
%T Interactive Evolution
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%I Oxford University Press
%K genetic algorithms, genetic programming
%O section C2.9
%@ 0-7503-0392-1

%A Wolfgang Banzhaf
%A Peter Nordin
%A Robert E. Keller
%A Frank D. Francone
%T Genetic Programming -- An Introduction; On the Automatic Evolution of Computer Programs and its Applications
%D 1998
%I Morgan Kaufmann
%C San Francisco, CA, USA
%K genetic algorithms, genetic programming
%U http://www.elsevier.com/wps/find/bookdescription.cws_home/677869/description#description
%8 January
%Z details from banzhaf Tue, 23 Sep 1997 12:58:06 PDT updated banzhaf Fri, 23 Jun 2006 11:40:44 -0230 FROM THE FOREWORD BY J.R. KOZA Genetic programming addresses the problem
   of automatic programming, namely the problem of how to enable a computer to do useful things without instructing it, step by step, on how to do it. The rapid growth of the
   field of genetic programming reflects the growing recognition that, after half a century of research in the fields of artificial intelligence, machine learning, adaptive
   systems, automated logic, expert systems, and neural networks, we may finally have a way to achieve automatic programming. Genetic programming is fundamentally different
   from other approaches in terms of (i) its representation (namely, programs), (ii) the role of knowledge (none), (iii) the role of logic (none), and (iv) its mechanism
   (gleaned from nature) for getting to a solution within the space of possible solutions. FROM THE FIRST SECTION OF THE BOOK Automated programming will be one of the most
   important areas of computer science research over the next twenty years. Hardware speed and capability has leapt forward exponentially. Yet software consistently lags years
   behind the capabilities of the hardware. The gap appears to be ever increasing. Demand for computer code keeps growing but the process of writing code is still mired in the
   modern day equivalent of the medieval ``guild'' days. Like swords in the 15th century, muskets before the early 19th century and books before the printing press, each piece
   of computer code is, today, handmade by a craftsman for a particular purpose. The history of computer programming is a history of attempts to move away from the
   ``craftsman'' approach -- structured programming, object oriented programming, object libraries, rapid prototyping. But each of these advances leaves the code that does the
   real work firmly in the hands of a craftsman, the programmer. The ability to enable computers to learn to program themselves is of the utmost importance in freeing the
   computer industry and the computer user from code that is obsolete before it is released.
%@ 3-920993-58-6

%T Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%I Springer-Verlag Berlin
%I EvoNet
%C Paris
%K genetic algorithms, genetic programming
%8 14-15  April
%Z EuroGP'98
%@ 3-540-64360-5

%T Les Robots inventeent la vie
%J Le Monde
%D 1998
%I 
%K genetic algorithms, genetic programming
%8 23 Avril
%Z in french, Description of EvoRobot'98 in particular: Stefanio Nolfi and Dario Floreano, Jean Arcady-Meyer, Henrik Lund, \citedittrich:1998:lmrrm, Nick Jakobi

%T GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%D 1999
%I Morgan Kaufmann San Francisco, CA, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.amazon.com/exec/obidos/ASIN/1558606114/qid%3D977054373/105-7666192-3217523
%8 13-17  July
%Z GECCO-99
%@ 1-55860-611-4

%A Wolfgang Banzhaf
%T Artificial Intelligence: Genetic Programming
%D 2000
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/400591.html
%X The term Genetic Programming describes a research area within the general field of Artificial Intelligence that deals with the evolution of computer code. This area is an
   out growth of two independent efforts in Artificial Intelligence, namely automatic programming and machine learning. Automatic programming is concerned with the induction
   of computer code which precisely fulfills certain functions, whereas machine learning studies improvement of computer programs through training and experience.
%O The Pennsylvania State University CiteSeer Archives
%O Contract no: 20851A2/2/102
%8 July ~04
%Z Survey/introduction to GP. See also \citeBanzhaf2001789

%A Wolfgang Banzhaf
%A Dirk Banscherus
%A Peter Dittrich
%T Hierarchical Genetic Programming Using Local Modules
%R Technical Report 50/98
%D 1998
%I 
%I University of Dortmund
%C Dortmund, Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/324880.html
%X This paper presents detailed experimental results for a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A
   module in a hGP program is context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same
   context. This new modular approach allows for a natural hierarchy in that local modules themselves may define local sub-modules.
%O The Pennsylvania State University CiteSeer Archives
%Z see also \citebanzhaf:2000:IJ

%A Wolfgang Banzhaf
%A Dirk Banscherus
%A Peter Dittrich
%T Hierarchical Genetic Programming using Local Modules
%J InterJournal Complex Systems
%V 228
%D 2000
%I 
%K genetic algorithms, genetic programming
%U https://eldorado.uni-dortmund.de/bitstream/2003/5365/1/ci56.pdf
%X This paper presents a new modular approach to Genetic Programming, hierarchical GP (hGP) based on the introduction of local modules. A module in a hGP program is
   context-dependent and should not be expected to improve all programs of a population but rather a very specific subset providing the same context. This new modular approach
   allows for a natural recursiveness in that local modules themselves may define local sub-modules.
%Z Category: Brief Article Status: Accepted Manuscript Number: [228] Submission Date: 981210 Revised On: 815 Subject(s): CX, CX.66 See also \citeoai:CiteSeerPSU:324880

%A W. Banzhaf
%A W. B. Langdon
%T Some considerations on the reason for bloat
%J Genetic Programming and Evolvable Machines
%V 3
%N 1
%D 2002
%P 81--91
%I 
%K genetic algorithms, genetic programming, linear genomes, effective fitness, neutral variations
%U http://web.cs.mun.ca/~banzhaf/papers/genp_bloat.pdf
%X A representation-less model for genetic programming is presented. The model is intended to examine the mechanisms that lead to bloat in genetic programming (GP). We discuss
   two hypotheses (fitness causes bloat and neutral code is protective) and perform simulations to examine the predictions deduced from these hypotheses. Our observation is
   that predictions from both hypotheses are realized in the simulated model.
%8 March
%Z Article ID: 395990

%A Wolfgang Banzhaf
%T Editorial Introduction
%J Genetic Programming and Evolvable Machines
%V 1
%N 1/2
%D 2000
%P 5--6
%I 
%8 April
%Z Article ID: 253701

%A Wolfgang Banzhaf
%T The artificial evolution of computer code
%J IEEE Intelligent Systems
%V 15
%N 3
%D 2000
%P 74--76
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/399369.html
%8 May -  June
%Z part of \citehirsh:2000:GP

%A W. Banzhaf
%T Acknowledgement
%J Genetic Programming and Evolvable Machines
%V 1
%N 4
%D 2000
%P 307
%I 
%8 October
%Z Article ID: 273809

%A W. Banzhaf
%T Artificial Intelligence: Genetic Programming
%B International Encyclopedia of the Social \& Behavioral Sciences
%E Neil J. Smelser and Paul B. Baltes
%D 2001
%P 789--792
%I Pergamon
%C Oxford
%U http://www.sciencedirect.com/science/article/B7MRM-4MT09VJ-403/2/fa4e06852750b95eb2734f9ca37ae6ad
%X Genetic Programming is a new method to generate computer programs. It was derived from the model of biological evolution. Programs are 'bred' through continuous improvement
   of an initially random population of programs. Improvements are made possible by stochastic variation of programs and selection according to prespecified criteria for
   judging the quality of a solution. Programs of Genetic Programming systems evolve to solve predescribed automatic programming and machine learning problems. In this
   contribution the origins and the context of Genetic Programming are discussed. The primary mechanisms behind the working of the method are then outlined. Next is a review
   of the state-of-the-art of Genetic Programming, including the major achievements of the method in recent years. This leads to an overview of the application areas where GP
   is most frequently used to present. Among these areas is robotics and the control of behavior, both of real and virtual agents. The article will conclude with a section on
   methodological issues and future directions. The use of Genetic Programming for simulation in the social sciences is briefly sketched.

%A W. Banzhaf
%T Editorial Introduction
%J Genetic Programming and Evolvable Machines
%V 2
%N 1
%D 2001
%P 5
%I 
%8 March
%Z Article ID: 319810

%A W. Banzhaf
%T Acknowledgement
%J Genetic Programming and Evolvable Machines
%V 2
%N 4
%D 2001
%P 315--315
%I 
%8 Decemeber
%Z Article ID: 386360

%A W. Banzhaf
%T Editorial Introduction
%J Genetic Programming and Evolvable Machines
%V 3
%N 1
%D 2002
%P 5--6
%I 
%8 March
%Z Article ID: 395987

%A W. Banzhaf
%T Acknowledgement
%J Genetic Programming and Evolvable Machines
%V 3
%N 4
%D 2002
%P 327
%I 
%8 Decemeber
%Z Article ID: 5103871

%A Wolfgang Banzhaf
%T Editorial Introduction
%J Genetic Programming and Evolvable Machines
%V 4
%N 1
%D 2003
%P 5--6
%I 
%8 March
%Z Article ID: 5113069

%A Wolfgang Banzhaf
%T Artificial Regulatory Networks and Genetic Programming
%B Genetic Programming Theory and Practice
%E Rick L. Riolo and Bill Worzel
%D 2003
%P 43--62
%I Kluwer
%K genetic algorithms, genetic programming, Regulatory Networks, Artificial Evolution, Evolutionary Algorithms, Development, Heterochrony
%O 4
%Z Part of \citeRioloWorzel:2003

%A Wolfgang Banzhaf
%A Markus Brameier
%A Marc Stautner
%A Klaus Weinert
%T Genetic Programming and Its Application in Machining Technology
%B Advances in Computational Intelligence: Theory and Practice
%S Natural Computing Series
%E Hans-Paul Schwefel and Ingo Wegener and Klaus Weinert
%D 2003
%P 194--242?
%I Springer
%K genetic algorithms, genetic programming, Linear Genetic Programming
%O 7
%Z Removal of Non-effective Code. Graph Interpretation. Linear Genetic Operators. Control of Variation Step Size. Control of Structural Diversity. Genetic Programming in
   Machining Technology. Tree-Based GP for Symbolic Regression. Graphical Representation. Parallelisation
%@ 3-540-43269-8

%A Wolfgang Banzhaf
%T Editorial Introduction
%J Genetic Programming and Evolvable Machines
%V 5
%N 1
%D 2004
%P 5
%I 
%8 March
%Z Article ID: 5264730

%A W. Banzhaf
%T Acknowledgement
%J Genetic Programming and Evolvable Machines
%V 5
%N 1
%D 2004
%P 7
%I 
%8 Decemeber
%Z Article ID: 5264731

%A Wolfgang Banzhaf
%A James Foster
%T Editorial Introduction
%J Genetic Programming and Evolvable Machines
%V 5
%N 2
%D 2004
%P 119--120
%I 
%K genetic algorithms, genetic programming, bioinformatics
%8 June
%Z BioGEC Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster

%A Wolfgang Banzhaf
%A Christian W. G. Lasarczyk
%T Genetic Programming of an Algorithmic Chemistry
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 175--190
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, artificial chemistry
%U http://www.cs.mun.ca/~banzhaf/papers/algochem.pdf
%X We introduce a new method of execution for GP-evolved programs consisting of register machine instructions. It is shown that this method can be considered as an artificial
   chemistry. It lends itself well to distributed and parallel computing schemes in which synchronisation and coordination are not an issue.
%O 11
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2 Sin, UCI thyroid
%@ 0-387-23253-2

%A Wolfgang Banzhaf
%A P. Dwight Kuo
%T Network motifs in natural and artificial transcriptional regulatory networks
%J Journal of Biological Physics and Chemistry
%V 4
%N 2
%D 2004
%P 50--63
%I 
%K artificial life
%U http://www.amsi.ge/jbpc/20404/2040405.html
%X We show that network motifs found in natural regulatory networks may also be found in an artificial regulatory network model created through a duplication / divergence
   process. It is shown that these network motifs exist more frequently in a genome created through the aforementioned process than in randomly generated genomes. These
   results are then compared with a network motif analysis of the gene expression networks of Escherichia Coli and Saccharomyces cerevisiae. In addition, it is shown that
   certain individual network motifs may arise directly from the duplication / divergence mechanism.
%@ 0-387-23253-2

%A Wolfgang Banzhaf
%A Julian Miller
%T The Challenge of Complexity
%B Frontiers of Evolutionary Computation
%S Genetic Algorithms And Evolutionary Computation Series
%E Anil Menon
%V 11
%D 2004
%P 73--99
%I Kluwer Academic Publishers
%C Boston, MA, USA
%K genetic algorithms, genetic programming, Evolutionary Algorithm, Complexity, Scaling Problem, Development, Heterochrony
%U http://www.cs.mun.ca/~banzhaf/papers/challenge_rev.pdf
%X the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face
   when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this ``challenge of complexity'' in
   Genetic Programming
%O 11
%@ 1-4020-7524-3

%A Wolfgang Banzhaf
%T Challenging the Program Counter
%B The Grand Challenge in Non-Classical Computation: International Workshop
%E Susan Stepney and Stephen Emmott
%D 2005
%I 
%I University of York and Microsoft Research
%C York, UK
%K genetic algorithms, genetic programming, artificial chemistry
%U http://www.cs.york.ac.uk/nature/workshop/papers/Banzhaf.pdf
%8 18-19  April
%Z http://www.cs.york.ac.uk/nature/workshop/

%A Wolfgang Banzhaf
%T Editorial
%J Genetic Programming and Evolvable Machines
%V 5
%N 2
%D 2005
%P 135--136
%I 
%8 June

%A W. Banzhaf
%T Acknowledgement
%J Genetic Programming and Evolvable Machines
%V 5
%N 2
%D 2005
%P 137--138
%I 
%8 June

%A Wolfgang Banzhaf
%A Andre Leier
%T Evolution on Neutral Networks in Genetic Programming
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 207--221
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Neutrality, Linear GP, Networks, Population Dynamics
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.7947
%X We examine the behaviour of an evolutionary search on neutral networks in a simple linear GP system of a Boolean function space problem. To this end we draw parallels
   between notions in RNA-folding problems and in Genetic Programming, observe parameters of neutral networks and discuss the population dynamics via the occupation
   probability of network nodes in runs on their way to the optimal solution.
%O 14
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Wolfgang Banzhaf
%T Introduction
%J Genetic Programming and Evolvable Machines
%V 6
%N 1
%D 2006
%P 5--6
%I 
%8 March

%A W. Banzhaf
%T Acknowledgement
%J Genetic Programming and Evolvable Machines
%V 6
%N 1
%D 2006
%P 7
%I 
%8 March
%Z list of reviewers

%A Wolfgang Banzhaf
%A Guillaume Beslon
%A Steffen Christensen
%A James Foster
%A Francois Kepes
%A Virginie Lefort
%A Julian Miller
%A Miroslav Radman
%A Jeremy J. Ramsden
%T From Artificial Evolution to Computational Evolution: A Research Agenda
%J Nature Reviews Genetics
%V 7
%N 9
%D 2006
%P 729--735
%I 
%K genetic algorithms, genetic programming
%X Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimisation and design problems by building
   solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new
   field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve
   previously unimaginable or intractable computational and biological problems.
%8 September
%Z We thank the people at Genopole Recherche, Evry, France, for generously sponsoring the meeting that initiated this paper.

%A Wolfgang Banzhaf
%T Editorial introduction
%J Genetic Programming and Evolvable Machines
%V 8
%N 1
%D 2007
%I 
%X As we have moved into the corporate sphere of Springer there were a number of changes, some subtle, some not so subtle. One change that is somewhat behind the scenes and
   eludes the eye of a reader is how Springer uses its distribution channels to spread the journal. Genetic Programming and Evolvable Machines is now accessible in over 5000
   libraries across the globe. I think that speaks to the ability of this publisher, and its will to get the word out about our community. In the absence of an officially
   calculated impact factor I have taken the initiative myself to address this issue. Using the citation base of Google Scholar, we have evaluated the impact of GPEM by
   looking at all the papers published since its inception in 2000, up to May 2006. It turns out that authors did very well if publishing in GPEM. Their GPEM papers regularly
   featured prominently among their papers in terms of citations. 50% of our authors can count their GPEM paper among the first, second or third most cited paper of theirs.
   While this is certainly only true for half of the authors, it is indeed an achievement. So if you publish in GPEM, be prepared that your work is read, and also cited.

%A Wolfgang Banzhaf
%A Simon Harding
%A William B. Langdon
%A Garnett Wilson
%T Accelerating Genetic Programming through Graphics Processing Units
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 229--249
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, graphics processing units, parallel processing, GPU
%X Graphics Processing Units (GPUs) are in the process of becoming a major source of computational power for numerical applications. Originally designed for application of
   time-consuming graphics operations, GPUs are stream processors that implement the SIMD paradigm. The true degree of parallelism of GPUs is often hidden from the user,
   making programming even more flexible and convenient. In this chapter we survey Genetic Programming methods currently ported to GPUs.
%O 15
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008

%A Yun Bao
%A Erbo Zhao
%A Xiaocong Gan
%A Dan Luo
%A Zhangang Han
%T A Review on Cutting-Edge Techniques in Evolutionary Algorithms
%B Fifth International Conference on Natural Computation, 2009. ICNC '09
%V 5
%D 2009
%P 347--351
%I 
%K genetic algorithms, genetic programming, EA performance improvements, convergence speed, cutting-edge techniques, evolutionary algorithms, nuclear power plant, evolutionary
   computation
%X There are vast amount researches in Evolutionary Algorithms (EA). We need an overview of the current state of EA research every few years. This paper reviews some of the
   interesting researches at the current state in both theory and application of EA. Works in EA performance improvements are introduced in the sense of balancing between
   convergence speed and diversity in the population. The combination of EA with other methods is highlighted as a prospective area that may give fertility results. Some smart
   applications are reviewed in this paper, for example, application in nuclear power plant. The authors point out some research highlights and drawbacks in the conclusion.
   Future research suggestions are also given.
%8 August
%Z Also known as \cite5364257

%A Martin Josephus Baptist
%T Modelling floodplain biogeomorphology
%R Ph.D. Thesis
%D 2005
%I Delft University Press
%I Technische Universiteit Delft
%C Holland
%K genetic algorithms, genetic programming
%U http://repository.tudelft.nl/assets/uuid...e2f6.../ceg_baptist_20050418.pdf
%X There is an increasing awareness that rivers need more room in order to safeguard flood safety under climate change conditions. Contemporary river management is creating
   room in the floodplains and allowing, within certain bounds, natural processes of sedimentation and erosion. One of the aims is to restore dynamic conditions, so as to get
   a sustainable and more diverse river ecosystem that can cope with floods. This new approach requires understanding of the interaction between the biotic and abiotic
   components of river systems. More specifically, it requires a better understanding of the interaction between flora and fauna and geomorphological factors. This is the
   object of investigation of the interdiscipline of biogeomorphology. Modelling biogeomorphological processes in river floodplains is the topic of this thesis. To reduce
   flood risks in the Netherlands, measures to increase the flood conveyance capacity of the Rhine River will be implemented. However, it is expected that floodplain
   sedimentation and softwood forest development in rehabilitated floodplains will gradually reduce the conveyance capacity and the biodiversity. Moreover, in regulated
   rivers, such as the Rhine River, erosion and sedimentation processes caused by channel migration, which periodically interrupt vegetation succession, cannot be allowed.
   Therefore, a floodplain management strategy was proposed that would meet both flood protection and nature rehabilitation objectives. This strategy, 'Cyclic Floodplain
   Rejuvenation (CFR)', aims at mimicking the effects of channel migration by removal of softwood forests, by lowering floodplains or by (re)constructing secondary channels.
   In chapter 2, the effects of CFR measures on reducing flood levels and enhancing biodiversity along the Waal River were assessed. A one-dimensional hydraulic modelling
   system, SOBEK, was applied together with rule-based models for floodplain vegetation succession and floodplain sedimentation. The model simulations demonstrated that the
   flood management strategy of Cyclic Floodplain Rejuvenation is able to sustain safe flood levels in the Waal River. Rejuvenation is then needed every 25 to 35 years on
   average, each time in an area of about 15percent of the total floodplain area. The rejuvenation strategy led to a diverse floodplain vegetation distribution that largely
   complies to the historical reference for the Waal River. Cyclic Floodplain Rejuvenation may be the appropriate answer to find symbiosis between flood protection and nature
   rehabilitation in highly regulated rivers. ...
%8 18  April
%Z Supervisor H.J. de Vriend. In english
%@ 90-407-2582-9

%A M. J. Baptist
%A Vladan Babovic
%A J. {Rodriguez Uthurburu}
%A M. Keijzer
%A R. E. Uittenbogaard
%A A. Mynett
%A A. Verwey
%T On inducing equations for vegetation resistance
%J Journal of Hydraulic Research
%V 45
%N 4
%D 2007
%P 435--450
%I 
%K genetic algorithms, genetic programming, vegetation, roughness, resistance, knowledge discovery
%X The paper describes the process of induction of equations for the description of vegetation-induced roughness from several angles. Firstly, it describes two approaches for
   obtaining theoretically well-founded analytical expressions for vegetation resistance. The first of the two is based on simplified assumptions for the vertical flow profile
   through and over vegetation, whereas the second is based on an analytical solution to the momentum balance for flow through and over vegetation. In addition to analytical
   expressions the paper also outlines a numerical 1-DV k-e turbulence model which includes several important features related to the influence plants exhibit on the flow.
   Last but not least, the paper presents a novel way of applying genetic programming to the results of the 1-DV model, in order to obtain an expression for roughness based on
   synthetic data. The resulting expressions are evaluated and compared with an independent data set of flume experiments

%A Albert-Laszlo Barabasi
%A Vincent W. Freeh
%A Hawoong Jeong
%A Jay B. Brockman
%T Parasitic Computing
%J Nature
%V 412
%D 2001
%P 894--897
%I 
%K 16-SAT
%U http://www.nd.edu/~alb/Publication06/082%20Parasitic%20computing/Parasitic%20computing.pdf
%X Reliable communication on the Internet is guaranteed by a standard set of protocols, used by all computers. Here we show that these protocols can be exploited to compute
   with the communication infrastructure, transforming the Internet into a distributed computer in which servers unwittingly perform computation on behalf of a remote node. In
   this model, which we call 'parasitic computing', one machine forces target computers to solve a piece of a complex computational problem merely by engaging them in standard
   communication. Consequently, the target computers are unaware that they have performed computation for the benefit of a commanding node. As experimental evidence of the
   principle of parasitic computing, we harness the power of several web servers across the globe, which unknown to them work together to solve an NP complete problem
%8 30  August
%Z fig1 p895 Segement (TCP packet) dropped due to invalid checksum One property of the TCP checksum function is that it forms a sufźcient logical basis for implementing any
   Boolean logic function, and by extension, any arithmetic operation. 65536 32 bit messages sent to http servers on three continents Cited by \citearXiv:cs/0701115v1

%A Igor Baradavka
%A Tatiana Kalganova
%T Assembling Strategies in Extrinsic Evolvable Hardware with Bidirectional Incremental Evolution
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 276--285
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming, evolvable hardware
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=276
%X Bidirectional incremental evolution (BIE) has been proposed as a technique to overcome the ``stalling'' effect in evolvable hardware applications. However preliminary
   results show perceptible dependence of performance of BIE and quality of evaluated circuit on assembling strategy applied during reverse stage of incremental evolution. The
   purpose of this paper is to develop assembling strategy that will assist BIE to produce relatively optimal solution with minimal computational effort (e.g. the minimal
   number of generations).
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Danny Barash
%A Ann Orel
%A V. Rao Vemuri
%T Micro Genetic Algorithms in Finding the Optimal Frequency for Stabilizing Atoms by High-intensity Laser Fields
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Renaud Barate
%A Antoine Manzanera
%T Automatic Design of Vision-Based Obstacle Avoidance Controllers Using Genetic Programming
%B Artificial Evolution
%S Lecture Notes in Computer Science
%E Nicolas Monmarch\'e and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton
%V 4926
%D 2007
%P 25--36
%I Springer
%C Tours, France
%K genetic algorithms, genetic programming
%X The work presented in this paper is part of the development of a robotic system able to learn context dependent visual clues to navigate in its environment. We focus on the
   obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an off-line procedure to automatically design algorithms adapted to the
   visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects
   meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming
   hand-designed controllers.
%8 October  29-31
%Z EA'07

%A Renaud Barate
%A Antoine Manzanera
%T Generalization performance of vision based controllers for mobile robots evolved with genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1331--1332
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, generalisation, Obstacle avoidance, robotic simulation, vision, Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1331.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389349

%A Renaud Barate
%A Antoine Manzanera
%T Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation
%B From Animals to Animats 10, Proceedings of the 10th International Conference on Simulation of Adaptive Behavior, SAB 2008
%S Lecture Notes in Computer Science
%E Minoru Asada and John C. T. Hallam and Jean-Arcady Meyer and Jun Tani
%V 5040
%D 2008
%P 73--82
%I Springer
%C Osaka, Japan
%K genetic algorithms, genetic programming
%X We present a system that automatically selects and parameterises a vision based obstacle avoidance method adapted to a given visual context. This system uses genetic
   programming and a robotic simulation to evaluate the candidate algorithms. As the number of evaluations is restricted, we introduce a novel method using imitation to guide
   the evolution toward promising solutions. We show that for this problem, our two-phase evolution process performs better than other techniques.
%8 July  7-12
%Z From Animals to Animats 10

%A Renaud Barate
%A Antoine Manzanera
%T Learning Vision Algorithms for Real Mobile Robots with Genetic Programming
%B ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS '08
%D 2008
%P 47--52
%I 
%K genetic algorithms, genetic programming, learning vision algorithms, mobile robots, obstacle avoidance algorithms, supervised learning system, control engineering
   computing, learning (artificial intelligence), mobile robots, robot vision
%X We present a genetic programming system to evolve vision based obstacle avoidance algorithms. In order to develop autonomous behavior in a mobile robot, our purpose is to
   design automatically an obstacle avoidance controller adapted to the current context. We first record short sequences where we manually guide the robot to move away from
   the walls. This set of recorded video images and commands is our learning base. Genetic programming is used as a supervised learning system to generate algorithms that
   exhibit this corridor centering behavior. We show that the generated algorithms are efficient in the corridor that was used to build the learning base, and that they
   generalize to some extent when the robot is placed in a visually different corridor. More, the evolution process has produced algorithms that go past a limitation of our
   system, that is the lack of adequate edge extraction primitives. This is a good indication of the ability of this method to find efficient solutions for different kinds of
   environments.
%8 August
%Z Also known as \cite4599426

%A Renaud Barate
%T Learning Visual Functions for a Mobile Robot with Genetic Programming
%R Ph.D. Thesis
%D 2008
%I 
%I ENSTA
%C 32 Bd Victor 75015 Paris
%K genetic algorithms, genetic programming, Vision, mobile robotics, obstacle avoidance
%U http://www.ensta.fr/~manzaner/Publis/these-barate.pdf
%X Existing techniques used to learn artificial vision for mobile robots generally represent an image with a set of visual features that are computed with a hard-coded method.
   This impairs the system's adaptability to a changing visual environment. We propose a method to describe and learn vision algorithms globally, from the perceived image to
   the final decision. The target application is the obstacle avoidance function, which is necessary for any mobile robot. We formally describe the structure of vision-based
   obstacle avoidance algorithms with a grammar. Our system uses this grammar and genetic programming techniques to learn controllers adapted to a given visual context
   automatically. We use a simulation environment to test this approach and evaluate the performance of the evolved algorithms. We propose several techniques to speed up the
   evolution and improve the performance and generalization abilities of evolved controllers. In particular, we compare several methods that can be used to guide the evolution
   and we introduce a new one based on the imitation of a recorded behavior. Next we validate these methods on a mobile robot moving in an indoor environment. Finally, we
   indicate how this system can be adapted for other vision based applications and we give some hints for the online adaptation of the robot's behavior.
%O In French
%8 November
%Z Francais

%A Alina Barbulescu
%A Elena Bautu
%T Meteorological time series modeling using an adaptive gene expression programming
%B Proceedings of the 10th WSEAS International Conference on Evolutionary Computation
%E Nikos E. Mastorakis and Anca Croitoru and Valentina Emilia Balas and Eduard Son and Valeri Mladenov
%D 2009
%P 17--22
%I World Scientific and Engineering Academy and Society (WSEAS) Stevens Point, Wisconsin, USA
%C Prague, Czech
%K genetic algorithms, genetic programming, Gene Expression Programming
%X The precipitations are characterised by important spatial and temporal variation. Model determination for such series is of high importance for hydrological purposes (e.g.
   weather forecasting, agriculture, flood areas, administrative planning), even if discovering patterns in such series is a very difficult problem. The objective of the
   current study is to describe the use of an adaptive evolutionary technique that give promising results for the development of non-linear time series models.
%8 23-25  March
%Z http://www.wseas.org/conferences/2009/prague/ec/index.html

%A Alina Barbulescu
%A Elena Bautu
%T ARIMA Models versus Gene Expression Programming in Precipitation Modeling
%B Proceedings of the 10th WSEAS International Conference on Evolutionary Computation
%E Nikos E. Mastorakis and Anca Croitoru and Valentina Emilia Balas and Eduard Son and Valeri Mladenov
%D 2009
%P 112--117
%I World Scientific and Engineering Academy and Society (WSEAS) Stevens Point, Wisconsin, USA
%C Prague, Czech
%K genetic algorithms, genetic programming, Gene Expression Programming
%X In this paper we present a case study: the application of some conceptually different approaches to the problem of identifying a model for a hydrological time series. The
   problem is particularly challenging, due to the size of the time series and more importantly, to the many complex phenomena that influence such time series and that reflect
   in the characteristics of the data. We use well established statistical methods to detect change points in the time series, and we model the subseries obtained by ARIMA,
   GEP and the adaptive variant and a combination of the two. The models obtained state the efficiency of combining pure statistical tests and methods with heuristic
   approaches.
%8 23-25  March
%Z http://www.wseas.org/conferences/2009/prague/ec/index.html

%A Alina Barbulescu
%A Elena Bautu
%T Alternative Models in Precipitation Analysis
%J Analele Stiintifice ale Universitatii Ovidius Constanta, Seria Matematica
%V XVII
%N 3
%D 2009
%P 45--68
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.anstuocmath.ro/mathematics/pdf19/Barbulescu_Bautu.pdf
%X Precipitation time series intrinsically contain important information concerning climate variability and change. Well-fit models of such time series can shed light upon
   past weather related phenomena and can help to explain future events. The objective of this study is to investigate the application of some conceptually different methods
   to construct models for large hydrological time series. We perform a thorough statistical analysis of the time series, which covers the identification of the change points
   in the time series. Then, the subseries delimited by the change points are modelled with classical Box-Jenkins methods to construct ARIMA models and with a computational
   intelligence technique, gene expression programming, which produces non-linear symbolic models of the series. The combination of statistical techniques with computational
   intelligence methods, such as gene expression programming, for modelling time series, offers increased accuracy of the models obtained. This affirmation is illustrated with
   examples.
%Z http://www.anstuocmath.ro/

%A Alina Barbulescu
%A Elena Bautu
%T Time Series Modeling Using an Adaptive Gene Expression Programming Algorithm
%J International Journal of Mathematical Models and Methods in Applied Sciences
%V 3
%N 2
%D 2009
%P 85--93
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.naun.org/journals/m3as/mmmas-134.pdf
%X Meteorological time series are characterised by important spatial and temporal variation. Model determination and the prediction of evolution of such series is of high
   importance for different practical purposes, even if discovering evolution patterns in such series is a very difficult problem. In this article we describe an adaptive
   evolutionary technique and we apply it for modelling the precipitation and temperatures collected in a region of Romania. The results are promising for the analysis of such
   time series.
%Z http://www.naun.org/journals/m3as/

%A Alina Barbulescu
%A Elena Bautu
%T Mathematical models of climate evolution in Dobrudja
%J Theoretical and Applied Climatology
%V 100
%D 2010
%P 29--44
%I Springer Wien
%K genetic algorithms, genetic programming, gene expression programming, ARIMA, Earth and Environmental Science
%X The understanding of processes that occur in climate change evolution and their spatial and temporal variations are of major importance in environmental sciences. Modelling
   these processes is the first step in the prediction of weather change. In this context, this paper presents the results of statistical investigations of monthly and annual
   meteorological data collected between 1961 and 2007 in Dobrudja (South-East of Romania between the Black Sea and the lower Danube River) and the models obtained using time
   series analysis and gene expression programming. Using two fundamentally different approaches, we provide a comprehensive analysis of temperature variability in Dobrudja,
   which may be significant in understanding the processes that govern climate changes in the region.
%8 March

%A Perry Barile
%A Victor Ciesielski
%A Karen Trist
%T Non-photorealistic Rendering Using Genetic Programming
%B Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)
%S Lecture Notes in Computer Science
%E Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb
   and Kay Chen Tan and J\"urgen Branke and Yuhui Shi
%V 5361
%D 2008
%P 299--308
%I Springer
%C Melbourne, Australia
%K genetic algorithms, genetic programming, non-photorealistic rendering, evolutionary computation
%X We take a novel approach to Non-Photorealistic Rendering by adapting genetic programming in combination with computer graphics drawing techniques. As a GP tree is
   evaluated, upon encountering certain nodes referred to as Draw nodes, information contained within such nodes are sent to one of three virtual canvasses and a mark is
   deposited on the canvas. For two of the canvasses the user is able to define custom brushes to be applied to the canvas. Drawing functions are supplied with little
   localised information regarding the target image. Based on this local data, the drawing functions are enabled to apply contextualized information to the canvas. The
   obtained results include a Shroud of Turin effect, a Decal effect and a Starburst effect.
%8 Decemeber  7-10

%A Perry Barile
%A Victor Ciesielski
%A Marsha Berry
%A Karen Trist
%T Animated drawings rendered by genetic programming
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 939--946
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X We describe an approach to generating animations of drawings that start as a random collection of strokes and gradually resolve into a recognizable subject. The strokes are
   represented as tree based genetic programs. An animation is generated by rendering the best individual in a generation as a frame of a movie. The resulting animations have
   an engaging characteristic in which the target slowly emerges from a random set of strokes. We have generated two qualitatively different kinds of animations, ones that use
   grey level straight line strokes and ones that use binary Bezier curve stokes. Around 100,000 generations are needed to generate engaging animations. Population sizes of 2
   and 4 give the best convergence behaviour. Convergence can be accelerated by using information from the target in drawing a stroke. Our approach provides a large range of
   creative opportunities for artists. Artists have control over choice of target and the various stroke parameters.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Gregory J. Barlow
%T Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming
%R M.S. Thesis
%D 2004
%I 
%I North Carolina State University
%C Raleigh, NC, USA
%K genetic algorithms, genetic programming, mobile robotics, evolutionary robotics, multi-objective optimization, incremental evolution, unmanned aerial vehicles
%U http://www.andrew.cmu.edu/user/gjb/includes/publications/thesis/barlow2004-thesis/barlow2004-thesis.pdf
%X Unmanned aerial vehicles (UAVs) have become increasingly popular for many applications, including search and rescue, surveillance, and electronic warfare, but almost all
   UAVs are controlled remotely by humans. Methods of control must be developed before UAVs can become truly autonomous. While the field of evolutionary robotics (ER) has made
   strides in using evolutionary computation (EC) to develop controllers for wheeled mobile robots, little attention has been paid to applying EC to UAV control. EC is an
   attractive method for developing UAV controllers because it allows the human designer to specify the set of high level goals that are to be solved by artificial evolution.
   In this research, autonomous navigation controllers were developed using multi-objective genetic programming (GP) for fixed wing UAV applications. Four behavioral fitness
   functions were derived from flight simulations. Multi-objective GP used these fitness functions to evolve controllers that were able to locate an electromagnetic energy
   source, to navigate the UAV to that source efficiently using on-board sensor measurements, and to circle around the emitter. Controllers were evolved in simulation. To
   narrow the gap between simulated and real controllers, the simulation environment employed noisy radar signals and a sensor model with realistic inaccuracies. All
   computations were performed on a 92-processor Beowulf cluster parallel computer. To gauge the success of evolution, baseline fitness values for a successful controller were
   established by selecting values for a minimally successful controller. Two sets of experiments were performed, the first evolving controllers directly from random initial
   populations, the second using incremental evolution. In each set of experiments, autonomous navigation controllers were evolved for a variety of radar types. Both the
   direct evolution and incremental evolution experiments were able to evolve controllers that performed acceptably. However, incremental evolution vastly increased the
   success rate of incremental evolution over direct evolution. The final incremental evolution experiment on the most complex radar investigated in this research evolved
   controllers that were able to handle all of the radar types. Evolved UAV controllers were successfully transferred to a wheeled mobile robot. An acoustic array on-board the
   mobile robot replaced the radar sensor, and a speaker emitting a tone was used as the target. Using the evolved navigation controllers, the mobile robot moved to the
   speaker and circled around it. Future research will include testing the best evolved controllers by using them to fly real UAVs.
%8 March
%Z ADA460111

%A Gregory J. Barlow
%A Choong K. Oh
%A Edward Grant
%T Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP011.pdf
%X Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using incremental evolution with multi-objective genetic
   programming (GP). We designed four fitness functions derived from flight simulations and used multi-objective GP to evolve controllers able to locate a radar source,
   navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. We selected realistic flight parameters and sensor
   inputs to aid in the transference of evolved controllers to physical UAVs. We used both direct and environmental incremental evolution to evolve controllers for four types
   of radars: 1) continuously emitting, stationary radars, 2) continuously emitting, mobile radars, 3) intermittently emitting, stationary radars, and 4) intermittently
   emitting, mobile radars. The use of incremental evolution drastically increased evolution's chances of evolving a successful controller compared to direct evolution. This
   technique can also be used to develop a single controller capable of handling all four radar types. In the next stage of research, the best evolved controllers will be
   tested by using them to fly real UAVs.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Gregory J. Barlow
%T Autonomous Controller Design for Unmanned Aerial Vehicles using Multi-objective Genetic Programming
%B Proceedings of the Graduate Student Workshop at the 2004 Genetic and Evolutionary Computation Conference (GECCO-2004)
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, evolutionary robotics, multi-objective optimisation, unmanned aerial vehicles
%U http://www.andrew.cmu.edu/user/gjb/includes/publications/conference/barlow2004-geccogsw/barlow2004-geccogsw.pdf
%X Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using multi-objective genetic programming (GP). Four fitness
   functions derived from flight simulations were designed and multi-objective GP was used to evolve controllers able to locate a radar source, navigate the UAV to the source
   efficiently using on-board sensor measurements, and circle around the emitter. Controllers were evolved for three different kinds of radars: stationary, continuously
   emitting radars, stationary, intermittently emitting radars, and mobile, continuously emitting radars. In this study, realistic flight parameters and sensor inputs were
   selected to aid in the transference of evolved controllers to physical UAVs.
%8 24-26  June
%Z Winner of Best Paper at the Graduate Student Workshop at the 2004 Genetic and Evolutionary Computation Conference (GECCO-2004).
   http://www-illigal.ge.uiuc.edu:8080/GECCO-2004/awards-winners.html GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A Gregory J. Barlow
%A Choong K. Oh
%A Edward Grant
%T Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming
%B Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems (CIS)
%D 2004
%P 688--693
%I IEEE
%C Singapore
%K genetic algorithms, genetic programming, incremental evolution, multi-objective optimisation
%U http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2004-cis/barlow2004-cis.pdf
%X Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using incremental evolution with multi-objective genetic
   programming (GP). We designed four fitness functions derived from flight simulations and used multi-objective GP to evolve controllers able to locate a radar source,
   navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. We selected realistic flight parameters and sensor
   inputs to aid in the transference of evolved controllers to physical UAVs. We used both direct and environmental incremental evolution to evolve controllers for four types
   of radars: 1) continuously emitting, stationary radars, 2) continuously emitting, mobile radars, 3) intermittently emitting, stationary radars, and 4) intermittently
   emitting, mobile radars. The use of incremental evolution drastically increased evolution's chances of evolving a successful controller compared to direct evolution. This
   technique can also be used to develop a single controller capable of handling all four radar types. In the next stage of research, the best evolved controllers will be
   tested by using them to fly real UAVs.
%8 1-3  Decemeber
%Z IEEE CIS RAM 2004 http://cis-ram.nus.edu.sg/

%A Gregory J. Barlow
%A Leonardo S. Mattos
%A Edward Grant
%A Choong K. Oh
%T Transference of Evolved Unmanned Aerial Vehicle Controllers to a Wheeled Mobile Robot
%B Proceedings of the IEEE International Conference on Robotics and Automation
%E Ruediger Dillmann
%D 2005
%I IEEE
%I IEEE Robotics and Automation Society
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%U http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2005-icra/barlow2005-icra.pdf
%X Transference of controllers evolved in simulation to real vehicles is an important issue in evolutionary robotics (ER). We have previously evolved autonomous navigation
   controllers for fixed wing UAV applications using multi-objective genetic programming (GP). Controllers were evolved to locate a radar source, navigate the UAV to the
   source efficiently using on-board sensor measurements, and circle around the emitter. We successfully tested an evolved UAV controller on a wheeled mobile robot. A passive
   sonar system on the robot was used in place of the radar sensor, and a speaker emitting a tone was used as the target in place of a radar. Using the evolved navigation
   controller, the mobile robot moved to the speaker and circled around it. The results from this experiment demonstrate that our evolved controllers are capable of
   transference to real vehicles. Future research will include testing the best evolved controllers by using them to fly real UAVs.
%8 18-22  April
%Z http://www.icra2005.org/

%A Gregory J. Barlow
%A Choong K. Oh
%T Robustness analysis of genetic programming controllers for unmanned aerial vehicles
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 135--142
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Artificial Life Evolutionary Robotics, Adaptive Behavior, autonomous vehicles, program synthesis, reliability, robustness,
   synthesis, transference, unmanned aerial vehicles
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p135.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Gregory J. Barlow
%A Choong K. Oh
%A Stephen F. Smith
%T Evolving cooperative control on sparsely distributed tasks for UAV teams without global communication
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 177--184
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, evolutionary robotics, multiagent systems, multiobjective optimisation, unmanned aerial vehicles, Artificial life, adaptive
   behaviour, evolvable hardware
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p177.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389125

%A Panagiotis Barmpalexis
%A Kyriakos Kachrimanis
%A Emanouil Georgarakis
%T Solid dispersions in the development of a nimodipine floating tablet formulation and optimization by artificial neural networks and genetic programming
%J European Journal of Pharmaceutics and Biopharmaceutics
%V 77
%N 1
%D 2011
%P 122--131
%I 
%K genetic algorithms, genetic programming, Solid dispersions, Nimodipine, Controlled release, Effervescent floating tablets, Artificial neural networks
%U http://www.sciencedirect.com/science/article/B6T6C-51696TP-1/2/61fc7d46e9a66d451646234b5e96dedb
%X The present study investigates the use of nimodipine-polyethylene glycol solid dispersions for the development of effervescent controlled release floating tablet
   formulations. The physical state of the dispersed nimodipine in the polymer matrix was characterised by differential scanning calorimetry, powder X-ray diffraction, FT-IR
   spectroscopy and polarised light microscopy, and the mixture proportions of polyethylene glycol (PEG), polyvinyl-pyrrolidone (PVP), hydroxypropylmethylcellulose (HPMC),
   effervescent agents (EFF) and nimodipine were optimised in relation to drug release (percent release at 60 min, and time at which the 90percent of the drug was dissolved)
   and floating properties (tablet's floating strength and duration), employing a 25-run D-optimal mixture design combined with artificial neural networks (ANNs) and genetic
   programming (GP). It was found that nimodipine exists as mod I microcrystals in the solid dispersions and is stable for at least a three-month period. The tablets showed
   good floating properties and controlled release profiles, with drug release proceeding via the concomitant operation of swelling and erosion of the polymer matrix. ANNs and
   GP both proved to be efficient tools in the optimization of the tablet formulation, and the global optimum formulation suggested by the GP equations consisted of PEG =
   9percent, PVP = 30percent, HPMC = 36percent, EFF = 11percent, nimodipine = 14percent.

%A P. Barmpalexis
%A K. Kachrimanis
%A A. Tsakonas
%A E. Georgarakis
%T Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation
%J Chemometrics and Intelligent Laboratory Systems
%V 107
%N 1
%D 2011
%P 75--82
%I 
%K genetic algorithms, genetic programming, Artificial neural networks, Controlled release, Experimental design, Optimisation
%U http://www.sciencedirect.com/science/article/B6TFP-523CDG2-4/2/67c4e87b7f04a0e4f5f6fe07a1127ef8
%X Symbolic regression via genetic programming (GP) was used in the optimisation of a pharmaceutical zero-order release matrix tablet, and its predictive performance was
   compared to that of artificial neural network (ANN) models. Two types of GP algorithms were employed: 1) standard GP, where a single population is used with a restricted or
   an extended function set, and 2) multi-population (island model) GP, where a finite number of populations is adopted. The amounts of four polymers, namely PEG4000, PVP K30,
   HPMC K100 and HPMC E50LV were selected as independent variables, while the percentage of nimodipine released in 2 and 8 h (Y2h, and Y8h), respectively, and the time at
   which 90% of the drug was dissolved (t90%), were selected as responses. Optimal models were selected by minimisation of the Euclidian distance between predicted and optimum
   release parameters. It was found that the prediction ability of GP on an external validation set was higher compared to that of the ANNs, with the multi population and
   standard GP combined with an extended function set, showing slightly better predictive performance. Similarity factor (f2) values confirmed GP's increased prediction
   performance for multi-population GP (f2 = 85.52) and standard GP using an extended function set (f2 = 84.47).

%A Howard Barnum
%A Herbert J. Bernstein
%A Lee Spector
%T A quantum circuit for OR
%D 1999
%I 
%K genetic algorithms, genetic programming
%U http://arxiv.org/PS_cache/quant-ph/pdf/9907/9907056.pdf
%X We give the first quantum circuit for computing $f(0)$ OR $f(1)$ more reliably than is classically possible with a single evaluation of the function. OR therefore joins XOR
   (i.e. parity, $f(0) \oplus f(1)$) to give the full set of logical connectives (up to relabeling of inputs and outputs) for which there is quantum speedup. The XOR algorithm
   is of fundamental importance in quantum computation; our OR algorithm (found with the aid of genetic programming), may represent a new quantum computational effect, also
   useful as a ``subroutine''.
%O arXiv.or
%8 October ~08
%Z Comment: 4 pages + 2 postscript figures. Version 3 restores the figures to Version 2, which changed the title, abstract, introduction and concluding paragraph, order of
   material, and emphasis from Version 1. No change in technical content

%A Howard Barnum
%A Herbert J Bernstein
%A Lee Spector
%T Quantum circuits for OR and AND of OR's
%R Technical Report
%D 2000
%I 
%I University of Bristol
%C UK
%K genetic algorithms, genetic programming
%U http://www.cs.bris.ac.uk/Publications/Papers/1000497.pdf
%X We give the first quantum circuit, derived with the aid of genetic programming, for computing $f(0)$ OR $f(1)$ more reliably than is classically possible with a single
   evaluation of the function. OR therefore joins XOR (i.e. parity, $f(0) \oplus f(1)$) to give the full set of logical connectives (up to relabeling of inputs and outputs)
   for which there is quantum speedup.
%8 August
%Z See also \citebarnum:2000:qc

%A Howard Barnum
%A Herbert J Bernstein
%A Lee Spector
%T Quantum circuits for OR and AND of ORs
%J Journal of Physics A: Mathematical and General
%V 33
%N 45
%D 2000
%P 8047--8057
%I 
%K genetic algorithms, genetic programming
%U http://hampshire.edu/lspector/pubs/jpa.ps
%X We give the first quantum circuit for computing f(0) or f(1) more reliably than is classically possible with a single evaluation function. Or therefor joins XOR (ie parity)
   to give the full set of logical connectives (up to relabelling of inputs and outputs) for which there is a quantum speedup
%8 17  November
%Z reports new quantum algorithms discovered by GP, with some details on the GP processes

%A Christophe Baron
%A Guy Gouarderes
%T Systemions to model alternative issues in problem solving
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 31--37
%I 
%C Orlando, Florida, USA
%8 13  July
%Z GECCO-99LB

%A Flavio Baronti
%A Antonina Starita
%T Enhancing Tournament Selection to Prevent Code Bloat in Genetic Programming
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 17--22
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp

%A David F. Barrero
%A David Camacho
%A Maria D. R-Moreno
%T Confidence intervals of success rates in evolutionary computation
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 975--976
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K Genetic programming: Poster
%X Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examined in detail. We address
   some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of
   the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical
   distribution with a large corpus of tools available that can be used in the context of EC research.One of those tools, confidence intervals (CIs), is studied.
%8 7-11  July
%Z Santa Fe trail artificial ant Also known as \cite1830657 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the
   fifteenth annual genetic programming conference (GP-2010)

%A David F. Barrero
%A Bonifacio Casta\~no
%A Maria D. R-Moreno
%A David Camacho
%T Statistical Distribution of Generation-to-Success in GP: Application to Model Accumulated Success Probability
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 154--165
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X Many different metrics have been defined in Genetic Programming. Depending on the experiment requirements and objectives, a collection of measures are selected in order to
   achieve an understanding of the algorithm behaviour. One of the most common metrics is the accumulated success probability, which evaluates the probability of an algorithm
   to achieve a solution in a certain generation. We propose a model of accumulated success probability composed by two parts, a binomial distribution that models the total
   number of success, and a lognormal approximation to the generation-to-success, that models the variation of the success probability with the generation.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A David F. Barrero
%A Maria R-Moreno
%A Bonifacio Castano
%A David Camacho
%T An Empirical Study on the Accuracy of Computational Effort in Genetic Programming
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 1169--1176
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming
%X Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of
   individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in
   the literature, their behaviour is not well known. In this paper we try to study the accuracy of these measures and advance in the understanding of the factors that
   influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation
   of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental
   setups, and under certain circumstances, might have a high relative error.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A John Barrett
%A Aneta Kostadinova
%A Juan Antonio Raga
%T Mining parasite data using genetic programming
%J Trends in Parasitology
%V 21
%N 5
%D 2005
%P 207--209
%I 
%K genetic algorithms, genetic programming
%X Genetic programming is a technique that can be used to tackle the hugely demanding data-processing problems encountered in the natural sciences. Application of genetic
   programming to a problem using parasites as biological tags demonstrates its potential for developing explanatory models using data that are both complex and noisy.
%8 May

%A S. J. Barrett
%T Recurring Analytical Problems within Drug Discovery and Development
%B Data Mining and Text Mining for Bioinformatics: Proceedings of the European Workshop
%E Tobias Scheffer and Ulf Leser
%D 2003
%P 6--7
%I 
%I KDnet
%C Dubrovnik, Croatia
%K genetic algorithms, genetic programming, SVM, SNP
%U http://www2.informatik.hu-berlin.de/~scheffer/publications/ProceedingsWS2003.pdf
%X The overall processes driving pharmaceuticals discovery and development research involve many disparate kinds of problems and problem-solving at multiple levels of
   generality and specificity. The discovery/pre-clinical processes are also highly technology-driven and specific aspects may be more dynamic over time compared to
   developmental research which is conducted in a more conservatively controlled manner, conducive to regulatory requirements.
%O Invited talk
%8 22  September
%Z Held in Conjunction with ECML / PKDD- 2003

%A S. J. Barrett
%A W. B. Langdon
%T Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development
%B Applications of Soft Computing: Recent Trends
%S Advances in Soft Computing
%E Ashutosh Tiwari and Joshua Knowles and Erel Avineri and Keshav Dahal and Rajkumar Roy
%D 2006
%P 99--110
%I Springer
%I World Federation of Soft Computing (WFSC), European Neural Network Society (ENNS), North American Fuzzy Information Processing Society (NAFIPS), European Society for Fuzzy
   Logic and Technology (EUSFLAT), and International Fuzzy Systems Association (IFSA)
%C On the World Wide Web
%K genetic algorithms, genetic programming, Pharmaceutical applications, Drug design, Particle swarm optimisation, Support vector machines
%U http://isxp1010c.sims.cranfield.ac.uk/Papers/paper196.pdf
%X Pharmaceutical discovery and development is a cascade of extremely complex and costly research encompassing many facets from: therapeutic target identification and
   bioinformatics study, candidate drug discovery and optimisation to pre-clinical organism-level evaluations and beyond to extensive clinical trials assessing effectiveness
   and safety of new medicines. Machine learning, in particular support vector machines SVM, particle swarm optimisation PSO and genetic programming GP, is increasingly used.
%8 19  September  - 7  October  2005
%Z http://www.cranfield.ac.uk/wsc10/ broken Original conference title= WSC10: 10th Online World Conference on Soft Computing in Industrial Applications
   http://isxp1010c.sims.cranfield.ac.uk/Presentations/presentation196.pdf broken slides (1Mbyte) Revised following conference.
%@ ISBN 3-540-29123-7

%A Steven J. Barrett
%T Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems John Wiley \& Sons Ltd., Chichester, UK, Keedwell, Edward and
   Narayanan, Ajit, 2005, 280 p., Hardcover, ISBN 0-470-02175-6
%J Genetic Programming and Evolvable Machines
%V 7
%N 3
%D 2006
%P 283--284
%I 
%K genetic algorithms, genetic programming
%O Book review
%8 October

%A Olivier Barriere
%A Evelyne Lutton
%A Cedric Baudrit
%A Mariette Sicard
%A Bruno Pinaud
%A Nathalie Perrot
%T Modeling human expertise on a cheese ripening industrial process using GP
%B Parallel Problem Solving from Nature - PPSN X
%S LNCS
%E Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume
%V 5199
%D 2008
%P 859--868
%I Springer
%C Dortmund
%K genetic algorithms, genetic programming
%X Industrial agrifood processes often strongly rely on human expertise, expressed as know-how and control procedures based on subjective measurements (colour, smell,
   texture), which are very difficult to capture and model. We deal in this paper with a cheese ripening process (of French Camembert), for which experimental data have been
   collected within a cheese ripening laboratory chain. A global and a monopopulation cooperative/coevolutive GP scheme (Parisian approach) have been developed in order to
   simulate phase prediction (i.e. a subjective estimation of human experts) from microbial proportions and Ph measurements. These two GP approaches are compared to Bayesian
   network modelling and simple multilinear learning algorithms. Preliminary results show the effectiveness and robustness of the Parisian GP approach.
%8 13-17  September
%Z GPLAB, Matlab, multi linear regression, INCALIN, Terminals: time derivatives of pH acidity, lactose and two bacteria concentrations. Gaussian random constants. Function
   set: arithmetics, log, exp?, Boolean ops. Fitness: parsimony, Euclidean sharing distance. tree GP. 30-40 nodes. Mutation Chi squared. 16 experiments each lasting 40 days.
   Missing data estimated by fitting splines. Log? distribution on floats. See also \citeinria-00381681 PPSN X
%@ 3-540-87699-5

%A Olivier Barriere
%A Evelyne Lutton
%A Pierre-Henri Wuillemin
%A Cedric Baudrit
%A Mariette Sicard
%A Bruno Pinaud
%A Nathalie Perrot
%T Modeling an agrifood industrial process using cooperative coevolution Algorithms
%R Technical Report inria-00381681, version 1
%D 2009
%I 
%I INRIA
%C Parc Orsay, France
%K genetic algorithms, genetic programming, Parisian, Computer Science, Artificial Intelligence, Life Sciences/Food and Nutrition, Agrifood, Cheese ripening, Cooperative
   coevolution, Parisian approach, Bayesian Network
%U http://hal.inria.fr/docs/00/38/16/81/PDF/RR2008.pdf
%X This report presents two experiments related to the modeling of an industrial agrifood process using evolutionary techniques. Experiments have been focused on a specific
   problem which is the modeling of a Camembert-cheese ripening process. Two elated complex optimisation problems have been considered: -- a deterministic modeling problem,
   the phase prediction problem, for which a search for a closed form tree expression has been performed using genetic programming (GP), -- a Bayesian network structure
   estimation problem, considered as a two-stage problem, i.e. searching first for an approximation of an independence model using EA, and then deducing, via a deterministic
   algorithm, a Bayesian network which represents the equivalence class of the independence model found at the first stage. In both of these problems, cooperative-coevolution
   techniques (also called ``Parisian'' approaches) have been proved successful. These approaches actually allow to represent the searched solution as an aggregation of
   several individuals (or even as a whole population), as each individual only bears a part of the searched solution. This scheme allows to use the artificial Darwinism
   principles in a more economic way, and the gain in terms of robustness and efficiency is important.
%8 6  May

%A Rodrigo C. Barros
%A Marcio P. Basgalupp
%A Andre C. P. L. F. {de Carvalho}
%A Alex A. Freitas
%T Towards the automatic design of decision tree induction algorithms
%B GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms
%E Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward
%D 2011
%P 567--574
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily
   understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the
   years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes two different approaches for automatically generating
   generic decision tree induction algorithms. Both approaches are based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological
   processes. We also propose guidelines to design interesting fitness functions for these evolutionary algorithms, which take into account the requirements and needs of the
   end-user.
%8 12-16  July
%Z Also known as \cite2002050 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Rodrigo C. Barros
%A Duncan D. Ruiz
%A Marcio P. Basgalupp
%T Evolutionary model trees for handling continuous classes in machine learning
%J Information Sciences
%V 181
%N 5
%D 2011
%P 954--971
%I 
%K genetic algorithms, genetic programming, Evolutionary algorithms, Model trees, Continuous classes, Machine learning
%U http://www.sciencedirect.com/science/article/B6V0C-51GHWYC-1/2/2ba74d92cb03abc637a4c377b47a4dbe
%X Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the
   end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously
   formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating
   the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not
   converge to the global optimal solution. we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model
   trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive
   performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary
   approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning
   applications.

%A Alwyn Barry
%T Aliasing in XCS and the Consecutive State Problem: 1 - Effects
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 19--26
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-317.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Alwyn Barry
%T Aliasing in XCS and the Consecutive State Problem: 2 - Solutions
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 27--34
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-336.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%T GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2002
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York
%K genetic algorithms, genetic programming, optimization, fuzzy model, design optimization, case-based reasoning, evolutionary algorithm, evolution strategies, simulated
   annealing, agents, evolutionary computation, co-evolution, parallel implementation, learning classifier system, time series prediction, grammatical evolution,
   multi-objective optimization, planning, scheduling, industrial applications, machine learning, niching, linkage learning
%8 8  July
%Z Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic
   Programming Conference (GP-2002)

%T GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2003
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C Chigaco
%K genetic algorithms, genetic programming, optimization, fuzzy model, design optimization, case-based reasoning, evolutionary algorithm, evolution strategies, simulated
   annealing, agents, evolutionary computation, co-evolution, parallel implementation, learning classifier system, time series prediction, grammatical evolution,
   multi-objective optimization, planning, scheduling, machine learning, representations
%8 11  July
%Z Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming
   Conference (GP-2003) part of barry:2003:GECCO:workshop

%A Alberto Bartoli
%A Giorgio Davanzo
%A Andrea {De Lorenzo}
%A Eric Medvet
%T GP-based Electricity Price Forecasting
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 37--48
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X The electric power market is increasingly relying on competitive mechanisms taking the form of day-ahead auctions, in which buyers and sellers submit their bids in terms of
   prices and quantities for each hour of the next day. Methods for electricity price forecasting suitable for these contexts are crucial to the success of any bidding
   strategy. Such methods have thus become very important in practice, due to the economic relevance of electric power auctions. In this work we propose a novel forecasting
   method based on Genetic Programming. Key feature of our proposal is the handling of outliers, i.e., regions of the input space rarely seen during the learning. Since a
   predictor generated with Genetic Programming can hardly provide acceptable performance in these regions, we use a classifier that attempts to determine whether the system
   is shifting toward a difficult-to-learn region. In those cases, we replace the prediction made by Genetic Programming by a constant value determined during learning and
   tailored to the specific subregion expected. We evaluate the performance of our proposal against a challenging baseline representative of the state-of-the-art. The baseline
   analyses a real-world dataset by means of a number of different methods, each calibrated separately for each hour of the day and recalibrated every day on a progressively
   growing learning set. Our proposal exhibits smaller prediction error, even though we construct one single model, valid for each hour of the day and used unmodified across
   the entire testing set. We believe that our results are highly promising and may open a broad range of novel solutions.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Alan J. Barton
%A Julio J. Valdes
%T Computational Intelligence Techniques Applied to Magnetic Resonance Spectroscopy Data of Human Brain Cancers
%B Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2008
%S Lecture Notes in Computer Science
%E Chien-Chung Chan and Jerzy W. Grzymala-Busse and Wojciech Ziarko
%V 5306
%D 2008
%P 485--494
%I Springer
%C Akron, OH, USA
%K genetic algorithms, genetic programming
%X Computational intelligence techniques were applied to human brain cancer magnetic resonance spectral data. In particular, two approaches, Rough Sets and a Genetic
   Programming-based Neural Network were investigated and then confirmed via a systematic Individual Dichotomization algorithm. Good preliminary results were obtained with
   100percent training and 100percent testing accuracy that differentiate normal versus malignant samples.
%8 October  23-25

%A Alan J. Barton
%A Julio J. Valdes
%A Robert Orchard
%T Learning the neuron functions within a neural network via Genetic Programming: Applications to geophysics and hydrogeology
%B International Joint Conference on Neural Networks, IJCNN 2009
%D 2009
%P 264--271
%I 
%C Atlanta, Georgia, USA
%K genetic algorithms, genetic programming, gene expression programming, geophysics, geophysics computing, hydrology, neural nets, geophysics, hydrogeology, neural network
   classifier, neural network neurons, neuron functions
%X A neural network classifier is sought. Classical neural network neurons are aggregations of a weight multiplied by an input value and then controlled via an activation
   function. This paper learns everything within the neuron using a variant of genetic programming called gene expression programming. That is, this paper does not explicitly
   use weights or activation functions within a neuron, nor bias nodes within a layer. Promising preliminary results are reported for a study of the detection of underground
   caves (a 1 class problem) and for a study of the interaction of water and minerals near a glacier in the Arctic (a 5 class problem).
%8 June  14-19
%Z one class membership. ANN variable with 0 mean 1 standard deviation. Also known as \cite5178731 See \citeBarton2009614

%A Alan J. Barton
%A Julio J. Valdes
%A Robert Orchard
%T Neural networks with multiple general neuron models: A hybrid computational intelligence approach using Genetic Programming
%J Neural Networks
%E S. Bressler and R. Kozma and L. Perlovsky and Venayagamoorthy
%V 22
%N 5-6
%D 2009
%P 614--622
%I 
%K genetic algorithms, genetic programming, General neuron model, Evolutionary Computation, Hybrid algorithm, Machine learning, Parameter space, Visualization
%U http://www.sciencedirect.com/science/article/B6T08-4WNRK15-3/2/d8803b07859caa7efcd99475af7005ae
%X Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of
   neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection)
   is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching
   a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network
   solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to
   illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the
   set of all network solutions.
%O Advances in Neural Networks Research: IJCNN2009, 2009 International Joint Conference on Neural Networks

%A Alan J. Barton
%T Searching for a single mathematical function to address the nonlinear retention time shifts problem in nanoLC-MS data: A fuzzy-evolutionary computational proteomics
   approach
%B 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
%D 2010
%I 
%K genetic algorithms, genetic programming, gene expression programming, fuzzy-evolutionary computational proteomics approach, liquid chromatography coupled mass spectrometry,
   mathematical function, nanoLC-MS, nanoLC-MS data, nonlinear retention time shifts problem, biocomputing, evolutionary computation, fuzzy set theory, proteins, proteomics
%X Proteomics involves collecting and analysing information about proteins within one or more complex samples in order to address a biological problem. One methodology is the
   use of high performance liquid chromatography coupled mass spectrometry (nanoLC-MS). In such a case, the accurate determination of non-linear peptide retention times
   between runs is expected to increase the number of identified peptides and hence, proteins. There are many approaches when using a computer for such a problem; including
   very interactive to completely non-interactive algorithms for finding global and local functions that may be either explicit or implicit. This paper extends previous work
   and explores finding an explicit global function for which two stages are involved: i) computation of a set of candidate functions (results) by the algorithm, and ii)
   searching within the set for patterns of interest. For the first stage, three classes of approximating global functions are considered: Class 1 functions that have a
   completely unknown structure, Class 2 functions that have a tiny amount of domain knowledge incorporated, and Class 3 functions that have a small amount of domain knowledge
   incorporated. For the second stage, some issues with current similarity measures for mathematical expressions are discussed and a new measure is proposed. Preliminary
   experimental results with an Evolutionary Computation algorithm called Gene Expression Programming (a variant of Genetic Programming) when used with a fuzzy membership
   within the fitness function are reported.
%8 May
%Z Also known as \cite5510688

%A Derek Bartram
%A Michael Burrow
%A Xin Yao
%T A Computational Intelligence Approach to Railway Track Intervention Planning
%B Evolutionary Computation in Practice
%S Studies in Computational Intelligence
%E Tina Yu and David Davis and Cem Baydar and Rajkumar Roy
%V 88
%D 2008
%P 163--198
%I Springer
%K genetic algorithms, genetic programming, k-means, RPCL, learning
%O 8
%Z Part of \citeTinaYu:2008:book Scheduling, railroad, maintenance, planning. Missing data. Missing values. p181 function set + - * / sin cos tan power

%A David Basanta
%A Mark A. Miodownik
%A Elizabeth A. Holm
%T Evolving Cellular Automata to Grow Microstructures
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 1--10
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=1
%X The properties of engineering structures such as cars, cell phones or bridges rely on materials and on the properties of these materials. The study of these properties,
   which are determined by the internal architecture of the material or microstructure, has significant importance for material scientists. One of the things needed for this
   study is a tool that can create microstructural patterns. In this paper we explore the use of a genetic algorithm to evolve the rules of an effector automata to recreate
   these microstructural patterns.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A David Basanta
%A Mark A. Miodownik
%A Peter J. Bentley
%A Elizabeth A. Holm
%T Evolving and Growing Microstructures of Materials using Biologically Inspired CA
%B 2004 NASA/DoD Conference on Evolvable Hardware
%E RS Zebulum
%D 2004
%P 275--275
%I IEEE Computer Society
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%X The properties of engineering structures, such as robotic arms, aircrafts or bridges, rely on the properties of the materials used to build them. The internal architecture
   of the material or microstructure determines its properties and therefore, its study is of great interest for engineers and material scientists. Although there are tools
   that can provide 2D microstructural information, tools that can be used to obtain 3D characterisations of microstructures for routine analysis are not yet available to
   material scientists. In this paper we will describe Microconstructor. Microconstructor comprises a genetic algorithm that evolves populations of Cellular Automata inspired
   by developmental biology that self organise into 3D patterns that can be used for microstructural analysis.
%8 June  24-26
%@ 0-7695-2145-2

%A Andreas Bastian
%T Identifying fuzzy models utilizing genetic programming
%J Fuzzy Sets and Systems
%V 113
%N 3
%D 2000
%P 333--350
%I 
%K genetic algorithms, genetic programming, System identification, Fuzzy modeling
%U http://www.sciencedirect.com/science/article/B6V05-4234BFC-1/1/261a04fa056f3f0dfe0fb79a773a971a
%X Fuzzy models offer a convenient way to describe complex nonlinear systems. Moreover, they permit the user to deal with uncertainty and vagueness. Due to these advantages
   fuzzy models are employed in various fields of applications, e.g. control, forecasting, and pattern recognition. Nevertheless, it has to be emphasised that the
   identification of a fuzzy model is a complex optimisation task with many local minima. Genetic programming provides a way to solve such complex optimization problems. In
   this work, the use of genetic programming to identify the input variables, the rule base and the involved membership functions of a fuzzy model is proposed. For this
   purpose, several new reproduction operators are introduced.
%8 1  August

%A Dmitry Batenkov
%T Hands-on introduction to genetic programming
%J XRDS Crossroads
%V 17
%N 1
%D 2010
%P 46--51
%I ACM
%K genetic algorithms, genetic programming, Coding Tools and Techniques, Expressions and Their Representation, Object-oriented Programming, Problem Solving, Control Methods,
   Search
%U http://xrds.acm.org/article.cfm?aid=1836558
%X The idea to mimic the principles of Darwinian evolution in computing has been around at least since the 1950s, so long, in fact, that it has grown into the field called
   evolutionary computing (EC). In this tutorial, we'll learn the basic principles of EC and its offspring, genetic programming (GP), on a "toy problem" of symbolic
   regression. We'll also learn how to use OpenBeagle, a generic C++ object-oriented EC framework.
%O The ACM Magazine for Students
%8 September  2010
%Z http://xrds.acm.org/ Christian Gagne's Open Beagle

%A Dmitry Batenkov
%T Open BEAGLE: a generic framework for evolutionary computations
%J Genetic Programming and Evolvable Machines
%V 12
%N 3
%D 2011
%P 329--331
%I Springer
%K genetic algorithms, genetic programming
%O Software review
%8 September

%A R. G. Bates
%A M. A. H. Dempster
%A Y. S. Romahi
%T Evolutionary reinforcement learning in FX order book and order flow analysis
%B IEEE International Conference on Computational Intelligence for Financial Engineering
%D 2003
%P 355--362
%I 
%C Hong Kong
%K genetic algorithms, genetic programming
%U http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2003/WP06.pdf
%X As macroeconomic fundamentals based modelling of FX time series have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved
   towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in
   foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from
   computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are
   preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.
%8 20-23  March
%Z Final report to HSBC Investment Bank, November (2002). Location: Technical report WP06/2003

%A Daryl Battle
%A Abdollah Homaifar
%A Edward Tunstel
%A Gerry Dozier
%T Genetic Programming of Full Knowledge Bases for Fuzzy Logic Controllers
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1463--1468
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-730.ps
%X Genetic programming (GP) is applied to automatic discovery of full knowledge bases for use in fuzzy logic control applications. An extension to a rule learning GP system is
   presented that achieves this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to
   design a mobile robot path tracking controller and performance is shown to be comparable to that of a manually designed controller.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Eric T. Bauer
%T Evolving Efficient Algorithms by Genetic Programming: A Case Study in Sorting
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 1--10
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Johannes M. Bauer
%A Kurt DeMaagd
%T Network Management Practices and Sector Performance - A Genetic Programming Approach
%B 36th Research Conference on Communications, Information, and Internet Policy.
%E Elizabeth Mateja
%D 2008
%I 
%C Arlington, VA, USA
%K genetic algorithms, genetic programming
%U http://www.tprcweb.com/images/stories/2008/Bauer-DeMaagd-Network-Management-2008-TPRC-fin.pdf
%X Introduction The migration to next-generation network architectures, in which platform and application/content layers are relatively distinct, has unleashed a very
   important and possibly far-reaching policy debate as to the rules that should govern the interaction of players operating on one or both of these layers. Started as a
   discussion on network neutrality, the debate recently shifted focus to delineating reasonable from unreasonable forms of network management. Legislation to strengthen
   regulatory powers (Markey Bill) or antitrust enforcement (Conyers Bill) is pending in Congress. The Federal Communications Commission has reasserted its willingness to
   enforce an open internet in its Comcast Decision in August 2008.
%8 September  27
%Z http://www.tprcweb.com/index.php?option=com_content&view=article&id=29&Itemid=18

%A Eric B. Baum
%A Igor Durdanovic
%T Toward Code Evolution By Artificial Economies
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB. See also \citebaum:1998:tceaeTR

%A Eric B. Baum
%A Igor Durdanovic
%T Toward Code Evolution By Artificial Economies
%R Technical Report
%D 1998
%I 
%I NEC Research Institute
%C 4 Independence Way, Princeto, NJ 08540, USA
%K genetic algorithms, genetic programming
%8 10  July
%Z Hayek2 blocks world "crossover is much better than headless chicken mutation" meta-agents, inherited wealth, rent, intellectual property, strong typing STGP. See also
   (\citebaum:1998:tceae, \citeoai:CiteSeerPSU:5199

%A Eric Baum
%A Igor Durdanovic
%T Toward Code Evolution By Artificial Economies (Extended Abstract)
%B Evolution as Computation, DIMACS Workshop, Princeton, January 1999
%S Natural Computing Series
%E Laura F. Landweber and Erik Winfree
%D 2001
%I Springer-Verlag
%C Princeton University
%K genetic algorithms, genetic programming
%U http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/2215/http:zSzzSzwww.neci.nj.nec.comzSzhomepageszSzericzSzevpap.pdf/toward-code-evolution-by.pdf
%X We have begun exploring code evolution by artificial economies. We implemented a reinforcement learning machine called Hayek2 consisting of agents, written in a machine
   language inspired by Ray's Tierra, that interact economically. The economic structure of Hayek2 addresses credit assignment at both the agent and meta levels. Hayek2
   succeeds in evolving code to solve Blocks World problems, and has been more effective at this than our hillclimbing program and our genetic program. Our hillclimber and our
   GP also performed well, learning algorithms as strong as a simple search program that incorporates hand-coded domain knowledge. We made efforts to optimize our hillclimbing
   program and it has features that may be of independent interest. Our genetic program using crossover performed far better than a version using other macro-mutations or our
   hillclimber, bearing on a controversy in the Genetic Programming literature.
%8 11-12  January
%Z see also \citebaum:1998:tceaeTR, http://dimacs.rutgers.edu/Workshops/Evolution/ Published Jan 2001
   http://www.amazon.com/exec/obidos/ASIN/3540667091/dominantsystems/107-7663466-9560554
%@ 3-540-66709-1

%A Laurent A. Baumes
%A Pierre Collet
%T Examination of genetic programming paradigm for high-throughput experimentation and heterogeneous catalysis
%J Computational Materials Science
%V 45
%N 1
%D 2009
%P 27--40
%I 
%K genetic algorithms, genetic programming, Heterogeneous catalysis, High-throughput, Materials, Combinatorial, Representation, Data structure
%U http://www.sciencedirect.com/science/article/B6TWM-4T4J19Y-1/2/809324138cc0b8f49634eae7f22e995f
%X The strong feature dependencies that exist in catalyst description do not permit using common algorithms while not loosing crucial information. Data treatments are
   restricted by the form of input data making the full use of the experimental information impossible, confining the experimentation studies, and reducing one of the primary
   goals of HTE: to enlarge the search space. Consequently, an advanced representation of the catalytic data supporting the intrinsic complexity of heterogeneous catalyst data
   structure is proposed. Likewise, an optimization strategy that can manipulate efficiently such data type, permitting a valuable connection between algorithms,
   high-throughput (HT) apparatus, and databases, is depicted. Such a new methodology enables the integration of domain knowledge through its configuration considering the
   study to be investigated. For the first time in heterogeneous catalysis, a conceptual examination of genetic programming (GP) is achieved.
%8 March
%Z Selected papers from the E-MRS 2007 Fall Meeting Symposium G: Genetic Algorithms in Materials Science and Engineering - GAMS-2007

%A L. A. Baumes
%A A. Blansche
%A P. Serna
%A A. Tchougang
%A N. Lachiche
%A P. Collet
%A A. Corma
%T Using Genetic Programming for an Advanced Performance Assessment of Industrially Relevant Heterogeneous Catalysts
%J Materials and Manufacturing Processes
%V 24
%N 3
%D 2009
%P 282--292
%I Taylor and Francis
%K genetic algorithms, genetic programming, Data mining, Heterogeneous catalysis, High-throughput, Materials science
%U http://lsiit.u-strasbg.fr/Publications/2009/BBSTLCC09
%X Beside the ease and speed brought by automated synthesis stations and reactors technologies in materials science, adapted informatics tools must be further developed in
   order to handle the increase of throughput and data volume, and not to slow down the whole process. This article reports the use of genetic programming (GP) in
   heterogeneous catalysis. Despite the fact that GP has received only little attention in this domain, it is shown how such an approach can be turned into a very singular and
   powerful tool for solid optimization, discovery, and monitoring. Jointly with neural networks, the GP paradigm is employed in order to accurately and automatically estimate
   the whole curve conversion vs. time in the epoxidation of large olefins using titanosilicates, Ti-MCM-41 and Ti-ITQ-2, as catalysts. In contrast to previous studies in
   combinatorial materials science and high-throughput screening, it was possible to estimate the entire evolution of the catalytic reaction for unsynthesized catalysts.
   Consequently, the evaluation of the performance of virtual solids is not reduced to a single point (e.g., the conversion level at only one given reaction time or the
   initial reaction rate). The methodology is thoroughly detailed, while stressing on the comparison between the recently proposed Context Aware Crossover (CAX) and the
   traditional crossover operator.
%8 March
%Z Affiliations: Institute of Chemical Technology, CSIC-UPV, Valencia, Spain Louis Pasteur University, LSIIT, FDBT, Illkirch, France

%A Andrei Bautu
%A Elena Bautu
%T Quantum Circuit Design By Means Of Genetic Programming
%J Romanian Journal of Physics
%V 52
%N 5-7
%D 2007
%P 697--704
%I Romanian Academy Publishing House
%C Bucharest, Romania
%K genetic algorithms, genetic programming, quantum gates
%U http://www.nipne.ro/rjp/2007_52_5-6/0697_0705.pdf
%X Research in quantum technology has shown that quantum computers can provide dramatic advantages over classical computers for some problems. The efficiency of quantum
   computing is considered to become so significant that the study of quantum algorithms has attracted widespread interest. Development of quantum algorithms and circuits is
   difficult for a human researcher, so automatic induction of computer programs by means of genetic programming, which uses almost no auxiliary information on the search
   space, proved to be useful in generating new quantum algorithms. This approach takes advantage of the intrinsic parallelism of the genetic algorithm and quantum computing
   parallelism. The paper begins with a brief review on some basic concepts in genetic algorithms and quantum computation. Next, it describes an application of genetic
   programming for evolving quantum computing circuits.
%Z S-expressions. Paper presented at the 7th International Balkan Workshop on Applied Physics, 5-7 July 2006, Constanta, Romania. http://www.nipne.ro/rjp/

%A Elena Bautu
%A Andrei Bautu
%A Henri Luchian
%T A GEP-based approach for solving Fredholm first kind integral equations
%B Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2005
%D 2005
%P 325
%I IEEE Computer Society
%K genetic algorithms, genetic programming, gene expression programming, Fredholm integral equations, GEA, GEP approach, evolutionary techniques, first kind integral
   equations, gene expression algorithm, symbolic technique, Fredholm integral equations, evolutionary computation, symbol manipulation
%X Evolutionary techniques have been widely accepted as an effective meta-heuristic for a wide variety of problems in different domains. The main purpose of this paper is to
   present a symbolic technique based on the evolutionary paradigm gene expression programming (GEP) for solving Fredholm first type integral equations. We present the main
   traits of the gene expression algorithm (GEA), and our implementation for solving first kind integral equations of Fredholm type. The results obtained on four model
   problems using the symbolic technique described in this paper prove it to be suitable to handle this class of problems.
%8 September
%@ 0-7695-2453-2

%A Elena Bautu
%A Andrei Bautu
%A Henri Luchian
%T Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming
%B Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05)
%D 2005
%P 321--324
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming
%X regression on a finite sample of noisy data. The purpose is to obtain a mathematical model for data which is both reliable and valid, yet the analytical expression is not
   restricted to any particular form. To obtain a statistical model of the noisy data set we use symbolic regression with pseudo-random number generators. We begin by
   describing symbolic regression and our implementation of this technique using genetic programming (GP) and gene expression programming (GEP). We present some results for
   symbolic regression on computer generated and real financial data sets in the final part of this paper.

%A Elena Bautu
%A Andrei Bautu
%A Henri Luchian
%T AdaGEP - An Adaptive Gene Expression Programming Algorithm
%B Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2007
%E Viorel Negru and Tudor Jebelean and Dana Petcu and Daniela Zaharie
%D 2007
%P 403--406
%I IEEE Computer Society
%C Timisoara, Romania
%K genetic algorithms, genetic programming, gene expression programming
%X Many papers focused on fine-tuning the gene expression programming (GEP) operators or their application rates in order to improve the performances of the algorithm. Much
   less work was done on optimizing the structural parameters of the chromosomes (i.e. number of genes and gene size). This is probably due to the fact that the no free lunch
   theorem states that no fixed values for these parameters will ever suit all problems. To counteract this fact, this paper presents a modified GEP algorithm, called AdaGEP,
   which automatically adapts the number of genes used by the chromosome. The adaptation process takes place at chromosome level, allowing chromosomes in the population to
   evolve with different number of genes.
%8 September  26-29
%Z p406 'The results presented in this paper demonstrate the superiority of AdaGEP over GEP on symbolic regression problems.'

%A Elena Bautu
%A Elena Pelican
%T Numerical Solution For Fredholm First Kind Integral Equations Occurring In Synthesis of Electromagnetic Fields
%J Romanian Journal of Physics
%V 52
%N 3-4
%D 2007
%P 245--256
%I Romanian Academy Publishing House
%K genetic algorithms, genetic programming, Gene Expression Programming, Fredholm integral equations of the first kind, inverse problems
%U http://www.nipne.ro/rjp/2007_52_3-4/0245_0257.pdf
%X It is known that Fredholm integral equations of the first kind with the kernel occur when solving with problems of synthesis of electrostatic and magnetic fields (m, n
   nonnegative rational numbers). This paper presents two approaches for solving such an equation. The first one involves discretisation by a collocation method and numerical
   solution using an approximate orthogonalisation algorithm. The second method is based on a nature inspired heuristic, namely genetic programming. It applies
   genetically-inspired operators to populations of potential solutions in the form of program trees, in an iterative fashion, creating new populations while searching for an
   optimal or near-optimal solution to the problem at hand. Results obtained in experiments are presented for both approaches.
%Z Paper presented at the 7th International Balkan Workshop on Applied Physics, 5-7 July 2006, Constanta, Romania. http://www.nipne.ro/rjp/

%A Elena Bautu
%A Andrei Bautu
%A Henri Luchian
%T An Evolutionary Approach for Modeling Time Series
%B 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC '08
%D 2008
%P 507--513
%I 
%K genetic algorithms, genetic programming, change point detection, data generation process, evolutionary approach, genetic operator, time series modeling, time series
%X Change points in time series appear due to variations in the data generation process. We consider the problem of modeling time series generated by dynamic processes, and we
   focus on finding the change points using a specially tailored genetic algorithm. The algorithm employs a new representation, described in detail in the paper. Suitable
   genetic operators are also defined and explained. The results obtained on computer generated time series provide evidence that the approach can be used for change point
   detection, and has good potential for time series modeling.
%8 September
%Z Also known as \cite5204862

%A Elena Bautu
%A Elena Pelican
%T Symbolic approach for the generalized airfoil equation
%J Creative Mathematics and Informatics
%V 17
%N 2
%D 2008
%P 52--60
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming, Generalised airfoil equation, Fredholm integral equation of the first kind, airfoil equation
%U http://creative-mathematics.ubm.ro/issues/down.php?f=creative_17_2008_no2_052_060.pdf
%X The generalised airfoil equation governs the pressure across an airfoil oscillating in a wind tunnel. In this paper we analyse the problem for an airfoil with a flap, by
   means of Gene Expression Programming (GEP). We present the main traits of the GEP metaheuristic and then we define its elements in order to be used for integral equations
   of the first kind. The results obtained by our symbolic approach confirm the suitability of this method for problems modelled by Fredholm first kind integral equations.
%Z http://creative-mathematics.ubm.ro/

%A Elena Bautu
%A Andrei Bautu
%A Henri Luchian
%T Evolving Gene Expression Programming Classifiers for Ensemble Prediction of Movements on the Stock Market
%B The Fourth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2010)
%E Leonard Barolli and Fatos Xhafa and Salvatore Vitabile and Hui-Huang Hsu
%D 2010
%P 108--115
%I IEEE Computer Society
%C Krakow, Poland
%K genetic algorithms, genetic programming, gene expression programming
%8 15-18  February

%A Elena Bautu
%T Intelligent Techniques for Data Modeling Problems
%R Ph.D. Thesis
%D 2010
%I 
%I Al. I. Cuza University
%C Iasi, Romania
%K genetic algorithms, genetic programming, gene expression programming, inverse problems, financial forecasting, data analysis, hypernetwork, hybridization
%U https://sites.google.com/site/ebautu/home/publications/thesis
%X Supervised learning deals with the problem of discovering models from data as relationships between input and output attributes. Two types of models are distinguished:
   regression models for continuous output and classification models (classiffiers) for discrete output. This thesis addresses both regression and classiffication problems,
   with an emphasis on new applications and on proposing new evolutionary techniques. First, we address the regression domain. Symbolic regression by means of evolutionary
   techniques is recommended when there is little or no a priori information on the modelled process. It relies on a set of input-output observations to infer mathematical
   models, posing no constraints on the structure, the coefficients or the size of the model. We introduce inverse problems modeled by Fredholm integral equations of the first
   kind and the inverse problem of log synthesis to be modelled by symbolic regression by means of gene expression programming. A new genetic programming scheme is formulated
   for the problem of automatically designing quantum circuits. An adaptive version of the gene expression programming algorithm is presented, which automatically tunes the
   complexity of the model by a gene (de)activation mechanism. For modelling time series produced by dynamic processes, we propose an evolutionary approach that uses a novel
   representation (and suitable genetic operators) to partition the time series based on change points. Empirical results prove the approach to be promising. Research on
   building classifiers for a given problem is also extensive, since there exists no best classifier at all tasks. The problem of predicting the direction of change of stock
   price on the market can be formulated as the search for a classifier that links past evolution to an increase or decrease. We explore new techniques for classification, in
   the context of predicting the direction of change of stock price, formulated as a binary classification
%O Romanian subtitle is Programare genetica pentru probleme de optimizare in Inteligenta artificiala
%8 June

%A Elena Bautu
%A Alina Barbulescu
%T A Hybrid Approach for Modelling Financial Time Series
%J The International Arab Journal of Information Technology (IAJIT)
%V 9
%D 2012
%I 
%K genetic algorithms, genetic programming

%A Cem M. Baydar
%A Kazuhiro Saitou
%T A Genetic Programming Framework for Error Recovery in Robotic Assembly Systems
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 756
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW036.pdf
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Cem M. Baydar
%A Kazuhiro Saitou
%T Off-Line Error Recovery Logic Synthesis in Automated Assembly Lines by using Genetic Programming
%B Proceedings Of The 2000 Japan/USA Symposium On Flexible Automation
%E Steven Y. Liang and Tatsuo Arai
%D 2000
%I 
%I ASME
%C Ann Arbor, MI, USA
%K genetic algorithms, genetic programming, Error Recovery Synthesis, Off-line Programming, Automated Assembly Lines
%U http://citeseer.ist.psu.edu/538284.html
%X Unexpected failures are one of the most important problems, which cause costly shutdowns in an assembly line. Generally the recovery process is done by the experts or
   automated error recovery logic controllers embedded in the system. The previous work in the literature is focused on the on-line recovery of the assembly lines which makes
   the process, time and money consuming. Therefore a novel approach is necessary which requires less time and hardware effort for the generation of error recovery logic. The
   proposed approach is based on three-dimensional geometric modelling of the assembly line coupled with the evolutionary computation techniques to generate error recovery
   logic in an off-line manner. The scope of this work is focused on finding an error recovery algorithm from a predefined error case. An automated assembly line is virtually
   modeled and the validity of the recovery algorithm is evaluated in a generate and test fashion by using a commercial software package. The obtained results showed that the
   developed framework is capable of generating recovery algorithms from an arbitrary part positioning error case. It is aimed that this approach will be coupled with the
   error generation in the future, providing efficient ways for the study of error recovery in automated assembly lines.
%8 23-26  July
%Z http://www.asme.org/divisions/med/enewsletter/2000oct/JapanUSAsymp.html http://members.asme.org/catalog/ItemView.cfm?ItemNumber=I464CD ASME Order #: I464CD
%@ 0-7918-1998-1

%A Cem M Baydar
%A Kazuhiro Saitou
%T Generation of Robust Recovery Logic in Assembly Systems using Multi-Level Optimization and Genetic Programming
%B Proceedings of DETC-00 ASME 2000 Design Engineering Technical Conferences and Computers and Information in Engineering Conference
%D 2000
%I 
%C Baltimore, Maryland, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/535775.html
%X Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, these errors are handled by human experts or logic controllers.
   However, these controller codes are based on anticipated error scenarios and are deficient in dealing with unforeseen situations. In our previous work (Baydar and Saitou,
   2000a), an approach for the automated generation of error recovery logic was discussed. The method is based on three-dimensional geometric modeling of the assembly line to
   generate error recovery logic in an "off-line" manner using Genetic Programming. The scope of our previous work was focused on finding an error recovery algorithm from a
   predefined error case. However due to the geometrical features of the assembly lines, there may be cases which can be detected as the same type of error by the sensors.
   Therefore robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. In this paper, an extension of our
   previous study is presented to overcome this problera An assembly line is modeled and from the given error cases optimum way of error recovery is investigated using
   multi-level optimization. The obtained results showed that the infrastructure is capable of finding robust error recovery algorithms and multi-level optimization procedure
   improved the process. It is expected that the results of this study will be combined with the automatic error generation, resulting in efficient ways to automated error
   recovery logic synthesis.
%O The Pennsylvania State University CiteSeer Archives
%8 10-13  September
%Z not verified

%A Cem M. Baydar
%A Kazuhiro Saitou
%T Off-line error prediction, diagnosis and recovery using virtual assembly systems
%B Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2001
%V 1
%D 2001
%P 818--823
%I IEEE
%C Seoul, Korea
%K genetic algorithms, genetic programming, 3D model, Bayesian reasoning, Monte Carlo simulation, assembly line, automated assembly systems, error scenarios, peg-in-hole
   assembly, unexpected failures, virtual assembly systems, Bayes methods, Monte Carlo methods, assembling, fault diagnosis, industrial robots, inference mechanisms, robot
   programming
%X Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of
   many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focusing
   on online diagnosing and recovering the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the
   possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the
   3D model of the assembly line to predict the possible errors in an offline manner. After that, these predicted errors can be diagnosed and recovered using Bayesian
   reasoning and genetic programming. A case study composed of a peg-in-hole assembly was performed and the results are discussed. It is expected that with this new approach,
   errors can be diagnosed and recovered accurately and costly downtime of robotic assembly systems will be reduced.
%8 21-26  May
%Z GP creates code in RAPID language. Also known as \cite932651
%@ 0-7803-6576-3

%A Cem M. Baydar
%T Off-line Error Prediction and Recovery Logic Synthesis using Virtual Assembly Systems
%R Ph.D. Thesis
%D 2001
%I 
%I The University of Michigan
%K genetic algorithms, genetic programming
%Z Chair: K. Saitou http://me.engin.umich.edu/news/pubs/ar/200209annualreportbw.pdf

%A Cem M. Baydar
%A Kazuhiro Saitou
%T Automated generation of robust error recovery logic in assembly systems using genetic programming
%J Journal of Manufacturing Systems
%V 20
%N 1
%D 2001
%P 55--68
%I 
%K genetic algorithms, genetic programming, robotics, Automated Assembly Systems, Error Recovery, Multi-Level Optimization
%U http://www.sciencedirect.com/science/article/B6VJD-441R1H8-6/2/cdebaddb30a67a67dc7cb6dd41fabf9f
%X Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, the recovery process is done 'on-line' by human experts or
   automated error recovery logic controllers embedded in the system. However, these controller codes are programmed based on anticipated error scenarios and, due to the
   geometrical features of the assembly lines, there may be error cases that belong to the same anticipated type but are present in different positions, each requiring a
   different way to recover. Therefore, robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. The
   proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the genetic programming and multi-level optimization techniques to
   generate robust error recovery logic in an 'off-line' manner. The approach uses genetic programming's flexibility to generate recovery plans in the robot language itself.
   An assembly line is modeled and from the given error cases an optimum way of error recovery is investigated using multi-level optimization in a 'generate and test' fashion.
   The obtained results showed that with the improved convergence gained by using multi-level optimisation, the infrastructure is capable of finding robust error recovery
   algorithms. It is expected that this approach will require less time for the generation of robust error recovery logic.
%Z IRB6000 KAREL2, ROUTINE GPcode26, Move to POS, Move Relative...

%A Cem Baydar
%A Kazuhiro Saitou
%T Off-line error prediction, diagnosis and recovery using virtual assembly systems
%J Journal of Intelligent Manufacturing
%V 15
%N 5
%D 2004
%P 679--692
%I Springer
%K genetic algorithms, genetic programming, Off-line programming, robotic assembly systems, virtual factories, error diagnosis and recovery
%X Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of
   many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focusing
   on on-line diagnosing and recovery of the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the
   possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the
   3-D model of the assembly line to predict the possible errors in an off-line manner. After that, these predicted errors are diagnosed and recovered using Bayesian reasoning
   and genetic algorithms. Several case studies are performed on single-station and multi-station assembly systems and the results are discussed. It is expected that with this
   new approach, errors can be diagnosed and recovered accurately and costly down times of robotic assembly systems will be reduced.
%8 October
%Z GP section 3.3. They generate error recovery code p688. linear chromosome Fig 4. Workspace Software. Pictures much better than \citeBaydar:2001:ICRA

%A Adil Baykasoglu
%A Turkay Dereli
%A Serkan Tanis
%T Prediction of cement strength using soft computing techniques
%J Cement and Concrete Research
%V 34
%N 11
%D 2004
%P 2083--2090
%I 
%K genetic algorithms, genetic programming, Gene expression programming, Modelling, Compressive strength, Cement manufacture
%U http://www.sciencedirect.com/science/article/B6TWG-4CBVDJS-1/2/46a55d4141904806cf09f3c92f56beb4
%X we aim to propose prediction approaches for the 28-day compressive strength of Portland composite cement (PCC) by using soft computing techniques. Gene expression
   programming (GEP) and neural networks (NNs) are the soft computing techniques that are used for the prediction of compressive cement strength (CCS). In addition to these
   methods, stepwise regression analysis is also used to have an idea about the predictive power of the soft computing techniques in comparison to classical statistical
   approach. The application of the genetic programming (GP) technique GEP to the cement strength prediction is shown for the first time in this paper. The results obtained
   from the computational tests have shown that GEP is a promising technique for the prediction of cement strength.
%8 November

%A Adil Baykasoglu
%T Soft computing approaches to production line design
%B ICRM'2005 3rd International Conference on Responsive Manufacturing
%E Nabil Gindy
%D 2005
%P 273--279
%I 
%I University of Nottingham, Guangdong University of Technology
%C Guangzhou, China
%K genetic algorithms, genetic programming, Gene Expression Programming, Manufacturing system design, soft computing
%X Gene Expression Programming (GEP) is used to develop a meta-model for the multiobjective design of a hypothetical production line. The developed meta-model is used to
   optimize production line design with Multiple Objective Tabu Search algorithm (MOTS). It is found out that GEP and MOTS can be effectively used to solve production line
   design problems which are known as complex design problems.
%8 12-14  September
%Z http://www.icrm2005.org/ broken Nov 2005

%A Adil Baykasoglu
%A Lale Ozbakir
%T MEPAR-miner: Multi-expression programming for classification rule mining
%J European Journal of Operational Research
%V 183
%N 2
%D 2007
%P 767--784
%I 
%K genetic algorithms, genetic programming, Data mining, Classification rules, Multi-expression programming, Evolutionary programming
%U http://www.sciencedirect.com/science/article/B6VCT-4MJS038-M/2/f780e675b2900eb28473dcbf6cfa03fb
%X Classification and rule induction are two important tasks to extract knowledge from data. In rule induction, the representation of knowledge is defined as IF-THEN rules
   which are easily understandable and applicable by problem-domain experts. In this paper, a new chromosome representation and solution technique based on Multi-Expression
   Programming (MEP) which is named as MEPAR-miner (Multi-Expression Programming for Association Rule Mining) for rule induction is proposed. Multi-Expression Programming
   (MEP) is a relatively new technique in evolutionary programming that is first introduced in 2002 by Oltean and Dumitrescu. MEP uses linear chromosome structure. In MEP,
   multiple logical expressions which have different sizes are used to represent different logical rules. MEP expressions can be encoded and implemented in a flexible and
   efficient manner. MEP is generally applied to prediction problems; in this paper a new algorithm is presented which enables MEP to discover classification rules. The
   performance of the developed algorithm is tested on nine publicly available binary and n-ary classification data sets. Extensive experiments are performed to demonstrate
   that MEPAR-miner can discover effective classification rules that are as good as (or better than) the ones obtained by the traditional rule induction methods. It is also
   shown that effective gene encoding structure directly improves the predictive accuracy of logical IF-THEN rules.

%A Adil Baykasoglu
%A Hamza Gullu
%A Hanifi Canakci
%A Lale Ozbakir
%T Prediction of compressive and tensile strength of limestone via genetic programming
%J Expert Systems with Applications
%V 35
%N 1-2
%D 2008
%P 111--123
%I 
%K genetic algorithms, genetic programming, multi expression programming, gene expression programming, Prediction, Limestone, Strength of materials
%U http://www.sciencedirect.com/science/article/B6V03-4NYJ0NK-1/2/00b6bf799aaf3df77a5e0fd846b85f20
%X Accurate determination of compressive and tensile strength of limestone is an important subject for the design of geotechnical structures. Although there are several
   classical approaches in the literature for strength prediction their predictive accuracy is generally not satisfactory. The trend in the literature is to apply artificial
   intelligence based soft computing techniques for complex prediction problems. Artificial neural networks which are a member of soft computing techniques were applied to
   strength prediction of several types of rocks in the literature with considerable success. Although artificial neural networks are successful in prediction, their inability
   to explicitly produce prediction equations can create difficulty in practical circumstances. Another member of soft computing family which is known as genetic programming
   can be a very useful candidate to overcome this problem. Genetic programming based approaches are not yet applied to the strength prediction of limestone. This paper makes
   an attempt to apply a promising set of genetic programming techniques which are known as multi expression programming (MEP), gene expression programming (GEP) and linear
   genetic programming (LGP) to the uniaxial compressive strength (UCS) and tensile strength prediction of chalky and clayey soft limestone. The data for strength prediction
   were generated experimentally in the University of Gaziantep civil engineering laboratories by using limestone samples collected from Gaziantep region of Turkey.

%A Adil Baykasoglu
%A Ahmet Oztas
%A Erdogan Ozbay
%T Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches
%J Expert Systems with Applications
%V 36
%N 3
%D 2009
%P 6145--6155
%I 
%K genetic algorithms, genetic programming, gene expression programming, Multiple objective optimization, Meta-heuristics, Prediction, High-strength concrete
%U http://www.sciencedirect.com/science/article/B6V03-4T0WJSK-G/2/2dd2cbea4bb9a919e91f3953aecaaa06
%X The optimization of composite materials such as concrete deals with the problem of selecting the values of several variables which determine composition, compressive
   stress, workability and cost etc. This study presents multi-objective optimization (MOO) of high-strength concretes (HSCs). One of the main problems in the optimization of
   HSCs is to obtain mathematical equations that represents concrete characteristic in terms of its constitutions. In order to solve this problem, a two step approach is used
   in this study. In the first step, the prediction of HSCs parameters is performed by using regression analysis, neural networks and Gen Expression Programming (GEP). The
   output of the first step is the equations that can be used to predict HSCs properties (i.e. compressive stress, cost and workability). In order to derive these equations
   the data set which contains many different mix proportions of HSCs is gathered from the literature. In the second step, a MOO model is developed by making use of the
   equations developed in the first step. The resulting MOO model is solved by using a Genetic Algorithm (GA). GA employs weighted and hierarchical method in order to handle
   multiple objectives. The performances of the prediction and optimization methods are also compared in the paper.
%8 April

%A Adil Baykasoglu
%A Mustafa Gocken
%T Gene expression programming based due date assignment in a simulated job shop
%J Expert Systems with Applications
%V 36
%N 10
%D 2009
%P 12143--12150
%I 
%K genetic algorithms, genetic programming, Gene expression programming, Due date assignment
%U http://www.sciencedirect.com/science/article/B6V03-4VY2C6B-1/2/d174ebf2e7f0566d9c964be7d6f4f2ab
%X In this paper, a new approach for due date assignment in a multi-stage job shop is proposed and evaluated. The proposed approach is based on a genetic programming technique
   which is known as gene expression programming (GEP). GEP is a relatively new member of the genetic programming family. The primary objective of this research is to compare
   the performance of the proposed due date assignment model with several previously proposed conventional due date assignment models. For this purpose, simulation models are
   developed and comparisons of the due date assignment models are made mainly in terms of the mean absolute percent error (MAPE), mean percent error (MPE) and mean tardiness
   (MT). Some additional performance measurements are also given. Simulation experiments revealed that for many test conditions the proposed due date assignment method
   dominates all other compared due date assignment methods.

%A Adil Baykasoglu
%A Mustafa Gocken
%A Lale Ozbakir
%T Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop
%J Simulation
%V 86
%N 12
%D 2010
%P 715--728
%I 
%K genetic algorithms, genetic programming, data mining, dispatching rules
%X In this paper, a genetic programming based data mining approach is proposed to select dispatching rules which will result in competitive shop performance under a given set
   of shop parameters (e.g. interarrival times, pre-shop pool length). The main purpose is to select the most appropriate conventional dispatching rule set according to the
   current shop parameters. In order to achieve this, full factorial experiments are carried out to determine the effect of input parameters on predetermined performance
   measures. Afterwards, a genetic programming based data mining tool that is known as MEPAR-miner (multi-expression programming for classification rule mining) is employed to
   extract knowledge on the selection of best possible conventional dispatching rule set according to the current shop status. The obtained results have shown that the
   selected dispatching rules are appropriate ones according to the current shop parameters. All of the results are illustrated via numerical examples and experiments on
   simulated data.

%A Michael D. Bayne
%T Vive l'evolution
%J Deep Magic
%D 1997
%I 
%K genetic algorithms, genetic programming, Java, www
%U http://samskivert.com/internet/deep/1997/02/12/
%X Evolutionary computing is a blanket term encompassing a host of methodologies and philosophies, all based upon the premise that mother nature is darned good at solving
   problems. The world is literally crawling with problem solvers of infinite variety. Although Charles Darwin planted the idea in 1859 with the publication of The Origin of
   Species, the concept of mimicking mother nature's problem solving techniques didn't start to flower until the mid-1960s, when the computing power to actually investigate
   such techniques was readily available.
%O www page
%8 12  February
%Z Quick overview of GP, ants GP java demo, http links to interesting places. Deep magic at http://www.go2net.com/internet/deep/ broken Feb 2012

%A Mohammad H. Baziar
%A Yaser Jafarian
%A Habib Shahnazari
%A Vahid Movahed
%A Mohammad Amin Tutunchian
%T Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach
%J Computer \& Geosciences
%V 37
%N 11
%D 2011
%P 1883--1893
%I 
%K genetic algorithms, genetic programming, Liquefaction, Capacity energy, Sand, Silt, Wildlife
%U http://www.sciencedirect.com/science/article/B6V7D-52R9DF5-2/2/08fa46566f649fc2348af34aa83ebbb2
%X Liquefaction is a catastrophic type of ground failure, which usually occurs in loose saturated soil deposits under earthquake excitations. A new predictive model is
   presented in this study to estimate the amount of strain energy density, which is required for the liquefaction triggering of sand-silt mixtures. A wide-ranging database
   containing the results of cyclic tests on sand-silt mixtures was first gathered from previously published studies. Input variables of the model were chosen from the
   available understandings evolved from the previous studies on the strain energy-based liquefaction potential assessment. In order to avoid over training, two sets of
   validation data were employed and a particular monitoring was made on the behaviour of the evolved models. Results of a comprehensive parametric study on the proposed model
   are in accord with the previously published experimental observations. Accordingly, the amount of strain energy required for liquefaction onset increases with increase in
   initial effective overburden pressure, relative density, and mean grain size. The effect of nonplastic fines on strain energy-based liquefaction resistance shows a more
   complicated behavior. Accordingly, liquefaction resistance increases with increase in fines up to about 10-15percent and then starts to decline for a higher increase in
   fines content. Further verifications of the model were carried out using the valuable results of some down hole array data as well as centrifuge model tests. These
   verifications confirm that the proposed model, which was derived from laboratory data, can be successfully used under field conditions.

%A Lawrence Beadle
%A Colin Johnson
%T Semantically Driven Crossover in Genetic Programming
%B Proceedings of the IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 111--116
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, Program Semantics, Crossover, Reduced Ordered Binary Decision Diagrams
%X Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic
   analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in
   better performance and smaller solutions in two separate genetic programming experiments.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Lawrence Beadle
%A Colin G. Johnson
%T Semantic Analysis of Program Initialisation in Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 10
%N 3
%D 2009
%P 307--337
%I 
%K genetic algorithms, genetic programming, Program initialisation, Program semantics, Program structure
%U http://www.springerlink.com/content/yn5p45723l6tr487
%X Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random
   combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of
   starting populations, the effects of purely semantic program initialisation and the importance of tree shape in the context of program initialisation. To achieve this, we
   create four different algorithms, in addition to using the traditional ramped half and half technique, applied to seven genetic programming problems. We present results to
   show that varying the choice and design of program initialisation can dramatically influence the performance of genetic programming. In particular, program behaviour and
   evolvable tree shape can have dramatic effects on the performance of genetic programming. The four algorithms we present have different rates of success on different
   problems.
%8 September

%A Lawrence Beadle
%A Colin G Johnson
%T Semantically Driven Mutation in Genetic Programming
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 1336--1342
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, Genetic programming, program semantics, semantically driven mutation, reduced ordered binary decision diagrams.
%X Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic
   changes in programs that result from the mutation operation. Using semantically driven mutation, we demonstrate increased performance in genetic programming on seven
   benchmark genetic programming problems over two different domains.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Lawrence Charles John Beadle
%T Semantic and Structural Analysis of Genetic Programming
%R Ph.D. Thesis
%D 2009
%I 
%I University of Kent
%C Canterbury
%K genetic algorithms, genetic programming
%U http://www.beadle.me/Me/LBeadle_PhD_Thesis.pdf
%X Genetic programming (GP) is a subset of evolutionary computation where candidate solutions are evaluated through execution or interpreted execution. The candidate solutions
   generated by GP are in the form of computer programs, which are evolved to achieve a stated objective. Darwinian evolutionary theory inspires the processes that make up GP
   which include crossover, mutation and selection. During a GP run, crossover, mutation and selection are performed iteratively until a program that satisfies the stated
   objectives is produced or a certain number of time steps have elapsed. The objectives of this thesis are to empirically analyse three different aspects of these evolved
   programs. These three aspects are diversity, efficient representation and the changing structure of programs during evolution. In addition to these analyses, novel
   algorithms are presented in order to test theories, improve the overall performance of GP and reduce program size. This thesis makes three contributions to the field of GP.
   Firstly, a detailed analysis is performed of the process of initialisation (generating random programs to start evolution) using four novel algorithms to empirically
   evaluate specific traits of starting populations of programs. It is shown how two factors simultaneously effect how strong the performance of starting population will be
   after a GP run. Secondly, semantically based operators are applied during evolution to encourage behavioural diversity and reduce the size of programs by removing
   inefficient segments of code during evolution. It is demonstrated how these specialist operators can be effective individually and when combined in a series of experiments.
   Finally, the role of the structure of programs is considered during evolution under different evolutionary parameters considering different problem domains. This analysis
   reveals some interesting effects of evolution on program structure as well as offering evidence to support the success of the specialist operators.
%8 July

%A Stuart Beale
%T Traffic Data: Less is More
%B Road Transport Information and Control
%D 2002
%I 
%I IEE
%C Savoy Place, London, UK
%K genetic algorithms, genetic programming
%X In support of the Governments 10 Year Transport Plan the Highways Agency has an ambitious programme to roll-out traffic systems on the English motorway network. The control
   methodologies within these systems can be further developed which will help meet the Government's targets to reduce congestion and accidents. This paper describes three
   innovative projects being undertaken by the Highways Agency. The approach to these projects departs from the traditional engineering approach, instead we have used
   mathematical techniques to evolve control functions that learn and operate on the available traffic data.
%8 19-21  March
%Z RTIC 2002 http://conferences.iee.org.uk/RTIC/ For "genetic algorithm" read "genetic programming"

%A Nick Beard
%T The joy of genetic programming
%J Personal Computer World
%D 1993
%P 471--472
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ga_beard93a.pdf
%8 June
%Z overview/introduction

%A K. Bearpark
%A A. J. Keane
%T Short term memory in genetic programming
%B Fourth International Conference on Adaptive Computing in Design and Manufacture, ACDM '00
%E I. C. Parmee
%D 2000
%P 309--320
%I Springer-Verlag
%C London, UK
%K genetic algorithms, genetic programming
%U http://eprints.soton.ac.uk/21399/
%X The recognition of useful information, its retention in memory, and subsequent use plays an important part in the behaviour of many biological species. Information gained
   by experience in one generation can be propagated to subsequent generations by some form of teaching. Each generation can then supplement its taught learning by its own
   experience. In this paper we explore the role of memorised information in the performance of a Genetic Programming (GP) system that uses a tree structure as its
   representation. Memory is implemented in the form of a set of subtrees derived from successful members of each generation. The memory is used by a genetic operator similar
   to the mutation operator but with the following difference. In a tree-structured system the mutation operator replaces randomly selected sub-trees by new randomly-generated
   sub-trees. The memory operator replaces randomly selected sub-trees by sub-trees randomly randomly selected from the memory. To study the memory operator's impact a GP
   system is used to evolve a well-known expression from classical kinetics using fitness-based selection. The memory operator is used together with the common crossover and
   mutation operators. It is shown that the addition of a memory operator increases the probability of a successful evolution for this particular problem. At this stage we
   make no claim for its impact on other problems that have been successfully addressed by Genetic Programming
%Z Evolutionary Design and Manufacture: Selected Papers from . (ACDM) One example physics integration of u*t+0.5*a*t*t t=1...10, u=20 or u=200 a=980 Reverse Polish RPN except
   for first (in Lisp) max length=11?? 19??, roulette wheel, crossover, mutation. Memory operator: when fitness improves over best of previous generation whole of tree and its
   subtrees are saved in memory. Later random choices from memory. elitism.Pop=2000, gen=20 40000 tests per minute (300 MHz).

%A Keith Bearpark
%T Learning and memory in genetic programming
%R Ph.D. Thesis
%D 2000
%I 
%I School of Engineering Sciences, University of Southampton
%K genetic algorithms, genetic programming
%U http://eprints.soton.ac.uk/45930/
%X Genetic Programming is a form of Evolutionary Computation in which computer programs are evolved by methods based on simulating the natural evolution of biological species.
   A new generation of a species acquires the characteristics of previous generations through the inheritance of genes by sexual reproduction and through random changes in
   alleles by random mutation. The new generation may enhance its ability to survive by the acquisition of cultural knowledge through learning processes. This thesis combines
   the transfer of knowledge by genetic means with the transfer of knowledge by cultural means. In particular, it introduces a new evolutionary operator, memory operator. In
   conventional genetic programming systems, a new generation is formed from a mating pool whose members are selected from the fittest members of previous generation. The new
   generation is produced by the exchange of genes between members of the mating pool and the random replacement of genes in the offspring. The new generation may or may not
   be able to survive better than its predecessor in a given environment. The memory operator augments the evolutionary process by inserting into new chromosomes genetic
   material known to often result in fitness improvements. This material is acquired through a learning process in which the system is required to evolve generations that
   survive in a less demanding environment. The cultural knowledge acquired in this learning process is applied as an intelligent form of mutation to aid survival in a more
   demanding environment.

%A Julie Beaulieu
%A Christian Gagn{\'e}
%A Marc Parizeau
%T Lens System Design And Re-engineering With Evolutionary Algorithms
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 155--162
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, evolvable hardware, evolutionary reengineering, evolvable optics, genetic algorithms, lens system design
%U http://citeseer.ist.psu.edu/532763.html
%X presents some lens system design and re-engineering experimentations with genetic algorithms and genetic programming. These Evolutionary Algorithms (EA) were successfully
   applied to a design problem that was previously presented to expert participants of an international lens design conference. Comparative results demonstrate that the use of
   EA for lens system design is very much human-competitive.
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
   Nominated for best at GECCO award
%@ 1-55860-878-8

%A Darren Beaumont
%A Susan Stepney
%T Grammatical Evolution of L-systems
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P -
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, grammatical evolution
%X L-systems are parallel generative grammars that can model branching structures. Taking a graphical object and attempting to derive an L-system describing it is a hard
   problem. Grammatical Evolution (GE) is an evolutionary technique aimed at creating grammars describing the legal structures an object can take. We use GE to evolve
   L-systems, and investigate the effect of elitism, and the form of the underlying grammar.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Matthias Bechmann
%A Angelika Sebald
%A Susan Stepney
%T From Binary to Continuous Gates - and Back Again
%B Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010
%S Lecture Notes in Computer Science
%E Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller
%V 6274
%D 2010
%P 335--347
%I Springer
%C York
%K genetic algorithms, genetic programming, cartesian genetic programming
%X We describe how nuclear magnetic resonance (NMR) spectroscopy can serve as a substrate for the implementation of classical logic gates. The approach exploits the inherently
   continuous nature of the NMR parameter space. We show how simple continuous NAND gates with sin/sin and sin/sinc characteristics arise from the NMR parameter space. We use
   these simple continuous NAND gates as starting points to obtain optimised target NAND circuits with robust, error-tolerant properties. We use Cartesian Genetic Programming
   (CGP) as our optimisation tool. The various evolved circuits display patterns relating to the symmetry properties of the initial simple continuous gates. Other circuits,
   such as a robust XOR circuit built from simple NAND gates, are obtained using similar strategies. We briefly mention the possibility to include other target objective
   functions, for example other continuous functions. Simple continuous NAND gates with sin/sin characteristics are a good starting point for the creation of error-tolerant
   circuits whereas the more complicated sin/sinc gate characteristics offer potential for the implementation of complicated functions by choosing some straightforward,
   experimentally controllable parameters appropriately.
%8 September  6-8

%A M. A. Beck
%A I. C. Parmee
%T Extending the bounds of the search space: A Multi-Population approach
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1469--1476
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-762.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Joerg D. Becker
%T Thinking, Learning, and Autonomous Problem Solving
%D 2002
%I 
%U http://arXiv.org/abs/cs/0212019
%X Ever increasing computational power will require methods for automatic programming. We present an alternative to genetic programming, based on a general model of thinking
   and learning. The advantage is that evolution takes place in the space of constructs and can thus exploit the mathematical structures of this space. The model is
   formalized, and a macro language is presented which allows for a formal yet intuitive description of the problem under consideration. A prototype has been developed to
   implement the scheme in PERL. This method will lead to a concentration on the analysis of problems, to a more rapid prototyping, to the treatment of new problem classes,
   and to the investigation of philosophical problems. We see fields of application in nonlinear differential equations, pattern recognition, robotics, model building, and
   animated pictures.
%O Comment: 9 pages, 4 figures
%8 Decemeber ~10

%A Lee A. Becker
%A Mukund Seshadri
%T Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules
%R Technical Report
%D 2003
%I 
%I Worcester Polytechnic Institute
%K genetic algorithms, genetic programming, comprehensibility , Occam's razor, overfitting, complexity penalising, S&P500, technical analysis, market timing
%U http://citeseer.ist.psu.edu/574013.html
%X This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity
   penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the
   search, can express complexity while retaining comprehensibility. Several of the learned technical trading rules outperform a buy and hold strategy for the S&P500 on the
   testing period from 1990-2002, even taking into account transaction costs.
%8 May

%A Lee A. Becker
%A Mukund Seshadri
%T Cooperative Coevolution of Technical Trading Rules
%R Technical Report
%D 2003
%I 
%I Worcester Polytechnic Institute
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-15.pdf
%X This paper describes how cooperative coevolution can be used for GP of technical trading rules. A number of different methods of choosing collaborators for fitness
   evaluation are investigated. Several of the methods outperformed, at a statistically significant level, a buy-and-hold strategy for the S&P500 on the testing period from
   1990-2002, even taking into account transaction costs.
%8 May

%A Lee A. Becker
%A Mukund Seshadri
%T GP-evolved Technical Trading Rules Can Outperform Buy and Hold
%B Procceedings of the Sixth International Conference on Computational Intelligence and Natural Computing
%D 2003
%I 
%C Embassy Suites Hotel and Conference Center, Cary, North Carolina USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Yan/gp-evolved-technical-trading.pdf
%X This paper presents a number of experiments in which GP-evolved technical trading rules outperform a buy-and-hold strategy on the S&P500, even taking into account
   transaction costs. Several methodology changes from previous work are discussed and tested. These include a complexity-penalising factor, a fitness function that considers
   consistency of performance, and coevolution of a separate buy and sell rule.
%8 September  26-30
%Z http://axon.cs.byu.edu/CINC/ http://www.ee.duke.edu/JCIS/ Worcester Polytechnic Institute

%A Ying Becker
%A Peng Fei
%A Anna M. Lester
%T Stock Selection : An Innovative Application of Genetic Programming Methodology
%B Genetic Programming Theory and Practice IV
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%V 5
%D 2006
%P 315--334
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, equity market, stock selection, quantitative asset management Capital Asset Pricing Model, Arbitrage Pricing Model, Technical
   trading rules, S&P 500, Stock selection models, Information ratio, Information coefficient, Quantitative asset management
%X One of the major challenges in an information-rich financial market is how effectively to derive an optimum investment solution among vast amounts of available information.
   The most efficacious combination of factors or information signals can be found by evaluating millions of possibilities, which is a task well beyond the scope of manual
   efforts. Given the limitations of the manual approach, factor combinations are typically linear. However, the linear combination of factors might be too simple to reflect
   market complexities and thus fully capture the predictive power of the factors. A genetic programming process can easily explore both linear and non-linear formulae. In
   addition, the ease of evaluation facilitates the consideration of broader factor candidates for a stock selection model. Based upon SSgA's previous research on using
   genetic programming techniques to develop quantitative investment strategies, we extend our application to develop stock selection models in a large investable stock
   universe, the S&P 500 index. Two different fitness functions are designed to derive GP models that accommodate different investment objectives. First, we demonstrate that
   the GP process can generate a stock selection model for an low active risk investment style. Compared to a traditional model, the GP model has significantly enhanced future
   stock return ranking capability. Second, to suit an active investment style, we also use the GP process to generate a model that identifies the stocks with future returns
   lying in the fat tails of the return distribution. A portfolio constructed based on this model aims to aggressively generate the highest returns possible compared to an
   index following portfolio. Our tests show that the stock selection power of the GP models is statistically significant. Historical backtest results indicate that portfolios
   based on GP models outperform the benchmark and the portfolio based on the traditional model. Further, we demonstrate that GP models are more robust in accommodating
   various market regimes and have more consistent performance than the traditional model.
%O 12
%8 11-13  May
%Z part of \citeRiolo:2006:GPTP Published Jan 2007 after the workshop Principal, Head of US Active Equity Research, Advanced Research Center, State Street Global Advisors,
   Boston, MA 02111;
%@ 0-387-33375-4

%A Ying L. Becker
%A Harold Fox
%A Peng Fei
%T An Empirical Study of Multi-Objective Algorithms for Stock Ranking
%B Genetic Programming Theory and Practice V
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2007
%P 239--259
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%X Quantitative models for stock selection and portfolio management face the challenge of determining the most efficacious factors, and how they interact, from large amounts
   of financial data. Genetic programming using simple objective fitness functions has been shown to be an effective technique for selecting factors and constructing
   multi-factor models for ranking stocks, but the resulting models can be somewhat unbalanced in satisfying the multiple objectives that portfolio managers seek: large excess
   returns that are consistent across time and the cross-sectional dimensions of the investment universe. In this study, we implement and evaluate three multi-objective
   algorithms to simultaneously optimise the information ratio, information coefficient, and intra-fractile hit rate of a portfolio. These algorithms the constrained fitness
   function, sequential algorithm, and parallel algorithm take widely different approaches to combine these different portfolio metrics. The results show that the
   multi-objective algorithms do produce well-balanced portfolio performance, with the constrained fitness function performing much better than the sequential and parallel
   multi-objective algorithms. Moreover, this algorithm generalises to the held-out test data set much better than any of the single fitness algorithms.
%O 14
%8 17-19  May
%Z part of \citeRiolo:2007:GPTP published Jan 2008

%A Ying L. Becker
%A Una-May O'Reilly
%T Genetic programming for quantitative stock selection
%B GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
%E Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding
%D 2009
%P 9--16
%I ACM New York, NY, USA
%I SigEvo
%C Shanghai, China
%K genetic algorithms, genetic programming
%X We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors
   identified by experts. We describe the multi-stage training, testing and validation process that we have integrated with GP selection to be appropriate for financial panel
   data and how the GP solutions are situated within a portfolio selection strategy. We share our experience with the pros and cons of evolved linear and non-linear models,
   and outline how we have used GP extensions to balance different objectives of portfolio managers and control the complexity of evolved models.
%8 June  12-14
%Z Also known as \citeDBLP:conf/gecco/BeckerO09 part of \citeDBLP:conf/gec/2009

%A Ilja Bedner
%T Evolving Light Cycle Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming, games
%X Evolution of autonomous agents that must compete for survival in the light-cycle game as seen in the movie tron
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Andreas Beham
%A Stephan Winkler
%A Stefan Wagner
%A Michael Affenzeller
%T A genetic programming approach to solve scheduling problems with parallel simulation
%B IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008
%D 2008
%P 1--5
%I 
%K genetic algorithms, genetic programming, dispatching, fitness evaluation, parallel simulation, production planning, scheduling problem, dispatching, production planning,
   scheduling
%X Scheduling and dispatching are two ways of solving production planning problems. In this work, based on preceding works, it is explained how these two approaches can be
   combined by the means of an automated rule generation procedure and simulation. Genetic programming is applied as the creator and optimizer of the rules. A simulator is
   used for the fitness evaluation and distributed over a number of machines. Some example results suggest that the approach could be successfully applied in the real world as
   the results are more than human competitive.
%8 April
%Z Also known as \cite4536379 \cite4536362

%A Saeed Behbahani
%A Clarence W. {de Silva}
%T Mechatronic Design Evolution Using Bond Graphs and Hybrid Genetic Algorithm With Genetic Programming
%J IEEE/ASME Transactions on Mechatronics
%I 
%K genetic algorithms, genetic programming, Bond graphs, electrohydraulic systems
%X A typical mechatronic problem (modelling, identification, and design) entails finding the best system topology as well as the associated parameter values. The solution
   requires concurrent and integrated methodologies and tools based on the latest theories. The experience on natural evolution of an engineering system indicates that the
   system topology evolves at a much slower rate than the parametric values. This paper proposes a two-loop evolutionary tool, using a hybrid of genetic algorithm (GA) and
   genetic programming (GP) for design optimisation of a mechatronic system. Specifically, GP is used for topology optimization, while GA is responsible for finding the elite
   solution within each topology proposed by GP. A memory feature is incorporated with the GP process to avoid the generation of repeated topologies, a common drawback of GP
   topology exploration. The synergic integration of GA with GP, along with the memory feature, provides a powerful search ability, which has been integrated with bond graphs
   (BG) for mechatronic model exploration. The software developed using this approach provides a unified tool for concurrent, integrated, and autonomous topological
   realisation of a mechatronic problem. It finds the best solution (topology and parameters) starting from an abstract statement of the problem. It is able to carry out the
   process of system configuration realization, which is normally performed by human experts. The performance of the software tool is validated by applying it to mechatronic
   design problems.
%O Early Access Article
%Z Also known as \cite6029337

%A Joey Beheler
%T Using Genetic Algorithms and Convolution to Find Optimal Strategies in Games without Perfect Information
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 11--18
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Morteza Beiki
%A Ali Bashari
%A Abbas Majdi
%T Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network
%J International Journal of Rock Mechanics and Mining Sciences
%V 47
%N 7
%D 2010
%P 1091--1103
%I 
%K genetic algorithms, genetic programming, Deformation modulus of rock mass, Relative strength of effect (RSE), Sensitivity analysis about the mean
%U http://www.sciencedirect.com/science/article/B6V4W-50RFN0V-1/2/fa0de8195c17e39f39b1ecead4df4da4
%X We use genetic programming (GP) to determine the deformation modulus of rock masses. A database of 150 data sets, including modulus of elasticity of intact rock (Ei),
   uniaxial compressive strength (UCS), rock mass quality designation (RQD), the number of joint per meter (J/m), porosity, and dry density for possible input parameters, and
   the modulus deformation of the rock mass determined by a plate loading test for output, was established. The values of geological strength index (GSI) system were also
   determined for all sites and considered as another input parameter. Sensitivity analyses are considered to find out the important parameters for predicting of the
   deformation modulus of rock mass. Two approaches of sensitivity analyses, based on statistical analysis of RSE values and sensitivity analysis about the mean, are
   performed. Evolution of the sensitivity analyses results establish the fact that variable of UCS, GSI, and RQD play more prominent roles for predicting modulus of the rock
   mass, and so those are considered as the predictors to design the GP model. Finally, two equations were achieved by GP. The statistical measures of root mean square error
   (RMSE) and variance account for (VAF) have been used to compare GP models with the well-known existing empirical equations proposed for predicting the deformation modulus.
   These performance criteria proved that the GP models give higher predictions over existing empirical models.

%A Ulas Beldek
%A Kemal Leblebicioglu
%T Strategy creation, decomposition and distribution in particle navigation
%J Information Sciences
%V 177
%N 3
%D 2007
%P 755--770
%I 
%K genetic algorithms, genetic programming, Rule-base, Strategy planning, Robot navigation, Maze solving, Optimization, Multi-agent systems
%X Strategy planning is crucial to control a group to achieve a number of tasks in a closed area full of obstacles. In this study, genetic programming has been used to evolve
   rule-based hierarchical structures to move the particles in a grid region to accomplish navigation tasks. Communications operations such as receiving and sending commands
   between particles are also provided to develop improved strategies. In order to produce more capable strategies, a task decomposition procedure is proposed. In addition, a
   conflict module is constructed to handle the challenging situations and conflicts such as blockage of a particle's pathway to destination by other particles.
%8 1  February

%A A. Belgasem
%A T. Kalganova
%A A. Almaini
%T Extrinsic Evolution of Finite State Machine
%B Proc. of ACDM2002
%E I. C. Parmee
%D 2002
%P 157--168
%I Springer
%K genetic algorithms, genetic programming, evolvable hardware
%X extrinsic evolvable hardware approach to evolve finite state machines (FSM). Both the genetic algorithm (GA) and Evolvable Hardware (EHW) are combined together to produce
   optimal logic circuit. GA is used to optimise the state assignment problem. EHW is used to design the combinational parts of the desired circuit. The approach is tested on
   a number of finite state machines from MCNC benchmark set. These circuits have been evolved using different functional sets of logic gates and GA parameters. The results
   show promise for the use of this approach as a design method for sequential logic circuits.

%A G. N. Beligiannis
%A E. N. Demiris
%A S. D. Likothanassis
%T Evolutionary Multimodel Partitioning Filters for Nonlinear Systems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1227
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, EHW, evolvable hardware, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-452.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Grigorios N. Beligiannis
%A Lambros V. Skarlas
%A Spiridon D. Likothanassis
%A Katerina G. Perdikouri
%T Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique
%J IEEE Transactions on Instrumentation and Measurement
%V 54
%N 6
%D 2005
%P 2184--2190
%I 
%K genetic algorithms, genetic programming, medical signal processing, nonlinear dynamical systems complex biomedical data identification, evolutionary multimodel partitioning
   filters, nonlinear model structure
%X In this contribution, a genetic programming (GP)-based technique, which combines the ability of GP to explore both automatically and effectively, the whole set of candidate
   model structures and the robustness of evolutionary multimodel partitioning filters, is presented. The method is applied to the nonlinear system identification problem of
   complex biomedical data. Simulation results show that the algorithm identifies the true model and the true values of the unknown parameters for each different model
   structure, thus assisting the GP technique to converge more quickly to the (near) optimal model structure. The method has all the known advantages of the evolutionary multi
   model partitioning filters, that is, it is not restricted to the Gaussian case; it is applicable to on-line/adaptive operation and is computationally efficient.
   Furthermore, it can be realized in a parallel processing fashion, a fact which makes it amenable to very large scale integration implementation.
%8 Decemeber
%Z Fig. 3. Plot of the real (solid line) versus the predicted (dashed line) values for an epoch consisting of 300 samples of an epileptic MEG (MEG measured in pT = 10 T). Fig.
   4. Plot of the real (solid line) versus the predicted (dashed line) values of an f-MCG in a normal pregnancy (f-MCG measured in pT = 10 T). TABLE II ABILITY OF THE
   ESTIMATED NONLINEAR MODEL IN PREDICTING ABNORMAL PREGNANCIES

%A Matt Bell
%T Evolving the Structure and Weights of Recurrent Neural Network though Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 11--20
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Tony Belpaeme
%T Evolution of Visual Feature Detectors
%B Late Breaking Papers at EvoISAP'99: the First European Workshop on Evolutionary Computation in Image Analysis and Signal Processing
%E Riccardo Poli and Stefano Cagnoni and Hans-Michael Voigt and Terry Fogarty and Peter Nordin
%D 1999
%P 1--10
%I 
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/362631.html
%X This paper describes how sets of visual feature detectors are evolved starting from simple primitives. The primitives, of which some are inspired on visual processing
   observed in mammalian visual pathways, are combined using genetic programming to form a feed-forward feature-extraction hierarchy. Input to the feature detectors consists
   of a series of real-world images, containing objects or faces. The results show how each set of feature detectors self-organizes into a set which is capable of returning
   feature vectors for discriminating the input images. We discuss the influence of different settings on the evolution of the feature detectors and explain some phenomena.
%8 28  May
%Z EvoIASP'99 Available as CSRP-99-10 from the School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. STGP. Information returned by each (of
   5) feature detector, entropy of the output vector p4 "if the outputs are weel spread, meaning the feature detectors return useful information, the fitness will be high.
   Explicit parsimony preasure, but not needed p8? LilGP.

%A Sheela V. Belur
%T CORE: Constrained Optimization by Random Evolution
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 280--286
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 MATLAB
%@ 0-18-206995-8

%A Amit Benbassat
%A Moshe Sipper
%T Evolving Lose-Checkers Players using Genetic Programming
%B IEEE Conference on Computational Intelligence and Game
%D 2010
%P 30--37
%I 
%C IT University of Copenhagen, Denmark
%K genetic algorithms, genetic programming, explicitly defined intron, full knowledge board game, genetic programming tree, local mutation, lose checker player, multitree
   individual, state evaluator, computer games, trees (mathematics)
%U http://game.itu.dk/cig2010/proceedings/papers/cig10_005_011.pdf
%X We present the application of genetic programming (GP) to the zero-sum, deterministic, full-knowledge board game of Lose Checkers. Our system implements strongly typed GP
   trees, explicitly defined introns, local mutations, and multitree individuals. Explicitly defined introns in the genome allow for information selected out of the population
   to be kept as a reservoir for possible future use. Multi-tree individuals are implemented by a method inspired by structural genes in living organisms, whereby we take a
   single tree describing a state evaluator and split it.
%8 18-21  August
%Z http://game.itu.dk/cig2010/proceedings/wp-content/acceptedpapers.html Also known as \cite5593376

%A Amit Benbassat
%A Moshe Sipper
%T Evolving board-game players with genetic programming
%B GECCO 2011 Graduate students workshop
%E Miguel Nicolau
%D 2011
%P 739--742
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X We present the application of genetic programming (GP) to zero-sum, deterministic, full-knowledge board games. Our work expands previous results in evolving board-state
   evaluation functions for Lose Checkers to a 10x10 variant of Checkers, as well as Reversi. Our system implements strongly typed GP trees, explicitly defined introns, and a
   selective directional crossover method.
%8 12-16  July
%Z Also known as \cite2002080 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Samy Bengio
%A Yoshua Bengio
%A Jocelyn Cloutier
%T Use of genetic programming for the search of a new learning rule for neutral networks
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 324--327
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/465154.html
%X In previous work ([1, 2, 3]) we explained how to use standard optimization methods such as simulated annealing, gradient descent and genetic algorithms to optimize a
   parametric function which could be used as a learning rule for neural networks. To use these methods, we had to choose a fixed number of parameters and a rigid form for the
   learning rule. In this article, we propose to use genetic programming to find not only the values of rule parameters but also the optimal number of parameters and the form
   of the rule. Experiments on classification tasks suggest genetic programming finds better learning rules than other optimization methods. Furthermore, the best rule found
   with genetic programming outperformed the well-known backpropagation algorithm for a given set of tasks
%8 27-29  June
%Z Uses GP to produce a learning rule for training a neural network. Evolved rule like back-propergation but better, differential is cubed. Says neural network is fully
   connected, IEEE Xplore link broken 16 Oct 2004

%A Sana {Ben Hamida}
%T Evolutionary Algorithms: Handling Constraints and Real-World Application
%R Ph.D. Thesis
%D 2001
%I 
%I Ecole Polytechnique
%C Paris
%K genetic algorithms, genetic programming
%U http://www.cmap.polytechnique.fr/~sana/indexAng.html
%X The present work is a heuristic and experimental study in the evolutionary computation domain, and starts with an introduction to the artificial evolution with a synthesis
   of the principal approaches. The first part is a heuristic study devoted to constraint handling in evolutionary computation. It presents an extensive review of previous
   constraint handling methods in the literature and their limitations. Two solutions are then proposed. The first idea is to improve genetic operator exploration capacity for
   constrained optimisation problems. The logarithmic mutation operator is conceived to explore both locally and globally the search space. The second solution introduces the
   original Adaptive Segregational Constraint Handling Evolutionary Algorithm (ASCHEA), the main idea of which is to maintain population diversity. In order to achieve this
   goal, three main ingredients are used: An original adaptive penalty method, a constraint-driven recombination, and a segregation selection that distinguishes between
   feasible and infeasible individuals to enhance the chances of survival of the feasible ones. Moreover, a niching method with an adaptive radius is added to ASCHEA in order
   to handle multimodal functions. Finally, to complete the ASCHEA system, a new equality constraint handling strategy is introduced, that reduces progressively the feasible
   domain in order to approach the actual null-measured domain as close as possible at the end of the evolution. The second part is a case study tackling a real-world problem.
   The goal is to design the 2-dimensional profile of an optical lens (phase plate) in order to control focal-plane irradiance of some laser beam. The aim is to design the
   phase plate such that a small circular target on the focal plane is uniformly illuminated without energy loss.
%8 mars
%Z In French. Chapter 7 GP v ES on laser. Supervisor: Marc Schoenauer

%A Luca Benini
%T Genetic Fitting: Evolutionary Search of Optimal Approximations for Discrete Functions
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 19--28
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Simon C. Benjamin
%T Evolutionary Route to Computation in Self-Assembled Nanoarrays
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X Ordered nanoarrays, i.e. regular patterns of quantum structures at the nanometre scale, can now be synthesised in a range of systems. In this paper I study a form of array
   computation where the internal dynamics are driven by intrinsic cell-cell interactions and global optical pulses addressing entire structure indiscriminately. The array
   would need to be ' wired' to conventional technologies only at its boundary. Any self assembled array would have a unique set of defects, therefore I employ an ab initio
   evolutionary process to subsume such flaws without any need to determine their location or nature. The approach succeeds for various forms of physical interaction within
   the array.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A E. Benkhelifa
%A G. Dragffy
%A A. G. Pipe
%A M. Nibouche
%T Design Innovation for Real World Applications, Using Evolutionary Algorithms
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P -
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X This paper discusses two important features of electronic design through evolutionary processes; creativity and innovation. Hence, conventional design methodologies are
   discussed and compared with their counterparts via evolutionary processes. An evolutionary search is used as an engine for discovering new designs for a real world
   application. Attempts to extract some useful principles from the evolved designs are presented and results are compared to conventional design topologies for the same
   problems.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Elhadj Benkhelifa
%A Ashutosh Tiwari
%A Anthony Pipe
%T Evolutionary design optimisation of a 32-Step Traffic Lights Controller
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X This paper shows a successful application of evolutionary algorithms for the design and optimisation of complex real world digital circuit that is a 32-Step Traffic Lights
   Controller. It discusses two important features of electronic design through evolutionary processes; creativity and innovation. Results are compared to conventional design
   topologies; and attempt to analyse the evolved designs is presented.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586108

%A Andrew Bennett
%A Derek Magee
%T Learning Sets of Sub-Models for Spatio-Temporal Prediction
%B AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence
%E Max Bramer and Richard Ellis
%D 2007
%I 
%I British Computer Society's Specialist Group on Artificial Intelligence (SGAI)
%C Cambridge, UK
%K genetic algorithms, genetic programming, card game playing
%U http://citeseerx.ist.psu.edu/viewdoc/download/10.1.1.150.6694.pdf
%X In this paper we describe a novel technique which implements a spatio-temporal model as a set of sub-models based on first order logic. These sub-models model different,
   typically independent, parts of the dataset; for example different spatio or temporal contexts. To decide which sub-models to use in different situations a context chooser
   is used. By separating the sub-models from where they are applied allows greater flexibility for the overall model. The sub-models are learnt using an evolutionary
   technique called Genetic Programming. The method has been applied to spatio-temporal data. This includes learning the rules of snap by observation, learning the rules of a
   traffic light sequence, and finally predicting a person's course through a network of CCTV cameras.
%8 10-12  Decemeber
%Z University of Leeds, UK

%A Andrew Bennett
%A Derek Magee
%T Using Genetic Programming to Learn Models Containing Temporal Relations from Spatio-Temporal Data
%B Proceedings of the 1st International Workshop on Combinations of Intelligent Methods and Applications
%E Ioannis Hatzilygeroudis and Constantinos Koutsojannis and Vasile Palade
%D 2008
%I 
%I CEUR
%C Patras, Greece
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6758
%X In this paper we describe a novel technique for learning predictive models from non-deterministic spatio-temporal data. Our technique learns a set of sub-models that model
   different, typically independent, aspects of the data. By using temporal relations, and implicit feature selection, based on the use of 1st order logic expressions, we make
   the sub-models general, and robust to irrelevant variations in the data.We use Allen's intervals [1], plus a set of four novel temporal state relations, which relate
   temporal intervals to the current time. These are added to the system as background knowledge in the form of functions. To combine the sub-models into a single model a
   context chooser is used. This probabilistically picks the most appropriate set of sub-models to predict in a certain context, and allows the system to predict in
   non-deterministic situations. The models are learnt using an evolutionary technique called Genetic Programming. The method has been applied to learning the rules of snap,
   and uno by observation; and predicting a person's course through a network of CCTV cameras.
%8 July  22
%Z CIMA'08 Combinations of Intelligent Methods and Applications http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/

%A Andrew David Bennett
%T Using genetic programming to learn predictive models from spatio-temporal data
%R Ph.D. Thesis
%D 2010
%I 
%I School of Computing, University of Leeds
%C UK
%K genetic algorithms, genetic programming
%U http://etheses.whiterose.ac.uk/1376/1/bennett_a.pdf
%X This thesis describes a novel technique for learning predictive models from nondeterministic spatio-temporal data. The prediction models are represented as a production
   system, which requires two parts: a set of production rules, and a conflict resolver. The production rules model different, typically independent, aspects of the
   spatio-temporal data. The conflict resolver is used to decide which sub-set of enabled production rules should be fired to produce a prediction. The conflict resolver in
   this thesis can probabilistically decide which set of production rules to fire, and allows the system to predict in non-deterministic situations. The predictive models are
   learnt by a novel technique called Spatio-Temporal Genetic Programming (STGP). STGP has been compared against the following methods: an Inductive Logic Programming system
   (Progol), Stochastic Logic Programs, Neural Networks, Bayesian Networks and C4.5, on learning the rules of card games, and predicting a person's course through a network of
   CCTV cameras. This thesis also describes the incorporation of qualitative temporal relations within these methods. Allen's intervals [1], plus a set of four novel temporal
   state relations, which relate temporal intervals to the current time are used. The methods are evaluated on the card game Uno, and predicting a person's course through a
   network of CCTV cameras. This work is then extended to allow the methods to use qualitative spatial relations. The methods are evaluated on predicting a person's course
   through a network of CCTV cameras, aircraft turnarounds, and the game of Tic Tac Toe. Finally, an adaptive bloat control method is shown. This looks at adapting the amount
   of bloat control used during a run of STGP, based on the ratio of the fitness of the current best predictive model to the initial fitness of the best predictive model.
%8 July
%Z noughts and crosses

%A Forrest H {Bennett III}
%T Automatic Creation of an Efficient Multi-Agent Architecture Using Genetic Programming with Architecture-Altering Operations
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 30--38
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Forrest H {Bennett III}
%T Emergence of a Multi-Agent Architecture and New Tactics For the Ant Colony Foraging Problem Using Genetic Programming
%B Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior: From animals to animats 4
%E Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer and Jordan Pollack and Stewart W. Wilson
%D 1996
%P 430--439
%I MIT Press Cambridge, MA, USA
%C Cape Code, USA
%K genetic algorithms, genetic programming
%8 9-13  September
%Z SAB-96 Each tree within individual treated as an "agent". Uses koza add/delete adf genetic operations to evolve the number of agents as well as their code.
%@ 0-262-63178-4

%A Forrest H {Bennett III}
%A John R. Koza
%A David Andre
%A Martin A. Keane
%T Evolution of a 60 Decibel op amp using genetic programming
%B Proceedings of International Conference on Evolvable Systems: From Biology to Hardware (ICES-96)
%S Lecture Notes in Computer Science
%E Tetsuya Higuchi and Iwata Masaya and Weixin Liu
%V 1259
%D 1996
%I Springer-Verlag Berlin, Germany
%C Tsukuba, Japan
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ices1996fhbamplifier60.pdf
%X Genetic programming was used to evolve both the topology and sizing (numerical values) for each component of a low-distortion, low-bias 60 decibel (1000-to-1) amplifier
   with good frequency generalization.
%8 7-8  October
%Z URL=version 1 as presented at the conference http://www.etl.go.jp:8080/etl/kikou/ICES96/
%@ 3-540-63173-9

%A Forrest H {Bennett III}
%T A Multi-Skilled Robot that Recognizes and Responds to Different Problem Environments
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 44--51
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/bennet_1997_msrrrdpe.pdf
%8 13-16  July
%Z GP-97 two memory cells SET-D0 and SET-D1. Max rpb size 600, up to 2 ADFs (up to 200 each). Architecture altering operations. OBJECT-DIST OBJECT-KIND and ROOM-COLOR. Fitness
   includes time penalty. 4 rooms in continous (ie floating point) world. Program is repeatedly evaluated until 1000 timesteps or hits mine. Claims [page 49] code cant
   remember locations

%A Forrest H {Bennett III}
%A John R. Koza
%A Martin A. Keane
%A David Andre
%T Darwinian Programming and Engineering Design using Genetic Programming
%B Proceedings of the 1st International Workshop on Soft Computing Applied to Software Engineering
%E Conor Ryan and Jim Buckley
%D 1999
%P 31--40
%I Limerick University Press
%I SCARE
%C University of Limerick, Ireland
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/scase1999.pdf
%X One of the central challenges of computer science is to build a system that can automatically create computer programs that are competitive with those produced by humans.
   This paper presents a candidate set of criteria that identify when a machine-created solution is competitive with a human-produced result. We argue that the field of design
   is a useful testbed for determining whether an automated technique can produce results that are competitive with human-produced results. We present several results that are
   competitive with the products of human creativity and inventiveness. This claim is supported by the fact that each of the results infringe on previously issued patents.
%8 12-14  April
%Z http://scare.csis.ul.ie/scase99/ SCASE'99 Automatic analog electrical circuit synthesis: Campbell 1917 Ladder Filter patent, Zobel 1925 "M-Derived Half Section" patent,
   Cauer 1934 - 1936 Elliptic patents, Darlington 1952 Emitter-Follower patent
%@ 1-874653-52-6

%A Forrest H {Bennett III}
%A Martin A. Keane
%A David Andre
%A John R. Koza
%T Automatic Synthesis of the Topology and Sizing for Analog Electrical Circuits Using Genetic Programming
%B Evolutionary Algorithms in Engineering and Computer Science
%E Kaisa Miettinen and Marko M. Makela and Pekka Neittaanmaki and Jacques Periaux
%D 1999
%P 199--229
%I John Wiley \& Sons Chichester, UK
%C Jyvaskyla, Finland
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/eurogen1999circuits.pdf
%X The design (synthesis) of an analog electrical circuit entails the creation of both the topology and sizing (numerical values) of all of the circuit's components. There has
   previously been no general automated technique for automatically creating the design for an analog electrical circuit from a high-level statement of the circuit's desired
   behavior. We have demonstrated how genetic programming can be used to automate the design of seven prototypical analog circuits, including a lowpass filter, a highpass
   filter, a passband filter, a bandpass filter, a frequency-measuring circuit, a 60 dB amplifier, a differential amplifier, a computational circuit for the square root
   function, and a time-optimal robot controller circuit. All seven of these genetically evolved circuits constitute instances of an evolutionary computation technique solving
   a problem that is usually thought to require human intelligence. The approach described herein can be directly applied to many other problems of analog circuit synthesis.
%8 30  May  - 3  June
%Z EUROGEN'99 http://www.wiley.com/Corporate/Website/Objects/Products/0,9049,91449,00.html
%@ 0-471-99902-4

%A Forrest H {Bennett III}
%A John R. Koza
%A Martin A. Keane
%A David Andre
%T Genetic programming: Biologically inspired computation that exhibits creativity in solving non-trivial problems
%B Proceedings of the AISB'99 Symposium on Scientific Creativity
%D 1999
%P 29--38
%I 
%I The Society for the Study of Artificial Intelligence and Simulation of Behaviour
%C Edingburgh
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/aisb1999.pdf
%X This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems. We
   argue that the field of design is a useful testbed for determining whether an automated technique can produce results that are competitive with human-produced results. We
   present several results that are competitive with the products of human creativity and inventiveness. This claim is supported by the fact that each of the results infringe
   on previously issued patents. This paper presents a candidate set of criteria that identify when a machine-created solution to a problem is competitive with a
   human-produced result.
%8 8-9  April
%Z AISB-99

%A Forrest H {Bennett III}
%A John R. Koza
%A James Shipman
%A Oscar Stiffelman
%T Building a Parallel Computer System for \$18,000 that Performs a Half Peta-Flop per Day
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1484--1490
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-788.ps
%X Techniques of evolutionary computation generally require significant computational resources to solve non-trivial problems of interest. Increases in computing power can be
   realized either by using a faster computer or by parallelizing the application. Techniques of evolutionary computation are especially amenable to parallelization. This
   paper describes how to build a 10-node Beowulf-style parallel computer system for $18,000 that delivers about a half peta-flop (1015 floating-point operations) per day on
   runs of genetic programming. Each of the 10 nodes of the system contains a 533 MHz Alpha processor and runs with the Linux operating system. This amount of computational
   power is sufficient to yield solutions (within a couple of days per problem) to 14 published problems where genetic programming has produced results that are competitive
   with human-produced results.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Forrest H {Bennett III}
%A John R. Koza
%A Martin A. Keane
%A Jessen Yu
%A William Mydlowec
%A Oscar Stiffelman
%T Evolution by Means of Genetic Programming of Analog Circuits that Perform Digital Functions
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1477--1483
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-787.ps
%X This paper demonstrates the ability of genetic programming to evolve analog circuits that perform digital functions and mixed analog-digital circuits. The evolved circuits
   include two purely digital circuits (a 100 nano-second NAND circuit and a two-instruction arithmetic logic unit circuit) and one mixed-signal circuit, namely a three-input
   digital-to-analog converter.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Forrest H {Bennett III}
%A John R. Koza
%A Jessen Yu
%A William Mydlowec
%T Automatic synthesis, placement, and routing of an amplifier circuit by means of genetic programming
%B Evolvable Systems: From Biology to Hardware Third International Conference, ICES 2000
%S LNCS
%E Julian Miller and Adrian Thompson and Peter Thomson and Terence C. Fogarty
%V 1801
%D 2000
%P 1--10
%I Springer-Verlag
%C Edinburgh, Scotland, UK
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/471655.html
%X The complete design of a circuit typically includes the tasks of creating the circuit's placement and routing as well as creating its topology and component sizing. Design
   engineers perform these four tasks sequentially. Each of these four tasks is, by itself, either vexatious or computationally intractable. This paper describes an automatic
   approach in which genetic programming starts with a high-level statement of the requirements for the desired circuit and simultaneously creates the circuit's topology,
   component sizing, placement, and routing as part of a single integrated design process. The approach is illustrated using the problem of designing a 60 decibel amplifier.
   The fitness measure considers the gain, bias, and distortion of the candidate circuit as well as the area occupied by the circuit after the automatic placement and routing.
%8 17-19  April
%Z ICES-2000
%@ 3-540-67338-5

%A Forrest H {Bennett III}
%A Eleanor G. Rieffel
%T Using Genetic Programming to Design Decentralized Controllers for Self-Reconfigurable Modular Robots
%B Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference
%E Darrell Whitley
%D 2000
%P 35--42
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%8 8  July
%Z Part of \citewhitley:2000:GECCOlb

%A F. H {Bennett III}
%A E. G. Rieffel
%T Design of Decentralized Controllers for Self-Reconfigurable Modular Robots Using Genetic Programming
%B Proceedings of the Second NASA / DoD Workshop on Evolvable Hardware
%D 2000
%P 43--52
%I IEEE Computer Society
%I Jet Propulsion Laboratory, California Institute of Technology
%C Palo Alto, California
%K genetic algorithms, genetic programming
%X Advantages of self-reconfigurable modular robots over conventional robots include physical adaptability, robustness in the presence of failures, and economies of scale.
   Creating control software for modular robots is one of the central challenges to realizing their potential advantages. Modular robots differ enough from traditional robots
   that new techniques must be found to create software to control them. The novel difficulties are due to the fact that modular robots are ideally controlled in a
   decentralized manner, dynamically change their connectivity topology, may contain hundreds or thousands of modules, and are expected to perform tasks properly even when
   some modules fail. We demonstrate the use of genetic programming to automatically create distributed controllers for self-reconfigurable modular robots. .
%8 July  13-15
%Z EH-2000 http://ic-www.arc.nasa.gov/ic/eh2000/index.html http://csdl.computer.org/comp/proceedings/eh/2000/0762/00/0762toc.htm
%@ 0-7695-0762-X

%A Forrest H {Bennett III}
%A Brad Dolin
%A Eleanor G. Rieffel
%T Programmable Smart Membranes: Using Genetic Programming to Evolve Scalable Distributed Controllers for a Novel Self-Reconfigurable Modular Robotic Application
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 234--245
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, modular robot, distributed control, smart membrane, self-reconfigurable, scalable, robust
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=234
%X Self-reconfigurable modular robotics represents a new approach to robotic hardware, in which the "robot" is composed of many simple, identical interacting modules. We
   propose a novel application of modular robotics: the programmable smart membrane, a device capable of actively filtering objects based on numerous measurable attributes.
   Creating control software for modular robotic tasks like the smart membrane is one of the central challenges to realizing their potential advantages. We use genetic
   programming to evolve distributed control software for a 2-dimensional smart membrane capable of distinguishing objects based on color. The evolved controllers exhibit
   scalability to a large number of modules and robustness to the initial configurations of the robotic filter and the particles.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Karl Benson
%T Evolving automatic target detection algorithms
%B Graduate Student Workshop
%E Conor Ryan and Una-May O'Reilly and William B. Langdon
%D 2000
%P 249--252
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%8 8  July
%Z GECCO-2000WKS Part of \citewu:2000:GECCOWKS

%A Karl A Benson
%A David Booth
%A James Cubillo
%A Colin Reeves
%T Automatic Detection of Ships in Spaceborne SAR Imagery
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 767
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW002.ps
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Karl A Benson
%T Evolving Finite State Machines with Embedded Genetic Programming for Automatic Target Detection within SAR Imagery
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 1543--1549
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, image processing applications
%X This paper presents a model comprising Finite State Machines (FSMs) with embedded Genetic Programs (GPs) which co-evolve to perform the task of Automatic Target Detection
   (ATD). The fusion of a FSM and GPs allows for a control structure (main program), the FSM, and sub-programs, the GPs, to co-evolve in a symbiotic relationship. The GP
   outputs along with the FSM state transition levels are used to construct confidence intervals that enable each pixel within the image to be classified as either target or
   non-target, or to cause a state transition to take place and further analysis of the pixel to be performed. The algorithms produced using this method consist of nominally
   four GPs, with a typical node cardinality of less than ten, that are executed in an order dictated by the FSM. The results of the experimentation performed are compared to
   those obtained in two independent studies of the same problem using Kohonen Neural Networks and a two stage Genetic Programming strategy.
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Karl Benson
%T Performing Classification with an Environment Manipulating Mutable Automata
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 264--271
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, system modeling and control
%X In this paper a novel approach to performing classification is presented. Hypersurface Discriminant functions are evolved using Genetic Programming. These discriminant
   functions reside in the states of a Finite State Automata, which has the ability to reason 1 and logically combine the hypersurfaces to generate a complex decision space.
   An object may be classified by one or many of the discriminant functions, this is decided by the automata. During the evolution of this symbiotic architecture, feature
   selection for each of the discriminant functions is achieved implicitly, a task which is normally performed before a classification algorithm is trained. Since each
   dis-criminant function has different features, and objects may be classified with one or more discriminant functions, no two objects from the same class need be classified
   using the same features. Instead, the most appropriate features for a given object are used.
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Karl A Benson
%A David Booth
%A James Cubillo
%A Colin Reeves
%T On the use of evolution to construct finite state machines and mathematical functions to perform automatic target detection
%B Proceedings of the 3rd IMA conference on image processing: mathematical methods, algorithms and applications
%D 2000
%I IEE
%I The Institute of Mathematics and its Applications, The Institute of Physics, The Institute of Electrical Engineers
%C Leicester, UK
%K genetic algorithms, genetic programming
%8 13-15  September

%A Karl A Benson
%T Evolving Automatic Target Detection Algorithms that logically Combine Decision Spaces
%B Proceedings of the 11th British Machine Vision Conference
%D 2000
%P 685--694
%I 
%C Bristol, UK
%K genetic algorithms, genetic programming
%U http://www.bmva.ac.uk/bmvc/2000/papers/p69.pdf
%X classification Discriminant functions are constructed by combining selected features from the feature set with simple mathematical functions such as ????? These
   discriminant functions are capable of forming nonlinear discontinuous hypersurfaces. For multimodal data more than one discriminant function may be combined with logical
   operators before classification is performed. An algorithm capable of making decisions as to whether a combination of discriminant functions is needed to classify a data
   sample, or whether a single discriminant function will suffice, is developed. The algorithms used to perform classification are not written by a human. The algorithms are
   learnt, or rather evolved, using Evolutionary Computing techniques
%8 11-14  September

%A Hilan N. Bensusan
%T Automatic bias learning: an inquiry into the inductive basis of induction
%R Ph.D. Thesis D. Phil.
%D 1999
%I 
%I University of Sussex
%K genetic algorithms, genetic programming, CIGA
%U http://www.cs.bris.ac.uk/Publications/Papers/1000410.pdf
%X This thesis combines an epistemological concern about induction with a computational exploration of inductive mechanisms. It aims to investigate how inductive performance
   could be improved by using induction to select appropriate generalisation procedures. The thesis revolves around a meta-learning system, called The Entrencher, designed to
   investigate how inductive performances could be improved by using induction to select appropriate generalisation procedures. The performance of The Entrencher is discussed
   against the background of epistemological issues concerning induction, such as the role of theoretical vocabularies and the value of simplicity. After an introduction about
   machine learning and epistemological concerns with induction, Part I looks at learning mechanisms. It reviews some concepts and issues in machine learning and presents The
   Entrencher. The system is the first attempt to develop a learning system that induces over learning mechanisms through algorithmic representations of tasks. Part II deals
   with the need for theoretical terms in induction. Experiments where The Entrencher selects between different strategies for representation change are reported. The system
   is compared to other methods and some conclusions are drawn concerning how best to use the system. Part III considers the connection between simplicity and inductive
   success. Arguments for Occam's razor are considered and experiments are reported where The Entrencher is used to select when, how and how much a decision tree needs to be
   pruned. Part IV looks at some philosophical consequences of the picture of induction that emerges from the experiments with The Entrencher and goes over the motivations for
   meta-learning. Based on the picture of induction that emerges in the thesis, a new position in the scientific realism debate, transcendental surrealism, is proposed and
   defended. The thesis closes with some considerations concerning induction, justification and epistemological naturalism.
%8 February
%Z System in \citebensusan:1996:ciGP called CIGA Constructive induction with a Genetic Algorithm

%A P. J. Bentley
%A J. P. Wakefield
%T Generic Evolutionary Design
%B Soft Computing in Engineering Design and Manufacturing
%E Pravir K. Chawdhry and Rajkumar Roy and Raj K. Pant
%D 1997
%P 289--298
%I Springer-Verlag Godalming, GU7 3DJ, UK
%K genetic algorithms, genetic programming
%U http://www.springer.com/engineering/mechanical+eng/book/978-3-540-76214-0
%X Generic evolutionary design means the creation of a range of different designs by evolution. This paper introduces generic evolutionary design by a computer, describing a
   system capable of the evolution of a wide range of solid object designs from scratch, using a genetic algorithm. The paper reviews relevant literature, and outlines a
   number of advances necessitated by the development of the system, including: a new generic representation of solid objects, a new multiobjective fitness ranking method, and
   variable-length chromosomes. A library of modular evaluation software is also described, which allows a user to define new design problems quickly and easily by picking
   combinations of modules to guide the evolution of designs. Finally, the feasibility of generic evolutionary design by a computer is demonstrated by presenting the
   successful evolution of both conventional and unconventional designs for a range of different solid-object design tasks, e.g. tables, heatsinks, prisms, boat hulls,
   aerodynamic cars.
%8 23-27  June
%Z published 1998?

%A P. J. Bentley
%A J. P. Wakefield
%T Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms
%B Soft Computing in Engineering Design and Manufacturing
%E P. K. Chawdhry and R. Roy and R. K. Pant
%D 1997
%P 231--240
%I Springer-Verlag London Godalming, GU7 3DJ, UK
%K genetic algorithms, MOGA
%U http://eprints.hud.ac.uk/4052/
%X This paper investigates the problem of using a genetic algorithm to converge on a small, user-defined subset of acceptable solutions to multiobjective problems, in the
   Pareto-optimal (P-O) range. The paper initially explores exactly why separate objectives can cause problems in a genetic algorithm (GA). A technique to guide the GA to
   converge on the subset of acceptable solutions is then introduced. The paper then describes the application of six multiobjective techniques (three established methods and
   three new, or less commonly used methods) to four test functions. The previously unpublished distribution of solutions produced in the P-O range(s) by each method is
   described. The distribution of solutions and the ability of each method to guide the GA to converge on a small, user-defined subset of P-O solutions is then assessed, with
   the conclusion that two of the new multiobjective ranking methods are most useful.
%8 23-27  June
%Z cited by \citeRoss:2011:GPEM. WSC2 Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing
%@ 3-540-76214-0

%A Peter J. Bentley
%T The Future of Evolutionary Design Research
%B AVOCAAD Second International Conference
%D 1999
%P 349--350
%I 
%C Brussels, Belgium
%K genetic algorithms, genetic programming, Computer, design, International
%U http://eprints.ucl.ac.uk/171652/
%8 8-10  April
%Z http://cumincad.scix.net/cgi-bin/works/BrowseTree?field=series&separator=:&recurse=0&order=AZ&value=AVOCAAD http://cumincad.scix.net/cgi-bin/works/Show?616c Workshop on
   Morphogenetic Design, in the 2nd International Conference on Added Value of Computer Aided Architectural Design (AVOCAAD), Brussels, Belgium

%A P. J. Bentley
%T Is evolution creative?
%B Proceedings of the AISB'99 Symposium on Creative Evolutionary Systems
%E P. J. Bentley and D. Corne
%D 1999
%P 28--34
%I The Society for the Study of Artificial Intelligence and Simulation of Behaviour
%C Edinburgh
%K genetic algorithms, genetic programming, gades, CE, sussex, System, systems
%U http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC6.pdf
%X Can evolution demonstrate some of the properties of creativity? This paper argues that it can, and provides examples which the author feels illustrate some of the awesome
   power and feats of design which resemble creativity. Is evolution, then, truly creative? This is clearly a much harder question, for it requires a definition of creativity
   Ăą that most subjective and controversial of words. This paper explores and discusses various aspects of creativity, attempting to determine to what extent evolution
   satisfies each definition. The paper ends by summarising the discussion, and presenting amalgamations of four different worldviews.
%Z The AISB'99 Convention took place in March 1999, hosted jointly by the University of Edinburgh and the Edinburgh College of Art
%@ 1-902956-03-6

%A Peter Bentley
%A Sanjeev Kumar
%T Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 35--43
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-329.ps
%X This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes within evolutionary systems. Following a summary of the significant
   features of embryogenies, the three main types of embryogenies in Evolutionary Computation are then identified and explained: external, explicit and implicit. An
   experimental comparison between these three different embryogenies and an evolutionary algorithm with no embryogeny is performed. The problem set to the four evolutionary
   systems is to evolve tessellating tiles. In order to assess the scalability of the embryogenies, the problem is increased in difficulty by enlarging the size of tiles to be
   evolved. The results are surprising, with the implicit embryogeny outperforming all other techniques by showing no significant increase in the size of the genotypes or
   decrease in accuracy of evolution as the scale of the problem is increased.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Peter J. Bentley
%T Evolving fuzzy detectives: An investigation into the evolution of fuzzy rules
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 38--47
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC7.pdf
%X This paper explores the use of genetic programming to evolve fuzzy rules for the purpose of fraud detection. The fuzzy rule evolver designed during this research is
   described in detail. Four key system evaluation criteria are identified: intelligibility, speed, handling noisy data, and accuracy. Three sets of experiments are then
   performed in order to assess the performance of different components of the system, in terms of these criteria. The paper concludes: 1. that many factors affect accuracy of
   classification, 2. intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions and 3. noise can cause loss of accuracy proportionate
   to the square of noise.
%8 13  July
%Z GECCO-99LB, fraud detection, pre-GP 3-way clustering of each attribute multi-objective fitness function. variable size tree genotypes, bitstring in tree specifies input
   field, start small. Newer version available bentley:2000:EA Iris and Wisconsin Breast Cancer. Perfomance falls lineraly or quadratically with noise.

%T Evolutionary Design by Computers
%E Peter J. Bentley
%D 1999
%I Morgan Kaufmann
%K genetic algorithms, genetic programming, Computers
%U http://www.cs.ucl.ac.uk/staff/p.bentley/evdes.html
%X By bringing together the highest achievers in these fields for the first time, including a foreword by Richard Dawkins, this book provides the definitive ...
%@ 1-55860-605-X

%A Peter Bentley
%T An introduction to evolutionary design by computers
%B Evolutionary Design by Computers
%E Peter J. Bentley
%D 1999
%P 1--73
%I Morgan Kaufman
%C San Francisco, USA
%K genetic algorithms, genetic programming, Computer, Computers, design
%O 1
%Z Part of \citeBentley:evdes

%A P. J. Bentley
%T Evolving fuzzy detectives: an investigation into the evolution of fuzzy rules
%B Soft Computing in Industrial Applications
%E Yukinori Suzuki and Seppo J. Ovaska and Takeshi Furuhashi and Rajkumar Roy and Yasuhiko Dote
%D 1999
%P 89--106
%I Springer-Verlag London
%K genetic algorithms, genetic programming, evolution, fuzzy, industrial, industrial application, Rules
%U http://www.amazon.com/Computing-Industrial-Applications-Yukinori-Suzuki/dp/185233293X
%X This paper explores the use of genetic programming to evolve fuzzy rules for the purpose of fraud detection. The fuzzy rule evolver designed during this research is
   described in detail. Four key system evaluation criteria are identified: intelligibility, speed, handling noisy data, and accuracy. Three sets of experiments are then
   performed in order to assess the performance of different components of the system, in terms of these criteria. The paper concludes: 1. that many factors affect accuracy of
   classification, 2. intelligibility and processing speed mainly seem to be affected by the fuzzy membership functions and 3. noise can cause loss of accuracy proportionate
   to the square of noise.
%@ 1-85233-293-X

%A P. J. Bentley
%T Exploring component-based representations - the secret of creativity by evolution?
%B Evolutionary Design and Manufacture: Selected Papers from ACDM'00
%E I. C. Parmee
%D 2000
%P 161--172
%I Springer-Verlag Berlin/Heidelberg, Germany
%C University of Plymouth, Devon, UK
%K genetic algorithms, genetic programming, Adaptive, design
%U http://www.springer.com/engineering/mechanical+eng/book/978-1-85233-300-3
%X This paper investigates one of the newest and most exciting methods in computer science to date: employing computers as creative problem solvers by using evolution to
   explore for new solutions. The paper introduces and discusses the new understanding that explorative evolution relies upon a representation based on components rather than
   a parameterisation of a known solution. Evolution explores how the components can be arranged, how many are needed, and the type or function of each. The extra freedom
   provided by this simple idea is remarkable. By using evolutionary computation for exploration instead of optimisation, this technique enables us to expand the capabilities
   of computers. The paper describes how the approach has already shown impressive results in the creation of novel designs and architecture, fraud detection, composition of
   music, and creation of art. A framework for explorative evolution is provided, with discussion of the significance and difficulties posed by each element. The paper ends
   with an example of creative problem solving for a simple application- showing how evolution can shape pieces of paper to make them fall slowly through the air, by spiraling
   down like sycamore seeds.
%8 April
%Z The Fourth International Conference on Adaptive Computing in Design and Manufacture (ACDM0) was held at the Evolutionary design and manufacture: selected papers from
   ACDM'00

%A Peter J. Bentley
%T ``Evolutionary, my dear Watson'' Investigating Committee-based Evolution of Fuzzy Rules for the Detection of Suspicious Insurance Claims
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 702--709
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW074.ps
%8 10-12  July
%Z See also \citebentley:1999:EA A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference
   (GP-2000) Part of \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Peter J. Bentley
%A Timothy Gordon
%A Jungwon Kim
%A Sanjeev Kumar
%T New Trends in Evolutionary Computation
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%V 1
%D 2001
%P 162--169
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, new trends, creative evolution, computation embryology, evolvable hardware, artificial immune systems
%X In the last five years, the field of evolutionary computation (EC) has seen a resurgence of new ideas, many stemming from new biological inspirations. The paper outlines
   four of these new branches of research: creative evolutionary systems, computational embryology, evolvable hardware and artificial immune systems, showing how they aim to
   extend the capabilities of EC. Recent, unpublished results by researchers in each area at the Department of Computer Science, UCL are provided
%8 27-30  May
%Z gades CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =
%@ 0-7803-6658-1

%A Peter J. Bentley
%A Una-May O'Reilly
%T Ten steps to make a perfect creative evolutionary design system
%B Non-Routine Design with Evolutionary Systems, GECCO-2001 Workshop
%E Peter Bentley and Mary Lou Maher and Josiah Poon
%D 2001
%I 
%K genetic algorithms, genetic programming, Agency GP, design, evolutionary, SYSTEM, SYSTEMS, WORKSHOP
%U http://sydney.edu.au/engineering/it/~josiah/gecco_workshop_bentley.pdf
%X A perfect creative evolutionary design system is impossible to achieve, but in this position paper we discuss 10 steps that might bring us a little closer to this dream.
   These important problems and requirements have been identified as a result of both authorsĂą experiences on a number of projects in this area. While our solutions may not
   solve all of the problems, they illustrate what we regard as the current state of the art in creative evolutionary design.
%8 7  July
%Z http://sydney.edu.au/engineering/it/~josiah/gecco2001_workshop_schedule.html

%A Peter J. Bentley
%A David W. Corne
%T An Introduction to Creative Evolutionary Systems
%B Creative Evolutionary Systems
%E Peter J. Bentley and David W. Corne
%D 2001
%P 1--75
%I Morgan Kaufmann
%K genetic algorithms, genetic programming
%8 July
%Z GP include amongst other EC techniques. Part of \citeBentley:2002:bookCES
%@ 1-55860-673-4

%T Creative evolutionary systems
%E Peter Bentley and David Corne
%D 2002
%I Morgan Kaufmann
%C USA
%K genetic algorithms, genetic programming, Computers
%U http://www.amazon.com/Creative-Evolutionary-Kaufmann-Artificial-Intelligence/dp/1558606734
%X This book concentrates on applying important ideas in evolutionary computation to creative areas, such as art, music, architecture, and design.
%Z Chapters on GP
%@ 1-55860-673-4

%A Peter J. Bentley
%T Digital Biology. How Nature is Transforming Our Technology and Our Lives
%D 2002
%I Simon and Schuster
%C USA
%K genetic algorithms, genetic programming, biology, digital, nature, technology
%U http://www.amazon.com/Digital-Biology-Peter-J-Bentley/dp/0743204476
%Z Hardback
%@ 0-7432-0447-6

%A Peter J. Bentley
%A Jon Timmis
%T Guest Editorial Special Issue on Artificial Immune Systems
%J Genetic Programming and Evolvable Machines
%V 4
%N 4
%D 2003
%P 307--309
%I 
%K artificial immune systems
%8 Decemeber
%Z Special issue on artificial immune systems. Article ID: 5144845

%A Peter J. Bentley
%T Fractal Proteins
%J Genetic Programming and Evolvable Machines
%V 5
%N 1
%D 2004
%P 71--101
%I 
%K genetic algorithms, fractal proteins, development, evolvability, scalability, complexity
%X The fractal protein is a new concept intended to improve evolvability, scalability, exploitability and provide a rich medium for evolutionary computation. Here the idea of
   fractal proteins and fractal proteins with concentration levels are introduced, and a series of experiments showing how evolution can design and exploit them within gene
   regulatory networks is described.
%8 March
%Z Article ID: 5264735

%T 7th International Conference on Artificial Immune Systems, ICARIS 2008
%S Lecture Notes in Computer Science
%E Peter J. Bentley and Doheon Lee and Sungwon Jung
%V 5132
%D 2008
%I Springer
%C Phuket, Thailand
%K Computers
%X This book constitutes the refereed proceedings of the 7th International Conference on Artificial Immune Systems, ICARIS 2008, held in Phuket, Thailand, in ...
%8 August  10-13
%@ 3-540-85071-6

%A Ilham Benyahia
%A J. Yves Potvin
%T Genetic Programming for Vehicle Dispatch
%B Proceedings of the 1997 IEEE International Conference on Evolutionary Computation
%D 1997
%P 547--552
%I IEEE Press Piscataway, NJ, USA
%C Indianapolis, USA
%K genetic algorithms, genetic programming
%X Vehicle dispatching is aimed at allocating real time service requests to a fleet of vehicles in movement. This task is modeled as a multiattribute choice problem. Namely,
   different attribute values are associated with each vehicle to describe its situation with respect to the current service request. Based on this attribute description, a
   utility function that approximates the decision process of a professional dispatcher is computed. This utility function evolves through genetic programming. Computational
   results are reported on requests collected from a courier service company
%8 13-16  April
%Z ICEC-97

%A Ilham Benyahia
%A Jean-Yves Potvin
%T Decision Support for Vehicle Dispatching Using Genetic Programming
%J IEEE Transactions on Systems, Man, and Cybernetics part A: systems and humans
%V 28
%N 3
%D 1998
%P 306--314
%I 
%K genetic algorithms, genetic programming
%U http://ieeexplore.ieee.org/iel4/3468/14669/00668962.pdf
%X Vehicle dispatching consists of allocating real-time service requests to a fleet of moving vehicles. In this paper, each vehicle is associated with a vector of attribute
   values that describes its current situation with respect to new incoming service requests. Using this attribute description, a utility function aimed at approximating the
   decision process of a professional dispatcher is constructed through genetic programming. Computational results are reported on requests collected from a courier service
   company and a comparison is provided with a neural network model and a simple dispatching policy.
%8 May

%A Ilham Benyahia
%A Vincent Talbot
%T Optimizing the Architecture of Adaptive Complex Applications Using Genetic Programming
%B The 14th International Conference on Distributed Multimedia Systems, DMS'2008
%D 2008
%P 27--31
%I Knowledge Systems Institute
%I Knowledge Systems Institute
%C Hyatt Harborside at Logan Int'l Airport, Boston, USA
%K genetic algorithms, genetic programming
%X unseen
%8 4-6  September
%Z http://www.ksi.edu/seke/dms08.html

%A L. Berardi
%A Z. Kapelan
%A O. Giustolisi
%A D. A. Savic
%T Development of pipe deterioration models for water distribution systems using EPR
%J Journal of Hydroinformatics
%V 10
%N 2
%D 2008
%P 113--126
%I 
%K genetic algorithms, genetic programming, data-driven modelling, evolutionary polynomial regression, failure analysis, performance indicators, water systems
%U http://www.iwaponline.com/jh/010/0113/0100113.pdf
%X The economic and social costs of pipe failures in water and wastewater systems are increasing, putting pressure on utility managers to develop annual replacement plans for
   critical pipes that balance investment with expected benefits in a risk-based management context. In addition to the need for a strategy for solving such a multi-objective
   problem, analysts and water system managers need reliable and robust failure models for assessing network performance. In particular, they are interested in assessing a
   conduit's propensity to fail and how to assign criticality to an individual pipe segment. pipe deterioration is modelled using Evolutionary Polynomial Regression. This
   data-driven technique yields symbolic formulae that are intuitive and easily understandable by practitioners. The case study involves a water quality zone within a
   distribution system and entails the collection of historical data to develop network performance indicators. Finally, an approach for incorporating such indicators into a
   decision support system for pipe rehabilitation/replacement planning is introduced and articulated.
%Z Fig 5 bathtub curve Hydroinformatics Group, Technical University of Bari, via Orabona 4, I-70125, Bari, Italy

%A Patrick Berarducci
%A Demetrius Jordan
%A David Martin
%A Jebbifer Seitzer
%T GEVOSH: Using Grammatical Evolution to Generate Hashing Functions
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WUGW001.pdf
%X In this paper, we present system GEVOSH, Grammatically Evolved Hashing. GEVOSH evolves hashing functions using grammatical evolution techniques. Hashing functions are used
   to expedite search in a wide number of domains. In our work, GEVOSH created hashing functions that, on average, perform better than many standard (human-generated) hash
   functions extracted from the literature. In this paper, we present the architecture of system GEVOSH, its main components and algorithms, and resultant generated hash
   functions along with comparisons to standard, human-generated functions.
%8 26-30  June
%Z GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A John P. Beretz
%T Evolution of Algorithms for Multi-Species Emergent Assembly Behavior using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 21--30
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2002:gagp

%A Steven Bergen
%A Brian J. Ross
%T Evolutionary Art Using Summed Multi-Objective Ranks
%B Genetic Programming Theory and Practice VIII
%S Genetic and Evolutionary Computation
%E Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva
%V 8
%D 2010
%P 227--244
%I Springer
%C Ann Arbor, USA
%K genetic algorithms, genetic programming
%U http://www.springer.com/computer/ai/book/978-1-4419-7746-5
%O 14
%8 20-22  May
%Z part of \citeRiolo:2010:GPTP

%A Steve Bergen
%T Automatic Structure Generation using Genetic Programming and Fractal Geometry
%R M.S. Thesis
%D 2011
%I 
%I Brock University
%K genetic algorithms, genetic programming

%A Steve Bergen
%A Brian Ross
%T Aesthetic 3D Model Evolution
%B Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012
%S LNCS
%E Penousal Machado and Juan Romero and Adrian Carballal
%V 7247
%D 2012
%P 11--22
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Aesthetics, L-systems, 3D models, multi-objective evaluation
%X Recently, evolutionary art has been exploring the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This
   paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image
   evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and
   symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming.
   Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey
   was conducted, and survey takers showed a clear preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic
   evolution of 3D structures is a promising new research area for evolutionary art.
%8 11-13  April
%Z Part of \citeMachado:2012:EvoMusArt EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012

%A Jean Berger
%A Mourad Sassi
%A Martin Salois
%T A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Itinerary Constraints
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 44--51
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Eric Berger
%T Development of a Minimal Information Line Following Algorithm using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 31--35
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2002/Berger.pdf
%8 June
%Z part of \citekoza:2002:gagp

%A Agneta Bergstrom
%A Patricija Jaksetic
%A Peter Nordin
%T Enhancing Information Retrieval by Automatic Acquisition of Textual Relations using Genetic Programming
%B IUI 2000
%D 2000
%I ACM Press
%K genetic algorithms, genetic programming, machine learning, natural language processing, semantic networks, information retrieval
%U http://web.media.mit.edu/~lieber/IUI/Bergstrom/Bergstrom.pdf
%X We have explored a novel method to find textual relations in electronic documents using genetic programming and semantic networks. This can be used for enhancing
   information retrieval and simplifying user interfaces. The automatic extraction of relations from text enables easier updating of electronic dictionaries and may reduce
   interface area both for search input and hit output on small screens such as cell phones and PDAs (Personal Digital Assistants).
%Z www

%A Agneta Bergstrom
%A Patricija Jaksetic
%A Peter Nordin
%T Acquiring Textual Relations Automatically on the Web using Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 237--246
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=237
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A F. J. Berlanga
%A M. J. {del Jesus}
%A M. J. Gacto
%A F. Herrera
%T A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems
%B Proceedings 8th International Conference on Artificial Intelligence and Soft Computing ICAISC
%S Lecture Notes on Artificial Intelligence (LNAI)
%E Leszek Rutkowski and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek Zurada
%V 4029
%D 2006
%P 182--191
%I Springer-Verlag
%C Zakopane, Poland
%K genetic algorithms, genetic programming
%X In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as
   objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the
   learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for
   the learning of an FRBCS, where disjunctive normal form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy
   rules, which presents a good classification performance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the
   population. The good results obtained with several classification problems support our proposal.
%8 June  25-29
%@ 3-540-35748-3

%A Francisco Jose Berlanga
%A Maria Jose {del Jesus}
%A Francisco Herrera
%T A novel genetic cooperative-competitive fuzzy rule based learning method using genetic programming for high dimensional problems
%B 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008
%D 2008
%P 101--106
%I 
%C Witten-Boommerholz, Germany
%K genetic algorithms, genetic programming, genetic cooperative-competitive fuzzy rule based learning method, high dimensional classification problems, high dimensional
   problems, token competition mechanism, fuzzy set theory, knowledge based systems, learning (artificial intelligence)
%X In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a
   different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means
   of a context-free grammar) and uses a token competition mechanism to maintain the diversity of the population. It makes the rules compete and cooperate among themselves,
   giving out a compact set of fuzzy rules that presents a good performance. The good results obtained in an experimental study involving several high dimensional
   classification problems support our proposal.
%8 4-7  March
%Z Also known as \cite4484575

%A F. J. Berlanga
%A A. J. Rivera
%A M. J. {del Jesus}
%A F. Herrera
%T GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems
%J Information Sciences
%V 180
%N 8
%D 2010
%P 1183--1200
%I 
%K genetic algorithms, genetic programming, Classification, Fuzzy rule-based systems, Genetic fuzzy systems, High-dimensional problems, Interpretability-accuracy trade-off
%U http://www.sciencedirect.com/science/article/B6V0C-4Y34R0J-1/2/82039ab1549f5a0d0fc4d73b2a30bfa6
%X In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional
   problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base,
   so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the
   rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of
   non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.

%A M. Bernal-Urbina
%A A. Flores-Mendez
%T Time Series Forecasting through Polynomial Artificial Neural Networks and Genetic Programming
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 3325--3330
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the
   information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analysed grow
   exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimisation algorithm that determines the architecture of the PANN
   through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic
   Algorithm.
%8 1-6  June
%Z Also known as \cite4634270. WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Marc Bernard
%A Amaury Habrard
%A Marc Sebban
%T Learning Stochastic Tree Edit Distance
%B Machine Learning: ECML 2006
%S Lecture Notes in Computer Science
%E Johannes Furnkranz and Tobias Scheffer and Myra Spiliopoulou
%V 4212
%D 2006
%P 42--53
%I Springer
%X Trees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree
   structured documents. In this context, many applications require the calculation of similarities between tree pairs. The most studied distance is likely the tree edit
   distance (ED) for which improvements in terms of complexity have been achieved during the last decade. However, this classic ED usually uses a priori fixed edit costs which
   are often difficult to tune, that leaves little room for tackling complex problems. In this paper, we focus on the learning of a stochastic tree ED. We use an adaptation of
   the Expectation-Maximization algorithm for learning the primitive edit costs. We carried out series of experiments that confirm the interest to learn a tree ED rather than
   a priori imposing edit costs.
%Z not GP but cited by \citemcdermott:2011:EuroGP

%A P. Bernardi
%A E. Sanchez
%A M. Schillaci
%A G. Squillero
%A M. {Sonza Reorda}
%T An Evolutionary Methodology to Enhance Processor Software-Based Diagnosis
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 3201--3206
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, microGP
%X The widespread use of cheap processor cores requires the ability to quickly point out the manufacturing process criticalities in an effort to enhance the production yield.
   Fault diagnosis is an integral part of the industrial effort towards these goals. This paper describes an innovative application of evolutionary algorithms: iterative
   refinement of a diagnostic test set. Several enhancements in the used evolutionary core are additionally outlined, highlighting their relevance for the specific problem.
   Experimental results are reported in the paper showing the effectiveness of the approach for a widely-known microcontroller core.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = "859--864",
%@ 0-7803-9487-9

%A Knut Bernhardt
%T Finding Alternatives and Reduced Formulations for Process-Based Models
%J Evolutionary Computation
%V 16
%N 1
%D 2008
%P 63--88
%I 
%K genetic algorithms, genetic programming, Model reduction, complexity, dimension reduction
%X This paper addresses the problem of model complexity commonly arising in constructing and using process-based models with intricate interactions. Apart from complex process
   details the dynamic behaviour of such systems is often limited to a discrete number of typical states. Thus, models reproducing the system's processes in all details are
   often too complex and over-parameterised. In order to reduce simulation times and to get a better impression of the important mechanisms, simplified formulations are
   desirable. In this work a data adaptive model reduction scheme that automatically builds simple models from complex ones is proposed. The method can be applied to the
   transformation and reduction of systems of ordinary differential equations. It consists of a multistep approach using a low dimensional projection of the model data
   followed by a Genetic Programming/Genetic Algorithm hybrid to evolve new model systems. As the resulting models again consist of differential equations, their process-based
   interpretation in terms of new state variables becomes possible. Transformations of two simple models with oscillatory dynamics, simulating a mathematical pendulum and
   predator-prey interactions respectively, serve as introductory examples of the method's application. The resulting equations of force indicate the predator-prey system's
   equivalence to a nonlinear oscillator. In contrast to the simple pendulum it contains driving and damping forces that produce a stable limit cycle.
%8 Spring
%Z CVODE, SUNDIALS

%A Tommaso F. Bersano-Begey
%A Jason M. Daida
%A John F. Vesecky
%A Frank L. Ludwig
%T A Platform-Independent Collaborative Interface for Genetic Programming Applications: Image Analysis for Scientific Inquiry
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 1--8
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 28--31  July
%Z GP-96LB java The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-201031-7

%A Tommaso F. Bersano-Begey
%A Jason M. Daida
%A John F. Vesecky
%A Frank L. Ludwig
%T A Java Collaborative Interface for Genetic Programming Applications: Image Analysis and Scientific Inquiry
%B Proceedings of the 1997 IEEE International Conference on Evolutionary Computation
%D 1997
%I IEEE Press Piscataway, NJ, USA
%C Indianapolis
%K genetic algorithms, genetic programming
%U ftp://ftp.eecs.umich.edu/people/daida/papers/ICEC97image.pdf
%8 13-16  April
%Z ICEC-97 Collaborative Interface Demonstration http://www.sprl.umich.edu/acers/gaia/collab.html GAIA (Genetic programming Assistant for Image Analysis) slides
   http://www-personal.umich.edu/~tombb/gaia74/

%A Tommaso F. Bersano-Begey
%T Controlling Exploration, Diversity and Escaping Local Optima in GP: Adapting Weights of Training Sets to Model Resource Consumption
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 7--10
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Tommaso F. Bersano-Begey
%A Jason M. Daida
%T A Discussion on Generality and Robustness and a Framework for Fitness Set Construction in Genetic Programming to Promote Robustness
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 11--18
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 Fri, 05 Sep 1997 06:14:54 EDT I did
   some follow-up work in trying to improve generality of code in the wall-following problem, and started to look at how to gain more information about generality by recording
   the distribution of hits (rather than just their total), an iterative algorithm to check for and correct ambiguous training sets (one which can be solved by other solutions
   besides the correct one), and an account of the relationship between size and generality of solutions. The following was a very preliminary work, but I am now working on
   expanding each topic and writing them in a more formal way. slides http://www-personal.umich.edu/~tombb/gp973/
%@ 0-18-206995-8

%A Tommaso F. Bersano-Begey
%A Patrick G. Kenny
%A Edmund H. Durfee
%T Multi-Agent Teamwork, Adaptive Learning and Adversarial Planning in Robocup Using a PRS Architecture
%B IJCAI97
%D 1997
%I 
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=BC06E9197308E7FDF6E8347CECE81DC1?doi=10.1.1.53.1962&rep=rep1&type=pdf
%X Our approach for the Robocup97 competition is to emphasise teamwork among agents by augmenting reactions (based on awareness of the current situation) with predictions
   (based on predefined multiagent manoeuvres). These predictions are accomplished by allowing agents to cooperatively accomplish predefined plans, which are elaborated
   reactively and hierarchically to ensure responsiveness to changing circumstances. By supporting the runtime construction of plans, our approach simplifies the introduction
   of new plans, strategies, and actions, and produces a framework for dynamic adaptation and plan recognition through automatically generating belief networks. Our
   implementation is built on top of UM-PRS, a procedural reasoning system architecture for real-time environments, which allows specifying, executing, and integrating plans
   based on subgoaling and preconditions
%O accepted
%Z um-prs.pdf broken 5-sep-97 http://www.sonycsl.co.jp/person/kitano/RoboCup/ws97.html

%A Hugues Bersini
%T Chemical Crossover
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 825--832
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/AA140.ps
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Yaniv Bernstein
%A Xiaodong Li
%A Vic Ciesielski
%A Andy Song
%T Multiobjective Parsimony Enforcement for Superior Generalisation Performance
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 83--89
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Multiobjective evolutionary algorithms, Combinatorial \& numerical optimization
%U http://goanna.cs.rmit.edu.au/~ybernste/papers/Bernstein_CEC_2004.pdf
%X Program Bloat - the phenomenon of ever-increasing program size during a GP run - is a recognised and widespread problem. Traditional techniques to combat program bloat are
   program size limitations or parsimony pressure (penalty functions). These techniques suffer from a number of problems, in particular their reliance on parameters whose
   optimal values it is difficult to a priori determine. In this paper we introduce POPE-GP, a system that makes use of the NSGA-II multiobjective evolutionary algorithm as an
   alternative, parameter-free technique for eliminating program bloat. We test it on a classification problem and find that while vastly reducing program size, it does
   improve generalisation performance.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Vincent Berthier
%A Hassen Doghmen
%A Olivier Teytaud
%T Consistency Modifications for Automatically Tuned Monte-Carlo Tree Search
%B Learning and Intelligent OptimizatioN, LION 4
%E Roberto Battiti
%D 2010
%I 
%C Venice
%K genetic algorithms, genetic programming, Game Go, Mathematics/Optimization and Control, Monte-Carlo Tree Search Consistency Ko-fights
%U HAL:http://hal.archives-ouvertes.fr/inria-00437146/en/
%X Monte-Carlo Tree Search algorithms (MCTS [4, 6]), including upper confidence trees (UCT [9]), are known for their impressive ability in high dimensional control problems.
   Whilst the main test bed is the game of Go, there are increasingly many applications [13, 12, 7]; these algorithms are now widely accepted as strong candidates for
   high-dimensional control applications. Unfortunately, it is known that for optimal performance on a given problem, MCTS requires some tuning; this tuning is often
   handcrafted or automated, with in some cases a loss of consistency, i.e. a bad behavior asymptotically in the computational power. This highly undesirable property led to a
   stupid behavior of our main MCTS program MoGo in a real-world situation described in section 3. This is a big trouble for our several works on automatic parameter tuning
   [3] and the genetic programming of new features in MoGo. We will see in this paper: -- A theoretical analysis of MCTS consistency; -- Detailed examples of consistent and
   inconsistent known algorithms; -- How to modify a MCTS implementation in order to ensure consistency, independently of the modifications to the scoring module (the module
   which is automatically tuned and genetically programmed in MoGo); -- As a by product of this work, we'll see the interesting property that some heavily tuned MCTS
   implementations are better than UCT in the sense that they do not visit the complete tree (whereas UCT asymptotically does), whilst preserving the consistency at least if
   consistency modifications above have been made.
%8 January  18-22
%Z LION4 http://lion.disi.unitn.it/intelligent-optimization//LION4/program.php

%A Robert R. Bertram
%A Jason M. Daida
%A John F. Vesecky
%A Guy A. Meadows
%A Christian Wolf
%T Reconstructing Incomplete Signals Using Nonlinear Interpolation and Genetic Algorithms
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 19--27
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 see also \citebertram:1998:risiGA
%@ 0-18-206995-8

%A Robert R. Bertram
%A Jason M. Daida
%A John F. Vesecky
%A Guy A. Meadows
%A Christian Wolf
%T Reconstructing Incomplete Signals Using Nonlinear Interpolation and Genetic Algorithms
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 447--454
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%U http://citeseer.ist.psu.edu/cache/papers/cs/10392/ftp:zSzzSzftp.eecs.umich.eduzSzpeoplezSzdaidazSzpaperszSzsga98reconstruct.pdf/reconstructing-incomplete-signals-using.pdf
%X This paper describes a general, nonanalytical method for deriving Fourier series coefficients using a genetic algorithm. Non-analytical methods are often needed in problems
   where lost portions of a complex signal require restoration. We discuss some of the difficulties involved in working with the associated trigonometric polynomials and
   propose an alternative solution for adapting genetic algorithms for this class of problems. We demonstrate the efficacy of our approach with a case study. Our particular
   case study features the processing of data that has been collected by a novel optical waveslope instrument, which measures the topography of water surfaces.
%8 22-25  July
%Z SGA-98 see also \citeBertram:1997:ris

%A Sireesha Besetti
%A Terence Soule
%T Function choice, resiliency and growth in genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1771--1772
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Poster, function choice, growth, resilience
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1771.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Michael L. Best
%T Coevolving Mutualists Guide Simulated Evolution
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 941
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming, poster papers
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A K. D. Bettenhausen
%A S. Gehlen
%A P. Marenbach
%A H. Tolle
%T BioX++ -- New results and conceptions concerning the intelligent control of biotechnological processes
%B 6th International Conference on Computer Applications in Biotechnology
%E A. Munack and K. Sch\"ugerl
%D 1995
%P 324--327
%I Elsevier Science
%I IFAC Publications
%K genetic algorithms, genetic programming, Expert systems, neural networks, fuzzy systems, learning control, fermentation, biotechnology
%U http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_03.pdf
%X BioX++ facilities the transparent generation of process control stratgies and sequences based on automatically self-organized structured process models. Experimental
   results showing the increased product yeild and the discussion of approach-specific problems are part of this paper as well as the new approaches actually examined.
%Z 14--17 May, Garmisch-Partenkirchen, Germany

%A Kurt Dirk Bettenhausen
%A Peter Marenbach
%T Self-organizing modeling of biotechnological batch and fed-batch fermentations
%B EUROSIM'95
%E F. Breitenecker and I. Husinsky
%D 1995
%I Elsevier
%C Vienna, Austria
%K genetic algorithms, genetic programming, fermentation, biotechnology
%U http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_23.ps.gz
%X An approach for the automatic generation of dynamic nonlinear process models obtained from experimantal process data and theoretical biological and chemical reflections
   using genetic programming for the supervision and coordination of the symbolic model structure during automatic development BioX++ includes (amongs fuzzy rule learning,
   expert system, NN also refered to) GP to produce process models, constants adapted using standard algorithmic techniques.
%Z 11--15 September, Vienna, Austria

%A K. D. Bettenhausen
%A P. Marenbach
%A Stephan Freyer
%A Hans Rettenmaier
%A Ullrich Nieken
%T Self-organizing Structured modeling of a Biotechnological Fed-batch fermentation by Means of Genetic Programming
%B First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA
%E A. M. S. Zalzala
%V 414
%D 1995
%P 481--486
%I IEE London, UK
%C Sheffield, UK
%K genetic algorithms, genetic programming, symbolic modelling, system identification, biotechnology, predictive control
%U http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_24.pdf
%X 12--14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm The article describes an approach for the
   self-organizing generation of models of complex and unknown processes by means of GP and its application on a biotechnological fed-batch production. First experiments of
   the symbolic generation of structured models within an industrial cooperation with BASF are presented.
%8 12-14  September
%Z Deals much more than bettenhausen:1995:ssmbff and \citebettenhausen:1995:biox with the idea of Genetic Programming. First results from an application of our approach for
   finding model of an industrial fed-batch fermentation process are presented which. This work was part of an cooperation of our Institute and the BASF AG, Ludwigshafen,
   Germany. This paper includes a more detailed description of how our GP system works.
%@ 0-85296-650-4

%A Hans-Georg Beyer
%A Dirk V. Arnold
%T Fitness Noise and Localization Errors of the Optimum in General Quadratic Fitness Models
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 817--824
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%U http://www.cs.dal.ca/~dirk/docs/GECCO99.ps.gz
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Hans-Georg Beyer
%A Thomas Jansen
%A Colin Reeves
%A Michael D. Vose
%T 04081 Abstracts Collection -- Theory of Evolutionary Algorithms
%B Theory of Evolutionary Algorithms
%S Dagstuhl Seminar Proceedings
%E Hans-Georg Beyer and Thomas Jansen and Colin Reeves and Michael D. Vose
%N 04081
%D 2004
%I Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, Germany
%C Dagstuhl, Germany
%K genetic algorithms, genetic programming, Evolutionary algorithms, co-evolution, run time analysis, landscape analysis, Markov chains
%U http://drops.dagstuhl.de/opus/volltexte/2006/498
%X From 15.02.04 to 20.02.04, the Dagstuhl Seminar 04081 ``Theory of Evolutionary Algorithms'' was held in the International Conference and Research Center (IBFI), Schloss
   Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given
   during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general.
   Links to extended abstracts or full papers are provided, if available.
%O $<$http://drops.dagstuhl.de/opus/volltexte/2006/498$>$ [date of citation: 2006-01-01]
%Z See also \citelangdon:2003:normal and \citemcphee:ots:gecco2004

%A Hans-Georg Beyer
%A Markus Olhofer
%A Bernhard Sendhoff
%T On the Impact of Systematic Noise on the Evolutionary Optimization Performance -- A Sphere Model Analysis
%J Genetic Programming and Evolvable Machines
%V 5
%N 4
%D 2004
%P 327--360
%I 
%K ES, evolution strategies, noisy optimisation, performance analysis, robust optimization
%X Quality evaluations in optimisation processes are frequently noisy. In particular evolutionary algorithms have been shown to cope with such stochastic variations better
   than other optimization algorithms. So far mostly additive noise models have been assumed for the analysis. However, we will argue in this paper that this restriction must
   be relaxed for a large class of applied optimization problems. We suggest systematic noise as an alternative scenario, where the noise term is added to the objective
   parameters or to environmental parameters inside the fitness function. We thoroughly analyse the sphere function with systematic noise for the evolution strategy with
   global intermediate recombination. The progress rate formula and a measure for the efficiency of the evolutionary progress lead to a recommended ratio between [mu] and
   [lambda]. Furthermore, analysis of the dynamics identifies limited regions of convergence dependent on the normalized noise strength and the normalised mutation strength. A
   residual localisation error R[infin] can be quantified and a second [mu] to [lambda] ratio is derived by minimising R[infin].
%8 Decemeber
%Z Article ID: 5272968

%A Hans-Georg Beyer
%A Dirk V. Arnold
%A Silja Meyer-Nieberg
%T A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise
%J Genetic Programming and Evolvable Machines
%V 6
%N 1
%D 2005
%P 7--24
%I 
%K ES, evolution strategies, final fitness error, noisy optimization, optimization quality, robust optimization
%X Differential-geometric methods are applied to derive steady state conditions for the (mgr/mgrI,lambda)-ES on the general quadratic test function disturbed by fitness noise
   of constant strength. A new approach for estimating the expected final fitness deviation observed under such conditions is presented. The theoretical results obtained are
   compared with real ES runs, showing a surprisingly excellent agreement.
%8 March

%T GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%D 2005
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, A-Life, Evolutionary Robotics and Adaptive Behaviour, Ant Colony Optimisation and Swarm Intelligence, Artificial Immune Systems,
   Biological Applications, Coevolution, Estimation of Distribution Algorithms, Evolutionary Combinatorial Optimisation, Evolutionary Multi-objective Optimization,
   Evolutionary Strategies, Evolutionary Programming, Evolvable Hardware, Meta-heuristics and Local Search, Real World Applications, Search-based Software Engineering
%U http://portal.acm.org/citation.cfm?id=1068009&jmp=cit&coll=GUIDE&dl=GUIDE&CFID=48779769&CFTOKEN=55479664#supp
%X The papers in this two volume proceedings are presented at the 7th Annual Genetic and Evolutionary Computation COnference (GECCO-2005), held in Washington, D.C., June
   25-29, 2005.This year is an exceptional one for the GECCO conference series. First, the International Society for Genetic and Evolutionary Computation (ISGEC) which has
   always been GECCO's sponsor has changed to become a Special Interest Group of the ACM named SIGEVO. Being part of ACM reflects the evolution and integration of our very
   successful discipline into the main stream of computer science. As a consequence, the GECCO-2005 proceedings are an ACM publication and they are incorporated into the ACM
   Digital Library. This guarantees an even broader dissemination of Darwinian and other nature-inspired computation methods.Second, we had 549 regular paper submissions
   representing the absolute record of all conferences emphasising the field of evolutionary computation. Paper reviewing has been done by double blind assignment. On average
   each paper was evaluated by five independent reviewers. Finally, 253 paper (46.1%) have been accepted as full (max. 8 pages) papers. Additionally, 120 submissions were
   accepted as posters.A goal of GECCO is to encourage new areas and paradigms of evolutionary computation to gather momentum and flourish. This is accomplished by the
   establishment of new independent tracks each year. This year, as a result of a recombinative and creative process, GECCO-2005 comprises 16 tracks consisting of core tracks
   ("C"), tracks previously in GECCOs ("P"), not yet belonging to the core track family), "recombined" tracks from GECCO 2004 ("R"), and newly created tracks ("N"):.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Hans-Georg Beyer
%T Special Issue: Best of GECCO 2005
%J Genetic Programming and Evolvable Machines
%V 7
%N 2
%D 2006
%P 129--130
%I 
%K genetic algorithms, genetic programming
%8 August
%Z Introduction to special issue

%A Hans-Georg Beyer
%A Silja Meyer-Nieberg
%T Self-adaptation of evolution strategies under noisy fitness evaluations
%J Genetic Programming and Evolvable Machines
%V 7
%N 4
%D 2006
%P 295--328
%I 
%K Evolution strategies, Self-adaptation, Noisy optimisation, Noisy sphere model
%X This paper investigates the self-adaptation behaviour of (1,L)-evolution strategies (ES) on the noisy sphere model. To this end, the stochastic system dynamics is
   approximated on the level of the mean value dynamics. Being based on this microscopic analysis, the steady state behavior of the ES for the scaled noise scenario and the
   constant noise strength scenario will be theoretically analysed and compared with real ES runs. An explanation will be given for the random walk like behaviour of the
   mutation strength in the vicinity of the steady state. It will be shown that this is a peculiarity of the (1,L)-ES and that intermediate recombination strategies do not
   suffer from such behaviour.
%8 Decemeber

%A Trevor Bezdek
%T Evolution and Analysis of DNA Classifiers
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 21--30
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Navneet Bhalla
%T Natalio Krasnogor, Steve Gustafson, David A. Pelta, and Jose L. Verdegay (eds): Systems self-assembly: multidisciplinary snapshots Elsevier, 2008, 310 pp, 41 colour plates,
   hard cover, \$160 USD list price, ISBN 978-0-444-52865-0
%J Genetic Programming and Evolvable Machines
%V 10
%N 4
%D 2009
%P 473--475
%I 
%O Book Review
%8 Decemeber

%A Bir Bhanu
%A Krzysztof Krawiec
%T Coevolutionary Construction of Features for Transformation of Representation in Machine Learning
%B GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2002
%P 249--254
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York
%U http://citeseer.ist.psu.edu/509773.html
%X The main objective of this paper is to study the usefulness of cooperative coevolutionary algorithms (CCA) for improving the performance of classification of machine
   learning (ML) classifiers, in particular those following the symbolic paradigm. For this purpose, we present a genetic programming (GP) -based coevolutionary feature
   construction procedure. In the experimental part, we confront the coevolutionary methodology with difficult real-world ML task with unknown internal structure and complex
   interrelationships between solution subcomponents (features), as opposed to artificial problems considered usually in the literature.
%8 8  July
%Z Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic
   Programming Conference (GP-2002) part of barry:2002:GECCO:workshop

%A Bir Bhanu
%A Yingqiang Lin
%T Learning Composite Operators For Object Detection
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 1003--1010
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, real world applications, composite operators, genetic image segmentation, object detection
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Bir Bhanu
%A Yingqiang Lin
%T Object detection in multi-modal images using genetic programming
%J Applied Soft Computing
%V 4
%N 2
%D 2004
%P 175--201
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6W86-4BV444R-1/2/7540dd938c0b2f3059b1afb5382bd28a
%X In this paper, we learn to discover composite operators and features that are synthesized from combinations of primitive image processing operations for object detection.
   Our approach is based on genetic programming (GP). The motivation for using GP-based learning is that we hope to automate the design of object detection system by
   automatically synthesizing object detection procedures from primitive operations and primitive features. There are many basic operations that can operate on images and the
   ways of combining these primitive operations to perform meaningful processing for object detection are almost infinite. The human expert, limited by experience, knowledge
   and time, can only try a very small number of conventional combinations. Genetic programming, on the other hand, attempts many unconventional combinations that may never be
   imagined by human experts. In some cases, these unconventional combinations yield exceptionally good results. To improve the efficiency of GP, we propose soft composite
   operator size limit to control the code-bloat problem while at the same time avoid severe restriction on the GP search. Our experiments, which are performed on selected
   regions of images to improve training efficiency, show that GP can synthesize effective composite operators consisting of pre-designed primitive operators and primitive
   features to effectively detect objects in images and the learned composite operators can be applied to the whole training image and other similar testing images.
%8 May

%A Krzysztof Krawiec
%A Bir Bhanu
%T Coevolution and Linear Genetic Programming for Visual Learning
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2723
%D 2003
%P 332--343
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, Coevolution
%X a novel genetically-inspired visual learning method is proposed. Given the training images, this general approach induces a sophisticated feature-based recognition system,
   by using cooperative coevolution and linear genetic programming for the procedural representation of feature extraction agents. The paper describes the learning algorithm
   and provides a firm rationale for its design. An extensive experimental evaluation, on the demanding real-world task of object recognition in synthetic aperture radar (SAR)
   imagery, shows the competitiveness of the proposed approach with human-designed recognition systems.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40602-6

%A Bir Bhanu
%A Jiangang Yu
%A Xuejun Tan
%A Yingqiang Lin
%T Feature Synthesis Using Genetic Programming for Face Expression Recognition
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 896--907
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030896.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Bir Bhanu
%A Yingqiang Lin
%T Synthesizing feature agents using evolutionary computation
%J Pattern Recognition Letters
%V 25
%N 13
%D 2004
%P 1519--1531
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V15-4CRY8J6-2/2/d245bfcfeee2d509066321e19d84a0fd
%X genetic programming (GP) with smart crossover and smart mutation is proposed to discover integrated feature agents that are evolved from combinations of primitive image
   processing operations to extract regions-of-interest (ROIs) in remotely sensed images. The motivation for using genetic programming is to overcome the limitations of human
   experts, since GP attempts many unconventional ways of combination, in some cases, these unconventional combinations yield exceptionally good results. Smart crossover and
   smart mutation identify and keep the effective components of integrated operators called "agents" and significantly improve the efficiency of GP. Our experimental results
   show that compared to normal GP, our GP algorithm with smart crossover and smart mutation can find good agents more quickly during training to effectively extract the
   regions-of-interest and the learned agents can be applied to extract ROIs in other similar images.
%O Pattern Recognition for Remote Sensing (PRRS 2002)
%8 1  October
%Z SAR

%A Bir Bhanu
%A Yingqiang Lin
%A Krzysztof Krawiec
%T Evolutionary Synthesis of Pattern Recognition Systems
%S Monographs in Computer Science
%D 2005
%I Springer-Verlag
%C New York
%K genetic algorithms, genetic programming, visual learning, feature synthesis, Computer vision, Image processing, Object detection, Pattern recognition
%U http://www.springer.com/west/home/computer/imaging?SGWID=4-149-22-39144807-detailsPage=ppmmedia|aboutThisBook
%@ 0-387-21295-7

%A P. Bhargavi
%A S. Jyothi
%T Soil Classification Using GATREE
%J International Journal of Computer Science \& Information Technology
%V 2
%N 5
%D 2010
%P 184--191
%I Academy \& Industry Research Collaboration Centre (AIRCC)
%K genetic algorithms, genetic programming, data mining, soil profile, soil database, classification
%U http://airccse.org/journal/jcsit/1010ijcsit14.pdf
%X This paper details the application of a genetic programming framework for classification of decision tree of Soil data to classify soil texture. The database contains
   measurements of soil profile data. We have applied GATree for generating classification decision tree. GATree is a decision tree builder that is based on Genetic
   Algorithms(GAs). The idea behind it is rather simple but powerful. Instead of using statistic metrics that are biased towards specific trees we use a more flexible, global
   metric of tree quality that try to optimise accuracy and size. GATree offers some unique features not to be found in any other tree inducers while at the same time it can
   produce better results for many difficult problems. Experimental results are presented which illustrate the performance of generating best decision tree for classifying
   soil texture for soil data set.

%A Maumita Bhattacharya
%A Baikunth Nath
%T Genetic Programming: A Review of Some Concerns
%B Proceedings of International Conference Computational Science Part~II - ICCS 2001
%S Lecture Notes in Computer Science
%E V. N. Alexandrov and J. J. Dongarra and B. A. Juliano and R. S. Renner and C. J. Kenneth Tan
%V 2074
%D 2001
%P 1031--1040
%I Springer
%C San Francisco, CA, USA
%K genetic algorithms, genetic programming, bloat
%U http://link.springer-ny.com/link/service/series/0558/papers/2074/20741031.pdf", acknowledgement = ack-nhfb
%X Genetic Programming (GP) is gradually being accepted as a promising variant of Genetic Algorithm (GA) that evolves dynamic hierarchical structures, often described as
   programs. In other words GP seemingly holds the key to attain the goal of 'automated program generation'. However one of the serious problems of GP lies in the 'code
   growth' or 'size problem' that occurs as the structures evolve, leading to excessive pressure on system resources and unsatisfying convergence. Several researchers have
   addressed the problem. However, absence of a general framework and physical constraints, viz, infinitely large resource requirements have made it difficult to find any
   generic explanation and hence solution to the problem. This paper surveys the major research works in this direction from a critical angle. Overview of a few other major GP
   concerns is covered in brief. We conclude with a general discussion on "code growth" and other critical aspects of GP techniques, while attempting to highlight on future
   research directions to tackle such problems.
%8 May  28-30

%A Maumita Bhattacharya
%A Ajith Abraham
%A Baikunth Nath
%T A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria
%B 2001 International Workshop on Hybrid Intelligent Systems
%S LNCS
%E Ajith Abraham and Mario Koppen
%D 2001
%P 379--394
%I Springer-Verlag Berlin
%C Adelaide, Australia
%K genetic algorithms, genetic programming, Linear genetic programming, neuro-fuzzy, neural networks, forecasting, electricity demand
%U http://citeseer.ist.psu.edu/510872.html
%X Genetic programming (GP), a relatively young and growing branch of evolutionary computation is gradually proving to be a promising method of modelling complex prediction
   and classification problems. This paper evaluates the suitability of a linear genetic programming (LGP) technique to predict electricity demand in the State of Victoria,
   Australia, while comparing its performance with two other popular soft computing techniques. The forecast accuracy is compared with the actual energy demand. To evaluate,
   we considered load demand patterns for ten consecutive months taken every 30 minutes for training the different prediction models. Test results show that while the linear
   genetic programming method delivered satisfactory results, the neuro fuzzy system performed best for this particular application problem, in terms of accuracy and
   computation time, as compared to LGP and neural networks.
%8 11-12  Decemeber
%Z HIS01 Possibly also of interest Applied Soft Computing Volume 1, Issue 2 , August 2001, Pages 127-138 doi:10.1016/S1568-4946(01)00013-8
%@ 3-7908-1480-6

%A Siddhartha Bhattacharyya
%A Olivier Pictet
%A Gilles Zumbach
%T Representational Semantics for Genetic Programming Based Learning in High-Frequency Financial Data
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 11--16
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Siddhartha Bhattacharyya
%A Parag C. Pendharkar
%T Inductive, Evolutionary, and Neural Computing Techniques for Discrimination: A Comparative Study
%J Decision Sciences
%V 29
%N 4
%D 1998
%P 871--899
%I 
%K genetic algorithms, genetic programming, Discriminant Analysis, Inductive Learning, Machine Learning, and Neural Networks
%U http://tigger.uic.edu/~sidb/papers/DiscCompPaper_DecSci.pdf
%X This paper provides a comparative study of machine learning techniques for two-group discrimination. Simulated data is used to examine how the different learning techniques
   perform with respect to certain data distribution characteristics. Both linear and nonlinear discrimination methods are considered. The data has been previously used in the
   comparative evaluation of a number of techniques and helps relate our findings across a range of discrimination techniques.
%8 Fall
%Z http://www.decisionsciences.org/dsj/ (USPS 884860) http://www.decisionsciences.org/dsj/Vol29_4/29_4_871.htm

%A Siddhartha Bhattacharyya
%T Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing
%B KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
%D 2000
%P 465--473
%I ACM Press New York, NY, USA
%I SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data AAAI : Am Assoc for Artifical Intelligence SIGART: ACM Special Interest Group on Artificial Intelligence
   SIGMOD: ACM Special Interest Group on Management of Data
%C Boston, Massachusetts, United States
%K genetic algorithms, genetic programming, Algorithms, Design, Experimentation, Human Factors, Management, Measurement, Performance, Theory, Pareto-optimal models, data
   mining, database marketing, evolutionary computation, multiple objectives
%U http://portal.acm.org/ft_gateway.cfm?id=347186&type=pdf&coll=GUIDE&dl=GUIDE&CFID=43813975&CFTOKEN=68162530
%X Predictive models in direct marketing seek to identify individuals most likely to respond to promotional solicitations or other intervention programs. While standard
   modelling approaches embody single objectives, real-world decision problems often seek multiple performance measures. Decision-makers here desire solutions that
   simultaneously optimise on multiple objectives, or obtain an acceptable tradeoff amongst objectives. Multi-criteria problems often characterise a range of solutions, none
   of which dominate the others with respect to the multiple objectives - these specify the Pareto-frontier of nondominated solutions, each offering a different level of
   tradeoff. This paper proposes the use of evolutionary computation based procedures for obtaining a set of nondominated models with respect to multiple stated objectives.
   The targeting depth-of-file presents a crucial real-world criterion in direct marketing, and models here are tailored for specified file-depths. Decision-makers are thus
   able to obtain a set of models along the Pareto-frontier, for a specific file-depth. The choice of a model to implement can be thus based on observed tradeoffs in the
   different objectives, based on possibly subjective and problem specific judgements. Given distinct models tailored for different file-depths, the implementation decision
   can also consider performance tradeoffs at the different depths-offile. Empirical results from a real-world problem illustrate the benefits of the proposed approach. Both
   linear and nonlinear models obtained by genetic search are examined.
%Z p470 "For the non-linear GP, results were found to be similar to those observed for the linear GA. "Elitism always provides improved performance".
%@ 1-58113-233-6

%A Siddhartha Bhattacharyya
%A Kumar Mehta
%T Evolutionary Induction of Trading Models
%B Evolutionary Computation in Economics and Finance
%S Studies in Fuzziness and Soft Computing
%E Shu-Heng Chen
%V 100
%D 2002
%P 311--332
%I Physica Verlag
%K genetic algorithms, genetic programming
%U http://tigger.uic.edu/~sidb/papers/EvolInductionOfTradingModels.pdf
%X Financial markets data present a challenging opportunity for the learning of complex patterns not readily discernable. This paper investigates the use of genetic algorithms
   for the mining of financial time-series for patterns aimed at the provision of trading decision models. A simple yet flexible representation for trading rules is proposed,
   and issues pertaining to fitness evaluation examined. Two key issues in fitness evaluation, the design of a suitable fitness function reflecting desired trading
   characteristics and choice of appropriate training duration, are discussed and empirically examined. Two basic measures are also proposed for characterising rules obtained
   with alternate fitness criteria.
%O 17
%8 2002
%Z http://btobsearch.barnesandnoble.com/booksearch/isbnInquiry.asp?sourceid=00395996645644787198&btob=Y&endeca=1&isbn=3790814768&itm=2
%@ 3-7908-1476-8

%A Siddhartha Bhattacharyya
%A Olivier V. Pictet
%A Gilles Zumbach
%T Knowledge-intensive genetic discovery in foreign exchange markets
%J IEEE Transactions on Evolutionary Computation
%V 6
%N 2
%D 2002
%P 169--181
%I 
%K genetic algorithms, genetic programming, data mining, financial data processing, foreign exchange trading, knowledge representation, learning (artificial intelligence),
   mathematical operators, symmetry, arithmetic operators, comparison operators, data mining, domain-related structuring, financial markets, high-frequency foreign exchange
   markets, indicator types, knowledge-intensive genetic discovery, logical operators, machine learning, optimisation fitness criteria, risk-adjusted return measure, robust
   performance, semantic restrictions, symmetry properties, trading decision model discovery, trading model specification
%U http://tigger.uic.edu/~sidb/papers/KnowIntenGPForex__IEEE_EC.pdf
%X This paper considers the discovery of trading decision models from high-frequency foreign exchange (FX) markets data using genetic programming (GP). It presents a
   domain-related structuring of the representation and incorporation of semantic restrictions for GP-based searching of trading decision models. A defined symmetry property
   provides a basis for the semantics of FX trading models. The symmetry properties of basic indicator types useful in formulating trading models are defined, together with
   semantic restrictions governing their use in trading model specification. The semantics for trading model specification have been defined with respect to regular
   arithmetic, comparison and logical operators. This study also explores the use of two fitness criteria for optimization, showing more robust performance with a
   risk-adjusted measure of returns
%8 April
%Z CODEN: ITEVF5 INSPEC Accession Number:7256658

%A Urvesh Bhowan
%A Mark Johnston
%A Mengjie Zhang
%T Differentiating Between Individual Class Performance in Genetic Programming Fitness for Classification with Unbalanced Data
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 2802--2809
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X This paper investigates improvements to the fitness function in Genetic Programming to better solve binary classification problems with unbalanced data. Data sets are
   unbalanced when there is a majority of examples for one particular class over the other class(es). We show that using overall classification accuracy as the fitness
   function evolves classifiers with a performance bias toward the majority class at the expense of minority class performance. We develop four new fitness functions which
   consider the accuracy of majority and minority class separately to address this learning bias. Results using these fitness functions show that good accuracy for both the
   minority and majority classes can be achieved from evolved classifiers while keeping overall performance high and balanced across the two classes.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Urvesh Bhowan
%A Mengjie Zhang
%A Mark Johnston
%T Genetic Programming for Image Classification with Unbalanced Data
%B Proceeding of the 24th International Conference Image and Vision Computing New Zealand, IVCNZ '09
%D 2009
%P 316--321
%I IEEE
%C Wellington
%K genetic algorithms, genetic programming
%X Image classification methods using unbalanced data can produce results with a performance bias. If the class representing important objects-of-interest is in the minority
   class, learning methods can produce the deceptive appearance of good looking results while recognition ability on the important minority class can be poor. This paper
   develops and compares two Genetic Programming (GP) methods for image classification problems with class imbalance. The first focuses on adapting the fitness function in GP
   to evolve classifiers with good individual class accuracy. The second uses a multi-objective approach to simultaneously evolve a set of classifiers along the trade-off
   surface representing minority and majority class accuracies. Evaluating our GP methods on two benchmark binary image classification problems with class imbalance, our
   results show that good solutions were evolved using both GP methods.
%8 23-25  November
%Z Also known as \cite5378388

%A Urvesh Bhowan
%A Mengjie Zhang
%A Mark Johnston
%T Multi-Objective Genetic Programming for Classification with Unbalanced Data
%B Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI'09)
%S Lecture Notes in Computer Science
%E Ann E. Nicholson and Xiaodong Li
%V 5866
%D 2009
%P 370--380
%I Springer
%C Melbourne, Australia
%K genetic algorithms, genetic programming
%X Existing learning and search algorithms can suffer a learning bias when dealing with unbalanced data sets. This paper proposes a Multi-Objective Genetic Programming (MOGP)
   approach to evolve a Pareto front of classifiers along the optimal trade-off surface representing minority and majority class accuracy for binary class imbalance problems.
   A major advantage of the MOGP approach is that by explicitly incorporating the learning bias into the search algorithm, a good set of well-performing classifiers can be
   evolved in a single experiment while canonical (single-solution) Genetic Programming (GP) requires some objective preference be a priori built into a fitness function. Our
   results show that a diverse set of solutions was found along the Pareto front which performed as well or better than canonical GP on four class imbalance problems.
%8 Decemeber  1-4

%A Urvesh Bhowan
%A Mengjie Zhang
%A Mark Johnston
%T Genetic Programming for Classification with Unbalanced Data
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 1--13
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X Learning algorithms can suffer a performance bias when data sets only have a small number of training examples for one or more classes. In this scenario learning methods
   can produce the deceptive appearance of good looking results even when classification performance on the important minority class can be poor. This paper compares two
   Genetic Programming (GP) approaches for classification with unbalanced data. The first focuses on adapting the fitness function to evolve classifiers with good
   classification ability across both minority and majority classes. The second uses a multi-objective approach to simultaneously evolve a Pareto front (or set) of classifiers
   along the minority and majority class trade-off surface. Our results show that solutions with good classification ability were evolved across a range of binary
   classification tasks with unbalanced data.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Urvesh Bhowan
%A Mengjie Zhang
%A Mark Johnston
%T AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 845--852
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%X Learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach using the
   accuracy of the minority and majority class as learning objectives. We focus our analysis on the classification ability of evolved Pareto-front solutions using the Area
   Under the ROC Curve (AUC) and investigate which regions of the objective trade-off surface favour high-scoring AUC solutions. We show that a diverse set of well-performing
   classifiers is simultaneously evolved along the Pareto-front using the MOGP approach compared to canonical GP where only one solution is found along the objective trade-off
   surface, and that in some problems the MOGP solutions had better AUC than solutions evolved with canonical GP using hand-crafted fitness functions.
%8 7-11  July
%Z Also known as \cite1830639 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Urvesh Bhowan
%A Mengjie Zhang
%A Mark Johnston
%T A Comparison of Classification Strategies in Genetic Programming with Unbalanced Data
%B Australasian Conference on Artificial Intelligence
%S Lecture Notes in Computer Science
%E Jiuyong Li
%V 6464
%D 2010
%P 243--252
%I Springer
%C Adelaide
%K genetic algorithms, genetic programming
%X Machine learning algorithms like Genetic Programming (GP) can evolve biased classifiers when data sets are unbalanced. In this paper we compare the effectiveness of two GP
   classification strategies. The first uses the standard (zero) class-threshold, while the second uses the best class-threshold determined dynamically on a
   solution-by-solution basis during evolution. These two strategies are evaluated using five different GP fitness across across a range of binary class imbalance problems,
   and the GP approaches are compared to other popular learning algorithms, namely, Naive Bayes and Support Vector Machines. Our results suggest that there is no overall
   difference between the two strategies, and that both strategies can evolve good solutions in binary classification when used in combination with an effective fitness
   function.
%8 Decemeber

%A Urvesh Bhowan
%A Mark Johnston
%A Mengjie Zhang
%T Evolving ensembles in multi-objective genetic programming for classification with unbalanced data
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1331--1338
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X Machine learning algorithms can suffer a performance bias when data sets are unbalanced. This paper proposes a Multi-objective Genetic Programming approach using negative
   correlation learning to evolve accurate and diverse ensembles of non-dominated solutions where members vote on class membership. We also compare two popular Pareto-based
   fitness schemes on the classification tasks. We show that the evolved ensembles achieve high accuracy on both classes using six unbalanced binary data sets, and that this
   performance is usually better than many of its individual members.
%8 12-16  July
%Z Also known as \cite2001756 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Authur S. Bickel
%A Riva Wenig Bickel
%T Tree Structured Rules in Genetic Algorithms
%B Genetic Algorithms and their Applications: Proceedings of the second International Conference on Genetic Algorithms
%E John J. Grefenstette
%D 1987
%P 77--81
%I Lawrence Erlbaum Associates Hillsdale, NJ, USA
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%X GA applied to variable length lists of tree structured production rules. Mutation applied within trees, eg > replaced by >=. Inversion applied by re-ordering rules, nb does
   change semantics of rules set because they are applied in order, not applied within trees. Crossover applied to lists NOT to contents of trees
%8 28-31  July

%A Philippe Bidaud
%A Frederic Chapelle
%A G. Dumont
%T Evolutionary optimization of mechanical and control design. Application to active endoscopes
%B Theory and Practice of Robots and Manipulators
%S RoManSy
%E Giovanni Bianchi and Jean-Claude Guinot and Cezary Rzymkowski
%N 14
%D 2002
%P 317--330
%I Springer Verlag Wien/New York
%I CISM - IFToMM
%C Udine, Italy
%K genetic algorithms, genetic programming
%8 July
%Z http://www.meil.pw.edu.pl/romansy2002/html/romansy14.htm

%A Cornelis J. Biesheuvel
%A Ivar Siccama
%A Diederick E. Grobbee
%A Karel G. M. Moons
%T Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism
%J Journal of Clinical Epidemiology
%V 57
%N 6
%D 2004
%P 551--560
%I 
%K genetic algorithms, genetic programming, Logistic regression, Prediction, Diagnostic research, Discrimination, Reliability
%U http://www.sciencedirect.com/science/article/B6T84-4CTB5RT-3/2/325f5e3699d990701839201564eff8d3
%X Objective Genetic programming is a search method that can be used to solve complex associations between large numbers of variables. It has been used, for example, for
   myoelectrical signal recognition, but its value for medical prediction as in diagnostic and prognostic settings, has not been documented. Study design and setting We
   compared genetic programming and the commonly used logistic regression technique in the development of a prediction model using empirical data from a study on diagnosis of
   pulmonary embolism. Using part (67%) of the data, we developed and internally validated (using bootstrapping techniques) a diagnostic prediction model by genetic
   programming and by logistic regression, and compared both on their predictive ability in the remaining data (validation set). Results In the validation set, the area under
   the ROC curve of the genetic programming model was significantly larger (0.73; 95%CI: 0.64-0.82) than that of the logistic regression model (0.68; 0.59-0.77). The
   calibration of both models was similar, indicating a similar amount of overoptimism. Conclusion Although the interpretation of a genetic programming model is less intuitive
   and this is the first empirical study quantifying its value for medical prediction, genetic programming seems a promising technique to develop prediction rules for
   diagnostic and prognostic purposes.
%8 June
%Z PMID: 15246123 [PubMed - indexed for MEDLINE]

%A Cornelis Jan Biesheuvel
%T Diagnostic Research : improvements in design and analysis
%R Ph.D. Thesis
%D 2005
%I 
%I Universiteit Utrecht
%C Holland
%K genetic algorithms, genetic programming, diagnosis, methodology, prediction research
%U http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/cv.pdf
%X Using polytomous logistic regression, one can directly model diagnostic test results in relation to several diagnostic outcome categories. Simultaneous prediction of
   several diagnostic outcome probabilities particularly applies to situations in which more than two disorders are considered in the differential diagnoses. As this is
   commonly the case, polytomous regression analysis may serve clinical practice better than conventional dichotomous regression analysis. Both alternatives deserve closer
   attention in future diagnostic research. We also showed that the development of a diagnostic prediction rule is not the end of the 'research line', even when a rule is
   subsequently adjusted for optimism using internal validation techniques e.g. bootstrap techniques. External validation of such rules in new patients is always required
   before introducing a rule in daily practice. This indicates that internal validation of prediction models may not be sufficient and indicative for the model's performance
   in future patients. Rather than viewing a validation data set as a separate study to estimate an existing rule's performance, validation data may be combined with data of
   previous derivation studies to generate more robust prediction models using recently suggested methods.
%Z * Title * Contents * Chapter 1: Introduction * Chapter 2: Test research versus diagnostic research * Chapter 3: Distraction from randomisation in diagnostic research *
   Chapter 4: Reappraisal of the nested case-control design in diagnostic research: updating the STARD guideline * Chapter 5: Validating and updating a prediction rule for
   neurological sequelae after childhood bacterial meningitis * Chapter 6: Genetic programming or multivariable logistic regression in diagnostic research * Chapter 7:
   Revisiting polytomous regression for diagnostic studies * Chapter 8: Concluding remarks * Summary * Samenvatting * Dankwoord * Curriculum Vitae * Volledig proefschrift (520
   kB) OMEGA KiQ Ltd.
%@ 90-393-2706-8

%A Franck Binard
%A Amy Felty
%T An abstraction-based genetic programming system
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2007)
%E Peter A. N. Bosman
%D 2007
%P 2415--2422
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, lambda calculus, languages, polymorphism, types
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2415.pdf
%X We extend tree-based typed Genetic Programming (GP) representation schemes by introducing System F, an expressive l-calculus, for representing programs and types. At the
   level of programs, System F provides higher-order programming capabilities with functions and types as first-class objects, e.g., functions can take other functions and
   types as parameters. At the level of types, System F provides parametric polymorphism. The expressiveness of the system provides the potential for a genetic programming
   system to evolve looping, recursion, lists, trees and many other typical programming structures and behaviour. This is done without introducing additional external symbols
   in the set of predefined functions and terminals of the system. In fact, we actually remove programming structures such as if/then/else, which we replace by two abstraction
   operators. We also change the composition of parse trees so that they may directly include types.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A Franck Binard
%A Amy Felty
%T Genetic programming with polymorphic types and higher-order functions
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1187--1194
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, lambda calculus, polymorphism, types
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1187.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389330

%A Wu Bing
%A Zhang Wen-qiong
%A Liang Jia-hong
%T A Genetic Multiple Kernel Relevance Vector Regression Approach
%B Second International Workshop on Education Technology and Computer Science (ETCS), 2010
%V 3
%D 2010
%P 52--55
%I 
%K genetic algorithms, GMK validation, Gaussian RBF, Sigmoid kernel, benchmark problems, genetic multiple kernel relevance vector regression, kernel function, multiple kernel
   function, multiple kernel learning, parameter selection, relevance vector machine, sparse Bayesian extension, state-of-the-art technique, support vector machine, Bayes
   methods, learning (artificial intelligence), pattern classification, regression analysis, support vector machines
%X Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The
   kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasised the requirement to
   multiple kernel learning. This paper proposes a novel regression technique, called Genetic Multiple Kernel Relevance Vector Regression (GMK RVR), which combines genetic
   programming and relevance vector regression to evolve a multiple kernel function. The proposed technique are compared with those of a standard RVR using the Polynomial,
   Gaussian RBF and Sigmoid kernel with various parameter settings, based on several benchmark problems. Numerical experiments show that the GMK performs better than such
   widely used kernels and prove the validation of the GMK.
%8 March
%Z Not a GP, fixed representation. Also known as \cite5460012

%A C. R. Birchenhall
%T Genetic Algorithms, Classifier Systems and Genetic Programming and their Use in the Models of Adaptive Behaviour and Learning
%J The Economic Journal
%V 105
%N 430
%D 1995
%P 788--795
%I 
%K genetic algorithms, genetic programming
%U http://links.jstor.org/sici?sici=0013-0133%28199505%29105%3A430%3C788%3AGACSAG%3E2.0.CO%3B2-%23
%Z Reviewed? in The Economic Journal, vol 106 number 434, 1996 APPROX pages 271

%A Andreas Birk
%A Wolfgang J. Paul
%T Schemas and Genetic Programming
%B Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic II
%S Studies in Cognitive Systems
%E Holk Cruse and Jeffrey Dean and Helge Ritter
%V 26
%D 2001
%I Kluwer
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/397549.html
%X With the help of schemas and genetic programming we describe systems which ffl interact with the real world ffl make theories about the consequences of their actions and
   ffl dynamically adjust inductive bias. We present experimental data related to learning geometric concepts and moving a block in a microworld.
%O The Pennsylvania State University CiteSeer Archives
%Z schemas_genetic_programming.pdf crashes my browser
%@ 0-7923-6666-2

%A Cosimo Birtolo
%A Roberto Armenise
%A Luigi Troiano
%T Supporting Menu Layout Design by Genetic Programming
%B Proceedings of the 12th International Conference on Enterprise Information Systems (ICEIS 2010)
%E Joaquim Filipe and Jos\'e Cordeiro
%D 2010
%I 
%C Funchal, Madeira, Portugal
%K genetic algorithms, genetic programming: poster
%X Graphical User Interfaces heavily rely on menus to access application functionalities. Therefore designing properly menus poses relevant usability issues to face. Indeed,
   trading off between semantic preferences and usability makes this task not so easy to be performed. Meta-heuristics represent a promising approach in assisting designers to
   specify menu layouts. In this paper, we propose a preliminary experience in adopting Genetic Programming as a natural means for evolving a menu hierarchy towards optimal
   structure.
%8 8 - 12  June
%Z http://www.iceis.org/iceis2010/index.htm http://www.iceis.org/Abstracts/2010/ICEIS_2010_Abstracts.htm Despite http://dblp.uni-trier.de/rec/bibtex/conf/iceis/BirtoloAT10
   does not appear to be in electronic proceedings published by Springer isbn 978-989-8425-08-9, pages 248-251

%A Mona T. Bisat
%A Charles W. Richter
%A Gerald B. Sheble
%T Using Adaptive Agents to Study Bilateral Contracts and Trade Networks
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A P. Bishop
%A R. Bloomfield
%T Conservative theory for long-term reliability-growth prediction [of software]
%J IEEE Transactions on Reliability
%V 45
%N 4
%D 1996
%P 550--560
%I 
%C Adelard, London, UK
%K software reliability, reliability theory, failure analysis, long-term reliability-growth prediction, software reliability growth modeling, program failure rate, use-time,
   initial fault number, worst-case bound, residual fault number, failure rate distribution
%U http://www.adelard.co.uk/resources/papers/pdf/issre96m.pdf
%X This paper describes a different approach to software reliability growth modeling which enables long-term predictions. Using relatively common assumptions, it is shown that
   the average value of the failure rate of the program, after a particular use-time, t, is bounded by N/(e/spl middot/t), where N is the initial number of faults. This is
   conservative since it places a worst-case bound on the reliability rather than making a best estimate. The predictions might be relatively insensitive to assumption
   violations over the longer term. The theory offers the potential for making long-term software reliability growth predictions based solely on prior estimates of the number
   of residual faults. The predicted bound appears to agree with a wide range of industrial and experimental reliability data. Less pessimistic results can be obtained if
   additional assumptions are made about the failure rate distribution of faults.
%8 Decemeber
%Z cf. \citebrady:murphy

%A Evandro Bittencourt
%A Sidney Schossland
%A Raul Landmann
%A Denio {Murilo de Aguiar}
%A Adilson Gomes {De Oliveira}
%T The Gene Expression Programming Applied to Demand Forecast
%B Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010), Guimar\~aes, Portugal, June 2010
%S Advances in Soft Computing
%E Emilio Corchado and Paulo Novais and Cesar Analide and Javier Sedano
%V 73
%D 2010
%P 197--200
%I Springer
%K genetic algorithms, genetic programming, gene expression programming
%U http://dx.doi.org/10.1007/978-3-642-13161-5

%A Jorge Blasco
%A Agustin Orfila
%A Arturo Ribagorda
%T Improving Network Intrusion Detection by Means of Domain-Aware Genetic Programming
%B International Conference on Availability, Reliability, and Security, ARES '10
%D 2010
%P 327--332
%I 
%K genetic algorithms, genetic programming, domain-aware genetic programming, fitness function, intrusive traffic, network intrusion detection, normal traffic, security of
   data
%X One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper we
   explore the use of Genetic Programming (GP) for such a purpose. Although GP has already been studied for this task, the inner features of network intrusion detection have
   been systematically ignored. To avoid the blind use of GP shown in previous research, we guide the search by means of a fitness function based on recent advances on IDS
   evaluation. For the experimental work we use a well-known dataset (i.e. KDD-99) that has become a standard to compare research although its drawbacks. Results clearly show
   that an intelligent use of GP achieves systems that are comparable (and even better in realistic conditions) to top state-of-the-art proposals in terms of effectiveness,
   improving them in efficiency and simplicity.
%8 February
%Z Also known as \cite5438073

%A Stefan Bleuler
%A Martin Brack
%A Lothar Thiele
%A Eckart Zitzler
%T Multiobjective Genetic Programming: Reducing Bloat Using SPEA2
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 536--543
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, SPEA, SPEA2, Pareto, external set
%U http://citeseer.ist.psu.edu/443099.html
%X This study investigates the use of multiobjective techniques in Genetic Programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating.
   The proposed approach considers the program size as a second, independent objective besides the program functionality. In combination with a recent multiobjective
   evolutionary technique, SPEA2, this method outperforms four other strategies to reduce bloat with regard to both convergence speed and size of the produced programs on a
   even-parity problem.
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = Solutions to
   even-9 parity.
%@ 0-7803-6658-1

%A Stefan Bleuler
%A Johannes Bader
%A Eckart Zitzler
%T Reducing Bloat in GP with Multiple Objectives
%B Multiobjective Problem Solving from Nature: from concepts to applications
%S Natural Computing
%E Joshua Knowles and David Corne and Kalyanmoy Deb
%D 2008
%I Springer
%K genetic algorithms, genetic programming
%O 9
%Z http://www.springer.com/west/home/computer/artificial?SGWID=4-147-22-173745027-0

%A Tobias Blickle
%A Lothar Thiele
%T Genetic Programming and Redundancy
%B Genetic Algorithms within the Framework of Evolutionary Computation (Workshop at KI-94, Saarbr\"ucken)
%E J. Hopf
%D 1994
%P 33--38
%I Max-Planck-Institut f\"ur Informatik (MPI-I-94-241)
%C Im Stadtwald, Building 44, D-66123 Saarbr\"ucken, Germany
%K genetic algorithms, genetic programming
%U http://www.tik.ee.ethz.ch/~tec/publications/bt94/GPandRedundancy.ps.gz
%Z From GP list Wed, 22 Mar 95 we did some work on the convergence problem and the redundancy in the trees in GP. It turned out that "bloating" is a property of GP that arises
   from the fact that more redundant trees have a higher probability to survive crossover. As a result, the redundant part of the trees grow bigger and bigger because the
   increased proportion of redundant "cut-sites" in the tree again lead to a higher probability to survive crossover. Gives a formula for tournament size related to proportion
   of crossover in a generational GP. Ie recommending T=10 for pc=0.9. This does not apply to steady state GA.

%A Tobias Blickle
%A Lothar Thiele
%T A Comparison of Selection Schemes Used in Genetic Algorithms
%R TIK-Report 11
%D 1995
%I 
%I TIK Institut fur Technische Informatik und Kommunikationsnetze, Computer Engineering and Networks Laboratory, ETH, Swiss Federal Institute of Technology
%C Gloriastrasse 35, 8092 Zurich, Switzerland
%K genetic algorithms, genetic programming
%U http://www.handshake.de/user/blickle/publications/tik-report11_v2.ps
%X Genetic Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection
   scheme for which a new description model is introduced in this paper. With this a mathematical analysis of tournament selection, truncation selection, linear and
   exponential ranking selection and proportional selection is carried out that allows an exact prediction of the fitness values after selection. The further analysis derives
   the selection intensity, selection variance, and the loss of diversity for all selection schemes. For completion a pseudo- code formulation of each method is included. The
   selection schemes are compared and evaluated according to their properties leading to an unified view of these different selection schemes. Furthermore the correspondence
   of binary tournament selection and ranking selection in the expected fitness distribution is proven.
%8 Decemeber
%Z Of special interest for the GP community may be the fact that in this report three analytic approximation formulas are obtained using GP for symbolic regression. The method
   is described in appendix A of the report. Second (extended and corrected) edition available via www and ftp Dec 1995

%A Tobias Blickle
%T Optimieren nach dem Vorbild der Natur, Evolutionare Algorithmen
%J Bulletin SEV/VSE
%V 86
%N 25
%D 1995
%P 21--26
%I 
%K genetic algorithms, genetic programming
%U http://www.handshake.de/user/blickle/publications/EA.ps
%Z Introduction to GA and GP in German

%A Tobias Blickle
%T YAGPLIC User Manual
%R Technical Report
%D 1995
%I 
%I Computer Engineering and Communication Network Lab (TIK), Swiss Federal Institute of Technology (ETH)
%C Gloriastrasse 35, CH-8092, Zurich
%K genetic algorithms, genetic programming
%U http://www.tik.ee.ethz.ch/~blickle/YAGPLIC.html broken
%Z Yet Another Genetic Programming Library In C Written in C for maximum performance. Object-oriented user-interface. Up to 32 data types possible in a tree and
   type-consistent crossover. Several selection schemes implemented: proportionate selection, ranking selection, tournament selection, truncation selection. Extensive output
   of statistical data for post processing with MATHEMATICA.

%A Tobias Blickle
%T Evolving Compact Solutions in Genetic Programming: A Case Study
%R TIK-Report
%D 1996
%I 
%I TIK Institut fur Technische Informatik und Kommunikationsnetze, Computer Engineering and Networks Laboratory, ETH, Swiss Federal Institute of Technology
%C Gloriastrasse 35, 8092 Zurich, Switzerland
%K genetic algorithms, genetic programming
%U http://www.handshake.de/user/blickle/publications/ppsn1.ps
%X Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional
   relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence $y=g(\bf x)$ that approximates a
   set of data points ($\bf x_i,y_i$). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore
   several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or
   redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional
   dependencies on numerical data.
%Z Presented at PPSN 4

%A Tobias Blickle
%T Evolving Compact Solutions in Genetic Programming: A Case Study
%B Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation
%S LNCS
%E Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel
%V 1141
%D 1996
%P 564--573
%I Springer-Verlag Heidelberg, Germany
%C Berlin, Germany
%K genetic algorithms, genetic programming, bloat, deleting crossover
%U http://citeseer.ist.psu.edu/blickle96evolving.html
%X Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional
   relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence y=g ( x ) that approximates a set
   of data points ( x i , y i ). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several
   suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy
   that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on
   numerical data.
%8 22-26  September
%Z http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 same as \citeblickle:1996:ecs Test of effectiveness of GP, EDI, deleting and adaptive anti-bloat techniques. Results
   differ continuous (symbolic regression) v. discrete 6-mux deleting crossover similar to code editing based on code interpretation during fitness evaluation.
%@ 3-540-61723-X

%A Tobias Blickle
%T Theory of Evolutionary Algorithms and Application to System Synthesis
%R Ph.D. Thesis
%D 1996
%I vdf Verlag CH-8092 Zurich
%I Swiss Federal Institute of Technology
%C Zurich
%K genetic algorithms, genetic programming
%U http://www.handshake.de/user/blickle/publications/diss.pdf
%X The subject of this thesis are Evolutionary Algorithms and their application to the automated synthesis and optimization of complex digital systems composed of hardware and
   software elements. In Part I the probabilistic optimization method of Evolutionary Algorithms (EAs) is presented. EAs apply the principles of natural evolution (selection
   and random variation) to a random set of points (population of individuals) in the search space. Evolutionary Algorithms are first embedded in the context of global
   optimization and the most important and widely used methods for constraint- handling are introduced, including a new method called IOS (individual objective switching).
   This is followed by a new formal description of selection schemes based on fitness distributions. This description enables an extensive and uniform examination of various
   selection schemes leading to new insights about the impact of the selection method parameters on the optimization process. Subsequently the variation (recombination)
   process of Evolutionary Algorithms is examined. As different analysis techniques are necessary depending on the representation of the problem (e.g. bit string, vector,
   tree, graph) only the recombination process for tree-representation (Genetic Programming) is considered. A major part of the explanation treats the problem of ``bloating'',
   i.e., the tree-size increase during optimization. Furthermore, a new redundancy based explanation of bloating is given and several methods to avoid bloating are compared.
   Part II is dedicated to the application of Evolutionary Algorithms to the optimization of complex digital systems. These systems are composed of hardware and software
   components and characterized by a high complexity caused by their heterogeneity (hardware/ software, electrical/mechanical, analog/digital). Computer-aided synthesis at the
   abstract system level is advantageous for application specific systems or embedded systems as it enables time-to-market to be reduced with a decrease in design errors and
   costs. The main task of system-synthesis is the transformation of a behavioral specification (for example given by an algorithm) into a structural specification, such as a
   composition of processors, general or dedicated hardware modules, memories and busses, while regarding various restrictions, e.g. maximum costs, data throughput rate,
   reaction time. Problems related to system synthesis are for example performance estimation, architecture optimization and design-space exploration. This thesis introduces a
   formal description of system-synthesis based on a new graph model where the specification is translated into a specification graph. The main tasks of system-synthesis
   (allocation, binding and scheduling) are defined for this graph and the process of system synthesis is formulated as a constrained global optimization problem. This
   optimization problem is solved by Evolutionary Algorithms using the results of Part I of the thesis. Finally, an example of synthesizing implementations of a video codec
   chip H.261 is described demonstrating the effectiveness of the proposed methodology and the capability of the EA to obtain the Pareto points of the design space in a single
   optimization run.
%8 November
%Z Of special interest for this community might be chapter 5 that deals with recombination and bloating in GP YAGPLIC
%@ 3-7281-2433-8

%A Tobias Blickle
%A Lothar Thiele
%T A Comparison of Selection Schemes used in Evolutionary Algorithms
%J Evolutionary Computation
%V 4
%N 4
%D 1996
%P 361--394
%I 
%K genetic algorithms, genetic programming, Selection, evolutionary algorithms, diversity, selection intensity, tournament selection, truncation selection, linear ranking
%U http://www.handshake.de/user/blickle/publications/ECfinal.ps
%X Evolutionary algorithms are a common probabilistic optimisation method based on the model of natural evolution. One important operator in these algorithms is the selection
   scheme, for which in this paper a new description model, based on fitness distributions, is introduced. With this, a mathematical analysis of tournament selection,
   truncation selection, ranking selection, and exponential ranking selection is carried out that allows an exact prediction of the fitness values after selection. The
   correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proved. Furthermore, several properties of selection schemes are
   derived (selection intensity, selection variance, loss of diversity), and the three selection schemes are compared using these properties.
%8 Winter
%Z Brief use of GP symbolic regression to find nice formulae. Theoretical analysis. NB see \citeDBLP:journals/ec/Motoki02 for update on loss of diversity under tournament
   selection

%A Christian Blume
%T Optimized Collision Free Robot Move Statement Generation by the Evolutionary Software GLEAM
%B Real-World Applications of Evolutionary Computing
%S LNCS
%E Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter
   and Terence C. Fogarty
%V 1803
%D 2000
%P 327--328
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming, Industrial Machining Robots
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=327
%8 17  April
%Z Robot command program is a vriable number of very high level command actions. EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh,
   Scotland, UK, April 17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61
%@ 3-540-67353-9

%A Dmitri Bobrovnikoff
%T SoccerBots: Evolving Intelligent Soccer Players
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 40--45
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Edward B. Boden
%A Gilford F. Martino
%T Testing Software using Order-Based Genetic Algorithms
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 461--466
%I MIT Press
%C Stanford University, CA, USA
%K Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 GA paper

%A Stefan Boettcher
%A Allon G. Percus
%T Extremal Optimization: Methods derived from Co-Evolution
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 825--832
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%U http://xxx.lanl.gov/abs/math.OC/9904056
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A G. D. Boetticher
%A K. Kaminsky
%T The Assessment and Application of Lineage Information in Genetic Programs for Producing Better Models
%B IEEE International Conference on Information Reuse and Integration
%D 2006
%P 141--146
%I IEEE
%C Waikoloa Village, HI, USA
%K genetic algorithms, genetic programming
%X One of the challenges in data mining, and in particular genetic programs, is to provide sufficient coverage of the search space in order to produce an acceptable model.
   Traditionally, genetic programs generate equations (chromosomes) and consider all chromosomes within a population for breeding purposes. Considering the enormity of the
   search space for complex problems, it is imperative to examine genetic programs breeding efforts in order to produce better solutions with less training. This research
   examines chromosome lineage within genetic programs in order to identify breeding patterns. Fitness values for chromosomes are sorted, then partitioned into five classes.
   Initial experiments reveal a distinct difference between upper, middle, and lower classes. Based upon initial results, a novel genetic programming process is proposed which
   breeds a new generation exclusively from the top 20 percent of a population. A second set of experiments statistically validate this proposed approach
%8 September
%Z Houston Univ., TX
%@ 0-7803-9788-6

%A Walter Bohm
%A Andreas Geyer-Schulz
%T Exact Uniform Initialization for Genetic Programming
%B Foundations of Genetic Algorithms IV
%E Richard K. Belew and Michael Vose
%D 1996
%P 379--407
%I Morgan Kaufmann San Francisco, California, USA
%C University of San Diego, CA, USA
%K genetic algorithms, genetic programming
%8 3--5  August
%Z FOGA4 k-bounded context-free languages May also use key Boehm96 Demonstrated on XOR problem
%@ 1-55860-460-X

%A Celia C. Bojarczuk
%A Heitor S. Lopes
%A Alex A. Freitas
%T Discovering comprehensible classification rules by using Genetic Programming: a case study in a medical domain
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 953--958
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, data mining, classification, medical applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-417.ps
%X This work it is intended to discover classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies,
   whose main symptom is chest pain. In order to discover these rules it was used genetic programming as well as some concepts of data mining, particularly the emphasis on the
   discovery of comprehensible knowledge.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99). See also
   \citebojarczuk:2000:kdcp
%@ 1-55860-611-4

%A Celia C. Bojarczuk
%A Heitor S. Lopes
%A Alex A. Freitas
%T Genetic programming for knowledge discovery in chest-pain diagnosis
%J IEEE Engineering in Medicine and Biology Magazine
%V 19
%N 4
%D 2000
%P 38--44
%I 
%K genetic algorithms, genetic programming, data mining, knowledge discovery, chest-pain diagnosis, predictive accuracy, rule set, comprehensible rules, background knowledge,
   preprocessing step, data sets, medical applications
%U http://citeseer.ist.psu.edu/459907.html
%X Explores a promising data mining approach. Despite the small number of examples available in the authors' application domain (taking into account the large number of
   attributes), the results of their experiments can be considered very promising. The discovered rules had good performance concerning predictive accuracy, considering both
   the rule set as a whole and each individual rule. Furthermore, what is more important from a data mining viewpoint, the system discovered some comprehensible rules. It is
   interesting to note that the system achieved very consistent results by working from "tabula rasa," without any background knowledge, and with a small number of examples.
   The authors emphasize that their system is still in an experiment in the research stage of development. Therefore, the results presented here should not be used alone for
   real-world diagnoses without consulting a physician. Future research includes a careful selection of attributes in a preprocessing step, so as to reduce the number of
   attributes (and the corresponding search space) given to the GP. Attribute selection is a very active research area in data mining. Given the results obtained so far, GP
   has been demonstrated to be a really useful data mining tool, but future work should also include the application of the GP system proposed here to other data sets, to
   further validate the results reported in this article.
%8 July -  August
%Z lilgp

%A Celia C. Bojarczuk
%A Heitor S. Lopes
%A Alex A. Freitas
%T Data mining with constrained-syntax genetic programming: applications to medical data sets
%B Proceedings Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2001)
%D 2001
%I 
%K genetic algorithms, genetic programming, data mining, classification, medical applications, Constrained-Syntax Genetic Programming
%U http://citeseer.ist.psu.edu/459555.html
%X This work is intended to discover classification rules for diagnosing certain pathologies. In order to discover these rules we have developed a new constrained-syntax
   genetic programming algorithm based on some concepts of data mining, particularly with emphasis on the discovery of comprehensible knowledge. We compare the performance of
   the proposed GP algorithm with a genetic algorithm and with the very well-known decision-tree algorithm C4.5.
%O a workshop at MedInfo-2001
%Z IDAMAP workshop http://www.ailab.si/idamap/idamap2001/ Evolves IFTHEN rules. GP syntax contrained similar to STGP. Size of rules used as component of fitness function
   (actually product of sensitivity, specificity and size releated coefficient. Demonstrated on 3 small medical datasets (2 UCI).

%A Celia C. Bojarczuk
%A Heitor S. Lopes
%A Alex A. Freitas
%T An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 11--21
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming, data mining, classification, medical applications
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=11
%X This paper proposes a constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several
   syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret.
   The GP is compared with C4.5 in a real-world medical data set. This data set represents a difficult classification problem, and a new preprocessing method was devised for
   mining the data
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Celia C. Bojarczuk
%A Heitor S. Lopes
%A Alex A. Freitas
%A Edson L Michalkiewicz
%T A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets
%J Artificial Intelligence in Medicine
%V 30
%N 1
%D 2004
%P 27--48
%I 
%K genetic algorithms, genetic programming, data mining, classification, medical applications
%U http://www.harcourt-international.com/journals/aiim/
%X We propose a constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic
   constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is
   compared with C4.5, a well-known decision-tree-building algorithm, and with another GP that uses Boolean inputs (BGP), in five medical data sets: chest pain, Ljubljana
   breast cancer, dermatology, Wisconsin breast cancer, and pediatric adrenocortical tumour. For this last data set a new preprocessing step was devised for survival
   prediction. Computational experiments show that, overall, the GP algorithm obtained good results with respect to predictive accuracy and rule comprehensibility, by
   comparison with C4.5 and BGP.
%8 January

%A Enzo Bolis
%A Christian Zerbi
%A Pierre Collet
%A Jean Louchet
%A Evelyne Lutton
%T A GP Artificial Ant for image processing: preliminary experiments with EASEA
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 246--255
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Image processing, Contour detection, EASEA, Animat
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=246
%X This paper describes how animat-based "food foraging" techniques may be applied to the design of low-level image processing algorithms. First, we show how we implemented
   the food foraging application using the EASEA software package. We then use this technique to evolve an animat and learn how to move inside images and detect high-gradient
   lines with a minimum exploration time. The resulting animats do not use standard "scanning + filtering" techniques but develop other image exploration strategies close to
   contour tracking. Experimental results on grey level images are presented.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Danushka Bollegala
%A Nasimul Noman
%A Hitoshi Iba
%T RankDE: learning a ranking function for information retrieval using differential evolution
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1771--1778
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, Real world applications
%X Learning a ranking function is important for numerous tasks such as information retrieval (IR), question answering, and product recommendation. For example, in information
   retrieval, a Web search engine is required to rank and return a set of documents relevant to a query issued by a user. We propose RankDE, a ranking method that uses
   differential evolution (DE) to learn a ranking function to rank a list of documents retrieved by a Web search engine. To the best of our knowledge, the proposed method is
   the first DE-based approach to learn a ranking function for IR. We evaluate the proposed method using LETOR dataset, a standard benchmark dataset for training and
   evaluating ranking functions for IR. In our experiments, the proposed method significantly outperforms previously proposed rank learning methods that use evolutionary
   computation algorithms such as Particle Swam Optimization (PSO) and Genetic Programming (GP), achieving a statistically significant mean average precision (MAP) of 0.339 on
   TD2003 dataset and 0.430 on the TD2004 dataset. Moreover, the proposed method shows comparable results to the state-of-the-art non-evolutionary computational approaches on
   this benchmark dataset. We analyze the feature weights learnt by the proposed method to better understand the salient features for the task of learning to rank for
   information retrieval.
%8 12-16  July
%Z Also known as \cite2001814 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Alessandro Bollini
%A Marco Piastra
%T Distributed and Persistent Evolutionary Algorithms: a Design Pattern
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 173--183
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=173
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP Java objectstore database
%@ 3-540-65899-8

%A Alessandro Bollini
%A Marco Piastra
%T A persistent blackboard for distributed evolutionary computation
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 48--56
%I 
%C Orlando, Florida, USA
%K Java
%8 13  July
%Z GECCO-99LB

%A Andrea Bonarini
%T Comparing Reinforcement Learning Algorithms Applied to Crisp and Fuzzy Learning Classifier Systems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 52--59
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-876.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Danilo Mattos Bonfim
%A Leandro Nunes {de Castro}
%T FranksTree: A Genetic Programming Approach to Evolve Derived Bracketed L-systems
%B Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part II
%S Lecture Notes in Computer Science
%E Lipo Wang and Ke Chen and Yew-Soon Ong
%V 3611
%D 2005
%P 1275--1278
%I Springer
%C Changsha, China
%K genetic algorithms, genetic programming, interactive evolution
%X L-system is a grammar-like formalism introduced to simulate the development of organisms. The L-system grammar can be viewed as a sort of genetic information that will be
   used to generate a specific structure. However, throughout development, the string (genetic information) that will effectively be used to 'draw' the phenotype of an
   individual is a result of the derivation of the L-system grammar. This work investigates the effect of applying a genetic programming approach to evolve derived L-systems
   instead of evolving the Lsystem grammar. The crossing over of plants from different species results in hybrid plants resembling a 'Frankstree', i.e. plants resultant from
   phenotypically different parents that present unusual body structures.
%8 August  27-29
%Z Crossover based on identifying branches in pictures? No mutation. population=6
%@ 3-540-28325-0

%A Josh C. Bongard
%T Coevolutionary Dynamics of a Multi-population Genetic Programming System
%B Advances in Artificial Life
%S LNAI
%E D. Floreano and J.-D. Nicoud and F. Mondada
%V 1674
%D 1999
%P 154
%I Springer Verlag
%C Lausanne
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/319504.html
%8 13-17  September
%Z ECAL-99
%@ 3-540-66452-1

%A Josh C. Bongard
%T The Legion System: A Novel Approach to Evolving Heterogeneity for Collective Problem Solving
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 16--28
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=16
%X We investigate the dynamics of agent groups evolved to peform a collective task, and in which the behavioural heterogeneity of the group is under evolutionary control. Two
   task domains are studied: solutions are evolved for the two tasks using an evolutionary algorithm called the Legion system. A new metric of heterogeneity is also
   introduced, which measures the heterogeneity of evolved group behaviours. It was found that the amount of heterogeneity evolved in an agent group is dependent on the given
   problem domain: for the first task, the Legion system evolved heterogeneous groups; for the second task, primarily homogeneous groups evolved. We conclude that the proposed
   system, in conjunction with the introduced heterogeneity measure, can be used as a tool for investigating various issues concerning redundancy, robustness and division of
   labour in the context of evolutionary approaches to collective problem solving.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Josh Bongard
%A Hod Lipson
%T Automated reverse engineering of nonlinear dynamical systems
%J PNAS, Proceedings of the National Academy of Sciences of the United States of America
%V 104
%N 24
%D 2007
%P 9943--9948
%I 
%K genetic algorithms, genetic programming, Physical Sciences, Computer Sciences, coevolution, modelling, symbolic identification
%X Complex nonlinear dynamics arise in many fields of science and engineering, but uncovering the underlying differential equations directly from observations poses a
   challenging task. The ability to symbolically model complex networked systems is key to understanding them, an open problem in many disciplines. Here we introduce for the
   first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data. This method is applicable to
   any system that can be described using sets of ordinary nonlinear differential equations, and assumes that the (possibly noisy) time series of all variables are observable.
   Previous automated symbolic modeling approaches of coupled physical systems produced linear models or required a nonlinear model to be provided manually. The advance
   presented here is made possible by allowing the method to model each (possibly coupled) variable separately, intelligently perturbing and destabilising the system to
   extract its less observable characteristics, and automatically simplifying the equations during modelling. We demonstrate this method on four simulated and two real systems
   spanning mechanics, ecology, and systems biology. Unlike numerical models, symbolic models have explanatory value, suggesting that automated reverse engineering approaches
   for model-free symbolic nonlinear system identification may play an increasing role in our ability to understand progressively more complex systems in the future.
%8 12  June
%Z Cited by Philosophy of Science Machine Science James A. Evans and Andrey Rzhetsky Science 23 July 2010: Vol. 329 no. 5990 pp. 399-400 DOI:10.1126/science.1189416

%A Josh C. Bongard
%T Accelerating Self-Modeling in Cooperative Robot Teams
%J IEEE Transactions on Evolutionary Computation
%V 13
%N 2
%D 2009
%P 321--332
%I 
%K genetic algorithms, genetic programming, Robots, Robot sensing systems, Training data, Sensors, Data models, Service robots, Computational modeling, self-modeling,
   Collective robotics, evolutionary robotics
%X One of the major obstacles to achieving robots capable of operating in real-world environments is enabling them to cope with a continuous stream of unanticipated
   situations. In previous work, it was demonstrated that a robot can autonomously generate self-models, and use those self-models to diagnose unanticipated morphological
   change such as damage. In this paper, it is shown that multiple physical quadrupedal robots with similar morphologies can share self-models in order to accelerate modeling.
   Further, it is demonstrated that quadrupedal robots which maintain separate self-modeling algorithms but swap self-models perform better than quadrupedal robots that rely
   on a shared self-modeling algorithm. This finding points the way toward more robust robot teams: a robot can diagnose and recover from unanticipated situations faster by
   drawing on the previous experiences of the other robots.
%8 April

%A Josh Bongard
%T A Functional Crossover Operator for Genetic Programming
%B Genetic Programming Theory and Practice VII
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Una-May O'Reilly and Trent McConaghy
%D 2009
%P 195--210
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, homologous crossover, crossover operators, system identification
%O 12
%8 14-16  May
%Z part of \citeRiolo:2009:GPTP

%A Josh C. Bongard
%T A probabilistic functional crossover operator for genetic programming
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 925--932
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%X The original mechanism by which evolutionary algorithms were to solve problems was to allow for the gradual discovery of sub-solutions to sub-problems, and the automated
   combination of these sub-solutions into larger solutions. This latter property is particularly challenging when recombination is performed on genomes encoded as trees, as
   crossover events tend to greatly alter the original genomes and therefore greatly reduce the chance of the crossover event being beneficial. A number of crossover operators
   designed for tree-based genetic encodings have been proposed, but most consider crossing genetic components based on their structural similarity. In this work we introduce
   a tree-based crossover operator that probabilistically crosses branches based on the behavioural similarity between the branches. It is shown that this method outperforms
   genetic programming without crossover, random crossover, and a deterministic form of the crossover operator in the symbolic regression domain.
%8 7-11  July
%Z Also known as \cite1830649 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Josh C. Bongard
%T Innocent Until Proven Guilty: Reducing Robot Shaping from Polynomial to Linear Time
%J IEEE Transactions on Evolutionary Computation
%V 15
%N 4
%D 2011
%P 571--585
%I 
%K genetic algorithms, genetic programming, Early stopping, Evolutionary computation, Joints, Manipulators, Neurons, Robot sensing systems, evolutionary robotics
%X In evolutionary algorithms, much time is spent evaluating inferior phenotypes that produce no offspring. A common heuristic to address this inefficiency is to stop
   evaluations early if they hold little promise of attaining high fitness. However, the form of this heuristic is typically dependent on the fitness function used, and there
   is a danger of prematurely stopping evaluation of a phenotype that may have recovered in the remainder of the evaluation period. Here a stopping method is introduced that
   gradually reduces fitness over the phenotype's evaluation, rather than accumulating fitness. This method is independent of the fitness function used, only stops those
   phenotypes that are guaranteed to become inferior to the current offspring-producing phenotypes, and realises significant time savings across several evolutionary robotics
   tasks. It was found that for many tasks, time complexity was reduced from polynomial to sublinear time, and time savings increased with the number of training instances
   used to evaluate a phenotype as well as with task difficulty.
%8 August
%Z Also known as \cite5703121

%A Christopher R. Bonham
%A Ian C. Parmee
%T An Investigation of Exploration and Exploitation Within Cluster Oriented Genetic Algorithms (COGAs)
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1491--1497
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-765.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Cesar Pedraza Bonilla
%A Carlos Ivan Camargo
%T Low Cost Platform for Evolvable-Based Boolean Synthesis
%B IEEE Second Latin American Symposium on Circuits and Systems (LASCAS), 2011
%D 2011
%I 
%K genetic algorithms, genetic programming, 32-bit processor, HPC implementation, PGP Boolean synthesis implementation, SIE, combinational synthesis, embedded platform,
   evolutionary algorithms, evolvable-based Boolean synthesis, hardware structures, low cost cluster, low cost platform, parallel genetic programming, spartan-3 FPGA, speedup
   values, Boolean functions, combinational circuits, embedded systems, field programmable gate arrays, logic design, microprocessor chips
%X Evolutionary algorithms are another option for combinational synthesis because they allow for the generation of hardware structures that cannot be obtained with other
   techniques. This paper shows a parallel genetic programming (PGP) Boolean synthesis implementation based on a low cost cluster of an embedded platform called SIE, based on
   a 32-bit processor and a Spartan-3 FPGA. Some tasks of the PGP have been accelerated in hardware and results have been compared with an HPC implementation, resulting in
   speedup values up to approximately 180.
%8 February
%Z Also known as \cite5750310

%A Bert Bonte
%A Bart Wyns
%T Automatically Designing Robot Controllers and Sensor Morphology with Genetic Programming
%B 6th IFIP Advances in Information and Communication Technology AIAI 2010
%S IFIP Advances in Information and Communication Technology
%E Harris Papadopoulos and Andreas Andreou and Max Bramer
%V 339
%D 2010
%P 86--93
%I Springer
%C Larnaca, Cyprus
%K genetic algorithms, genetic programming
%X Genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. In this contribution we use genetic programming to
   automatically evolve efficient robot controllers for a corridor following task. Based on tests executed in a simulation environment we show that very robust and efficient
   controllers can be obtained. Also, we stress that it is important to provide sufficiently diverse fitness cases, offering a sound basis for learning more complex behaviour.
   The evolved controller is successfully applied to real environments as well. Finally, controller and sensor morphology are co-evolved, clearly resulting in an improved
   sensor configuration.
%8 October  6-7
%Z http://www.cs.ucy.ac.cy/aiai2010/

%A Lashon B. Booker
%A David B. Fogel
%A Darrell Whitley
%A Peter J. Angeline
%A A. E. Eiben
%T Recombination
%B Evolutionary Computation 1 Basic Algorithms and Operators
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 2000
%P 256--307
%I Institute of Physics Publishing
%C Bristol
%K genetic algorithms, genetic programming
%O 33
%Z http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=IP274 section 33.5 parse trees p286--289
%@ 0-7503-0664-5

%A Richard F. Booth
%A Alexandre V. Borovik
%T Coevolution of Algorithms and Deterministic Solutions of Equations in Free Groups
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 11--22
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=11
%X We discuss the use of evolutionary algorithms for solving problems in combinatorial group theory, using a class of equations in free groups as a test bench. We find that,
   in this context, there seems to be a correlation between successful evolutionary algorithms and the existence of good deterministic algorithms. We also trace the
   convergence of co-evolution of the population of fitness functions to a deterministic solution.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Cruz E. Borges
%A Cesar L. Alonso
%A Jose L. Montana
%T Model selection in genetic programming
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 985--986
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, Poster
%X In this paper we discuss the problem of model selection in Genetic Programming. We present empirical comparisons between classical statistical methods (AIC, BIC) adapted to
   Genetic Programming and the Structural Risk Minimisation method (SRM) based on Vapnik-Chervonenkis theory (VC), for symbolic regression problems with added noise. We also
   introduce a new model complexity measure for the SRM method that tries to measure the non-linearity of the model. The experimentation suggests practical advantages of using
   VC-based model selection with the new complexity measure, when using genetic training.
%8 7-11  July
%Z Also known as \cite1830662 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Cruz Enrique Borges
%A Cesar L. Alonso
%A Jose Luis Montana
%T Coevolutionary Architectures with Straight Line Programs for solving the Symbolic Regression Problem
%B Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)
%E Agostinho Rosa
%D 2010
%P Paper Nr: 37
%I 
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X To successfully apply evolutionary algorithms to the solution of increasingly complex problems we must develop effective techniques for evolving solutions in the form of
   interacting coadapted subcomponents. In this paper we present an architecture which involves cooperative coevolution of two subcomponents: a genetic program and an
   evolution strategy. As main difference with work previously done, our genetic program evolves straight line programs representing functional expressions, instead of tree
   structures. The evolution strategy searches for good values for the numerical terminal symbols used by those expressions. Experimentation has been performed over symbolic
   regression problem instances and the obtained results have been compared with those obtained by means of Genetic Programming strategies without coevolution. The results
   show that our coevolutionary architecture with straight line programs is capable to obtain better quality individuals than traditional genetic programming using the same
   amount of computational effort.
%8 24-26  October
%Z http://www.icec.ijcci.org/ICEC2010/home.asp http://www.ecta.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm

%A Neven Boric
%A Pablo A. Estevez
%T Genetic Programming-Based Clustering Using an Information Theoretic Fitness Measure
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 31--38
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X A clustering method based on multitree genetic programming and an information theoretic fitness is proposed. A probabilistic interpretation is given to the output of trees
   that does not require a conflict resolution phase. The method can cluster data with irregular shapes, estimate the underlying models of the data for each class and use
   those models to classify unseen patterns. The proposed scheme is tested on several real and artificial data sets, outperforming k-means algorithm in all of them.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A A. Borrelli
%A I. {De Falco}
%A A. {Della Cioppa}
%A M. Nicodemi
%A G. Trautteura
%T Performance of genetic programming to extract the trend in noisy data series
%J Physica A: Statistical and Theoretical Physics
%V 370
%N 1
%D 2006
%P 104--108
%I 
%K genetic algorithms, genetic programming, Multiobjective genetic programming, Stochastic time series
%X In this paper an approach based on genetic programming for forecasting stochastic time series is outlined. To obtain a suitable test-bed some well-known time series are
   dressed with noise. The GP approach is endowed with a multiobjective scheme relying on statistical properties of the faced series, i.e., on their momenta. Finally, the
   method is applied to the MIB30 Index series.
%O Econophysics Colloquium - Proceedings of the International Conference "Econophysics Colloquium"
%8 1  October

%A Mariusz Boryczka
%A Zbigniew J. Czech
%T Solving Approximation Problems By Ant Colony Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 133
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, artificial life, adaptive behavior, agents, ant colony optimization, poster paper, ant colony programming, approximation problems,
   automatic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-02.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Mariusz Boryczka
%A Zbigniew J. Czech
%T Solving Approximation Problems by Ant Colony Programming
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 39--46
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming, automatic programming, ant colony programming, approximation problems
%U http://www-zo.iinf.polsl.gliwice.pl/pub/zjc/bc02.ps.Z
%X A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm....
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp

%A Indranil Bose
%A Xi Chen
%T Quantitative models for direct marketing: A review from systems perspective
%J European Journal of Operational Research
%V 195
%N 1
%D 2009
%P 1--16
%I 
%K genetic algorithms, genetic programming, Marketing, Data mining, Customer profiling, Customer targeting, Statistical modelling, Performance evaluation
%U http://www.sciencedirect.com/science/article/B6VCT-4S7SV3H-3/2/39d97985eecf3aa2b863955e4227cbb0
%X In this paper, quantitative models for direct marketing models are reviewed from a systems perspective. A systems view consists of input, processing, and output and the six
   key activities of direct marketing that take place within these constituent parts. A discussion about inputs for direct marketing models is provided by describing the
   various types of data used, by determining the significance of the data, and by addressing the issue of selection of appropriate data. Two types of models, statistical and
   machine learning based, are popularly used for conducting direct marketing activities. The advantages and disadvantages of these two approaches are discussed along with
   enhancements to these models. The evaluation of output for direct marketing models is done on the basis of accuracy and profitability. Some challenges in conducting
   research in the area of quantitative direct marketing models are listed and some significant research questions are proposed.
%Z Survey

%A Mark Boslough
%A Michael Peters
%A Arthurine Pierson
%T Graduated Embodiment for Sophisticated Agent Evolution and Optimization
%R Technical Report SAND2005-0014
%D 2005
%I 
%I Sandia National Laboratories
%C P.O. Box 5800, Albuquerque, NM 87185-0318, USA
%K genetic algorithms, genetic programming
%U http://www.cs.sandia.gov/web1433/pubsagent/Graduated_Embodiment.pdf
%X We summarise the results of a project to develop evolutionary computing methods for the design of behaviours of embodied agents in the form of autonomous vehicles. We
   conceived and implemented a strategy called graduated embodiment. This method allows high-level behavior algorithms to be developed using genetic programming methods in a
   low-fidelity, disembodied modelling environment for migration to high-fidelity, complex embodied applications. This project applies our methods to the problem domain of
   robot navigation using adaptive waypoints, which allow navigation behaviors to be ported among autonomous mobile robots with different degrees of embodiment, using
   incremental adaptation and staged optimisation. Our approach to biomimetic behaviour engineering is a hybrid of human design and artificial evolution, with the application
   of evolutionary computing in stages to preserve building blocks and limit search space. The methods and tools developed for this project are directly applicable to other
   agent-based modeling needs, including climate-related conflict analysis, multiplayer training methods,and market-based hypothesis evaluation.
%8 January
%Z Unlimited Release Mark Boslough Michael Peters Evolutionary Computing & Agent Based Modeling Department Arthurine Pierson Intelligent Systems Principles Department

%A Peter A. N. Bosman
%A Dirk Thierens
%T Linkage Information Processing In Distribution Estimation Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 60--67
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-812.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Peter A. N. Bosman
%A Edwin D. {de Jong}
%T Grammar Transformations in an EDA for Genetic Programming
%R Technical Report UU-CS-2004-047
%D 2004
%I 
%I Department of Information and Computing Sciences, Utrecht University
%C The Netherlands
%K genetic algorithms, genetic programming, EDA, grammar
%U http://www.cs.uu.nl/research/techreps/UU-CS-2004-047.html
%X In this paper we present a new Estimation of Distribution Algorithm (EDA) for Genetic Programming (GP). We propose a probability distribution for the space of trees, based
   on a grammar. To introduce dependencies into the distribution, grammar transformations are performed that facilitate the description of specific subfunctions. We present
   some results from experiments on two benchmark problems and show some of the subfunctions that were introduced during optimization as a result of the transformations that
   were applied.
%Z Royal Tree. See also \citebosman:2004:obu:panbos

%A Peter A. N. Bosman
%A Edwin D. {de Jong}
%T Grammar Transformations in an EDA for Genetic Programming
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WOBU001.pdf
%X we present a new Estimation-of-Distribution Algorithm (EDA) for Genetic Programming (GP). We propose a probability distribution for the space of trees, based on a grammar.
   To introduce dependencies into the distribution, grammar transformations are performed that facilitate the description of specific subfunctions. We present some results
   from experiments on two benchmark problems and show some of the subfunctions that were introduced during optimisation as a result of the transformations that were applied.
%8 26-30  June
%Z See also \citeUUCS2004047 GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A Peter A. N. Bosman
%A Edwin D. {de Jong}
%T Learning Probabilistic Tree Grammars for Genetic Programming
%B Parallel Problem Solving from Nature - PPSN VIII
%S LNCS
%E Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\vno Ata Kab\'an and Hans-Paul
   Schwefel
%V 3242
%D 2004
%P 192--201
%I Springer-Verlag Berlin
%C Birmingham, UK
%K genetic algorithms, genetic programming, EDA
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=192
%X Genetic Programming (GP) provides evolutionary methods for problems with tree representations. A recent development in Genetic Algorithms (GAs) has led to principled
   algorithms called Estimation-of-Distribution Algorithms (EDAs). EDAs identify and exploit structural features of a problemrsquos structure during optimization. Here, we
   investigate the use of a specific EDA for GP. We develop a probabilistic model that employs transformations of production rules in a context-free grammar to represent local
   structures. The results of performing experiments on two benchmark problems demonstrate the feasibility of the approach.
%8 18-22  September
%@ 3-540-23092-0

%A Martijn Bot
%T Application of Genetic Programming to the Induction of Linear Programming Trees
%R M.S. Thesis
%D 1999
%I 
%I Vrije Universiteit
%C Amsterdam, The Netherlands
%K genetic algorithms, genetic programming, data mining
%U http://citeseer.ist.psu.edu/243957.html
%8 1  July
%Z See also \citebot:1999:GPilct, \citebot:2000:GPilct

%A Martijn Bot
%A William B. Langdon
%T Application of Genetic Programming to Induction of Linear Classification Trees
%B Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
%E Eric Postma and Marc Gyssens
%D 1999
%P 107--114
%I 
%I BNVKI, Dutch and the Belgian AI Association
%C Kasteel Vaeshartelt, Maastricht, Holland
%K genetic algorithms, genetic programming, data mining
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/BNAIC99.bot.18aug99.ps.gz
%8 3-4  November
%Z http://www.cs.unimaas.nl/~bnvki/

%A Martijn C. J. Bot
%A William B. Langdon
%T Application of Genetic Programming to Induction of Linear Classification Trees
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 247--258
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=247
%X A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a set of benchmark classification
   problems. Using GP we are able to induce decision trees with a linear combination of variables in each function node. A new representation of decision trees using strong
   typing in GP is introduced. With this representation it is possible to let the GP classify into any number o f classes. Results indicate that GP can be applied successfully
   to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified.
%8 15-16  April
%Z See also \citebot:1999:GPilct EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Martijn C. J. Bot
%T Improving Induction of Linear Classification Trees with Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 403--410
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/316984.html
%X Decision trees are a well known technique in machine learning for describing the underlying structure of a dataset. In [Bot and Langdon, 2000] a new representation of
   decision trees using strong typing in GP was introduced. In the function nodes, a linear combination of variables is made. The effects of techniques such as limited error
   fitness, fitness sharing Pareto scoring and domination Pareto scoring are evaluated on a set of benchmark classification problems. Comparisons with current state-of-the-art
   algorithms in machine learning are presented and areas of future research are identified. Results indicate that GP can be applied successfully to classification problems.
   Limited error fitness reduces runtime while maintaing equal accuracy. Pareto scoring works well against bloat. Fitness sharing Pareto works better than domination Pareto.
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Martijn C. J. Bot
%T Feature Extraction for the k-Nearest Neighbour Classifier with Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 256--267
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Feature Extraction, Machine Learning
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=256
%X In pattern recognition the curse of dimensionality can be handled either by reducing the number of features, e.g. with decision trees or by extraction of new features. We
   propose a genetic programming (GP) framework for automatic extraction of features with the express aim of dimension reduction and the additional aim of improving accuracy
   of the k-nearest neighbour (k-NN) classifier. We will show that our system is capable of reducing most datasets to one or two features while k-NN accuracy improves or stays
   the same. Such a small number of features has the great advantage of allowing visual inspection of the dataset in a two-dimensional plot. Since k-NN is a non-linear
   classification algorithm, we compare several linear fitness measures. We will show the a very simple one, the accuracy of the minimal distance to means (mdm) classifier
   outperforms all other fitness measures. We introduce a stopping criterion gleaned from numeric mathematics. New features are only added if the relative increase in training
   accuracy is more than a constant d, for the mdm classifier estimated to be 3.3%.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Martijn C. J. Bot
%T Feature Extraction for the k-Nearest Neighbour Classifier with Genetic Programming
%B Graduate Student Workshop
%E Conor Ryan
%D 2001
%P 397--400
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming
%8 7  July
%Z GECCO-2001WKS Part of heckendorn:2001:GECCOWKS

%A Michael Botros
%T Evolving Controllers for Miniature Robots
%B Evolvable Machines: Theory \& Practice
%S Studies in Fuzziness and Soft Computing
%E Nadia Nedjah and Luiza de Macedo Mourelle
%V 161
%D 2004
%P 73--100
%I Springer
%C Berlin
%K genetic algorithms, genetic programming
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html
%O 4
%Z Springer says published in 2005 but available Nov 2004
%@ 3-540-22905-1

%A Michael Botros
%T Evolving Complex Robotic Behaviors Using Genetic Programming
%B Genetic Systems Programming: Theory and Experiences
%S Studies in Computational Intelligence
%E Nadia Nedjah and Ajith Abraham and Luiza de Macedo Mourelle
%V 13
%D 2006
%P 175--194
%I Springer
%C Germany
%K genetic algorithms, genetic programming
%Z http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html
%@ 3-540-29849-5

%A J. Botzheim
%A L. T. Koczy
%T Model Identification by Bacterial Optimization
%B Proceedings of the 5th International Symposium of Hungarian Researchers on Computational Intelligence
%D 2004
%P 91--102
%I 
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.7233
%X In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate.
   Increasing interest in biologically inspired learning algorithms for control techniques such as artificial neural networks and fuzzy systems is in progress. In this paper a
   recent kind of evolutionary method called bacterial algorithm is introduced. This method can be used for fuzzy rule extraction and optimization. Bacterial Programming is
   also proposed in this paper. This approach is the combination of the bacterial algorithm and the genetic programming techniques and can be applied for the optimization of
   the structure of Bspline neural networks.
%8 November

%A Janos Botzheim
%A Cristiano Cabrita
%A Laszlo T. Koczy
%A Antonio E. Ruano
%T Genetic and Bacterial Programming for B-Spline Neural Networks Design
%J Journal of Advanced Computational Intelligence and Intelligent Informatics
%V 11
%N 2
%D 2007
%P 220--231
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/BotzheimCabritaKoczyRuano07.pdf
%X The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions
   employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In
   this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient
   topology search, obtaining additionally more consistent solutions.
%8 February

%A Janos Botzheim
%T Intelligens szamitastechnikai modellek identifiacioja evolucios es gradiens alapu tanulo algoritmusokkal
%R Ph.D. Thesis Ph.D. thesis
%D 2007
%I 
%I Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics
%C Budapest
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/thesisbooklet.pdf
%X The thesis discusses identification techniques of soft computing models. Its goal is to develop identification methods based on numerical data that can produce results
   better in terms of quality criteria (e.g. mean square error) relevant for the given applications than other techniques known from the literature. The first statement
   proposes the Bacterial Evolutionary Algorithm for the extraction of Mamdani-type fuzzy rules with trapezoidal membership functions. The second statement proposes the
   application of the Levenberg-Marquardt algorithm for local optimisation of fuzzy rules. The third statement introduces the Bacterial Memetic Algorithm, a combination of the
   Bacterial Evolutionary and the Levenberg-Marquardt algorithm. The fourth statement deals with Takagi-Sugeno-type fuzzy systems. The fifth statement proposes a new technique
   called Bacterial Programming for the design process of B-spline neural networks. Finally, the sixth statement presents the application of Bacterial Evolutionary Algorithm
   for the feature selection problem.
%8 11  November
%Z In Hungarian. 24 page english summary

%A Amine M. Boumaza
%A Jean Louchet
%T Dynamic Flies: Using Real-Time Parisian Evolution in Robotics
%B Applications of Evolutionary Computing
%S LNCS
%E Egbert J. W. Boers and Stefano Cagnoni and Jens Gottlieb and Emma Hart and Pier Luca Lanzi and Gunther R. Raidl and Robert E. Smith and Harald Tijink
%V 2037
%D 2001
%P 288--297
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, fly algorithm, robot
%U http://minimum.inria.fr/evo-lab/Publications/evoiasp2001_Louchet_Boumaza.ps.gz
%X The Fly algorithm is a Parisian evolution strategy devised for parameter space exploration in computer vision applications, which has been applied to stereovision. The
   resulting scene model is a set of 3-D points which concentrate upon the surfaces of obstacles. In this paper, we present how the evolutionary scene analysis can be
   continuously updated and integrated into a specific real-time mobile robot navigation system. Simulation-based experimental results are presented.
%8 18  April
%Z EvoWorkshops2001
%@ 3-540-41920-9

%A Amine M. Boumaza
%A Jean Louchet
%T Mobile Robot Sensor Fusion Using Flies
%B Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM
%S LNCS
%E G\"unther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and
   Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf
%V 2611
%D 2003
%P 357--367
%I Springer-Verlag Berlin
%I EvoNet
%C University of Essex, England, UK
%K genetic algorithms, genetic programming, evolutionary computation, applications
%8 14-16  April
%Z EvoWorkshops2003

%A A. Bourmistrova
%A S. Khantsis
%T Control System Design Optimisation via Genetic Programming
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 1993--2000
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X This paper describes a stochastic approach for comprehensive diagnostics and validation of control system architecture for Unmanned Aerial Vehicle (UAV). Mathematically
   based diagnostics of a 6 DoF system provides capability for a complex evaluation of system components behaviour, but are typically both memory and computationally
   expensive. Design and optimisation of the flight controllers is a demanding task which usually requires deep engineering knowledge of intrinsic aircraft behaviour.
   Evolutionary Algorithms (EAs) are known for their robustness for a wide range of optimising functions, when no a priori knowledge of the search space is available. Thus it
   makes evolutionary approach a promising technique to design the task controllers for complex dynamic systems such as an aircraft. In this study, EAs are used to design a
   controller for recovery (landing) of a small fixed-wing UAV on a frigate ship deck. The control laws are encoded in a way common for Evolutionary Programming. However,
   parameters (numeric coefficients in the control equations) are optimised independently using effective Evaluation Strategies, while structural changes occur at a slower
   rate. The fitness evaluation is made via test runs on a comprehensive 6 degree-of-freedom non-linear UAV model. The need of a well defined approach to the control system
   validation is dictated by the nature of UAV application, where the major source of mission success is based on autonomous control system architecture reliability. The
   results show that an effective controller can be designed with little knowledge of the aircraft dynamics using appropriate evolutionary techniques. An evolved controller is
   evaluated and a set of reliable algorithm parameters is validated.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Anna Bourmistrova
%A Sergey Khantsis
%T Flight Control System Design Optimisation via Genetic Programming
%B Aerial Vehicles
%E Thanh Mung Lam
%D 2009
%I InTech
%K genetic algorithms, genetic programming, mobile robotics
%U http://www.intechopen.com/articles/show/title/flight_control_system_design_optimisation_via_genetic_programming
%X In this chapter, an application of the Evolutionary Design (ED) is demonstrated. The aim of the design was to develop a controller which provides recovery of a fixed-wing
   UAV onto a ship under the full range of disturbances and uncertainties that are present in the real world environment. The controller synthesis is a multistage process.
   However, the approach employed for synthesis of each block is very similar. Evolutionary algorithm is used as a tool to evolve and optimise the control laws. One of the
   greatest advantages of this methodology is that minimum or no a priori knowledge about the control methods is used, with the synthesis starting from the most basic
   proportional control or even from `null' control laws. During the evolution, more complex and capable laws emerge automatically. As the resulting control laws demonstrate,
   evolution does not tend to produce parsimonious solutions. The method demonstrating remarkable robustness in terms of convergence indicating that a near optimal solution
   can be found. In very limited cases, however, it may take too long time for the evolution to discover the core of a potentially optimal solution, and the process does not
   converge. More often than not, this hints at a poor choice of the algorithm parameters. The most important and difficult problem in Evolutionary Design is preparation of
   the fitness evaluation procedure with predefined special intermediate problems. Computational considerations are also of the utmost importance. Robustness of EAs comes at
   the price of computational cost, with many thousands of fitness evaluations required. The simulation testing covers the entire operational envelope and highlights several
   conditions under which recovery is risky. All environmental factors--sea wave, wind speed and turbulence--have been found to have a significant effect upon the probability
   of success. Combinations of several factors may result in very unfavourable conditions, even if each factor alone may not lead to a failure. For example, winds up to 12 m/s
   do not affect the recovery in a calm sea, and a severe ship motion corresponding to Sea State 5 also does not represent a serious threat in low winds. At the same time,
   strong winds in a high Sea State may be hazardous for the aircraft.
%O 7

%A Anna Bourmistrova
%A Sergey Khantsis
%T Genetic Programming in Application to Flight Control System Design Optimisation
%B New Achievements in Evolutionary Computation
%E Peter Korosec
%D 2010
%I InTech
%K genetic algorithms, genetic programming, UAV
%U http://www.intechopen.com/download/pdf/pdfs_id/8542
%O 10
%8 February
%Z the first seminal book to introduce GP as a solid and practical technique is John Koza's Genetic Programming, dated 1992. RMIT

%A Bradley J. Bozarth
%T Programmatic Compression of Video using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 46--53
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Behzad Bozorgtabar
%A Farzad Noorian
%A Gholam Ali Rezai Rad
%T Comparison of different PCA based Face Recognition algorithms using Genetic Programming
%B 5th International Symposium on Telecommunications (IST 2010)
%D 2010
%P 801--805
%I 
%K genetic algorithms, genetic programming, eigenfaces method, face recognition algorithms, multilinear PCA, principal component analyses, security systems automation, two
   dimensional PCA, eigenvalues and eigenfunctions, face recognition, principal component analysis
%X Face Recognition plays a vital role in automation of security systems; therefore many algorithms have been invented with varying degrees of effectiveness. After successful
   try out of principal component analyses (PCA) in eigenfaces method, many different PCA based algorithms such as Two Dimensional PCA (2DPCA) and Multilinear PCA (MLPCA),
   combined with several classifying algorithms were studied. This paper uses Genetic Programming (GP) as a clustering tool, to classify features extracted by PCA, 2DPCA and
   MLPCA. Results of different algorithms are compared with each other and also previous studies and it is shown that Genetic Programming can be used in combination with PCA
   for face recognition problems.
%8 Decemeber
%Z Also known as \cite5734132

%A Behzad Bozorgtabar
%A Farzad Noorian
%A Rezai Rad {Gholam Ali}
%T A Genetic Programming approach to face recognition
%B IEEE GCC Conference and Exhibition (GCC), 2011
%D 2011
%P 194--197
%I IEEE
%C Dubai, United Arab Emirates
%K genetic algorithms, genetic programming, data mining, face recognition technology, feature extraction, image group classification, pattern recognition, principal component
   analysis, relation discovery methodology, data mining, face recognition, feature extraction, image classification, principal component analysis
%X Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now,
   Genetic Programming (GP), an acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper
   tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further
   improve the results, a leveraging method is also used. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable
   to other Face recognition solutions.
%8 February  19-22
%Z Iran University of Science and Technology Also known as \cite5752477

%A Tony Brabazon
%A M. O'Neill
%A C. Ryan
%A J. J. Collins
%T Uncovering Technical Trading Rules Using Evolutionary Automatic Programming
%B Proceedings of 2001 AAANZ Conference (Accounting Association of Australia and NZ)
%D 2001
%I 
%C Auckland, New Zealand
%K genetic algorithms, genetic programming, grammatical evolution, financial prediction
%8 1-3  July

%A Anthony Brabazon
%A Michael O'Neill
%A Conor Ryan
%A Robin Matthews
%T Evolving classifiers to model the relationship between strategy and corporate performance using grammatical evolution
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 103--112
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming, grammatical evolution
%X This study examines the potential of grammatical evolution to construct a linear classifier to predict whether a firm's corporate strategy will increase or decrease
   shareholder wealth. Shareholder wealth is measured using a relative fitness criterion, the change in a firm's market-value-added ranking in the Stern-Stewart Performance
   1000 list, over a four year period, 1992-1996. Model inputs and structure are selected by means of grammatical evolution. The best classifier correctly categorised the
   direction of performance ranking change in 66.38percent of the firms in the training set and 65percent in the out-of-sample validation set providing support for a
   hypothesis that changes in corporate strategy are linked to changes in corporate performance.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Anthony Brabazon
%A Michael O'Neill
%A Robin Matthews
%A Conor Ryan
%T Grammatical Evolution And Corporate Failure Prediction
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 1011--1018
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, real world applications, corporate failure prediction, genotype to phenotype mapping, grammars, grammatical evolution
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Tony Brabazon
%A Michael O'Neill
%T Trading Foreign Exchange Markets Using Evolutionary Automatic Programming
%B GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2002
%P 133--136
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.grammatical-evolution.org/gews2002/brabazon.ps
%8 8  July
%Z Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic
   Programming Conference (GP-2002) part of barry:2002:GECCO:workshop

%A Anthony Brabazon
%A Michael O'Neill
%T A Grammar Model for Foreign-Exchange Trading
%B Proceedings of the International Conference on Artificial Intelligence
%E H. R. Arabnia et al.
%V II
%D 2003
%P 492--498
%I CSREA Press
%K genetic algorithms, genetic programming
%X This study examines the potential of Grammatical Evolution to uncover a series of useful technical trading rules which can be used to trade foreign exchange markets. In
   this study, each of the evolved programs represents a market trading system and implicitly, a predictive model. The form of these programs is not specified ex-ante but
   emerges by means of an evolutionary process. Daily US Dollar-DM exchange rates for the period 9/3/93 to 13/10/97 are used to train and test the model. The preliminary
   findings suggest that the developed rules earn positive returns in hold-out sample test periods after allowing for trading and slippage costs. This suggests potential for
   future research to determine whether further refinement of the methodology adopted in this study could improve the returns earned by the developed rules.
%8 23-26  June
%@ 1-932415-13-0

%A Anthony Brabazon
%A Robin Matthews
%A Michael O'Neill
%T Grammars, Representations, Mental Maps and Corporate Strategy
%B Business Research Yearbook: Global Business Perspectives. Proceedings of the Fifteenth Annual International Conference of the International Academy of Business Disciplines
%E C. Gardner and J. Biberman and A. Alkhafaji
%V 11
%D 2004
%P 1054--1058
%I Saline, Michigan, USA
%C San Antonio, USA
%K genetic algorithms, genetic programming,grammatical evolution
%8 March  24-27
%Z http://academic.scranton.edu/faculty/BIBERMANG1/pres.htm

%A Anthony Brabazon
%A Michael O'Neill
%T Bond-Issuer Credit Rating with Grammatical Evolution
%B Applications of Evolutionary Computing, EvoWorkshops2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC
%S LNCS
%E Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori
   and Franz Rothlauf and George D. Smith and Giovanni Squillero
%V 3005
%D 2004
%P 270--279
%I Springer Verlag Berlin
%C Coimbra, Portugal
%K genetic algorithms, genetic programming, grammatical evolution, evolutionary computation
%X This study examines the utility of Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial statements
   of bond-issuing firms. Financial data, and the associated Standard & Poor's issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test
   the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment-grade and junk bond ratings with an average accuracy
   of 87.59 (84.92)% across a five-fold cross validation. The results suggest that the two classifications of credit rating can be predicted with notable accuracy from a
   relatively limited subset of firm-specific financial data, using Grammatical Evolution.
%8 5-7  April
%Z EvoWorkshops2004
%@ 3-540-21378-3

%A Anthony Brabazon
%A Katrina Meagher
%A Edward Carty
%A Michael O'Neill
%A Peter Keenan
%T Grammar-mediated time-series prediction
%J Journal of Intelligent Systems
%V 14
%N 2--3
%D 2004
%P 123--143
%I 
%K genetic algorithms, genetic programming, grammatical evolution, time-series

%A A. Brabazon
%A K. Meagher
%A E. Carty
%A M. O'Neill
%A P. Keenan
%T Grammar-Mediated Time-Series Prediction
%J Journal of Intelligent Systems
%V 14
%N 2-3
%D 2005
%P 123--143
%I Freund \& Pettman Publishers
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.freundpublishing.com/Journal_Intelligent_Systems/Intellileaf14_2_3.htm
%O Special Issue

%A Anthony Brabazon
%A Michael O'Neill
%T Credit Rating with pi Grammatical Evolution
%B Proceedings of Computer Methods and Systems Conference
%E R. Tadeusiewicz and A. Ligeza and M. Szymkat
%V 1
%D 2005
%P 253--260
%I Oprogramowanie Naukowo-Techniczne Tadeusiewicz Krakow
%C Krakow, Poland
%K genetic algorithms, genetic programming, grammatical evolution
%X This study examines the utility of pi Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial
   statements of bond-issuing firms. Financial data, and the associated Standard and Poor's issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to
   train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment grade and junk bond ratings with an
   average accuracy of 86 (87)percent across a five-fold cross validation.
%8 14-16  November
%@ 83-916420-3-8

%A Anthony Brabazon
%A Michael O'Neill
%T Biologically Inspired Algorithms for Financial Modelling
%S Natural Computing Series
%D 2006
%I Springer
%K genetic algorithms, genetic programming, ant colony systems, artificial immune systems, biologically inspired algorithms (BIAs), computer trading, evolutionary
   methodologies, financial markets, financial trading, grammatical evolution, (GE), multilayer perceptrons, neural networks (NNs), particle swarm optimisation (PSO)
%Z reviewed by \citeKaboudan:2006:GPEM also Brad G. Kyer, The Book Review Column 40(4), 2009, p11-17, William Gasarch, http://www.cs.umd.edu/~gasarch/bookrev/
%@ 3-540-26252-0

%A Anthony Brabazon
%A Michael O'Neill
%T Credit Classification Using Grammatical Evolution
%J Informatica
%V 30
%N 3
%D 2006
%P 325--335
%I 
%K genetic algorithms, genetic programming, grammatical evolution, Povzetek: Metoda gramaticne evolucije je uporabljena za klasificiranje kreditov.
%U http://ai.ijs.si/informatica/PDF/30-3/07_Brabazon_Credit%20Classification%20Using.pdf
%X Grammatical Evolution (GE) is a novel data driven, model induction tool, inspired by the biological genetoprotein mapping process. This study provides an introduction to
   GE, and demonstrates the methodology by applying it to model the corporate bond-issuer credit rating process, using information drawn from the financial statements of
   bond-issuing firms. Financial data and the associated Standard & Poor's issuer credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the
   model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment grade and junk bond ratings with an average accuracy of
   87.59 (84.92)percent across a five-fold cross validation.

%A Anthony Brabazon
%A Michael O'Neill
%T Bond Rating with piGrammatical Evolution
%B Knowledge Engineering and Intelligent Computations
%S Studies in Computational Intelligence
%E C. Cotta and S. Reich and R. Schaefer and A. Ligeza
%V 102
%D 2008
%P 17--30
%I Springer
%K genetic algorithms, genetic programming, Grammatical Evolution
%X Most large firms use both share and debt capital to provide long-term finance for their operations. The debt capital may be raised from a bank loan, or may be obtained by
   selling bonds directly to investors. As an example of the scale of US bond markets, the value of new bonds issued in 2004 totaled $5.48 trillion, and the total value of
   outstanding marketable bond debt at 31 December 2004 was $23.6 trillion [1]. In comparison, the total global market capitalisation of all companies quoted on the New York
   Stock Exchange (NYSE) at 31/12/04 was $19.8 trillion [2]. Hence, although company stocks attract most attention in the business press, bond markets are actually
   substantially larger. When a company issues traded debt (e.g. bonds), it must obtain a credit rating for the issue from at least one recognised rating agency (Standard and
   Poor's (S&P), Moody's and Fitches'). The credit rating represents an agency's opinion, at a specific date, of the credit worthiness of a borrower in general (a bond-issuer
   credit-rating), or in respect of a specific debt issue (a bond credit rating). These ratings impact on the borrowing cost, and the marketability of issued bonds. Although
   several studies have examined the potential of both statistical and machine-learning methodologies for credit rating prediction [3-6], many of these studies used relatively
   small sample sizes, making it difficult to generalise strongly from their findings. This study by contrast, uses a large dataset of 791 firms, and introduces pi GE to this
   domain.
%O 2

%T Natural Computing in Computational Finance
%S Studies in Computational Intelligence
%E Anthony Brabazon and Michael O'Neill
%V 100
%D 2008
%I Springer
%K genetic algorithms, genetic programming, computational finance, evolution strategies, differential evolution, bacterial foraging, quantum-inspired evolutionary algorithms
%U http://www.springer.com/engineering/book/978-3-540-77476-1
%X Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based
   modelling in modern computational finance. Following an introductory chapter the book is organised into three sections. The first section deals with optimisation
   applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies,
   quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset
   pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model
   induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including
   the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear
   description of the financial application addressed. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and
   students, in the fields of both natural computing and finance.
%8 April

%A Anthony Brabazon
%A Michael O'Neill
%A Ian Dempsey
%T An Introduction to Evolutionary Computation in Finance
%J IEEE Computational Intelligence Magazine
%V 3
%N 4
%D 2008
%P 42--55
%I 
%K genetic algorithms, genetic programming, grammatical evolution, finance, evolutionary computation, financial data processing computational intelligence methodologies,
   evolutionary computation approach, finance
%U http://ieeexplore.ieee.org/xpl/tocresult.jsp?isYear=2008&isnumber=4625777&Submit32=Go+To+Issue
%X The world of finance is an exciting and challenging environment. Recent years have seen an explosion in the application of computational intelligence methodologies in
   finance. In this article we provide an overview of some of these applications concentrating on those employing an evolutionary computation approach.
%8 November
%Z Also known as \cite4625793

%T Natural Computing in Computational Finance (Volume 2)
%S Studies in Computational Intelligence
%E Anthony Brabazon and Michael O'Neill
%V 185
%D 2009
%I Springer
%K genetic algorithms, genetic programming, computational Finance, Computational Intelligence
%U http://www.springer.com/engineering/book/978-3-540-95973-1
%X About this book Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws
   inspiration from phenomena in the natural world) for the purposes of financial modeling and optimisation. A related stream of work has also seen the application of learning
   mechanisms drawn from Natural Computing algorithms for the purposes of agent based modelling in finance and economics. In this book we have collected a series of chapters
   which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to
   uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems. This book follows on from Natural
   Computing in Computational Finance (Volume 100 in Springer's Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very
   first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007. Written for: Engineers, researchers, and graduate students
   in Computational Intelligence and Computer Finance
%8 March

%T Natural Computing in Computational Finance (Volume 3)
%S Studies in Computational Intelligence
%E A. Brabazon and M. O'Neill and D. G. Maringer
%V 293
%D 2010
%I Springer
%K genetic algorithms, genetic programming, natural computing, computational finance, computational intelligence
%U http://www.springer.com/engineering/book/978-3-642-13949-9
%X This book consists of eleven chapters each of which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range
   of cutting-edge natural computing and agent-based methodologies in computational finance and economics. While describing cutting edge applications, the chapters are written
   so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and
   economics. The inspiration for this book was due in part to the success of EvoFIN 2009, the 3rd European Workshop on Evolutionary Computation in Finance and Economics. This
   book follows on from Natural Computing in Computational Finance Volumes I \citeBrabazon:2008:edbook and II \citeBrabazon:2009:book

%A A. Brabazon
%A M. O'Neill
%T Natural Computing and Finance
%D 2010
%I 
%C Krakow, Poland
%K genetic algorithms, genetic programming, grammatical evolution, finance
%U ncra.ucd.ie/papers/PPSN_tutorial_2010_published.pdf
%O PPSN 2010 11th International Conference on Parallel Problem Solving From Nature
%O Tutorial
%8 11-15  September

%A Jeremy S. Bradbury
%A Kevin Jalbert
%T Automatic Repair of Concurrency Bugs
%B Proceedings of the 2nd International Symposium on Search Based Software Engineering (SSBSE '10)
%E Massimiliano Di Penta and Simon Poulding and Lionel Briand and John Clark
%D 2010
%I 
%C Benevento, Italy
%K genetic algorithms, genetic programming, SBSE, concurrency, mutation :poster?
%U http://www.ssbse.org/2010/fastabstracts/ssbse2010_fastabstract_04.pdf
%X Bugs in concurrent software are difficult to identify and fix since they may only exhibit abnormal behaviour on certain thread interleavings. We propose the use of genetic
   programming to incrementally create a solution that fixes a concurrency bug automatically. Bugs in a concurrent program are fixed by iteratively mutating the program and
   evaluating each mutation using a fitness function that compares the mutated program with the previous version. We propose three mutation operators that can fix concurrency
   bugs: synchronise an unprotected shared resource, expand synchronization regions to include unprotected source code, and interchange nested lock objects.
%O Fast abstract
%8 7-9  September
%Z focus on deadlock and dead race bugs. Add synchronisation primitives around shared variables. Expand code region protected by existing synchronisation primitives (locks).
   Swap existing locks. Test based fitness. IBM ConTest. Hill climbing. Mutant chosen according to bug. Fast abstracts not in proceedings? http://www.ssbse.org/program.php

%A Katie Braden
%T A simple Approach to Protein Structure Prediction using Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 36--44
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2002/Braden.pdf
%8 June
%Z part of \citekoza:2002:gagp

%A Robert Gregory Bradley
%A Anthony Brabazon
%A Michael O'Neill
%T Evolving Trading Rule-Based Policies
%B EvoFIN
%S LNCS
%E Cecilia Di Chio and Anthony Brabazon and Gianni A. Di Caro and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and
   Michael O'Neill and Ernesto Tarantino and Neil Urquhart
%V 6025
%D 2010
%P 251--260
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming, grammatical evolution
%X Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based
   policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented
   as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are
   well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading
   strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number
   of suggestions for future work.
%8 7-9  April
%Z EvoFIN'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Robert Bradley
%A Anthony Brabazon
%A Michael O'Neill
%T Objective Function Design in a Grammatical Evolutionary Trading System
%B 2010 IEEE World Congress on Computational Intelligence
%D 2010
%P 3487--3494
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Barcelona, Spain
%K genetic algorithms, genetic programming, grammatical evolution
%X Designing a suitable objective function is an essential step in successfully applying an evolutionary algorithm to a problem. In this study we apply a grammar-based Genetic
   Programming algorithm called Grammatical Evolution to the problem of trading model induction. A number of experiments were performed to assess the effect of objective
   function design on the trading characteristics of the evolved trading strategies. Empirical results suggest that the choice of objective function has a significant impact.
   The paper concludes with in and out-of-sample results, and indicates a number of avenues of future work.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586020

%A Robert M. Brady
%A Ross J. Anderson
%A Robin C. Ball
%T Murphy's law, the fitness of evolving species, and the limits of software reliability
%R Technical Report
%D 1996?
%I 
%I Computer Laboratory, Cambridge
%U http://www.ftp.cl.cam.ac.uk/ftp/users/rja14/babtr.pdf
%X We tackle two problems of interest to the software assurance community. Firstly, existing models of software development (such as the waterfall and spiral models) are
   oriented towards one-off software development projects, while the growth of mass market computing has led to a world in which most software consists of packages which
   follow an evolutionary development model. This leads us to ask whether anything interesting and useful may be said about evolutionary development. We answer in the
   affirmative. Secondly, existing reliability growth models emphasise the Poisson distribution of individual software bugs, while the empirically observed reliability growth
   for large systems is asymptotically slower than this. We provide a rigorous explanation of this phenomenon. Our reliability growth model is inspired by statistical
   thermodynamics, but also applies to biological evolution. It is in close agreement with experimental measurements of the fitness of an evolving species and the reliability
   of commercial software products. However, it shows that there are significant differences between the evolution of software and the evolution of species. In particular, we
   establish maximisation properties corresponding to Murphy?s law which work to the advantage of a biological species, but to the detriment of software reliability.
%Z cf \citeBishop96 Takes huge liberties, dressing them in maths, "the number of defects which survive a selection process is maximised" "debugging removes the minimum
   possible number of bugs that must be removed in order to pass the test sequence". "we have a dsitribution of deffects that e behaves statisically as if they were in thermal
   equilibrium at this" [1/t] "temperature".

%A Valentino Braitenberg
%T Vehicles
%D 1984
%I MIT Press, Cambridge MA, 1984
%I Europe?
%K NEURAL MOBILE SIMULATION EVOLUTION MOTOR-SCHEMA REACTIVE MODULAR
%O Braitenberg describes a set of thought experiments in which increasingly complex vehicles are built from simple mechanical and electronic components. Each of these
   imaginary vehicles in some way mimics intelligent behavior, and each one is given a name that corresponds to the behavior it imitates: "Fear," "Love," "Values," "Logic,"
   etc. Braitenberg uses these thought experiments to explore psychological ideas and the nature of intelligence. Progressing through the book, the reader sees very intricate
   behaviors emerge from the interaction of simple component parts. In a sense, Braitenberg "constructs" intelligent behavior---a process he calls "synthetic psychology." -
   from [Hogg Martin \& Resnick 91]
%Z amazon says 1986
%@ 0-262-52112-1

%A Tomaz Brajlih
%A Igor Drstvensek
%A Miha Kovacic
%A Joze Balic
%T Compensation of the size of the finished part for the PolyJet rapid prototyping procedure
%B Proceedings of the International Conference Polymers \& Moulds Innovations PMI 2005
%D 2005
%I 
%C Gent, Belgium
%K genetic algorithms, genetic programming, hitra izdelava prototipov, PolyJet postopek, izravnalni faktor, prototipi, rapid prototyping, polyjet procedure, compensation
   factor
%X The main accuracy problem of rapid prototyping procedures, which are using polymers as a building material is shrinking of a finished layer in the phase of polymerization.
   Different procedures are using different approaches to handle the problem but none of them can actually reach the accuracy that users of traditional cutting techniques are
   used to. To achieve better quality of the PolyJet procedure, which originally employs a method of size compensation to reach a desired accuracy, we decided to improve the
   procedure's performance by adjusting the compensation factor for every part separately. To this purpose some traditional methods of statistics were used, which were later
   combined with some newer, less traditional methods like genetic programming. The later enabled us to acquire a formula for compensation factor determination based upon the
   geometry of the actual part. It also showed the importance or unimportance of some influencing parameters respectively. The method resulted in a better compensation factor
   and better overall performance of the PolyJet procedure compared to other rapid prototyping techniques used nowadays.
%8 April  20-23
%Z http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9636118

%A T. Brajlih
%A I. Drstvensek
%A B. Valentan
%A J. Balic
%T Improving the Accuracy of Rapid Prototyping Procedures by Genetic Programming
%B Proceedings of the 5TH International conference of DAAAM Baltic -- Industrial Engineering
%E R. Kyeener
%D 2006
%P 113--116
%I DAAAM
%I BALTECH Consortium, Estonian Academy of Sciences, Federation of Estonian Engineering Industries, Association of Estonian Mechanical Engineers, Leonardo National Agency of
   Estonia, INNOMET
%C Tallinn, Estonia
%K genetic algorithms, genetic programming
%U http://innomet.ttu.ee/daaam06/proceedings/Production%20Engineering/24brajilih.pdf
%X To achieve better quality of the PolyJet Rapid Prototyping procedure, which originally employs a method of size compensation by scale factors to reach a desired accuracy,
   we decided to improve the procedure's performance by adjusting scale factors for every part separately. The main accuracy problem of rapid prototyping procedures that are
   using polymers as a building material is shrinking of a finished layer in the phase of polymerization. To this purpose we used genetic programming that enabled us to
   acquire a formula for scale factor's determination based upon the geometry of the actual part. The method resulted in optimized scale factors and better overall performance
   of the PolyJet procedure compared to other rapid prototyping techniques used nowadays.
%8 20-22  April

%A Tomaz Brajlih
%A Igor Drstvensek
%A Miha Kovacic
%A Joze Balic
%T Optimizing scale factors of the PolyJet rapid prototyping procedure by genetic programming
%J Journal of achievements in materials and manufacturing engineering
%V 16
%N 1-2
%D 2006
%P 101--106
%I 
%K genetic algorithms, genetic programming, rapid prototyping, PolyJet
%U http://www.journalamme.org/papers_cams05/167.pdf
%X The main problem of assuring a high dimensional accuracy of rapid prototyping procedures, that are using polymers as a building material, is shrinking of a finished layer
   during the phase of polymerisation. Therefore, the finished object is slightly smaller then the object's CAD three-dimensional model, that was used to build the prototype.
   Commonly used method to minimise this problem is to scale (enlarge) the original CAD model in order to compensate for the material's shrinkage during manufacturing. The
   scaling is usually done by the number factor (in percentages) that is recommended by the rapid prototyping machine's manufacturer. With a long-term use of the certain rapid
   prototyping machine the end-users can determine their own scale factor's values, which are more suited to their model's properties. This research has established a method
   that enables the user of a PolyJet RP machine to determine the optimal scale factor regardless of his previous experience. For that purpose the genetic programming methods
   were used to establish a mathematical model that enables the user to calculate optimal scale factor values for each axis (X,Y,Z) regarding a certain object's properties.
   This method was later tested on a series of prototypes that were scaled with factor values acquired with the established mathematical model.
%O Special Issue of CAM3S'2005
%8 May -  June
%Z http://www.journalamme.org/ http://157.158.19.167/index.php?id=69 Formerly Proceedings of Achievements in Mechanical and Materials Engineering. Faculty of mechanical
   engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia *Corresponding author. E-mail address: brajlih@yahoo.com [COBISS.SI-ID 10526486]

%A Markus Brameier
%A Wolfgang Kantschik
%A Peter Dittrich
%A Wolfgang Banzhaf
%T SYSGP -- A C++ library of different GP variants
%R Technical Report CI-98/48
%D 1998
%I 
%I Collaborative Research Center 531, University of Dortmund
%C Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/323834.html
%X In recent years different variants of genetic programming (GP) have emerged all following the basic idea of GP, the automatic evolution of computer programs. Today, three
   basic forms of representation for genetic programs are used, namely tree, graph and linear structures. We introduce a multi-representation system, SYSGP, that allows
   researchers to experiment with different representations with only a minimum implementation overhead. The system further offers the possibility to combine modules of
   different representation forms into one genetic program. SYSGP has been implemented as a C++ library using templates that operate with a generic data type.
%O The Pennsylvania State University CiteSeer Archives

%A Markus Brameier
%A Frank Hoffmann
%A Peter Nordin
%A Wolfgang Banzhaf
%A Frank Francone
%T Parallel Machine Code Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1228
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-439.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Markus Brameier
%A Wolfgang Banzhaf
%T Effective Linear Genetic Programming
%R Technical Report Reihe CI 108/01, SFB 531
%D 2001
%I 
%I Department of Computer Science, University of Dortmund
%C 44221 Dortmund, Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/488546.html
%X Different variants of genetic operators are introduced and compared for linear genetic programming including program induction without crossover. Variation strength of
   crossover and mutations is controlled based on the genetic code. Effectivity of genetic operations improves on code level and on fitness level. Thereby algorithms for
   creating code efficient solutions are presented.
%O The Pennsylvania State University CiteSeer Archives

%A Markus Brameier
%A Wolfgang Banzhaf
%T A Comparison of Genetic Programming and Neural Networks in Medical Data Analysis
%R Reihe CI 43/98, SFB 531
%D 1998
%I 
%I Dortmund University
%C Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/324837.html
%X We apply an interpreting variant of linear genetic programming to several diagnosis problems in medicine. We compare our results to results obtained with neural networks
   and argue that genetic programming is able to show similar performances in classification and generalization even when using a relatively small number of generations.
   Finally, an efficient algorithm for the elimination of introns in linear genetic programs is presented
%O The Pennsylvania State University CiteSeer Archives

%A Markus Brameier
%A Wolfgang Banzhaf
%T A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining
%J IEEE Transactions on Evolutionary Computation
%V 5
%N 1
%D 2001
%P 17--26
%I 
%K genetic algorithms, genetic programming, Data mining, evolutionary computation, neural networks
%U http://web.cs.mun.ca/~banzhaf/papers/ieee_taec.pdf
%X We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs.
   This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in real-world applications like medicine. We
   compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even
   within a relatively small number of generations.
%8 February
%Z proben1/UCI LGP variable length string of C instruction. Branching. steady state tournament selection. two-point string crossover "high mutation rates have been experienced
   to produced better results" p19. Size<=256 "it is much easier for the GP system to implement structural introns [than semantic ones]" p20 "for all problems discussed, the
   performance of GP in generalization comes close to or even better then the results documented for NNs" (MLP, RPROP) p21 Ten demes of 500 connected in one direction circle.
   5% mutation rate. "On average, the number of effective generations is reduced by a factor of three when using demes. Tests with and without conditionals. Runtime
   comparison. Intron removal (dead code) at run time.

%A Markus Brameier
%A Wolfgang Banzhaf
%T Evolving Teams of Predictors with Linear Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 2
%N 4
%D 2001
%P 381--407
%I 
%K genetic algorithms, genetic programming, evolution of teams, combination of multiple predictors, linear genetic programming
%U http://citeseer.ist.psu.edu/411995.html
%X This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team
   programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a real numbered
   vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and
   generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP. In
   particular, the processing time of linear genetic programs is reduced significantly by removing intron code before program execution.
%8 Decemeber
%Z Article ID: 386363

%A Markus Brameier
%A Wolfgang Banzhaf
%T Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming
%R Technical Report
%D 2002
%I 
%I Dortmund University
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/552561.html
%X We investigate structural and semantic distance metrics for linear genetic programs. Causal connections between changes of the genotype and fitness changes form a necessary
   condition for analyzing structural differences between genetic programs and for the two major objectives of this paper: (i) Distance information betweenin-dividuals is used
   to control structural diversity of population individuals actively by a two-level tournament selection. (ii) Variation distance of effective code is controlled for
   different genetic operators - including an effective variant of the mutation operator that works closely with the used distance metric. Numerous experiments have been
   performed for a regression problem, a classification task, and a Boolean problem
%O The Pennsylvania State University CiteSeer Archives
%8 February ~25
%Z see also \citebrameier:2002:EuroGP 123.pdf crashes SUSE 10.0 KDE Konqueror 3.4.2b, Nov 2006

%A Markus Brameier
%A Wolfgang Banzhaf
%T Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 37--49
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780037.pdf
%X We have investigated structural distance metrics for linear genetic programs. Causal connections between changes of the genotype and changes of the phenotype form a
   necessary condition for analyzing structural differences between genetic programs and for the two objectives of this paper: (i) The distance information between individuals
   is used to control structural diversity of population individuals actively by a two-level tournament selection. (ii) Variation distance of effective code is controlled for
   different genetic operators - including a mutation operator that works closely with the applied distance measure. Numerous experiments have been performed for three
   benchmark problems.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP Best paper See also \citeoai:CiteSeerPSU:552561
%@ 3-540-43378-3

%A Markus Brameier
%A Wolfgang Banzhaf
%T Neutral Variations Cause Bloat in Linear GP
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 286--296
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=286
%X In this contribution we investigate the influence of different variation effects on the growth of code. A mutation-based variant of linear GP is applied that operates with
   minimum structural step sizes. Results show that neutral variations are a direct cause for (and not only a result of) the emergence and the growth of intron code. The
   influence of non-neutral variations has been found to be considerably smaller. Neutral variations turned out to be beneficial by solving two classification problems more
   successfully.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003 Section 2.3 PerlGP 'In PerlGP \citemaccallum03, evolved code is expanded from a tree-based genotype into a string
   before being evaluated with Perl's eval() function. The trees of each individual are built (and later, mutated) according to a grammar and are strongly typed. In this
   application, we want the evolved code to look like the example given in Figure 3; that is to say, the solution should be some arithmetic expression containing constants and
   RE matches against a protein sequence. The matches() function feeds the number of separate RE matches into the arithmetic expression. If the result of the expression for a
   given sequence is greater than zero, it is predicted/classified as nuclear, otherwise it is non-nuclear.'
%@ 3-540-00971-X

%A Markus Brameier
%T On Linear Genetic Programming
%R Ph.D. Thesis
%D 2004
%I 
%I Fachbereich Informatik, Universit\"at Dortmund
%C Germany
%K genetic algorithms, genetic programming, Evolutionary algorithms, Machine learning
%U https://eldorado.uni-dortmund.de/bitstream/2003/20098/1/Brameier.ps
%X The thesis is about linear genetic programming (LGP), a machine learning approach that evolves computer programs as sequences of imperative instructions. Two fundamental
   differences to the more common tree-based variant (TGP) may be identified. These are the graph-based functional structure of linear genetic programs, on the one hand, and
   the existence of structurally noneffective code, on the other hand.The two major objectives of this work comprise(1) the development of more advanced methods and variation
   operators to produce better and more compact program solutions and (2) the analysis of general EA/GP phenomena in linear GP, including intron code, neutral variations, and
   code growth, among others.First, we introduce efficient algorithms for extracting features of the imperative and functional structure of linear genetic programs.In doing
   so, especially the detection and elimination of noneffective code during runtime will turn out as a powerful tool to accelerate the time-consuming step of fitness
   evaluation in GP.Variation operators are discussed systematically for the linear program representation. We will demonstrate that so called effective instruction mutations
   achieve the best performance in terms of solution quality.These mutations operate only on the (structurally) effective code and restrict the mutation step size to one
   instruction.One possibility to further improve their performance is to explicitly increase the probability of neutral variations. As a second, more time-efficient
   alternative we explicitly control the mutation step size on the effective code (effective step size).Minimum steps do not allow more than one effective instruction to
   change its effectiveness status. That is, only a single node may be connected to or disconnected from the effective graph component. It is an interesting phenomenon that,
   to some extent, the effective code becomes more robust against destructions over the generations already implicitly. A special concern of this thesis is to convince the
   reader that there are some serious arguments for using a linear representation.In a crossover-based comparison LGP has been found superior to TGP over a set of benchmark
   problems. Furthermore, linear solutions turned out to be more compact than tree solutions due to (1) multiple usage of subgraph results and (2) implicit parsimony pressure
   by structurally noneffective code.The phenomenon of code growth is analysed for different linear genetic operators. When applying instruction mutations exclusively almost
   only neutral variations may be held responsible for the emergence and propagation of intron code. It is noteworthy that linear genetic programs may not grow if all neutral
   variation effects are rejected and if the variation step size is minimum.For the same reasons effective instruction mutations realize an implicit complexity control in
   linear GP which reduces a possible negative effect of code growth to a minimum.Another noteworthy result in this context is that program size is strongly increased by
   crossover while it is hardly influenced by mutation even if step sizes are not explicitly restricted. Finally, we investigate program teams as one possibility to increase
   the dimension of genetic programs. It will be demonstrated that much more powerful solutions may be found by teams than by individuals. Moreover, the complexity of team
   solutions remains surprisingly small compared to individual programs. Both is the result of specialisation and cooperation of team members.
%8 February
%Z Day of Submission: 2003-05-28, Committee: Wolfgang Banzhaf and Martin Riedmiller and Peter Nordin.
   \onlineAvailableThttps://eldorado.uni-dortmund.de/handle/2003/20098http://hdl.handle.net/2003/200982007-08-17

%A Markus Brameier
%A Josien Haan
%A Andrea Krings
%A Robert M MacCallum
%T Automatic discovery of cross-family sequence features associated with protein function
%J BMC bioinformatics [electronic resource]
%V 7
%N 16
%D 2006
%I BioMed Central Ltd.
%K genetic algorithms, genetic programming
%U http://www.biomedcentral.com/1471-2105/7/16
%X Background Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative
   genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional
   categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying
   biology, and consequently important sequence-to-function relationships may be missed. Results We show that a self-supervised data mining approach is able to find
   relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of
   protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and
   keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily
   detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and
   computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis,
   transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in
   the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the "transcription"
   function than to the general "nuclear" function/location. Conclusion We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The
   technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the
   approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription.
%8 January ~12
%Z PMID: 16409628

%A Markus Brameier
%A Wolfgang Banzhaf
%T Linear Genetic Programming
%S Genetic and Evolutionary Computation
%N XVI
%D 2007
%I Springer
%K genetic algorithms, genetic programming
%U http://www.springer.com/west/home/default?SGWID=4-40356-22-173660820-0
%X Table of contents Preface, About the Authors, Acknowledgments, Introduction, I Fundamental Analyses: Basic Concepts, Representation Characteristics, A Comparison with
   Neural Networks, II Method Design: Segment Variations, Instruction Mutations, Analysis of Control Parameters, A Comparison with Tree-Based GP, III Advanced Techniques and
   Phenomena: Control of Diversity and Step Size, Code Growth and Neutral Variations, Evolution of Program Teams, References, Index.
%@ 0-387-31029-0

%A Markus Brameier
%A Andrea Krings
%A Robert M. MacCallum
%T NucPred Predicting nuclear localization of proteins
%J Bioinformatics
%V 23
%N 9
%D 2007
%P 1159--1160
%I 
%K genetic algorithms, genetic programming
%X NucPred analyses patterns in eukaryotic protein sequences and predicts if a protein spends at least some time in the nucleus or no time at all. Subcellular location of
   proteins represents functional information, which is important for understanding protein interactions, for the diagnosis of human diseases and for drug discovery. NucPred
   is a novel web tool based on regular expression matching and multiple program classifiers induced by genetic programming. A likelihood score is derived from the programs
   for each input sequence and each residue position. Different forms of visualisation are provided to assist the detection of nuclear localisation signals (NLSs). The NucPred
   server also provides access to additional sources of biological information (real and predicted) for a better validation and interpretation of results. Availability: The
   web interface to the NucPred tool is provided at http://www.sbc.su.se/~maccallr/nucpred. In addition, the Perl code is made freely available under the GNU Public Licence
   (GPL) for simple incorporation into other tools and web servers.
%Z PMID: 17332022 [PubMed - indexed for MEDLINE]

%A Markus Brameier
%A Carsten Wiuf
%T Ab initio identification of human microRNAs based on structure motifs
%J BMC Bioinformatics
%V 8
%D 2007
%P 478
%I 
%K genetic algorithms, genetic programming, linear genetic programming
%U http://www.biomedcentral.com/content/pdf/1471-2105-8-478.pdf
%X BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNA molecules that are directly involved in post-transcriptional regulation of gene expression. The mature miRNA
   sequence binds to more or less specific target sites on the mRNA. Both their small size and sequence specificity make the detection of completely new miRNAs a challenging
   task. This cannot be based on sequence information alone, but requires structure information about the miRNA precursor. Unlike comparative genomics approaches, ab initio
   approaches are able to discover species-specific miRNAs without known sequence homology. RESULTS: MiRPred is a novel method for ab initio prediction of miRNAs by genome
   scanning that only relies on (predicted) secondary structure to distinguish miRNA precursors from other similar-sized segments of the human genome. We apply a machine
   learning technique, called linear genetic programming, to develop special classifier programs which include multiple regular expressions (motifs) matched against the
   secondary structure sequence. Special attention is paid to scanning issues. The classifiers are trained on fixed-length sequences as these occur when shifting a window in
   regular steps over a genome region. Various statistical and empirical evidence is collected to validate the correctness of and increase confidence in the predicted
   structures. Among other things, we propose a new criterion to select miRNA candidates with a higher stability of folding that is based on the number of matching windows
   around their genome location. An ensemble of 16 motif-based classifiers achieves 99.9 percent specificity with sensitivity remaining on an acceptable high level when
   requiring all classifiers to agree on a positive decision. A low false positive rate is considered more important than a low false negative rate, when searching larger
   genome regions for unknown miRNAs. 117 new miRNAs have been predicted close to known miRNAs on human chromosome 19. All candidate structures match the free energy
   distribution of miRNA precursors which is significantly shifted towards lower free energies. We employed a human EST library and found that around 75 percent of the
   candidate sequences are likely to be transcribed, with around 35 percent located in introns. CONCLUSION: Our motif finding method is at least competitive to
   state-of-the-art feature-based methods for ab initio miRNA discovery. In doing so, it requires less previous knowledge about miRNA precursor structures while programs and
   motifs allow a more straightforward interpretation and extraction of the acquired knowledge.
%8 18  Decemeber
%Z PMID: 18088431 [PubMed - indexed for MEDLINE]

%A Jurgen Branke
%A Massimo Cutaia
%A Heinrich Dold
%T Reducing Genetic Drift in Steady State Evolutionary Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 68--74
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jurgen Branke
%A Pablo Funes
%A Frederik Thiele
%T Evolutionary design of en-route caching strategies
%J Applied Soft Computing
%V 7
%N 3
%D 2006
%P 890--898
%I 
%K genetic algorithms, genetic programming, En-route caching, Robustness
%X Nowadays, large distributed databases are commonplace. Client applications increasingly rely on accessing objects from multiple remote hosts. The Internet itself is a huge
   network of computers, sending documents point-to-point by routing packeted data over multiple intermediate relays. As hubs in the network become over used, slowdowns and
   timeouts can disrupt the process. It is thus worth to think about ways to minimise these effects. Caching, i.e. storing replicas of previously-seen objects for later reuse,
   has the potential for generating large bandwidth savings and in turn a significant decrease in response time. En-route caching is the concept that all nodes in a network
   are equipped with a cache, and may opt to keep copies of some documents for future reuse [X. Tang, S.T. Chanson, Coordinated en-route web caching, IEEE Transact. Comput. 51
   6 (2002) 595-607]. The rules used for such decisions are called caching strategies. Designing such strategies is a challenging task, because the different nodes interact,
   resulting in a complex, dynamic system. In this paper, we use genetic programming to evolve good caching strategies, both for specific networks and network classes. An
   important result is a new innovative caching strategy that outperforms current state-of-the-art methods.
%8 June

%T GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%I ACM New York, NY, USA
%I ACM SIGEVO
%C Portland, OR, USA
%K genetic algorithms, genetic programming, Ant Colony Optimisation and Swarm Intelligence, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware,
   Bioinformatics and Computational Biology, Combinatorial Optimisation and Metaheuristics, Estimation of Distribution Algorithms, Evolution Strategies and Evolutionary
   Programming, Evolutionary Multiobjective, Optimisation, Generative and Developmental Systems, Genetics-Based Machine Learning, Parallel Evolutionary Systems, Real World
   Application, Search Based Software Engineering, Theory
%U http://portal.acm.org/citation.cfm?id=1830483&coll=DL&dl=ACM&CFID=12039329&CFTOKEN=58660565
%X These proceedings contain the papers presented at the 12th Annual Genetic and Evolutionary Computation Conference (GECCO-2010), held in Portland, USA, July 7-11, 2010. This
   year, we received 373 submissions, of which 168 were accepted as full eight-page publication with 25 minute presentation during the conference. This corresponds to an
   acceptance rate of 45percent. In addition, 110 submissions (29percent) have been accepted for poster presentation with two-page abstracts in the proceedings. GECCO works
   according to the motto one conference, many mini-conferences. This year, there were 15 separate tracks that operated independently from each other. Each track had its own
   track chair(s) and individual program committee. To ensure an unbiased reviewing process, all reviews were conducted double blind; no authors' names were revealed to the
   reviewers. About 560 researchers participated in the reviewing process. We want to thank them for all their work, which is highly appreciated and absolutely vital to ensure
   the high quality of the conference. In addition to the presentation of the papers contained in these proceedings, GECCO-2010 also included free tutorials, workshops, a
   series of sessions on Evolutionary Computation in Practice, various competitions, and late-breaking papers.
%8 July  07-11
%Z GECCO-2019 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010).
   ACM Order Number 910102.

%A Gursewak S Brar
%A Yadwinder S Brar
%A Yaduvir Singh
%T A Fuzzy Entropy Algorithm For Data Extrapolation In Multi-Compressor System
%B Proceedings of the World Congress on Engineering, WCE 2007
%V I
%D 2007
%P 105--110
%I 
%C London
%K genetic algorithms, genetic programming, fuzzy entropy, incomplete data, classification, knowledge discovery, multi-compressor system
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.2111
%X In this paper incomplete quantitative data has been dealt by using the concept of fuzzy entropy. Fuzzy entropy has been used to extrapolate the data pertaining to the
   compressor current. Certain attributes related to the compressor current have been considered. Test data of compressor current used in this knowledge discovery algorithm
   knows the entire attribute clearly. The developed algorithm is very effective and can be used in the various application related to knowledge discovery and machine
   learning. The developed knowledge discovery algorithm using fuzzy entropy has been tested on a multi-compressor system for incomplete compressor current data and it is
   found that the error level is merely 4.40percent, which is far better than other available knowledge discovery algorithms
%8 July  2-4
%Z pdf broken?

%A Scott Brave
%T Evolution of Planning: Using recursive techniques in Genetic Planning
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 1--10
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford
   University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Scott Brave
%T Using Genetic Programming to Evolve Recursive Programs for Tree Search
%B Fourth Golden West Conference on Intelligent Systems
%E Sushil J. Louis
%D 1995
%P 60--65
%I International Society for Computers and their Applications - ISCA San Francisco, California, USA
%K genetic algorithms, genetic programming
%8 12-14  June
%Z GWICS ISCA-GW-95 http://www.isca-hq.org/proc-lst.htm
%@ 1-880843-12-9

%A Scott Brave
%T Using Genetic Programming to Evolve Mental Models
%B Fourth Golden West Conference on Intelligent Systems
%E Sushil J. Louis
%D 1995
%P 91--96
%I International Society for Computers and their Applications - ISCA San Francisco, California, USA
%K genetic algorithms, genetic programming, memory
%8 12-14  June
%Z GWICS ISCA-GW-95 http://www.isca-hq.org/proc-lst.htm
%@ 1-880843-12-9

%A Scott Brave
%T Evolving Recursive Programs for Tree Search
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 203--220
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/220
%X This article compares basic genetic programming, genetic programming with automatically defined functions (ADFs), and genetic programming with ADFs using a restricted form
   of recursion on a planning problem involving tree search. The results show that evolution of a recursive program is possible and further that, of the three techniques
   explored, genetic programming with recursive ADFs performs the best for the tree search problem. Additionally, genetic programming using ADFs (recursive and non-recursive)
   outperforms genetic programming without ADFs. The scalability of these techniques is also investigated. The computational effort required to reach a solution using ADFs
   with recursion is shown to remain essentially constant with world size, while genetic programming with non-recursive ADFs scales linearly at best, and basic genetic
   programming scales exponentially. Finally, many solutions were found using genetic programming with recursive ADFs which generalised to any world size.
%O 10
%Z Recursive ADFs, non-recursive ADFs and non-ADF GP compared on a tree searching problem. Tree depths 2-7 (ie up to 127 leaf nodes) containing one goal node. Problem arranged
   so can only be solved (by luck?) or by using memory. READ+WRITE update a single memory cell per tree node, ie no index, just access current cell. WRITE not as Teller but
   returns its argument. ADF1 and ADF2 syntax set up so one can search tree and one can move within it, cf. Andre. Recursive ADFs much better than ADFs much better than
   non-ADFs, difference increase as tree size increases. "random"? program search can find recursive ADF programs which solve problem. DGPC
%@ 0-262-01158-1

%A Scott Brave
%T Evolving Deterministic Finite Automata Using Cellular Encoding
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 39--44
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X his paper presents a method for the evolution of deterministic finite automata that combines genetic programming and cellular encoding. Programs are evolved that specify
   actions for the incremental growth of a deterministic finite automata from an initial single-state zygote. The results show that, given a test bed of positive and negative
   samples, the proposed method is successful at inducing automata to recognise several different languages. 1. Introduction The automatic creation of finite...
%8 28--31  July
%Z GP-96 DGPC "inremental growth of finite automata from an initial single-state zygote", "Induced automata to recognise several different (formal) languages" eg Tomita
   "applies cellular encoding to the evolution of determistic finite (state) automata."

%A Scott Brave
%T The Evolution of Memory and Mental Models Using Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 261--266
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming, memory
%U http://citeseer.ist.psu.edu/cache/papers/cs/1745/http:zSzzSzbrave.www.media.mit.eduzSzpeoplezSzbravezSzpublicationszSzmodels.pdf/brave96evolution.pdf
%X his paper applies genetic programming to the evolution of intelligent agents that gradually build internal representations of their surroundings for later use in planning.
   The method used allows for the creation of dynamically determined representations that are not pre-designed by the human creator of the system. In an illustrative
   path-planning problem, evolved programs learn a model of their world and use this internal representation to plan their successive actions. The results show that...
%8 28--31  July
%Z GP-96. cf. \citebrave:1994:mmGW

%T Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, Evolutionary Programming, fuzzy rules
%8 13  July
%Z GECCO-99LB

%A Karl J. Brazier
%A Graeme Richards
%A Wenjia Wang
%T Implicit Fitness Sharing Speciation and Emergent Diversity in Tree Classifier Ensembles.
%B Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Proceedings
%S Lecture Notes in Computer Science
%E Zheng Rong Yang and Richard M. Everson and Hujun Yin
%V 3177
%D 2004
%P 333--338
%I Springer
%I IEEE
%C Exeter, UK
%K genetic algorithms, genetic programming, gene expression programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3177&spage=333
%X Implicit fitness sharing is an approach to the stimulation of speciation in evolutionary computation for problems where the fitness of an individual is determined as its
   success rate over a number trials against a collection of succeed/fail tests. By fixing the reward available for each test, individuals succeeding in a particular test are
   caused to depress the size of one another's fitness gain and hence implicitly co-operate with those succeeding in other tests. An important class of problems of this form
   is that of attribute-value learning of classifiers. Here, it is recognised that the combination of diverse classifiers has the potential to enhance performance in
   comparison with the use of the best obtainable individual classifiers. However, proposed prescriptive measures of the diversity required have inherent limitations from
   which we would expect the diversity emergent from the self-organisation of speciating evolutionary simulation to be free. The approach was tested on a number of the
   popularly used real-world data sets and produced encouraging results in terms of accuracy and stability.
%8 August  25-27
%Z http://www.dcs.ex.ac.uk/ideal04/ a) Cleveland heart data b) Thyroid data c) Pima Indians diabetes data d) E. coli data
%@ 3-540-22881-0

%A N. Bredeche
%A E. Haasdijk
%A A. E. Eiben
%T On-Line, On-Board Evolution of Robot Controllers
%B 9th International Conference, Evolution Artificielle, EA 2009
%S Lecture Notes in Computer Science
%E Pierre Collet and Nicolas Monmarche and Pierrick Legrand and Marc Schoenauer and Evelyne Lutton
%V 5975
%D 2009
%P 110--121
%I Springer
%C Strasbourg, France
%K genetic algorithms, genetic programming
%U http://www.cs.vu.nl/~gusz/papers/2009-bredeche09ea_final2-LNCS.pdf
%X This paper reports on a feasibility study into the evolution of robot controllers during the actual operation of robots (on-line), using only the computational resources
   within the robots themselves (on-board). We identify the main challenges that these restrictions imply and propose mechanisms to handle them. The resulting algorithm is
   evaluated in a hybrid system, using the actual robots' processors interfaced with a simulator that represents the environment. The results show that the proposed algorithm
   is indeed feasible and the particular problems we encountered during this study give hints for further research.
%O Revised Selected Papers
%8 October  26-28
%Z EA'09 Published 2010

%A Joseph L. Breeden
%A Todd W. Allen
%T Using an optimization toolkit for Java to evolve market strategies for European seeds
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 57--64
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms
%8 13  July
%Z GECCO-99LB

%A Jose Carlos Ribeiro
%A Mario Zenha-Rela
%A Francisco {Fernandez de Vega}
%T An Evolutionary Approach for Performing Structural Unit-Testing on Third-Party Object-Oriented Java Software
%B Proceedings of International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO '07)
%S Studies in Computational Intelligence
%E Natalio Krasnogor and Giuseppe Nicosia and Mario Pavone and David Pelta
%V 129
%D 2007
%P 379--388
%I Springer
%C Acireale, Italy
%K genetic algorithms, genetic programming, SBSE
%U http://jcbribeiro.googlepages.com/NICSO2007-053.pdf
%X Evolutionary Testing is an emerging methodology for automatically generating high quality test data. The focus of this paper is on presenting an approach for generating
   test cases for the unit-testing of object-oriented programs, with basis on the information provided by the structural analysis and interpretation of Java bytecode and on
   the dynamic execution of the instrumented test object. The rationale for working at the bytecode level is that even when the source code is unavailable, insight can still
   be obtained and used to guide the search-based test case generation process. Test cases are represented using the Strongly Typed Genetic Programming paradigm, which
   effectively mimics the polymorphic relationships, inheritance dependences and method argument constraints of object-oriented programs.
%8 8-10  November

%A Jose Carlos {Bregieiro Ribeiro}
%A Mario Alberto Zenha-Rela
%A Francisco {Fernandez de Vega}
%T eCrash: a framework for performing evolutionary testing on third-party Java components
%B I Jornadas sobre Algoritmos Evolutivos y Metaheuristicas (JAEM 2007)
%E Enrique Alba and Francisco Herrera
%D 2007
%P 137--144
%I 
%C Zaragoza, Spain
%K genetic algorithms, genetic programming, SBSE, STGP
%U http://jcbribeiro.googlepages.com/jribeiro_jaem07.pdf
%X The focus of this paper is on presenting a tool for generating test data by employing evolutionary search techniques, with basis on the information provided by the
   structural analysis and interpretation of the Java bytecode of third-party Java components, and on the dynamic execution of the instrumented test object. The main objective
   of this approach is that of evolving a set of test cases that yields full structural code coverage of the test object. Such a test set can be used for effectively
   performing the testing activity, providing confidence in the quality and robustness of the test object. The rationale of working at the bytecode level is that even when the
   source code is unavailable structural testing requirements can still be derived, and used to assess the quality of a test set and to guide the evolutionary search towards
   reaching specific test goals.
%8 11-14  September
%Z http://neo.lcc.uma.es/jaem07/ With CEDI 2007

%A Jose Carlos {Bregieiro Ribeiro}
%A Mario Alberto Zenha-Rela
%A Francisco {Fernandez de Vega}
%T A strategy for evaluating feasible and unfeasible test cases for the evolutionary testing of object-oriented software
%B AST '08: Proceedings of the 3rd international workshop on Automation of software test
%D 2008
%P 85--92
%I ACM New York, NY, USA
%C Leipzig, Germany
%K genetic algorithms, genetic programming, SBSE, Search-Based Test Case Generation, Evolutionary Testing, Object-Orientation, Strongly-Typed Genetic Programming, Software
   Engineering, Testing and Debugging| Testing tools, Verification
%U http://jcbribeiro.googlepages.com/ast12-ribeiro.pdf
%X Evolutionary Testing is an emerging methodology for automatically producing high quality test data. The focus of our on-going work is precisely on generating test data for
   the structural unit-testing of object-oriented Java programs. The primary objective is that of efficiently guiding the search process towards the definition of a test set
   that achieves full structural coverage of the test object. However, the state problem of object-oriented programs requires specifying carefully ne-tuned methodologies that
   promote the traversal of problematic structures and difficult controlflow paths - which often involves the generation of complex and intricate test cases, that dene
   elaborate state scenarios. This paper proposes a methodology for evaluating the quality of both feasible and unfeasible test cases - i.e., those that are effectively
   completed and terminate with a call to the method under test, and those that abort prematurely because a runtime exception is thrown during test case execution. With our
   approach, unfeasible test cases are considered at certain stages of the evolutionary search, promoting diversity and enhancing the possibility of achieving full coverage.
%Z also known as \cite1370061

%A Jose Carlos {Bregieiro Ribeiro}
%A Mario Alberto Zenha-Rela
%A Francisco {Fernandez de Vega}
%T Strongly-typed genetic programming and purity analysis: input domain reduction for evolutionary testing problems
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1783--1784
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, Input domain reduction, search-based test case generation, strongly-Typed genetic programming, Search-based software engineering:
   Poster, Testing, Debugging, Testing tools, data generators, coverage testing, stack, bitset, STGP, EMCDG, IDR
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1783.pdf
%X Search-based test case generation for object-oriented software is hindered by the size of the search space, which encompasses the arguments to the implicit and explicit
   parameters of the test object's public methods. The performance of this type of search problems can be enhanced by the definition of adequate Input Domain Reduction
   strategies. The focus of our on-going work is on employing evolutionary algorithms for generating test data for the structural unit-testing of Java programs. Test cases are
   represented and evolved using the Strongly-Typed Genetic Programming paradigm; Purity Analysis is particularly useful in this situation because it provides a means to
   automatically identify and remove Function Set entries that do not contribute to the definition of interesting test scenarios. Categories and Subject Descriptors
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389439

%A Jose Carlos {Bregieiro Ribeiro}
%T Search-based test case generation for object-oriented java software using strongly-typed genetic programming
%B GECCO-2008 Graduate Student Workshops
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 1819--1822
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, dynect-orientation, evolutionary testing, search-based test case generation, strongly-Typed genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1819.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1388979

%A Jose Carlos {Bregieiro Ribeiro}
%A Mario Alberto Zenha-Rela
%A Francisco {Fernandez de Vega}
%T Test Case Evaluation and Input Domain Reduction Strategies for the Evolutionary Testing of Object-Oriented Software
%J Information and Software Technology
%V 51
%N 11
%D 2009
%P 1534--1548
%I 
%K genetic algorithms, genetic programming, Evolutionary Testing, Search-Based Software Engineering, Test Case Evaluation, Input Domain Reduction
%U http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2
%X In Evolutionary Testing, meta-heuristic search techniques are used for generating test data. The focus of our research is on employing evolutionary algorithms for the
   structural unit-testing of object-oriented programs. Relevant contributions include the introduction of novel methodologies for automation, search guidance and input domain
   reduction; the strategies proposed were empirically evaluated with encouraging results.Test cases are evolved using the Strongly-Typed Genetic Programming technique. Test
   data quality evaluation includes instrumenting the test object, executing it with the generated test cases, and tracing the structures traversed in order to derive coverage
   metrics. The methodology for efficiently guiding the search process towards achieving full structural coverage involves favouring test cases that exercise problematic
   structures. Purity Analysis is employed as a systematic strategy for reducing the search space."
%8 November
%Z Third IEEE International Workshop on Automation of Software Test (AST 2008); Eighth International Conference on Quality Software (QSIC 2008)

%A Jose Carlos {Bregieiro Ribeiro}
%A Mario {Zenha Rela}
%A Francisco {Fernandez de Vega}
%T An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1949--1950
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Poster
%X This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that
   of proposing an adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the
   instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals
   evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the algorithm's efficiency considerably, while
   introducing a negligible computational overhead.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Jose Carlos {Bregieiro Ribeiro}
%A Mario Alberto Zenha-Rela
%A Francisco {Fernandez de Vega}
%T Test Case Evaluation and Input Domain Reduction strategies for the Evolutionary Testing of Object-Oriented software
%J Information and Software Technology
%V 51
%N 11
%D 2009
%P 1534--1548
%I 
%K genetic algorithms, genetic programming, SBSE, Evolutionary Testing, Search-Based Software Engineering, Test Case Evaluation, Input Domain Reduction
%U http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2
%X In Evolutionary Testing, meta-heuristic search techniques are used for generating test data. The focus of our research is on employing evolutionary algorithms for the
   structural unit-testing of Object-Oriented programs. Relevant contributions include the introduction of novel methodologies for automation, search guidance and Input Domain
   Reduction; the strategies proposed were empirically evaluated with encouraging results. Test cases are evolved using the Strongly-Typed Genetic Programming technique. Test
   data quality evaluation includes instrumenting the test object, executing it with the generated test cases, and tracing the structures traversed in order to derive coverage
   metrics. The methodology for efficiently guiding the search process towards achieving full structural coverage involves favouring test cases that exercise problematic
   structures. Purity Analysis is employed as a systematic strategy for reducing the search space.
%O Third IEEE International Workshop on Automation of Software Test (AST 2008); Eighth International Conference on Quality Software (QSIC 2008)

%A Jose Carlos {Bregieiro Ribeiro}
%A Mario Alberto Zenha-Rela
%A Francisco {Fernandez de Vega}
%T Enabling Object Reuse on Genetic Programming-based Approaches to Object-Oriented Evolutionary Testing
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 220--231
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming, SBSE
%X Recent research on search-based test data generation for Object-Oriented software has relied heavily on typed Genetic Programming for representing and evolving test data.
   However, standard typed Genetic Programming approaches do not allow Object Reuse; this paper proposes a novel methodology to overcome this limitation. Object Reuse means
   that one instance can be passed to multiple methods as an argument, or multiple times to the same method as arguments. In the context of Object-Oriented Evolutionary
   Testing, it enables the generation of test programs that exercise structures of the software under test that would not be reachable otherwise. Additionally, the
   experimental studies performed show that the proposed methodology is able to effectively increase the performance of the test data generation process.
%8 7-9  April
%Z AT-nodes P-nodes \citelopez:2004:eurogp. Java Red-Black tree and vector classes. pop=25. Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with
   EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Paul Bremner
%A Mohammad Samie
%A Gabriel Dragffy
%A Tony Pipe
%A James Alfred Walker
%A Andy M. Tyrrell
%T Evolving Digital Circuits Using Complex Building Blocks
%B Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010
%S Lecture Notes in Computer Science
%E Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller
%V 6274
%D 2010
%P 37--48
%I Springer
%C York
%K genetic algorithms, genetic programming
%X This work is a study of the viability of using complex building blocks (termed molecules) within the evolutionary computation paradigm of CGP; extending it to MolCGP.
   Increasing the complexity of the building blocks increases the design space that is to be explored to find a solution; thus, experiments were undertaken to find out whether
   this change affects the optimum parameter settings required. It was observed that the same degree of neutrality and (greedy) 1+4 evolution strategy gave optimum
   performance. The Computational Effort used to solve a series of benchmark problems was calculated, and compared with that used for the standard implementation of CGP.
   Significantly less Computational Effort was exerted by MolCGP in 3 out of 4 of the benchmark problems tested. Additionally, one of the evolved solutions to the 2-bit
   multiplier problem was examined, and it was observed that functionality present in the molecules, was exploited by evolution in a way that would be highly unlikely if using
   standard design techniques.
%8 September  6-8

%A Paul Bremner
%A Mohammad Samie
%A Anthony G. Pipe
%A Gabriel Dragffy
%A Yang Liu
%T Evolving Cell Array Configurations Using CGP
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 73--84
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X A cell array is a proposed type of custom FPGA, where digital circuits can be formed from interconnected configurable cells. In this paper we have presented a means by
   which CGP might be adapted to evolve configurations of a proposed cell array. As part of doing so, we have suggested an additional genetic operator that exploits modularity
   by copying sections of the genome within a solution, and investigated its efficacy. Additionally, we have investigated applying selection pressure for parsimony during
   functional evolution, rather than in a subsequent stage as proposed in other work. Our results show that solutions to benchmark problems can be evolved with a good degree
   of efficiency, and that compact solutions can be found with no significant impact on the required number of circuit evaluations.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Paul Bremner
%A Mohammad Samie
%A Anthony Pipe
%A Andy Tyrrell
%T Multi-Objective Optimisation of Cell-Array Circuit Evolution
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 440--446
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, cartesian genetic programming, Multiobjective optimization, Evolvable hardware and software
%X In this paper we have investigated the efficacy of applying multi-objective optimisation to Cartesian genetic programming (CGP) when used for evolution of cell-array
   configurations. A cell-array is a proposed type of custom FPGA, where digital circuits can be formed from interconnected configurable cells; thus, the CGP nodes are more
   complex than in its standard implementation. We have described modifications to a previously described optimisation algorithm that has led to significant improvements in
   performance; circuits close to a hand designed equivalent have been found, in terms of the optimised objectives. Additionally we have investigated the effect of circuit
   decomposition techniques on evolutionary performance. We found that using a hybrid of input and output decomposition techniques substantial reductions in evolution time
   were observed. Further, while the number of circuit inputs is the key factor for functional evolution time, the number of circuit outputs is the key factor for optimisation
   time.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Markus M. Breunig
%T Location Independent Pattern Recognition using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 29--38
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming, ADF
%U http://www.dbs.informatik.uni-muenchen.de/~breunig/HomepageResearch/Papers/PatternRecog.pdf
%X This paper describes an application of genetic programming. Programs able of recognising a pattern independent of its location are evolved. Usually the evolution of
   programs is controlled primarily by the fitness evaluation function. This paper demonstrates how genetic programming can be encouraged to evolve programs with properties
   not being explicitly considered in the fitness measure like location independence. The measurements taken include the use of automatically defined functions allowing the
   problem to be decomposed into sub-functions, a special implementation of iteration and carefully chosen function and terminal sets. A main purpose was to minimise the
   restrictions imposed on the solution, i.e. giving the genetic programming as much freedom as possible while still encouraging the desired properties.
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Miran Brezocnik
%A Joze Balic
%T System for discovering and optimizung mathematical models using genetic programming and genetic algorithms
%B Proceedings of the 8th International DAAAM Symposium
%E Branko Katalinic
%D 1997
%P 37--38
%I DAAAM International Vienna
%C Dubrovnik, Croatia
%K genetic algorithms, genetic programming, adaptive systems, evolutionary computation
%X In this paper, we propose a system for discovering and optimising of various mathematical models. The system consists of two parts. In the first part, we discover unknown
   mathematical models on the basis of empirical given data (learning data). In the second part, we optimise parameters of the discovered mathematical models. Genetic
   programming (GP) and genetic algorithm (GA) are used for discovering and optimizing of models, respectively. GP and GA are evolutionary optimization methods based on the
   Darwinian natural selection and survival of the fittest.
%8 23-25  October
%Z http://www.daaam.com/daaam/Past_Activities/DAAAM_International_Activities_1990-2005.pdf
%@ 3-901509-04-6

%A Miran Brezocnik
%A Joze Balic
%T Comparison of genetic programming with genetic algorithm
%B 3rd International Conference Design to Manufacture in Modern Industry: Design to manufacture in modern industry
%E Anton Jezernik and Bojan Dolsak
%D 1997
%P 150--156
%I University of Maribor, Faculty of Mechanical Engineering Slovenia
%K genetic algorithms, genetic programming
%X The paper is concerned about the conventional genetic algorithm (GA) and, particularly, the recently proposed paradigm: genetic programming (GP). The well-known basic
   knowledge of the conventional GA is briefly presented, but only for comparison with GP. On the contrary, the GP method is discussed in detail. The GP is an evolutionary
   process, where the fittest computer program in the space of possible computer programs is searched for. The fittest computer program represents a solution in the observed
   problem domain. One of the most powerful abilities of the GP that allows inclusion of rich information into computer programs is clearly presented and emphasised. Our
   personal views concerning the complementary nature of the conventional GA and GP are discussed. Finally, we briefly presented the implementation of the GP on the wide
   variety of different problems.
%8 September
%@ 86-435-0192-1

%A Miran Brezocnik
%A Joze Balic
%T A genetic programming approach for modelling of self-organizing assembly systems
%B Intelligent assembly and disassembly - IAD'98: A proceedings volume from the IFAC Workshop
%E Peter Kopacek and Dragica Noe
%D 1998
%P 47--52
%I Pergamon Oxford, UK
%I IFAC
%C Bled, Slovenia
%K genetic algorithms, genetic programming, self-organising systems, intelligent manufacturing systems, assembly, simulation
%U http://books.elsevier.com/elsevier/?isbn=0080430422
%X The paper proposes a genetic programming approach to the modelling of the assembly of the basic components (cells) into an integrated (whole) organism. The concept is based
   on the simulation of self-organising uniting of live cells into tissues, organs and individuals. The assembly is treated as the basic and general principle, therefore, the
   basic cells can be very different. Assembling takes place on the basis of the genetic content in the basic components and is influenced by the environmental conditions. The
   genetic content can be topological, geometrical, technological, ecological, economical, etc. The simulation of the self-organizing genetic assembly of the variant product
   consisting of many basic components is given. The basic feature of genetic assembly is that it takes place in a distributed, nondeterministical, bottom-up, and
   self-organising manner.
%8 21-23  May
%@ 0-08-043042-2

%A Miran Brezocnik
%T MODELING OF TECHNOLOGICAL SYSTEMS BY THE USE OF GENETIC METHODS
%R Ph.D. Thesis phdthesis
%D 1998
%I 
%I University of Maribor, Faculty of Mechanical Engineering
%C Smetanova ulica 17, SI-2000 Maribor, Slovenia
%K genetic algorithms, genetic programming, intelligent manufacturing systems, technological system, forming, assembly, robots, self-organisation, genetic methods, modelling,
   optimisation
%X In this work we propose modelling of different technological systems by a general approach. The research starts with searching for common characteristics of the
   technological systems. After they have been found out, they are synthesised into uniform principle serving for conceiving a general method for their modeling. The method
   imitates associating of living cells into tissues, organs and organisms. The disturbances resulting from limited human knowledge, unpredictability of technological systems,
   and unexpected events in production environment are automatically eliminated during the evolutionary process. More and more intelligent behaviour of the individual
   technological system, which is expressed as an increasingly successful synchronisation of the material, energy and information, is obtained gradually with self-organization
   and without centralised instruction. In order to support the theoretical researches a system for genetic programming is developed. It is successfully used for genetic
   modeling of: 1. forming efficiency, 2. assembly and classification, and 3. trajectories of robots in the production environment. The results of modeling of forming
   efficiency show excellent correspondence between analytically obtained models, experimental results, and genetically developed models. In case of genetic modeling of
   assembly the basic components are integrated into the final product in a self-organising manner. Genetic modelling the trajectory of the robot, striving to arrive at the
   aim through a dynamic production environment, discovers the intelligent robot navigation formed during the evolutionary process.

%A Miran Brezocnik
%A Joze Balic
%A Leo Gusel
%T Artificial intelligence approach to determination of flow curve
%J Journal for technology of plasticity
%V 25
%N 1-2
%D 2000
%P 1--7
%I 
%K genetic algorithms, genetic programming, forming, flow curve, artificial intelligence
%X For the control of the forming process it is necessary to know as precisely as possible the flow curve of the material formed. The paper presents the determination of the
   equation for the flow curve with the artificial intelligence approach. The genetic programming method (GP) was used. It is an evolutionary optimisation technique based on
   the Darwinian natural selection and the survival of the fittest organisms. The comparison between the experimental results, the analytical solution and the solution
   obtained genetically clearly shows that the genetic programming method is a very promising approach.
%Z http://www.scindeks.nbs.bg.ac.yu/arhiva.php?issn=0350-2368&je=en

%A Miran Brezocnik
%T Uporaba genetskega programiranja v inteligentnih proizvodnih sistemih
%D 2000
%I University of Maribor, Faculty of mechanical engineering
%C Maribor, Slovenia
%K genetic algorithms, genetic programming, manufacturing, intelligent manufacturing systems, modelling, assembly, metal forming, autonomous robot, evolutionary algorithms
%U http://maja.uni-mb.si/slo/Knjige/2000-03-mon/index.htm
%O In Slovenian
%@ 86-435-0306-1

%A Miran Brezocnik
%A Joze Balic
%A Zlatko Kampus
%T Modeling of forming efficiency using genetic programming
%J Journal of Materials Processing Technology
%V 109
%N 1-2
%D 2001
%P 20--29
%I 
%K genetic algorithms, genetic programming, Metal-forming, Yield stress, Forming efficiency, Modeling, Adaptation, Artificial intelligence
%U http://www.sciencedirect.com/science/article/B6TGJ-423HM9M-5/1/bcc93a13fbb04521236d3a8e16f8850b
%X This paper proposes new approach for modeling of various processes in metal-forming industry. As an example, we demonstrate the use of genetic programming (GP) for modeling
   of forming efficiency. The forming efficiency is a basis for determination of yield stress which is the fundamental characteristic of metallic materials. Several different
   genetically evolved models for forming efficiency on the basis of experimental data for learning were discovered. The obtained models (equations) differ in size, shape,
   complexity and precision of solutions. In one run out of many runs of our GP system the well-known equation of Siebel was obtained. This fact leads us to opinion that GP is
   a very powerful evolutionary optimization method appropriate not only for modeling of forming efficiency but also for modeling of many other processes in metal-forming
   industry. [COBISS.SI-ID 5979414]
%8 1  February
%Z Journal of Materials Processing Technology http://www.elsevier.com/wps/find/journaldescription.cws_home/505656/description#description

%A Miran Brezocnik
%A Joze Balic
%T A genetic-based approach to simulation of self-organizing assembly
%J Robotics and Computer-Integrated Manufacturing
%V 17
%N 1-2
%D 2001
%P 113--120
%I 
%K genetic algorithms, genetic programming, Intelligent manufacturing systems, Self-organizing assembly, Evolution
%U http://www.sciencedirect.com/science/article/B6V4P-42DP1Y1-J/1/175033beb3ddb787b75c22253e5534c2
%X The paper proposes a new and innovative biologically oriented idea in conceiving intelligent systems in modern factories of the future. The intelligent system is treated as
   an autonomous organization structure efficiently adapting itself to the dynamic changes in the production environment and the environment in a wider sense. Simulation of
   self-organizing assembly of mechanical parts (basic components) into the product is presented as an example of the intelligent system. The genetic programming method is
   used. The genetic-based assembly takes place on the basis of the genetic content in the basic components and the influence of the environment. The evolution of solutions
   happens in a distributed way, nondeterministically, bottom-up, and in a self-organizing manner. The paper is also a contribution to the international research and
   development program intelligent manufacturing systems, which is one of the biggest projects ever introduced.
%8 February
%Z Robotics and Computer-Integrated Manufacturing http://www.elsevier.com/wps/find/journaldescription.cws_home/704/description#description

%A Miran Brezocnik
%A Miha Kovacic
%T Survey of the evolutionary computation and its application in manufacturing systems
%B 3rd International Conference on Revitalization and Modernization of Production RIM 2001
%E Milan Jurkovic and Isak Karabegovic
%D 2001
%P 501--508
%I 
%I Bihac, Tehnieki fakultet
%C University of Bihac, Bihacu, Bosnia and Herzegovina
%K genetic algorithms, genetic programming
%8 September
%@ 9958-624-10-9

%A Miran Brezocnik
%A Joze Balic
%A Karl Kuzman
%T Genetic programming approach to determining of metal materials properties
%J Journal of Intelligent Manufacturing
%V 13
%N 1
%D 2002
%P 5--17
%I 
%K genetic algorithms, genetic programming, materials properties, metal forming, modeling, self-organisation
%U http://www.springerlink.com/openurl.asp?genre=article&eissn=1572-8145&volume=13&issue=1&spage=5
%X The paper deals with determining metal materials properties by use of genetic programming (GP). As an example, the determination of the flow stress in bulk forming is
   presented. The flow stress can be calculated on the basis of known forming efficiency. The experimental data obtained during pressure test serve as an environment to which
   models for forming efficiency have to be adapted during simulated evolution as much as possible. By performing four experiments, several different models for forming
   efficiency are genetically developed. The models are not a result of the human intelligence but of intelligent evolutionary process. With regard to their precision, the
   successful models are more or less equivalent; they differ mainly in size, shape, and complexity of solutions. The influence of selection of different initial model
   components (genes) on the probability of successful solution is studied in detail. In one especially successful run of the GP system the Siebel's expression was genetically
   developed. In addition, redundancy of the knowledge hidden in the experimental data was detected and eliminated without the influence of human intelligence. Researches
   showed excellent agreement between the experimental data, existing analytical solutions, and models obtained genetically.
%8 February
%Z Journal of Intelligent Manufacturing http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40528-70-35668245-0,00.html

%A Miran Brezocnik
%A Miha Kovacic
%T Prediction of surface roughness with genetic programming
%B Proceedings of the 11th International Scientific Conference Achievements in Mechanical and Materials Engineering, AMME'2002
%E Leszek A. Dobrzanski
%D 2002
%P 23--26
%I 
%K genetic algorithms, genetic programming
%Z http://www.wamme.org/index.php?id=37&PHPSESSID=8b9ce9355f0dbdaebee40f5d6ddec320 See also \citeBrezocnik:2004:JMPT
%@ 83-914458-7-9

%A Miran Brezocnik
%T On intelligent learning systems for next-generation manufacturing
%B DAAAM International Scientific Book 2002
%E Branko Katalinic
%V 1
%D 2002
%P 39--48
%I DAAAM International
%C Vienna
%K genetic algorithms, genetic programming, manufacturing systems, artificial intelligence, learning, evolutionary computation, emergence
%U http://www.daaam.com/
%X In the first part of the paper we analyse the basic scientific and philosophical facts, as well as social circumstances, that have a great impact on manufacturing concepts.
   Then we propose a shift from the present manufacturing paradigm favouring particularly determinism, rationalism, and top-down organisational principles towards intelligent
   systems in next-generation manufacturing involving phenomena such as non-determination, emergence, learning, complexity, self-organization, bottom-up organisation, and
   co-existence with natural environment. In the second part we give two examples from metal forming industry and autonomous intelligent vehicles. Both systems are based on
   learning and imitate some excellent properties of living systems. The stable global order (i.e. the solution) of each presented system gradually emerges as a result of
   interactions between basic entities of which the system consists and the environment.
%O 6
%8 October
%Z http://www.daaam.com/daaam/Publications/Publications.htm
%@ 3-901509-30-5

%A Miran Brezocnik
%A Miha Kovacic
%T Integrated evolutionary computation environment for optimizing and modeling of manufacturing processes
%B 6th International Research/Expert Conference "Trends in the development of Machinery and Associated Technology"
%E Safet Brdarevia and Sabahudin Ekinovia and Ramon Compamys Pascual and Joan Calvet Vivancos
%D 2002
%P TMT02--073
%I 
%I FACULTY OF MECHANICAL ENGINEERING IN ZENICA, UNIVERSITY OF SARAJEVO, BOSNIA AND HERZEGOVINA. UNIVERSITAT POLITECNICA DE CATALUNYA BARCELONA, DEP. D'ENGINYERIA MECANICA
   (SPAIN)
%C Neum, Bosnia and Herzegovina
%K genetic algorithms, genetic programming, Poster
%8 18-22  September
%Z http://www.mf.unze.ba/tmt2002/
%@ 9958-617-11-0

%A Miran Brezocnik
%A Joze Balic
%A Zmago Brezocnik
%T Emergence of intelligence in next-generation manufacturing systems
%J Robotics and Computer-Integrated Manufacturing
%V 19
%N 1-2
%D 2003
%P 55--63
%I 
%K genetic algorithms, genetic programming, Intelligent manufacturing systems, Emergence, Learning
%U http://www.sciencedirect.com/science/article/B6V4P-47XW4VG-1/2/f88aada395a16da3031d89d272dae207
%X In the paper we propose a fundamental shift from the present manufacturing concepts and problem solving approaches towards new manufacturing paradigms involving phenomena
   such as emergence, intelligence, non-determinism, complexity, self-organisation, bottom-up organization, and coexistence with the ecosystem. In the first part of the paper
   we study the characteristics of the past and the present manufacturing concepts and the problems they caused. According to the analogy with the terms in cognitive
   psychology four types of problems occurring in complex manufacturing systems are identified. Then, appropriateness of various intelligent systems for solving of these four
   types of problems is analysed. In the second part of the paper, we study two completely different problems. These two problems are (1) identification of system in metal
   forming industry and (2) autonomous robot system in manufacturing environment. A genetic-based approach that imitates integration of living cells into tissues, organs, and
   organisms is used. The paper clearly shows how the state of the stable global order (i.e., the intelligence) of the overall system gradually emerges as a result of
   low-level interactions between entities of which the system consists and the environment.
%8 February -  April

%A Miran Brezocnik
%A Miha Kovacic
%T Modelling of intelligent mobility for next-generation manufacturing systems
%B DAAAM International Scientific Book 2003
%E B. Katalinic
%V 2
%D 2003
%P 95--102
%I DAAAM International Vienna
%C Vienna
%K genetic algorithms, genetic programming
%X We present the modelling of the intelligent mobility for next-generation manufacturing systems. The modelling took place in the simplified dynamic manufacturing environment
   with several loads, obstacles and one robot placed in it. Each agent is freely movable on the floor. The aim of the robot is to pick up all loads and to come to the goal
   point. For optimisation of the robot path between loads and for planning of the robot travel the genetic algorithm and the genetic programming were used, respectively. The
   research showed that intelligent behaviour of the robot results from the interactions of the robot with the dynamic environment.
%8 July
%Z publication@daaam.com http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm
%@ 3-901509-30-5

%A Miran Brezocnik
%A Miha Kovacic
%A Mirko Ficko
%T Genetic-based approach to predict surface roughness in end milling
%B 7th International Research/Expert Conference "Trends in the Development Machinery and Associated Technology"
%D 2003
%P 529--532
%I 
%I UNIVERSITAT POLITECNICA DE CATALUNYA UNIVERSITY OF SARAJEVO ESCOLA TECNICA SUPERIOR D'ENGINYERIA INDUSTRIAL DE BARCELONA FACULTY OF MECHANICAL ENGINEERING IN ZENICA (Bosnia
   and Herzegovina) DEPARTAMENT D'ENGINYERIA MECANICA (Spain)
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%8 15-16  September
%Z http://www.mf.unze.ba/tmt2003/papers.htm
%@ 9958-617-18-8

%A Miran Brezocnik
%A Miha Kovacic
%A Mirko Ficko
%T Intelligent systems for next-generation manufacturing
%J Academic Journal of Manufacturing Engineering
%V 2
%N 1
%D 2004
%P 34--37
%I 
%K genetic algorithms, genetic programming
%Z http://www.eng.utt.ro/auif/rev/issue/no-05/no-05.html

%A Miran Brezocnik
%A Leo Gusel
%T Predicting stress distribution in cold-formed material with genetic programming
%J International journal of advanced manufacturing technology
%V 23
%N 7-8
%D 2004
%P 467--474
%I 
%K genetic algorithms, genetic programming, metal forming, stress distribution, modelling
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0268-3768&volume=23&issue=7&spage=467
%X In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy
   were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables
   (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the
   dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during
   simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be
   developed; therefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known.

%A Miran Brezocnik
%A Mirko Ficko
%A Miha Kovacic
%T Genetic based approach to predict surface roughness
%B 8th International Research/Expert Conference Trends in the Development Machinery and Associated Technology
%D 2004
%P 91--94
%I 
%C Neum, Bosnia and Herzegovina
%K genetic algorithms, genetic programming, celno frezanje, povrsinska hrapavost, napoved hrapavosti, genetsko programiranje, end milling, surface roughness, prediction of
   surface roughness
%X In this paper we propose genetic programming to predict surface roughness in end milling. Two independent data sets were obtained from measurements: the training data set
   and the testing data set. Spindle speed, feed rate, depth of cut and vibrations were used as independent input variables (parameters), while surface roughness was the
   output variable. Different surface roughness models were obtained with the training data set and genetic programming. The testing data set was used to prove the accuracy of
   the best model. The conclusion is that surface roughness is most influenced by the feed rate, while vibrations increase the prediction accuracy.
%8 15-19  September
%Z http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9009686
%@ 9958-617-21-8

%A M. Brezocnik
%A M. Kovacic
%A M. Ficko
%T Prediction of surface roughness with genetic programming
%J Journal of Materials Processing Technology
%V 157-158
%D 2004
%P 28--36
%I 
%K genetic algorithms, genetic programming, Manufacturing systems, Surface roughness; Milling, Evolutionary algorithms
%X In this paper, we propose genetic programming to predict surface roughness in end-milling. Two independent data sets were obtained on the basis of measurement: training
   data set and testing data set. Spindle speed, feed rate, depth of cut, and vibrations are used as independent input variables (parameters), while surface roughness as
   dependent output variable. On the basis of training data set, different models for surface roughness were developed by genetic programming. Accuracy of the best model was
   proved with the testing data. It was established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.
%8 20  Decemeber  2004
%Z Originally in AMME 2000-2002 conference \citeBrezocnik:2002:AMME. Achievements in Mechanical and Materials Engineering Conference. Selected for publication as full paper in
   the Special Issue of the Journal of Materials Processing Technology (Elsevier, the Netherlands)

%A Miha Kovacic
%A Miran Brezocnik
%A Joze Balic
%T Genetic Programming Approach for Autonomous Vehicles
%B Mechatronics 2004 9th Mechatronics Forum International Conference
%D 2004
%I 
%I Atilim University
%C METU, Ankara, Turkey
%K genetic algorithms, genetic programming
%U http://mechatronics.atilim.edu.tr/mechatronics2004/papers/Mechatronics2004_Abstract_026.pdf
%X GP was used for intelligent path planning of an autonomous vehicle in 2D production environment. Robot had to find loads, to avoid all the obstacles and to reach the target
   point. The production environment (robot, loads and obstacles) are represented as free 2D shapes. The robot discretely rotates for 30 degrees left and right and moves
   forward by two different steps. Step decreases if the sensor detects the load or obstacle. The GP system tries to find gradually optimal program for robot navigation
   through production environment as a consequence of interactions between the robot and detected environment. Program for navigation can be randomly constructed of logical
   operators (IFLOAD, IF-OBSTACLE), basic commands (MOVE, RIGHT, LEFT), and connection functions (CF2, CF3). Each program is run several times until 100 time units for the
   robot's task are used or the target point is reached. The system for genetic programming was run 50-times. Robot travelled safely with all collected loads to the target
   point 2-times, which means that the probability of the finding successful navigation program is 4 percent. In future the researches will be oriented particularly towards
   conceiving an improved GP system with the possibility of use 3D models of the production environment. Preliminary results of the concept are encouraging.
%8 30  August -1  September
%Z University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia

%A Miran Brezocnik
%A Miha Kovacic
%A Joze Balic
%A Bogdan Sovilj
%T Programming CNC measuring machines by genetic algorithms
%J Academic Journal of Manufacturing Engineering
%V 2
%N 4
%D 2004
%P 15--20
%I 
%K genetic algorithms, genetic programming, optimisation, coordinate measuring machines, computer aided quality control, evolutionary computation
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/brezocnik_2004_AJME.pdf
%X The need for efficient and reliable tools for programming of CNC coordinate measuring machine is rapidly increasing in modern production. The proposed concept based on
   genetic algorithms assures generation and optimization of NC programs for measuring machine. Therefore the structure, undergoing simulated evolution, is the population of
   NC programs. The NC programs control the tactile probe which performs simple elementary motions in the discretized measuring area. During the simulated evolution the probe
   movement becomes more and more optimized and intelligent solutions emerge gradually as a result of the low level interaction between the simple probe movements and the
   measuring environment. Example of CNC programming of measuring machine is given. Results show universality and inventiveness of the approach
%Z http://www.eng.utt.ro/auif/ http://www.eng.utt.ro/auif/rev/issue/no-08/no-08.html#C2

%A Miran Brezocnik
%A Miha Kovacic
%T Integrated genetic programming and genetic algorithm approach to predict surface roughness
%J Materials and Manufacturing Processes
%V 18
%N 3
%D 2003
%P 475--491
%I 
%K genetic algorithms, genetic programming, Manufacturing systems, Surface roughness, Milling
%X we propose a new integrated genetic programming and genetic algorithm approach to predict surface roughness in end-milling. Four independent variables, spindle speed, feed
   rate, depth of cut, and vibrations, were measured. Those variables influence the dependent variable (i.e., surface roughness). On the basis of training data set, different
   models for surface roughness were developed by genetic programming. The floating-point constants of the best model were additionally optimised by a genetic algorithm.
   Accuracy of the model was proved on the testing data set. By using the proposed approach, more accurate prediction of surface roughness was reached than if only modelling
   by genetic programming had been carried out. It was also established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the
   prediction accuracy.
%8 May

%A Miran Brezocnik
%A Bostjan Vaupotic
%A Janez Fridrih
%A Ivo Pahole
%T Cost estimation for punch dies by genetic programming
%B RIM 2005 / 5th International scientific conference on Production engineering
%E Milan Jurkovic and Vlatko Dolecek
%D 2005
%P 167--172
%I Faculty of Technical Engineering, Bihac, Bosnia and Hercegovina
%K genetic algorithms, genetic programming, punch dies, cost estimation
%X The paper presents a new approach for cost estimation of punch dies used in metal-forming industry. In the modern business world fast and accurate information is the
   principal advantage in securing orders and establishing the company's renowned. Often, the offer for the manufacturing and supply of the tool must be sent within a short
   time. However, precise preparation of the offer requires much work. The paper presents an approach ensuring fast determination of the relatively precise cost estimate of
   the punch dies on the basis of the tool input parameters (e.g., outside dimensions, number of blades, number of directions of cutting). The proposed approach is based on
   the evolutionary searching for the adequate general equation describing the influence of the tool input parameters on punch die manufacturing cost. Evolutionary development
   of the equation was performed by the genetic programming and the base of the punch dies already made.
%8 14-17  September
%@ 9958-9262-0-2

%A Miran Brezocnik
%A Miha Kovacic
%A Leo Gusel
%T Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution
%J Materials and Manufacturing Processes
%V 20
%N 3
%D 2005
%P 497--508
%I 
%K genetic algorithms, genetic programming, Metal forming, Stress distribution, System modelling
%U http://journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=1042-6914&volume=20&issue=3&spage=497
%X We compare genetic algorithm (GA) and genetic programming (GP) for system modelling in metal forming. As an example, the radial stress distribution in a cold-formed
   specimen (steel X6Cr13) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analysed by the visioplasticity method. After each extrusion,
   the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These
   variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were
   developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling.
   However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate.
%8 May
%Z A1 Laboratory for Intelligent Manufacturing Systems, University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia A2 Laboratory for Material Forming,
   University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia

%A Miran Brezocnik
%A Miha Kovacic
%A Matej Psenicnik
%T Prediction of steel machinability by genetic programming
%J Journal of achievements in materials and manufacturing engineering
%V 16
%N 1-2
%D 2006
%P 107--113
%I 
%K genetic algorithms, genetic programming, Steel machinability, Extra machinability, Modelling
%U http://157.158.19.167/papers_cams05/1123.pdf
%X The steels with extra machinability are made according to a special technological process. Such steels can be machined at high cutting speeds. In addition, the resistance
   of the tools used for machining, is higher than in the case of ordinary steels. It depends on several parameters, particularly on the steel chemical composition, whether
   the steel will meet the criterion of extra machinability. Special tests for each batch separately show whether the steel has extra machinability or not. In our research,
   the prediction of machinability of steels, depending on input parameters, was performed by genetic programming and data on the batches of steel already made. The model
   developed during the simulated evolution was tested also with the testing data set. The results show that the proposed concept can be successfully used in practice.
%O Special Issue of CAM3S'2005
%8 May -  June
%Z http://www.journalamme.org/ http://157.158.19.167/index.php?id=69 Formerly Proceedings of Achievements in Mechanical and Materials Engineering. (1.123) Intelligent
   Manufacturing Systems Laboratory, University of Maribor, Faculty of Mechanical Engineering, Smetanova ulica 17, SI-2000 Maribor, Slovenia

%A Lionel C. Briand
%A Yvan Labiche
%A Marwa Shousha
%T Using genetic algorithms for early schedulability analysis and stress testing in real-time systems
%J Genetic Programming and Evolvable Machines
%V 7
%N 2
%D 2006
%P 145--170
%I 
%K genetic algorithms, Software verification and validation, Schedulability theory
%X Reactive real-time systems have to react to external events within time constraints: Triggered tasks must execute within deadlines. It is therefore important for the
   designers of such systems to analyse the schedulability of tasks during the design process, as well as to test the system's response time to events in an effective manner
   once it is implemented. This article explores the use of genetic algorithms to provide automated support for both tasks. Our main objective is then to automate, based on
   the system task architecture, the derivation of test cases that maximise the chances of critical deadline misses within the system; we refer to this testing activity as
   stress testing. A second objective is to enable an early but realistic analysis of tasks' schedulability at design time. We have developed a specific solution based on
   genetic algorithms and implemented it in a tool. Case studies were run and results show that the tool (1) is effective at identifying test cases that will likely stress the
   system to such an extent that some tasks may miss deadlines, (2) can identify situations that were deemed to be schedulable based on standard schedulability analysis but
   that, nevertheless, exhibit deadline misses.
%O Special Issue: Best of GECCO 2005
%8 August

%A Forrest Briggs
%A Melissa O'Neill
%T Functional genetic programming with combinators
%B Proceedings of the Third Asian-Pacific workshop on Genetic Programming
%E The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen
%D 2006
%P 110--127
%I 
%C Military Technical Academy, Hanoi, VietNam
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/fsb-meo-combs.pdf
%X Prior program representations for genetic programming that incorporated features of modern programming languages solved harder problems than earlier representations, but
   required more complex genetic operators. We develop the idea of using combinator expressions as a program representation for genetic programming. This representation makes
   it possible to evolve programs with a variety of programming language constructs using simple genetic operators. We investigate the effort required to evolve
   combinator-expression solutions to several problems: linear regression, even parity on N inputs, and implementation of the stack and queue data structures. Genetic
   programming with combinator expressions compares favourably to prior approaches, namely the works of Yu [37], Kirshenbaum [18], Agapitos and Lucas [1], Wong and Leung [35],
   Koza [20], Langdon [21], and Katayama [17].
%Z http://www.aspgp.org

%A Forrest Briggs
%A Melissa O'Neill
%T Functional genetic programming and exhaustive program search with combinator expressions
%J International Journal of Knowledge-Based and Intelligent Engineering Systems
%V 12
%N 1
%D 2008
%P 47--68
%I IOS Press
%K genetic algorithms, genetic programming
%U http://iospress.metapress.com/content/u6l4j13p67w66370/
%X Using a strongly typed functional programming language for genetic programming has many advantages, but evolving functional programs with variables requires complex genetic
   operators with special cases to avoid creating ill-formed programs. We introduce combinator expressions as an alternative program representation for genetic programming,
   providing the same expressive power as strongly typed functional programs, but in a simpler format that avoids variables and other syntactic clutter. We outline a complete
   genetic-programming system based on combinator expressions, including a novel generalised genetic operator, and also show how it is possible to exhaustively enumerate all
   well-typed combinator expressions up to a given size. Our experimental evidence shows that combinator expressions compare favourably with prior representations for
   functional genetic programming and also offers insight into situations where exhaustive enumeration outperforms genetic programming and vice versa.
%Z KES

%A Kristin Briney
%A Tod Karpinski
%T An Interdisciplinary Investigation of the Evolution and Maintenance of Conditional Strategies in Chthamalus anisopoma, using Genetic Programming and a Quantitative Genetic
   Model
%B GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2003
%P 258--261
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C Chigaco
%K genetic algorithms, genetic programming
%8 11  July
%Z Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming
   Conference (GP-2003) part of barry:2003:GECCO:workshop

%A Carlos A. Brizuela
%A Nobuo Sannomiya
%T A Diversity Study in Genetic Algorithms for Job Shop Scheduling Problems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 75--82
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-333.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Oliver Brock
%T Evolving Reusable Subroutines for Genetic Programming
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 11--19
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/156902.html
%X Although automatically defined functions (ADFs) are able to significantly reduce the computational effort required in genetic programming, reasonably di\AEcult problems
   still require large amounts of computation time. However, every time genetic programming evolves a program to solve a problem those ADFs have to be rediscovered from
   scratch. If the ADFs of a correct program contain partial solutions that are generally useful, they can be used to solve similar problems. This paper proposes a technique
   to make the information of successful ADFs accessible to genetic programming in order to reduce the computational costs of solving related problems with less computational
   effort and demonstrates its utility using the example of the even n-parity function.
%8 June
%Z This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford
   University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html ADFS previously evolved may be used by subsequent GP runs. Ie
   become part of fitness set for rpb and adf of later runs.
%@ 0-18-182105-2

%A Rodney A. Brooks
%T Artificial Life and Real Robots
%B Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life
%E Francisco J. Varela and Paul Bourgine
%D 1992
%P 3--10
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://people.csail.mit.edu/brooks/papers/real-robots.pdf
%X The first part of this paper explores the general issues in using Artificial Life techniques to program actual mobile robots. In particular it explores the difficulties
   inherent in transferring programs evolved in a simulated environment to run on an actual robot. It examines the dual evolution of organism morphology and nervous systems in
   biology. It proposes techniques to capture some of the search space pruning that dual evolution offers in the domain of robot programming. It explores the relationship
   between robot morphology and program structure, and techniques for capturing regularities across this mapping. The second part of the paper is much more specific. It
   proposes techniques which could allow realistic explorations concerning the evolution of programs to control physically embodied mobile robots. In particular we introduce a
   new abstraction for behaviour-based robot programming which is specially tailored to be used with genetic programming techniques. To compete with hand coding techniques it
   will be necessary to automatically evolve programs that are one to two orders of magnitude more complex than those previously reported in any domain. Considerable
   extensions to previously reported approaches to genetic programming are necessary in order to achieve this goal.

%A T. Broughton
%A P. Coates
%A H. Jackson
%T Exploring 3D design worlds using Lindenmayer systems and Genetic Programming
%R Technical Report
%D 1998
%I 
%I University of East London
%K genetic algorithms, genetic programming
%U http://homepages.uel.ac.uk/0483p/chapter12.html
%Z www info only

%A T. Broughton
%A P. S. Coates
%A H. Jackson
%T Exploring Three-dimensional design worlds using Lindenmeyer Systems and Genetic Programming
%B Evolutionary Design Using Computers
%E Peter Bentley
%D 1999
%P 323--341
%I Academic press
%C London, UK
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/P.Bentley/evdes.html
%X The raw Lindenmeyer-system (L-system) generates random branching structures in the isospatial grid. Using a three dimensional L-system, early experiments (reported CAAD
   Futures 97, \citecoates:1997:GPx3dw ) showed that globally defined useful form (the flytrap) can evolve quite quickly using one fitness function This paper will describe
   further experiments undertaken using an improved L-system and multigoal evolution to evolve space/enclosure systems that satisfy both the requirements of space use and
   those of enclosure. This is implemented as symbiotic coevolution between: 1) L-system branching tree system whose goal is to surround the largest volume of empty space
   (defined as space which is "invisible" to an outside observer). 2) Circulation system using walking three dimensional turtles to measure the spatial property of the
   enclosed space. The resulting enclosure phenotypes can be realised using the occupied isospatial grid points as nodes of a nurbs surface. The chapter covers: 1.0
   Introduction to Genetic Programming, L-Systems and the Isospatial Grid 2.0 Three dimensional L-systems, production rules and s- expressions 3.0 Evolutionary Experiments in
   Simple Environments 4.0 Symbiotic Coevolution
%O 14
%@ 0-12-089070-4

%A Janelle Brown
%T AI, Teamwork is Goal of Robot Soccer Tourney
%J Wired News
%V 5
%N 10
%D 1997
%I 
%K genetic algorithms, genetic programming
%U http://www.wired.com/culture/lifestyle/news/1997/08/6388
%X It's got all the excitement of real soccer, but without the bad haircuts and big egos. This week the Robot Soccer World Cup debuts at the International Joint Conferences on
   Artificial Intelligence in Japan. Matching robot against robot, RoboCup is making breakthroughs in artificial life and multi-agent collaboration, while providing a few
   kicks in the process.
%8 3:04pm PDT 26  August
%Z Report on RoboCup robot competition (held at IJCAI 1997 in Nagoya, Japan) http://www.robocup.org/RoboCup/ see also \citeluke:1997:csstcGP and
   http://www.cs.umd.edu/users/seanl/soccerbots/

%A Joseph A. Brown
%A Daniel Ashlock
%T Using Evolvable Regressors to Partition Data
%B ANNIE 2010, Intelligent Engineering Systems through Artificial Neural Networks
%E Cihan H. Dagli
%V 20
%D 2010
%P 187--194
%I ASME
%I Smart Engineering Systems Laboratory, Systems Engineering Graduate Programs, Missouri University of Science and Technology, 600 W. 14th St., Rolla, MO 65409 USA
%C St. Louis, Mo, USA
%K genetic algorithms, genetic programming
%U http://asmedl.aip.org/ebooks/asme/asme_press/859599/859599_paper24
%X This manuscript examines permitting multiple populations of evolvable regressors to compete to be the best model for the largest number of data points. Competition between
   populations enables a natural process of specialisation that implicitly partitions the data. This partitioning technique uses function-stack based regressors and has the
   ability to discover the natural number of clusters in a data set via a process of sub-population collapse.
%8 November  1-3
%Z ASME Order Number: 859599

%A W. Michael Brown
%A Aidan P. Thompson
%A Peter A. Schultz
%T Efficient hybrid evolutionary optimization of interatomic potential models
%J Journal of Chemical Physics
%V 132
%N 2
%D 2010
%P 024108
%I 
%K genetic algorithms, genetic programming, potential energy functions, search problems
%X The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a
   hybrid, population based optimisation strategy intended for the automated development of material specific inter atomic potentials. We compare two strategies for parallel
   genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We
   evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time
   with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model
   development and demonstrate that vectorisation can help to address this issue. Finally, we show that with the use of this method, we are able to 'rediscover' the exact
   model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations.
%Z 34.20.Cf Department of Multiscale Dynamic Material Modeling, Sandia National Laboratories, Albuquerque, New Mexico 87185-1322, USA

%A H. {Brown Cribbs III}
%A Robert E. Smith
%T Classifier System Renaissance: New Analogies, New Directions
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 547--552
%I MIT Press
%C Stanford University, CA, USA
%K Classifier Systems, Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 Classifier paper

%A Cameron Browne
%T Automatic Generation and Evaluation of Recombination Games
%R Ph.D. Thesis
%D 2008
%I 
%I Faculty of Information Technology, Queensland University of Technology
%C Australia
%K genetic algorithms, genetic programming, Combinatorial, Games, Design, Aesthetics, Evolutionary, Search, Yavalath
%U http://www.cameronius.com/cv/publications/thesis-2.47.zip
%X Many new board games are designed each year, ranging from the unplayable to the truly exceptional. For each successful design there are untold numbers of failures; game
   design is something of an art. Players generally agree on some basic properties that indicate the quality and viability of a game, however these properties have remained
   subjective and open to interpretation. The aims of this thesis are to determine whether such quality criteria may be precisely defined and automatically measured through
   self-play in order to estimate the likelihood that a given game will be of interest to human players, and whether this information may be used to direct an automated search
   for new games of high quality. Combinatorial games provide an excellent test bed for this purpose as they are typically deep yet described by simple well defined rule sets.
   To test these ideas, a game description language was devised to express such games and a general game system implemented to play, measure and explore them. Key features of
   the system include modules for measuring statistical aspects of self-play and synthesising new games through the evolution of existing rule sets. Experiments were conducted
   to determine whether automated game measurements correlate with rankings of games by human players, and whether such correlations could be used to inform the automated
   search for new high quality games. The results support both hypotheses and demonstrate the emergence of interesting new rule combinations.
%8 February

%A Cameron Browne
%T Evolutionary Game Design
%D 2011
%I Springer
%K genetic algorithms, genetic programming
%U http://www.springer.com/computer/ai/book/978-1-4471-2178-7
%X This book tells the story of Yavalath, the first computer-generated board game to be commercially released... Table of contents Introduction Games in General The Ludi
   System Measuring Games Evolving Games Viable Games Yavalath Conclusion
%Z Softcover

%A David Browne
%T Vision-Based Obstacle Avoidance: A Coevolutionary Approach
%R Bachelor of Computing with Honours
%D 1996
%I 
%I Department of Software Development, Monash University
%C Australia
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/browne/browne_thesis.ps.gz
%X This thesis investigates the design of robust obstacle avoidance strategies. Specifically, simulated coevolution is used to breed steering agents and obstacle courses in a
   `computational arms race'. Both steering agent strategies and obstacle courses are represented by computer programs, and are coevolved according to the genetic programming
   paradigm. Previous research has found it difficult to evolve robust vision based obstacle avoidance agents. By independently evolving obstacle avoidance agents against a
   competing evolving species (ie the obstacle courses), it is hypothesised that the robustness of the agents will be increased. The simon system, an existing genetic
   programming tool, is modified and used to evolve both the obstacle avoidance agents and the obstacle courses. A comparison is made between the robustness of coevolved
   obstacle avoidance agents and traditionally evolved (non-coevolved) agents. Robustness is measured by average performance in a series of randomly generated obstacle
   courses. Experimental results show that the average robustness of the coevolved oa agents is greater than that of the traditionally evolved, and statistically it is shown
   that this data is representative of all cases. It is therefore concluded that coevolution is applicable to oa type problems, and can be used to evolve more robust, general
   purpose Vision-Based Obstacle Avoidance agents.
%8 October

%A Nigel P. A. Browne
%A Marcus V. {dos Santos}
%T Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming
%J Applied Computational Intelligence and Soft Computing
%V 2010
%D 2010
%P Article ID 409045
%I 
%K genetic algorithms, genetic programming, gene expression programming
%U http://downloads.hindawi.com/journals/acisc/2010/409045.pdf
%X Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the
   genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach
   introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm
   does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations,
   and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our
   experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.

%A Wilker Shane Bruce
%T Automatic Generation of Object-Oriented Programs Using Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 267--272
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming, memory
%U http://citeseer.ist.psu.edu/bruce96automatic.html
%X This research addresses the application of genetic programming to the generation of object-oriented programs. An extended chromosome data structure is presented where the
   set of methods associated with an object is stored as an array of program trees. Modified genetic operators are defined to manipulate this structure. Indexed memory is used
   to allow the programs generated by the system to access and modify object memory. These extensions to the standard genetic programming...
%8 28--31  July
%Z GP-96 Early version available from http://www.scis.nova.edu/~brucews/PUBLICATIONS/gp-96.ps (broken) Uses GP to induce stack, queue and P queue. Represents objects as array
   of trees, one per method. Mutation and crossover. "Strongly typed GP generally out performed untyped GP as was expected". STGP. Says details in \citebruce:thesis.

%A Wilker Shane Bruce
%T The Application of Genetic Programming to the Automatic Generation of Object-Oriented Programs
%R Ph.D. Thesis
%D 1995
%I 
%I School of Computer and Information Sciences, Nova Southeastern University
%C 3100 SW 9th Avenue, Fort Lauderdale, Florida 33315, USA
%K genetic algorithms, genetic programming, memory
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bruce.thesis.ps.gz
%8 December

%A Wilker Shane Bruce
%T The Lawnmower Problem Revisited: Stack-Based Genetic Programming and Automatically Defined Functions
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 52--57
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/bruce97lawnmower.html
%X Stack-based genetic programming is an alternative to Koza-style tree-based genetic programming that generates linear programs that are executed on a virtual machine using a
   FORTH-style operand stack instead of tree-based function calls. A stack-based genetic programming system was extended to include the ability to generate programs containing
   automatically defined functions. Experiments were run to test the system using Koza's lawnmower problem. The stack-based system using automatically...
%8 13-16  July
%Z GP-97 Zero fitness if attempts to pop empty stack. LEFT primitive removed from population. ARG0 never in best best of run. "SBGP required significantly more search than
   tree-based GP" "comparisons ... may be problem dependant". "In both systems [GP and SBGP] the use of ADFs appreciably improved the ability of the GP system to quickly find
   a solution to the [lawn mower] problem." failure of SBGP without ADFs to solve 8x12 "is most probably due to our limit of a maximium of 256 elements in a solution".

%A Eva Brucherseifer
%A Peter Bechtel
%A Stephan Freyer
%A Peter Marenbach
%T An Indirect Block-Oriented Representation for Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 268--279
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Block-oriented representation, Biotechnology, Process modelling, Controller design, Causality
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=268
%X When Genetic Programming (GP) is applied to system identification or controller design different codings can be used for internal representation of the individuals. One
   common approach is a block-oriented representation where nodes of the tree structure directly correspond to blocks in a block diagram. In this paper we present an indirect
   block-oriented representation, which adopts some aspects of the way humans perform the modelling in order to increase the GP system's performance. A causality measure based
   on an edit distance is examined to compare the direct an the indirect representation. Finally, results from a real world application of the indirect block-oriented
   representation are presented.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Peter Bruhn
%A Andreas Geyer-Schulz
%T Genetic Programming over Context-Free Languages with Linear Constraints for the Knapsack Problem: First Results
%J Evolutionary Computation
%V 10
%N 1
%D 2002
%P 51--74
%I 
%K genetic algorithms, genetic programming, grammatical evolution, grammar-based genetic, programming, combinatorial, optimization, context-free grammars, with linear
   constraints, knapsack problems
%U http://www.ingentaconnect.com/content/mitpress/evco/2002/00000010/00000001/art00004
%X we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the
   multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach,
   we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over
   context-free languages with linear constraints is ideally suited to modeling complementarities between items in a knapsack problem: The more complementarities in the
   problem, the stronger the performance in comparison to its competitors.
%8 Spring

%A S. P. Brumby
%A J. Theiler
%A S. J. Perkins
%A N. R. Harvey
%A J. J. Szymanski
%A J. J. Bloch
%A M. Mitchell
%T Investigation of image feature extraction by a genetic algorithm
%B Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, Proceedings of SPIE
%E Bruno Bosacchi and David B. Fogel and James C. Bezdek
%V 3812
%D 1999
%P 24--31
%I 
%K genetic algorithms, genetic programming
%8 19-20  July
%Z http://www.spie.org/web/meetings/programs/sd99/confs/3812.html Los Alamos National Lab; Santa Fe Institute [3812-03]

%A S. P. Brumby
%A N. R. Harvey
%A S. Perkins
%A R. B. Porter
%A J. J. Szymanski
%A J. Theiler
%A J. J. Bloch
%T A genetic algorithm for combining new and existing image processing tools for multispectral imagery
%B Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI. Proceedings of SPIE
%E Sylvia S. Shen and Michael R. Descour
%V 4049
%D 2000
%P 480--490
%I 
%K genetic algorithms, genetic programming

%A S. P. Brumby
%A J. J. Bloch
%A N. R. Harvey
%A J. Theiler
%A S. Perkins
%A A. C. Young
%A J. J. Szymanski
%T Evolving forest fire burn severity classification algorithms for multi-spectral imagery
%B In Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, Proceedings of SPIE
%E Sylvia S. Shen and Michael R. Descour
%V 4381
%D 2001
%P 236--245
%I 
%K genetic algorithms, genetic programming, Multispectral imagery, Supervised classification, Forest fire, Wildfire, GENIE, Aladdin
%U http://public.lanl.gov/perkins/webdocs/brumby.aerosense01.pdf
%X Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial
   estimates of forest damage included 17,000 acres (6,900 ha) of 70-100per cent tree mortality. Restoration efforts following the fire were complicated by the large scale of
   the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the
   burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on
   the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+
   multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and
   manual interpretation of high-resolution aerial colour/infrared photography.
%Z p3 Max size directed acyclic graph, not tree GP. GENIE object-oriented Perl. RSI's IDL language and image processing environment. C. UNIX Linux. Aladdin JAVA. Output is
   written to one of a number of scratch planes (memory) 'temporary workspaces where an image plane can be stored.' 'the gene [ADDP rD0 rS1 wS2] applies pixel-by-pixel
   addition to two input planes, read from data plane 0 and from scratch plane 1, and writes its output to scratch plane 2.' 'GENIE performs an analysis of chromosome graphs
   when they are created and only carries out those processing steps that actually affect the final result. Therefore, the fixed length of the chromosome acts as a maximum
   effective length.' hamming distance fitness pop=50. 30 gens. max chrome size 20. 3 scratch registers. 'The best evolved image-processing algorithm had the chromosome, [OPEN
   rD1 wS1 1 1][ADDS rD4 wS3 0.34][NEG rS1 wS1][MULTP rD4 rS3 wS2] [LINCOMB rS1 rD6 wS3 0.11][ADDP rS1 rS3 wS1][SUBP rS1 rD5 wS1]' 'The final values of S1, S2, and S3 are then
   combined in the linear sum, where the coefficients and intercept have been chosen by the Fisher discriminant, as described in Section 2.3, above, to produce our real-valued
   answer plane A (Figure 6): A = 0.0147*S1 - 0.0142*S2 + 0.0134*S3 + 1.554' 'Adjusting the threshold on A to fall at the between-peak minimum of the histogram at 0.7930 (a
   different optimisation criterion for the threshold than that used by default by GENIE) produces a new Boolean mask, Figure 9, in which almost all the false positives have
   been removed, and the remaining pixels marked as burn correspond very closely to the high severity burn regions in the BAER map'

%A Steven P. Brumby
%A James Theiler
%A Simon Perkins
%A Neal R. Harvey
%A John J. Szymanski
%T Genetic programming approach to extracting features from remotely sensed imagery
%B FUSION 2001: Fourth International Conference on Image Fusion
%D 2001
%I 
%C Montreal, Quebec, Canada
%K genetic algorithms, genetic programming, Evolutionary Computation, Image Processing, Remote Sensing, Multispectral Imagery, Panchromatic imagery
%U http://public.lanl.gov/perkins/webdocs/brumbyFUSION2001.pdf
%X Multi-instrument data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problems of spatial co-registration, the remote
   sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. We describe a
   genetic programming/supervised classifier software system, called Genie, which evolves and combines spatio-spectral image processing tools for remotely sensed imagery. We
   describe our representation of candidate image processing pipelines, and discuss our set of primitive image operators. Our primary application has been in the field of
   geospatial feature extraction, including wildfire scars and general land-cover classes, using publicly available multi-spectral imagery (MSI) and hyper-spectral imagery
   (HSI). Here, we demonstrate our system on Landsat 7 Enhanced Thematic Mapper (ETM+) MSI. We exhibit an evolved pipeline, and discuss its operation and performance.
%8 7-10  August
%Z oai:CiteSeerPSU:567526 seems to be wrong

%A Steven P. Brumby
%A James Theiler
%A Jeffrey J. Bloch
%A Neal R. Harvey
%A Simon Perkins
%A John J. Szymanski
%A A. Cody Young
%T Evolving land cover classification algorithms for multispectral and multitemporal imagery
%B Proc. SPIE Imaging Spectrometry VII
%E Michael R. Descour and Sylvia S. Shen
%V 4480
%D 2002
%I 
%K genetic algorithms, genetic programming, Feature Extraction, Supervised classification, K-means clustering, Multi-spectral imagery, Land cover, Wildfire
%U http://citeseer.ist.psu.edu/445835.html
%X The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the
   adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote
   sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced
   Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and
   multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software
   package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from
   before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that
   distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification
   results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification.
%Z Los Alamos National Lab.

%A C. H. Bryant
%A S. H. Muggleton
%A S. G. Oliver
%A D. B. Kell
%A P. G. K. Reiser
%A R. D. King
%T Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes
%J Electronic Transactions in Artificial Intelligence
%V 6
%N 12
%D 2001
%I 
%K ILP
%U http://www.stancomb.co.uk/~prr/Papers/bryant-ETAI.pdf
%Z online only?

%A Randal E. Bryant
%T Graph-Based Algorithms for Boolean Function Manipulation
%J IEEE Transactions on Computers
%V C-35
%N 8
%D 1986
%P 677--691
%I 
%K DEC VAX, Boolean functions, binary decision diagrams, logic design verification, symbolic manipulation
%X In this paper we present a new data structure for representing Boolean functions and an associated set of manipulation algorithms. Functions are represented by directed,
   acyclic graphs in a manner similar to the representations introduced by Lee [1] and Akers [2], but with further restrictions on the ordering of decision variables in the
   graph. Although a function requires, in the worst case, a graph of size exponential in the number of arguments, many of the functions encountered in typical applications
   have a more reasonable representation. Our algorithms have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long
   as the graphs do not grow too large. We present experimental results from applying these algorithms to problems in logic design verification that demonstrate the
   practicality of our approach.
%8 August
%Z NOT GP, exhaustive depth first search?

%A Gunnar Buason
%A Nicklas Bergfeldt
%A Tom Ziemke
%T Brains, Bodies, and Beyond: Competitive Co-Evolution of Robot Controllers, Morphologies and Environments
%J Genetic Programming and Evolvable Machines
%V 6
%N 1
%D 2005
%P 25--51
%I 
%K genetic algorithms, neuronal robot controller, CCE, khepera, YAKS simulator
%X We present a series of simulation experiments that incrementally extend previous work on neural robot controllers in a predator-prey scenario, in particular the work of
   Floreano and Nolfi, and integrates it with ideas from work on the co-evolution of robot morphologies and control systems. The aim of these experiments has been to further
   systematically investigate the tradeoffs and interdependencies between morphological parameters and behavioral strategies through a series of predator-prey experiments in
   which increasingly many aspects are subject to self-organization through competitive co-evolution. Motivated by the fact that, despite the emphasis of the interdependence
   of brain, body and environment in much recent research, the environment has actually received relatively little attention, the last set of experiments lets robots/species
   actively adapt their environments to their own needs, rather than just adapting themselves to a given environment. This paper is an extended version of: Buason and Ziemke.
   "Co-evolving task-dependent visual morphologies in predator-prey experiments," in Genetic and Evolutionary Computation Conference, Cantu-Paz et al. (Eds.), Springer Verlag:
   Berlin, 2003, pp. 458-469.
%8 March
%Z 20/100 rule

%A Thomas Buchsbaum
%A Siegfried V{\"o}ssner
%T Information-Dependent Switching of Identification Criteria in a Genetic Programming System for System Identification
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 300--309
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050300.pdf
%X Genetic Programming (GP) can be used to identify the nonlinear differential equations of dynamical systems. If, however, the fitness function is chosen in a classical way,
   the optimisation will not work very well. In this article, we explain the reasons for the failure of the GP approach and present a solution strategy for improving
   performance. Using more than one identification criterion (fitness function) and switching based on the information content of the data enable standard GP algorithms to
   find better solutions in shorter times. A computational example illustrates that identification criteria switching has a bigger influence on the results than the choice of
   the GP parameters has.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Thomas Buchsbaum
%T Toward a Winning GP Strategy for Continuous Nonlinear Dynamical System Identification
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 1269--1275
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X System identification is the scientific art of building models from data. Good models are of essential importance in many areas of science and industry. Models are used to
   analyse, simulate, and predict systems and their states. Model structure selection and estimation of the model parameters with respect to a chosen criterion of fit are
   essential parts of the identification process. In this article, we investigate the suitability of genetic programming for creating continuous nonlinear state-space models
   from noisy time series data. We introduce methodologies from the field of chaotic time series estimation and present concepts for integrating them into a genetic
   programming system. We show that even small changes of the fitness evaluation approach may lead to a significantly improved performance. In combination with multiobjective
   optimisation, a multiple shooting approach is able to create powerful models from noisy data.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Muneer Buckley
%A Zbigniew Michalewicz
%A Ralf Zurbruegg
%T An Application of Genetic Programming to Forecasting Foreign Exchange Rates
%B Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering
%E Raymond Chiong
%D 2010
%P 26--48
%I IGI Global
%K genetic algorithms, genetic programming
%U http://hdl.handle.net/2440/54525
%O 2
%Z http://www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=794&DetailsType=Description Muneer Buckley (University of Adelaide, Australia) Zbigniew Michalewicz
   (University of Adelaide, Australia) Ralf Zurbruegg (Institute of Computer Science, Polish Academy of Sciences & Polish-Japanese Institute of Information Technology, Poland)

%A Muneer Buckley
%A Zbigniew Michalewicz
%A Ralf Zurbruegg
%T An Application of Genetic Programming to Forecasting Foreign Exchange Rates
%B Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering
%E Raymond Chiong
%D 2009
%P 26--48
%I IGI Global
%K genetic algorithms, genetic programming
%U http://hdl.handle.net/2440/54525
%X There is a great need for accurate predictions of foreign exchange rates. Many industries participate in foreign exchange scenarios with little idea where the exchange rate
   is moving, and what the optimum decision to make at any given time is. Although current economic models do exist for this purpose, improvements could be made in both their
   flexibility and adaptability. This provides much room for models that do not suffer from such constraints. This chapter proposes the use of a genetic program (GP) to
   predict future foreign exchange rates. The GP is an extension of the DyFor GP tailored for forecasting in dynamic environments. The GP is tested on the Australian / US
   (AUD/USD) exchange rate and compared against a basic economic model. The results show that the system has potential in forecasting long term values, and may do so better
   than established models. Further improvements are also suggested.
%O 2

%A Tai D. Bui
%A Alan A. Smith
%T Water Resource Engineers and Environmental Hydraulics
%B World Water Congress 2001
%E Don Phelps and Gerald Sehlke
%V 111
%D 2001
%P 286--286
%I ASCE
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://link.aip.org/link/?ASC/111/286/1
%X In past decades, the fundamental notion of employing a multi-disciplinary approach to water resource projects was well received and promoted. According to this approach,
   water resource practitioners (especially engineers) should change their solution techniques and evaluation so that a solution would encompass a plethora of issues ? both
   structural and non-structural ? related to the project. It was recognized that solutions could not be based solely on mathematical models of flow conditions. Aspects such
   as ecology and non-technical issues such as recreational and societal needs should all be considered in the solution derivation process. Undoubtedly, sophisticated
   technical and mathematical tools (such as artificial neural network and genetic programming, and other tools related to Hydro-informatics) are essential to implement the
   approach. Added to this is the involvement of various professionals in certain projects. Planners, biologists, limnologists, economists, landscape architects, etc. are some
   of the other disciplines, besides engineers, involved in dealing with water resource projects. To address the issues, a distinct branch of engineering is imperative. The
   International Association of Hydraulic Engineering and Research initiated the Eco-hydraulic branch, the American Society of Civil Engineers formed the Environmental
   Hydraulic Technical Committee and the Canadian Society of Civil Engineers has the Hydrotechnical branch. All in all, these efforts are intended to ensure not only that
   levels of awareness are elevated but also those levels of engineering practice are adjusted to suit. As a result, solutions would be environmentally friendly and/or
   sympathetic.
%8 20-24  May
%Z number = 40569 Conference Proceeding Paper

%A Thai Bui
%T Solving the 8-Puzzle with Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 11--17
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Zdenek Buk
%A Jan Koutni
%A Miroslav Snorek
%T NEAT in HyperNEAT Substituted with Genetic Programming
%B 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009
%S Lecture Notes in Computer Science
%E Mikko Kolehmainen and Pekka Toivanen and Bartlomiej Beliczynski
%V 5495
%D 2009
%P 243--252
%I Springer
%C Kuopio, Finland
%K genetic algorithms, genetic programming
%X In this paper we present application of genetic programming (GP) [1] to evolution of indirect encoding of neural network weights. We compare usage of original HyperNEAT
   algorithm with our implementation, in which we replaced the underlying NEAT with genetic programming. The algorithm was named HyperGP. The evolved neural networks were used
   as controllers of autonomous mobile agents (robots) in simulation. The agents were trained to drive with maximum average speed. This forces them to learn how to drive on
   roads and avoid collisions. The genetic programming lacking the NEAT complexification property shows better exploration ability and tends to generate more complex solutions
   in fewer generations. On the other hand, the basic genetic programming generates quite complex functions for weights generation. Both approaches generate neural controllers
   with similar abilities.
%O Revised selected papers
%8 23-25  April
%Z ICANNGA 2009

%A Vladimir V. Bukhtoyarov
%A Olga E. Semenkina
%T Comprehensive evolutionary approach for neural network ensemble automatic design
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Neural network ensemble is an approach based on cooperative usage of many neural networks for problem solving. Often this approach enables to solve problem more efficiently
   than approach where only one network is used. The two major stages of the neural network ensemble construction are: design and training component networks, combining of the
   component networks predictions to produce the ensemble output. In this paper, a probability-based method is proposed to accomplish the first stage. Although this method is
   based on the genetic algorithm, it requires fewer parameters to be tuned. A method based on genetic programming is proposed for combining the predictions of component
   networks. This method allows us to build nonlinear combinations of component networks predictions providing more flexible and adaptive solutions. To demonstrate robustness
   of the proposed approach, its results are compared with the results obtained using other methods.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586516

%A Lawrence Bull
%A Terence C. Fogarty
%T Evolutionary Computing in Multi-Agent Environments: Speciation and Symbiogenesis
%B Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation
%S LNCS
%E Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel
%V 1141
%D 1996
%P 12--21
%I Springer-Verlag Heidelberg, Germany
%C Berlin, Germany
%K genetic algorithms
%X In this paper we introduce two macro-level operators to enhance the use of population-based evolutionary computing techniques in multiagent environments: speciation and
   symbiogenesis. We describe their use in conjunction with the genetic algorithm to evolve Pittsburgh-style classifier systems, where each classifier system represents an
   agent in a cooperative multi-agent system. The reasons for implementing these kinds of operators are discussed and we then examine their performance in developing a
   controller for the gait of a wall-climbing quadrupedal robot, where each leg of the quadruped is controlled by a classifier system. We find that the use of such operators
   can give improved performance over static population/agent configurations.
%8 22-26  September
%Z http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 Wall climbing quadruped robot simulation
%@ 3-540-61723-X

%A Larry Bull
%A Owen Holland
%T Evolutionary Computing in Multi-Agent Environments: Eusociality
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 347--352
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Genetic Algorithms
%8 13-16  July
%Z GP-97

%A Larry Bull
%T On using ZCS in a Simulated Continuous Double-Auction Market
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 83--90
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-806.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Larry Bull
%A Richard Preen
%T On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 37--48
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Larry Bull
%T On dynamical genetic programming: simple Boolean networks in learning classifier systems
%J International Journal of Parallel, Emergent and Distributed Systems
%V 24
%N 5
%D 2009
%P 421--442
%I Taylor \& Francis
%K genetic algorithms, genetic programming, discrete, dynamical systems, evolution, multiplexer, unorganised machines
%X Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to
   achieve this end. Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within conventional genetic
   programming (GP). This paper presents results from an initial investigation into using simple dynamical GP representations within a learning classifier system. It is shown
   possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are
   considered.
%8 October
%Z a Department of Computer Science, University of the West of England, Bristol, UK Formerly Parallel Algorithms and Applications

%A James B. Bullard
%A John Duffy
%T learning and the Stability of Cycles
%J Macroeconomic Dynamics
%V 2
%N 1
%D 1998
%P 22--48
%I 
%K genetic algorithms, Learning, Multiple Equilibria, Coordination
%X We investigate the extent to which agents can learn to coordinate on stationary perfect-foresight cycles in a general-equilibrium environment. Depending on the value of a
   preference parameter, the limiting backward (direction of time reversed) perfect-foresight dynamics are characterized by steady-state, periodic, or chaotic trajectories for
   real money balances. We relax the perfect-foresight assumption and examine how a population of artificial, heterogeneous adaptive agents might learn in such an environment.
   These artificial agents optimize given their forecasts of future prices, and they use forecast rules that are consistent with steady-state or periodic trajectories for
   prices. The agents' forecast rules are updated by a genetic algorithm. We find that the population of artificial adaptive agents is able eventually to coordinate on steady
   state and low-order cycles, but not on the higher-order periodic equilibria that exist under the perfect-foresight assumption.
%8 March
%Z Also available as working paper 1995-006B http://research.stlouisfed.org/wp/1995/95-006.pdf

%A James Bullard
%A John Duffy
%T A model of learning and emulation with artificial adaptive agents
%J Journal of Economic Dynamics and Control
%V 22
%N 2
%D 1998
%P 179--207
%I 
%K genetic algorithms, Learning, Coordination, Overlapping generations
%X We study adaptive learning in a sequence of overlapping generations economies in which agents live for n periods. Agents initially have heterogeneous beliefs, and form
   multi-step-ahead forecasts using a forecast rule chosen from a vast set of candidate rules. Agents learn in every period by creating new forecast rules and by emulating the
   forecast rules of other agents. Computational experiments show that systems so defined can yield three qualitatively different types of long-run outcomes: (1) coordination
   on a low inflation, stationary perfect foresight equilibrium; (2) persistent currency collapse; and (3) coordination failure within the allotted time frame.
%8 February
%Z JEL classification codes: D83; C63 Also available as working paper 1994-014C http://research.stlouisfed.org/wp/1994/94-014.pdf

%A Frances V. Buontempo
%A Xue Zhong Wang
%A Mulaisho Mwense
%A Nigel Horan
%A Anita Young
%A Daniel Osborn
%T Genetic Programming for the Induction of Decision Trees to Model Ecotoxicity Data
%J Journal of Chemical Information and Modeling
%V 45
%D 2005
%P 904--912
%I 
%K genetic algorithms, genetic programming, decision trees, model ecotoxicity, EPTree, C5.0 See5, recursive partitioning, S-Plus, SIMCA-P 8.0, QSAR
%X Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention, and
   the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at
   each node. These approaches can be successful; however, the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the
   applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options
   and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two data sets giving improved accuracy and
   generalization ability over a popular decision tree inducer.
%O ASAP article. Web Release Date: May 12, 2005
%Z http://pubs.acs.org/journals/jcisd8/index.html S1549-9596(04)09652-4 ACS Publications Division cites EPtree \citedelisle:2004:CIM y-scrambling. at least 10\% data coverage
   required of decision trees. Tournament size 16. No parsimony fitness preassure. Trees regrown. Lots of mutation if pop stagnated. Elitist but gives no improvement.
   -Log(LC50) vibrio fischeri. 1093 features. 60 training compounds. 100 generation. Pop 600. 1 second per generation. Department of Chemical Engineering and School of Civil
   Engineering, University of Leeds, Leeds LS2 9JT, U.K., AstraZeneca UK Ltd., Brixham Environmental Laboratory, Freshwater Quarry, Brixham, Devon TQ5 8BA, U.K., and Centre of
   Ecology and Hydrology, Monks Wood, Huntingdon PE28 2LS, U.K.

%A Robert Burbidge
%T A Contribution to the Foundations of AI: Genetic Programming and Support Vector Machines
%B Workshop and Summer School on Evolutionary Computing Lecture Series by Pioneers
%E T. M. McGinnity
%D 2008
%I 
%I School of Computing and Intelligent Systems, University of Ulster
%C University of Ulster
%K genetic algorithms, genetic programming, SVM
%U http://users.aber.ac.uk/rvb/wssec-rb-final.pdf
%X The aim of genetic programming is to automatically find computer programs that solve problems; using an algorithm inspired by biological evolution. The aim of the support
   vector machine is to model empirical data; using an algorithm based on statistical optimality. Fundamentally, both these techniques, and all artificial intelligence
   disciplines, use search; with differing representations, search operators and objective functions. We formally compare these two techniques as a contribution toward the
   foundations of artificial intelligence, and less grandiosely, in order to encourage transfer of knowledge between the two disciplines.
%8 18-20  August
%Z http://isel.infm.ulst.ac.uk/conference/wssec2008/ "The search space for GP is hard". "The search space for the SVM is easy". "inherent capacity control in GP" VC-dimension.

%A Robert Burbidge
%A Joanne H. Walker
%A Myra S. Wilson
%T A Grammar for Evolution of a Robot Controller
%B TAROS 2009 Towards Autonomous Robotic Systems
%S Intelligent Systems Research Centre Technical Report Series
%E Theocharis Kyriacou and Ulrich Nehmzow and Chris Melhuish and Mark Witkowski
%D 2009
%P 182--189
%I 
%C University of Ulster, Londonderry, United Kingdom
%K genetic algorithms, genetic programming, grammatical evolution, robot control
%U http://isrc.ulster.ac.uk/images/stories/publications/report-series/TAROS_2009.pdf
%X An autonomous mobile robot requires an onboard controller that allows it to perform its tasks for long periods in isolation. One possibility is for the robot to adapt to
   its environment using some form of artificial intelligence. Evolutionary techniques such as genetic programming (GP) offer the possibility of automatically programming the
   controller based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been successfully applied to various
   problems, particularly those for which GP has been successful. We present a method for applying GE to autonomous robot control and evaluate it in simulation for the Khepera
   robot.
%8 August  31 -  September  2
%Z http://www.infm.ulst.ac.uk/~ulrich/Taros09/

%A Robert Burbidge
%A Joanne H. Walker
%A Myra S. Wilson
%T Grammatical evolution of a robot controller
%B IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
%D 2009
%P 357--362
%I 
%C St. Louis, USA
%K genetic algorithms, genetic programming, grammatical evolution, Khepera robot, artificial intelligence, autonomous mobile robot, evolutionary algorithm, evolutionary
   technique, onboard controller, robot controller, grammars, mobile robots
%X An autonomous mobile robot requires an on board controller that allows it to perform its tasks for long periods in isolation. One possibility is for the robot to adapt to
   its environment using some form of artificial intelligence. Evolutionary techniques such as genetic programming (GP) offer the possibility of automatically programming the
   controller based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been successfully applied to various
   problems, particularly those for which GP has been successful. We present a method for applying GE to autonomous robot control and evaluate it in simulation for the Khepera
   robot.
%8 11-15  October
%Z Also known as \cite5354411

%A Colin J. Burgess
%A Martin Lefley
%T Can genetic programming improve software effort estimation? A comparative evaluation
%J Information and Software Technology
%V 43
%N 14
%D 2001
%P 863--873
%I 
%K genetic algorithms, genetic programming, Case-based reasoning, Machine learning, Neural networks, Software effort estimation
%U http://www.sciencedirect.com/science/article/B6V0B-44D4196-7/1/20f45986fc0a4827ad09169178379d73
%X Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently,
   attention has turned to a variety of machine learning (ML) methods. This paper attempts to evaluate critically the potential of genetic programming (GP) in software effort
   estimation when compared with previously published approaches, in terms of accuracy and ease of use. The comparison is based on the well-known Desharnais data set of 81
   software projects derived from a Canadian software house in the late 1980s. The input variables are restricted to those available from the specification stage and
   significant effort is put into the GP and all of the other solution strategies to offer a realistic and fair comparison. There is evidence that GP can offer significant
   improvements in accuracy but this depends on the measure and interpretation of accuracy used. GP has the potential to be a valid additional tool for software effort
   estimation but set up and running effort is high and interpretation difficult, as it is for any complex meta-heuristic technique.

%A C. J. Burgess
%A M. Lefley
%T Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation
%B Machine Learning Applications In Software Engineering: Series on Software Engineering and Knowledge Engineering
%E Du Zhang and Jeffrey J. P. Tsai
%V 16
%D 2005
%P 95--105
%I World Scientific Publishing Co.
%K genetic algorithms, genetic programming, Artificial Intelligence, Machine Learning, SBSE
%X Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently
   attention has turned to a variety of machine learning methods. This paper attempts to critically evaluate the potential of genetic programming (GP) in software effort
   estimation when compared with previously published approaches. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian
   software house in the late 1980s. It shows that GP can offer some significant improvements in accuracy and has the potential to be a valid additional tool for software
   effort estimation.
%8 May
%Z This paper is not on-line. Contact the author
%@ 981-256-094-7

%A Glenn Burgess
%T Finding Approximate Analytic Solutions To Differential Equations Using Genetic Programming
%R Technical Report DSTO-TR-0838
%D 1999
%I 
%I Surveillance Systems Division, Defence Science and Technology Organisation, Australia
%C Salisbury, SA, 5108, Austrlia
%K genetic algorithms, genetic programming, differential equations
%U http://www.dsto.defence.gov.au/corporate/reports/DSTO-TR-0838.pdf
%X The computational optimisation technique, genetic programming, is applied to the analytic solution of general differential equations. The approach generates a mathematical
   expression that is an approximate or exact solution to the particular equation under consideration. The technique is applied to a number of differential equations of
   increasing complexity in one and two dimensions. Comparative results are given for varying several parameters of the algorithm such as the size of the calculation stack and
   the variety of available mathematical operators. Several novel approaches gave negative results. Angeline's module acquisition (MA) and Koza's automatically defined
   functions (ADF) are considered and the results of some modifications are presented. One result of significant theoretical interest is that the syntax-preserving crossover
   used in Genetic Programming may be generalised to allow the exchange of n-argument functions without adverse effects. The results show that Genetic Programming is an
   effective technique that can give reasonable results, given plenty of computing resources. The technique used here can be applied to higher dimensions; although in practice
   the algorithmic complexity may be too high.
%8 February
%Z Based on author's 1997 Dept. Phys. Honours Thesis, Flinders University of South Australia

%A Mark Burgin
%A Eugene Eberbach
%T Bounded and periodic evolutionary machines
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X The aim of this paper is the development of foundations for evolutionary computation. We introduce and study two classes of evolutionary automata: bounded and periodic
   evolutionary machines.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586271

%A Keki M. Burjorjee
%T Genetic Algorithms Go to Grade School
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 31--40
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Donald S. Burke
%A Kenneth A. {De Jong}
%A John J. Grefenstette
%A Connie Loggia Ramsey
%A Annie S. Wu
%T Putting More Genetics into Genetic Algorithms
%J Evolutionary Computation
%V 6
%N 4
%D 1998
%P 387--410
%I 
%K genetic algorithms, Models of viral evolution, variable-length representation, length penalty functions, genome length adaptation, noncoding regions, duplicative genes
%U http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.387
%X The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a
   criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more
   engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an
   interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). The VIV incorporates a
   number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can
   vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent
   phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs
   more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the
   early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final
   population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the
   individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion
   that noncoding regions serve a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing
   better tools for understanding evolving biological systems.
%8 Winter
%Z Evolutionary Computation (Journal) Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms Edited by Annie S. Wu and Wolfgang
   Banzhaf

%A Donald S. Burke
%A Kenneth A. {De Jong}
%A John J. Grefenstette
%A Connie Loggia Ramsey
%A Annie S. Wu
%T Putting More Genetics into Genetic Algorithms
%D 1998
%I 
%K genetic algorithms, Models of viral evolution, variable-length representation, length penalty functions, genome length adaptation, noncoding regions, duplicative genes
%U http://www.ib3.gmu.edu/gref/papers/burke-ec98.ps
%O preprint of \citeburk:1998:pmgGA
%8 19  October

%A Edmund Burke
%A Steven Gustafson
%A Graham Kendall
%T A Survey And Analysis Of Diversity Measures In Genetic Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 716--723
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, diversity, population diversity, population dynamics
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
   Nominated for best at GECCO award
%@ 1-55860-878-8

%A Edmund Burke
%A Steven Gustafson
%A Graham Kendall
%A Natalio Krasnogor
%T Advanced Population Diversity Measures in Genetic Programming
%B Parallel Problem Solving from Nature - PPSN VII
%S Lecture Notes in Computer Science, LNCS
%E Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel
%N 2439
%D 2002
%P 341--350
%I Springer-Verlag
%C Granada, Spain
%K genetic algorithms, genetic programming, Theory of EC, Evolution dynamics
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=341
%X This paper presents a survey and comparison of significant diversity measures in the genetic programming literature. This study builds on previous work by the authors to
   gain a deeper understanding of the conditions under which genetic programming evolution is successful.
%O Available from http://link.springer.de/link/service/series/0558/papers/2439/243900341.pdf
%8 7-11  September
%@ 3-540-44139-5

%A Edmund Burke
%A Steven Gustafson
%A Graham Kendall
%T Ramped Half-n-Half Initialisation Bias in GP
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1800--1801
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, poster
%U http://www.cs.nott.ac.uk/~smg/research/publications/gecco-poster-2003.pdf
%X Tree initialisation techniques for genetic programming (GP) are examined in [4,3], highlighting a bias in the standard implementation of the initialisation method Ramped
   Half-n-Half (RHH) [1]. GP trees typically evolve to random shapes, even when populations were initially full or minimal trees [2]. In canonical GP, unbalanced and sparse
   trees increase the probability that bigger subtrees are selected for recombination, ensuring code growth occurs faster and that subtree crossover will have more difficultly
   in producing trees within specified depth limits. The ability to evolve tree shapes which allow more legal crossover operations, by providing more possible crossover points
   (by being bushier), and control code growth is critical. The GP community often uses RHH [4]. The standard implementation of the RHH method selects either the grow or full
   method with 0.5 probability to produce a tree. If the tree is already in the initial population it is discarded and another is created by grow or full. As duplicates are
   typically not allowed, this standard implementation of RHH favours full over grow and possibly biases the evolutionary process.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Edmund K. Burke
%A Matthew R. Hyde
%A Graham Kendall
%A John Woodward
%T Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1559--1565
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, bin packing, heuristics, hyper heuristic, reliability
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1559.pdf
%X It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as
   the worst case or average case performance. However, this method of assessing a heuristic would only be relevant to a user who employs the heuristic over a set of problems
   which is actually representative of the set of all possible bin packing problems. On the other hand, a real world user will often only deal with packing problems that are
   representative of a particular sub-set. Their piece sizes will all belong to a particular distribution. The contribution of this paper is to show that a Genetic Programming
   system can automate the process of heuristic generation and produce heuristics that are human-competitive over a range of sets of problems, or which excel on a particular
   sub-set. We also show that the choice of training instances is vital in the area of automatic heuristic generation, due to the trade-off between the performance and
   generality of the heuristics generated and their applicability to new problems.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A E. K. Burke
%A M. R. Hyde
%A G. Kendall
%A J. R. Woodward
%T The Scalability of Evolved on Line Bin Packing Heuristics
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 2530--2537
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we
   used genetic programming to evolve heuristics for this problem, which beat the human designed 'bestfit' algorithm. Here we examine the performance of the evolved heuristics
   on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently
   apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much
   larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by 'best fit'. Interestingly, our evolved
   heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that,
   when solutions are explicitly constructed for single problem instances, the size of the search space explodes. How- ever, when working in the space of algorithmic
   heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A John J. Burke
%T Genetic Programming of Crops to Sustain or Increase Yields under Reduced Irrigation
%B World Water and Environmental Resources Congress 2005
%E Raymond Walton
%D 2005
%I 
%C Anchorage, Alaska, USA
%X Crop productivity is determined by the plant's capacity to convert energy, nutrients, and water into harvestable yield of high quality and high value. The challenge is to
   sustain or enhance the outputs with a declining land base, reduced water supplies, and a changing global environment. The process of crop adaptation to the environment is
   restricted by the genetic potential of the plant. Improving the capacity of crops to overcome or adapt to factors that limit growth would increase yield and quality, while
   reducing demand for irrigation. Research identifying the molecular and biochemical factors underlying crop productivity, adaptation to stressful environments, and
   production of high-value end products is providing new insights into strategies for germplasm improvement. Characterisation of existing genetic diversity within U.S
   germplasm collections for water-deficit and temperature stress resistance; and the use of biotechnology to enhance yield stabilisation in water limited environments will
   ensure farming sustainability in the future.
%8 May  15-19
%Z Perhaps not about GP? c2005 ASCE

%A E. K. Burke
%A M. R. Hyde
%A G. Kendall
%T Evolving Bin Packing Heuristics with Genetic Programming
%B Parallel Problem Solving from Nature - PPSN IX
%S LNCS
%E Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao
%V 4193
%D 2006
%P 860--869
%I Springer-Verlag Berlin
%C Reykjavik, Iceland
%K genetic algorithms, genetic programming
%U http://www.cs.nott.ac.uk/~mvh/ppsn2006.pdf
%X The bin-packing problem is a well known NP-Hard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This
   paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presented with the sum of the pieces already in the
   bin and the size of the piece that is about to be packed. This heuristic operates in a fixed framework that iterates through the open bins, applying the heuristic to each
   one, before deciding which bin to use. The best evolved programs emulate the functionality of the human designed first-fit heuristic. Thus, the contribution of this paper
   is to demonstrate that genetic programming can be employed to automatically evolve bin packing heuristics which are the same as high quality heuristics which have been
   designed by humans.
%8 9-13  September
%Z PPSN-IX
%@ 3-540-38990-3

%A Edmund K. Burke
%A Matthew Hyde
%A Graham Kendall
%A Gabriela Ochoa
%A Ender Ozcan
%A John R. Woodward
%T A Classification of Hyper-heuristics Approaches
%B Handbook of Metaheuristics
%S International Series in Operations Research \& Management Science
%E Michel Gendreau and Jean-Yves Potvin
%V 57
%D 2010
%P 449--468
%I Springer
%K genetic algorithms, genetic programming
%U http://www.cs.nott.ac.uk/~gxo/papers/ChapterClassHH.pdf
%X The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic
   methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of
   previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We
   distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in
   detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research.
%O 15

%A Edmund K. Burke
%A Matthew R. Hyde
%A Graham Kendall
%T Grammatical Evolution of Local Search Heuristics
%J IEEE Transactions on Evolutionary Computation
%I 
%K genetic algorithms, genetic programming, Grammatical Evolution, Grammar, Heuristic algorithms, Production, Search problems, Bin packing, heuristics, local search, stock
   cutting
%X Genetic programming approaches have been employed in the literature to automatically design constructive heuristics for cutting and packing problems. These heuristics
   obtain results superior to human-created constructive heuristics, but they do not generally obtain results of the same quality as local search heuristics, which start from
   an initial solution and iteratively improve it. If local search heuristics can be successfully designed through evolution, in addition to a constructive heuristic which
   initialises the solution, then the quality of results which can be obtained by automatically generated algorithms can be significantly improved. This paper presents a
   grammatical evolution methodology which automatically designs good quality local search heuristics that maintain their performance on new problem instances.
%O Accepted for future publication
%Z iGE also known as \cite6029980

%A Forbes Burkowski
%T Optimization via Gene Expression Algorithms
%B Handbook of Bioinspired Algorithms and Applications
%S Computer \& Information Science Series
%E Stephan Olariu and Albert Y. Zomaya
%D 2005
%P Pages 8--121--8--134
%I Chapman and Hall/CRC
%K SVM
%O 8
%Z Not on GP?

%A Jens Busch
%A Jens Ziegler
%A Wolfgang Banzhaf
%A Andree Ross
%A Daniel Sawitzki
%A Christian Aue
%T Automatic Generation of Control Programs for Walking Robots Using Genetic Programming
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 258--267
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780258.pdf
%X We present the system SIGEL that combines the simulation and visualization of robots with a Genetic Programming system for the automated evolution of walking. It is
   designed to automatically generate control programs for arbitrary robots without depending on detailed analytical information of the robots' kinematic structure. Different
   fitness functions as well as a variety of parameters allow the easy and interactive configuration and adaptation of the evolution process and the simulations.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Stephen F. Bush
%A Amit B. Kulkarni
%T Genetically Induced Communication Network Fault Tolerance
%D 2002
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/572931.html
%X This paper presents the architecture and initial feasibility results of a proto-type communication network that uses genetic programming to evolve services and protocols as
   part of network operation. The network evolves responses to environmental conditions in a manner that could not be pre-programmed within legacy network nodes apriori.
   Apriori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and
   execution of code in support of the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network
   service code occurs via a genetic programming paradigm. Complexity and Algorithmic Information Theory play a key role in understanding and guiding code evolution within the
   network.
%O The Pennsylvania State University CiteSeer Archives
%O Invited Paper: SFI Workshop: Resilient and Adaptive Defence of Computing Networks 2002
%Z No confirmation

%A Stephen F. Bush
%T Genetically induced communication network fault tolerance
%J Complexity
%V 9
%N 2
%D 2003
%P 19--33
%I John Wiley \& Sons, Inc.
%K genetic algorithms, genetic programming, active networks, algorithmic information theory, Kolmogorov complexity, complexity theory, self-healing networks
%U http://www.crd.ge.com/~bushsf/pdfpapers/ComplexityJournal.pdf
%X This article presents the architecture and initial feasibility results of a proto-type communication network that uses genetic programming to evolve services and protocols
   as part of network operation. The network evolves responses to environmental conditions in a manner that could not be pre-programmed within legacy network nodes a priori. A
   priori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and
   execution of code in support of the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network
   service code occurs via a genetic programming paradigm. Complexity and algorithmic information theory play a key role in understanding and guiding code evolution within the
   network.

%A William S. Bush
%A Alison A. Motsinger
%A Scott M. Dudek
%A Marylyn D. Ritchie
%T Can neural network constraints in GP provide power to detect genes associated with human disease?
%B Applications of Evolutionary Computing, EvoWorkshops2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC
%S LNCS
%E Franz Rothlauf and Juergen Branke and Stefano Cagnoni and David W. Corne and Rolf Drechsler and Yaochu Jin and Penousal Machado and Elena Marchiori and Juan Romero and
   George D. Smith and Giovanni Squillero
%V 3449
%D 2005
%P 44--53
%I Springer Verlag Berlin
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming, evolutionary computation, ANN
%X A major goal of human genetics is the identification of susceptibility genes associated with common, complex diseases. Identifying gene-gene and gene-environment
   interactions which comprise the genetic architecture for a majority of common diseases is a difficult challenge. To this end, novel computational approaches have been
   applied to studies of human disease. Previously, a GP neural network (GPNN) approach was employed. Although the GPNN method has been quite successful, a clear comparison of
   GPNN and GP alone to detect genetic effects has not been made. In this paper, we demonstrate that using NN evolved by GP can be more powerful than GP alone. This is most
   likely due to the confined search space of the GPNN approach, in comparison to a free form GP. This study demonstrates the benefits of using GP to evolve NN in studies of
   the genetics of common, complex human disease.
%8 30  March -1  April
%Z EvoWorkshops2005
%@ 3-540-25396-3

%A James M. Butler
%A Edward P. K. Tsang
%T EDDIE Beats the Bookies
%R Technical Report CSM-259
%D 1995
%I 
%I Computer Science, University of Essex
%C Colchester CO4 3SQ, UK
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/tsang98eddie.html
%X Betting on a horse race is, in many ways, like investing in a financial market. You invest your money on the horse that you believe is going to win the race, in the hope of
   a return on your investment. Like some financial investments, horse race betting is a high risk investment, in that you can lose all of your money. As with making the right
   financial decision, the return on your investment, if you bet on the winning horse, can be considerable. In this paper, we present EDDIE, a genetic...
%8 15  Decemeber
%Z EDDIE, which stands for Evolutionary Dynamic Data Investment Evaluator, is designed as a tool to help channelling expert's knowledge into computer programs for making
   rules, which can then be examined by experts and used by other people. EDDIE is based on the concept of Genetic Programming, which borrows its ideas from evolution. EDDIE
   been applied to real horse races. We used the first 150 handicap races results in 1993 together with the expert knowledge that we could find from a text on horse racing to
   train EDDIE, which generates rules about betting. These rules were used to bet on the remaining 30 races in that season and obtained 88% return on investment. As
   scientists, we should always be cautious about experimental results. The sample size is small and luck may have a part to play in the success of EDDIE. However, the results
   justifies the investment of more time and effort into this research, which is what we are doing. See also \citetsang:1998:eddie

%A Matthew Butler
%A Vlado Keselj
%T Optimizing a Pseudo Financial Factor Model with Support Vector Machines and Genetic Programming
%B 22nd Canadian Conference on Artificial Intelligence, Canadian AI 2009
%S Lecture Notes in Computer Science
%E Yong Gao and Nathalie Japkowicz
%V 5549
%D 2009
%P 191--194
%I Springer
%C Kelowna, Canada
%K genetic algorithms, genetic programming, support vector machines, financial forecasting, principle component analysis
%X We compare the effectiveness of Support Vector Machines (SVM) and Tree-based Genetic Programming (GP) to make accurate predictions on the movement of the Dow Jones
   Industrial Average (DJIA). The approach is facilitated though a novel representation of the data as a pseudo financial factor model, based on a linear factor model for
   representing correlations between the returns in different assets. To demonstrate the effectiveness of the data representation the results are compared to models developed
   using only the monthly returns of the inputs. Principal Component Analysis (PCA) is initially used to translate the data into PC space to remove excess noise that is
   inherent in financial data. The results show that the algorithms were able to achieve superior investment returns and higher classification accuracy with the aid of the
   pseudo financial factor model. As well, both models outperformed the market benchmark, but ultimately the SVM methodology was superior in terms of accuracy and investment
   returns.
%8 May  25-27

%A Martin V. Butz
%A Kumara Sastry
%A David E. Goldberg
%T Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection
%J Genetic Programming and Evolvable Machines
%V 6
%N 1
%D 2005
%P 53--77
%I 
%K genetic algorithms, classifier systems, LCS, learning classifier systems, XCS, tournament selection, genetics based machine learning
%X Recent analysis of the XCS classifier system have shown that successful genetic learning strongly depends on the amount of fitness pressure towards accurate classifiers.
   Since the traditionally used proportionate selection is dependent on fitness scaling and fitness distribution, the resulting evolutionary fitness pressure may be neither
   stable nor sufficiently strong. Thus, we apply tournament selection to XCS. In particular, we exhibit the weakness of proportionate selection and suggest tournament
   selection as a more reliable alternative. We show that tournament selection results in a learning classifier system that is more parameter independent, noise independent,
   and more efficient in exploiting fitness guidance in single-step problems as well as multistep problems. The evolving population is more focused on promising subregions of
   the problem space and thus finds the desired accurate, maximally general representation faster and more reliably.
%8 March

%A Martin V. Butz
%A David E. Goldberg
%A Pier Luca Lanzi
%A Kumara Sastry
%T Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and the impact on computational complexity
%J Genetic Programming and Evolvable Machines
%V 8
%N 1
%D 2007
%P 5--37
%I 
%K genetic algorithms, classifier systems, Learning classifier systems, LCS, XCS, Niching, Markov chain analysis, Solution sustenance, Mutation
%X Michigan-style learning classifier systems iteratively evolve a distributed solution to a problem in the form of potentially overlapping subsolutions. Each problem niche is
   covered by subsolutions that are represented by a set of predictive rules, termed classifiers. The genetic algorithm is designed to evolve classifier structures that
   together cover the whole problem space and represent a complete problem solution. An obvious challenge for such an online evolving, distributed knowledge representation is
   to continuously sustain all problem subsolutions covering all problem niches, that is, to ensure niche support. Effective niche support depends both on the probability of
   reproduction and on the probability of deletion of classifiers in a niche. In XCS, reproduction is occurrence-based whereas deletion is support-based. In combination, niche
   support is assured effectively. we present a Markov chain analysis of the niche support in XCS, which we validate experimentally. Evaluations in diverse Boolean function
   settings, which require non-overlapping and overlapping solution structures, support the theoretical derivations. We also consider the effects of mutation and crossover on
   niche support. With respect to computational complexity, the paper shows that XCS is able to maintain (partially overlapping) niches with a computational effort that is
   linear in the inverse of the niche occurrence frequency.
%8 March

%A B. F. Buxton
%A W. B. Langdon
%A S. J. Barrett
%T Data Fusion by Intelligent Classifier Combination
%J Measurement and Control
%E Qing-Ping Yang
%V 34
%N 8
%D 2001
%P 229--234
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/mc/
%X The use of hybrid intelligent systems in industrial and commercial applications is briefly reviewed. The potential for application of such systems, in particular those that
   combine results from several constituent classifiers, to problems in drug design is discussed. It is shown that, although there are no general rules as to how a number of
   classifiers should best be combined, effective combinations can automatically be generated by genetic programming (GP). A robust performance measure based on the area under
   classifier receiver-operating-characteristic (ROC) curves is used as a fitness measure in order to facilitate evolution of multi-classifier systems that outperform their
   constituent individual classifiers. The approach is illustrated by application to publicly available Landsat data and to pharmaceutical data of the kind used in one stage
   of the drug design process.
%8 October
%Z http://www.instmc.org.uk/pubs/measandcontrol.htm "Measurement + Control is neither a "learned" journal nor a commercial trade publication" feature issue of M&C on Signal
   Processing Awarded best paper prize by the Worshipful Company of Instrument Makers.

%A B. F. Buxton
%A S B Holden
%A P C Treleaven
%T Intelligent Data Analysis and Fusion Techniques in Pharmaceuticals, Bioprocessing and Process Control
%D 2002
%I 
%K genetic algorithms, genetic programming, boosting, support vector machines
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/rocket/EPSRC-final-report.htm
%8 October
%Z End of project report. INTErSECT Faraday Partnership Flagship Project, 4 January 1999- 3 July 2002 Grant Reference GR/M43975

%A Maxim Buzdalov
%T Generation of tests for programming challenge tasks using evolution algorithms
%B GECCO 2011 Graduate students workshop
%E Miguel Nicolau
%D 2011
%P 763--766
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, SBSE
%X In this paper, an automated method for generation of tests in order to detect inefficient (slow) solutions for programming challenge tasks is proposed. The method is based
   on genetic algorithms. The proposed method was applied to a task from the Internet problem archive - the Timus Online Judge. For this problem, none of the existed solutions
   passed the generated set of tests.
%8 12-16  July
%Z Also known as \cite2002086 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Chad M. Byers
%A Betty H. C. Cheng
%A Philip K. McKinley
%T Digital enzymes: agents of reaction inside robotic controllers for the foraging problem
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 243--250
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, Artificial life/robotics/evolvable hardware
%X Over billions of years, natural selection has continued to select for a framework based on (1) parallelism and (2) cooperation across various levels of organisation within
   organisms to drive their behaviours and responses. We present a design for a bottom-up, reactive controller where the agent's response emerges from many parallelled,
   enzymatic interactions (bottom-up) within the biologically-inspired process of signal transduction (reactive). We use enzymes to explore the potential for evolving
   simulated robot controllers for the central-place foraging problem. The properties of the robot and stimuli present in its environment are encoded in a digital format
   (molecule) capable of being manipulated and altered through self-contained computational programs (enzymes) executing in parallel inside each controller to produce the
   robot's foraging behaviour. Evaluation of this design in unbounded worlds reveals evolved strategies employing one or more of the following complex behaviors: (1) swarming,
   (2) coordinated movement, (3) communication of concepts using a primitive language based on sound and colour, (4) cooperation, and (5) division of labour.
%8 12-16  July
%Z Also known as \cite2001610 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A M. D. Byington
%A B. E. Bishop
%T Cooperative Robot Swarm Locomotion Using Genetic Algorithms
%B System Theory, 2008. SSST 2008. 40th Southeastern Symposium on
%D 2008
%P 252--256
%I 
%K genetic algorithms, cooperative robot swarm locomotion, decentralized controller design, locomotion controllers, robotic agents, control system synthesis, decentralised
   control, mobile robots, motion control, multi-robot systems
%8 March
%Z Not GP, real coded GA applied to ANN

%A Jonathan Byrne
%A Michael O'Neill
%A Erik Hemberg
%A Anthony Brabazon
%T Analysis of Constant Creation Techniques on the Binomial-3 Problem with Grammatical Evolution
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 568--573
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, grammatical evolution
%X This paper studies the difference between Persistent Random Constants (PRC) and Digit Concatenation as methods for generating constants. It has been shown that certain
   problems have different fitness landscapes depending on how they are represented, independent of changes to the combinatorial search space, thus changing problem
   difficulty. In this case we show that the method for generating the constants can also influence how hard the problem is for Genetic Programming.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Jonathan Byrne
%A Michael O'Neill
%A Anthony Brabazon
%T Structural and nodal mutation in grammatical evolution
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1881--1882
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, grammatical evolution, Poster
%X This study focuses on mutation in Grammatical Evolution and divides mutation events into those that are structural in nature and those that are nodal. A structural event
   being one that alters the length of the phenotype. A nodal event simply alters the value at any node of a derivation tree. We analyse and compare the effect of integer,
   nodal and structural mutations on fitness for randomly generated individuals before continuing this analysis to their relative problem-solving performance over full runs.
   The study highlights the importance of understanding how the search operators of an evolutionary algorithm behave. The result in this case being a form of mutation for
   Grammatical Evolution, node mutation, with a better property of locality than standard integer-based mutation, which does not discriminate between structural and nodal
   contexts.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Jonathan Byrne
%A James McDermott
%A Michael O'Neill
%A Anthony Brabazon
%T An Analysis of the Behaviour of Mutation in Grammatical Evolution
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 14--25
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X This study attempts to decompose the behaviour of mutation in Grammatical Evolution (GE). Standard GE mutation can be divided into two types of events, those that are
   structural in nature and those that are nodal. A structural event can alter the length of the phenotype whereas a nodal event simply alters the value at any terminal (leaf
   or internal node) of a derivation tree. We analyse the behaviour of standard mutation and compare it to the behaviour of its nodal and structural components. These results
   are then compared with standard GP operators to see how they differ. This study increases our understanding of how the search operators of an evolutionary algorithm behave.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A J. Byrne
%A M. O'Neill
%A A. Brabazon
%T Optimising Offensive Moves in Toribash
%B Proceedings of Mendel 2010 16th International Conference on Soft Computing
%E R. Matousek
%D 2010
%P 78--85
%I Brno University of Technology
%C Brno, Czech Republic
%8 23-25  June
%Z 0102 http://www.mendel-conference.org/

%A Jonathan Byrne
%A James McDermott
%A Edgar Galvan-Lopez
%A Michael O'Neill
%T Implementing an Intuitive Mutation Operator for Interactive Evolutionary 3D Design
%B 2010 IEEE World Congress on Computational Intelligence
%D 2010
%P 2919--2925
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Barcelona, Spain
%K genetic algorithms, genetic programming, grammatical evolution
%X Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been described as a key element in Evolutionary Computation. Grammatical Evolution
   (GE) is a generative system as it uses grammar rules to derive a program from an integer encoded genome. The genome, upon which the evolutionary process is carried out,
   goes through several transformations before it produces an output. The aim of this paper is to investigate the impact of locality during the generative process using both
   qualitative and quantitative techniques. To explore this, we examine the effects of standard GE mutation using distance metrics and conduct a survey of the output designs.
   There are two different kinds of event that occur during standard GE Mutation. We investigate how each event type affects the locality on different phenotypic stages when
   applied to the problem of interactive design generation.
%8 18-23  July
%Z CEC 2010. WCCI 2010. Also known as \cite5586485

%A Jonathan Byrne
%A Michael Fenton
%A Erik Hemberg
%A James McDermott
%A Michael O'Neill
%A Elizabeth Shotton
%A Ciaran Nally
%T Combining Structural Analysis and Multi-Objective Criteria for Evolutionary Architectural Design
%B Applications of Evolutionary Computing, EvoApplications 2011: EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, EvoTRANSLOG
%S LNCS
%E Cecilia Di Chio and Anthony Brabazon and Gianni Di Caro and Rolf Drechsler and Marc Ebner and Muddassar Farooq and Joern Grahl and Gary Greenfield and Christian Prins and
   Juan Romero and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Neil Urquhart and A. Sima Uyar
%V 6625
%D 2011
%P 200--209
%I Springer Verlag Berlin
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming, grammatical evolution
%8 27-29  April
%Z Part of \citeDiChio:2011:evo_b EvoApplications2011 held inconjunction with EuroGP'2011, EvoCOP2011 and EvoBIO2011

%A Jonathan Byrne
%A Erik Hemberg
%A Michael O'Neill
%T Interactive operators for evolutionary architectural design
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 43--44
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution, Digital entertainment technologies and arts: Poster
%X In this paper we explore different techniques that allow the user to direct interactive evolutionary search. Broadening interaction beyond simple evaluation increases the
   amount of feedback and bias a user can apply to the search. Increased feedback will have the effect of directing the algorithm to more fruitful areas of the search space.
   This paper examines whether additional feedback from the user can be a benefit to the problem of evolutionary design. We find that the interface between the user and the
   search space plays a vital role in this process.
%8 12-16  July
%Z Also known as \cite2001884 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Ali Firat Cabalar
%A Abdulkadir Cevik
%A Ibrahim H. Guzelbey
%T Constitutive modeling of Leighton Buzzard Sands using genetic programming
%J Neural Computing and Applications
%V 19
%N 5
%D 2010
%P 657--665
%I Springer London
%K genetic algorithms, genetic programming
%X This paper investigates the results of laboratory experiments and numerical simulations conducted to examine the behaviour of mixtures composed of coarse (i.e. Leighton
   Buzzard Sand fraction B) and fine (i.e. Leighton Buzzard Sand fraction E) sand particles. Emphasis was placed on assessing the role of fines content in mixture and strain
   level on the deviatoric stress and pore water pressure generation using experimental (i.e. Triaxial testing) and numerical approaches (i.e. genetic programming, GP). The
   experimental database used for GP modelling is based on a laboratory study of the properties of saturated coarse and fine sand mixtures with various mix ratios under a 100
   kPa effective stresses in a 100 mm diameter conventional triaxial testing apparatus. Experimental results show that coarse-fine sand mixtures exhibit clay-like behavior due
   to particle-particle effects with the increase in fines content. It is shown that GP modeling of coarse-fine sand mixtures is observed to be quite satisfactory. The results
   have implications in the design of compressible particulate systems and in the development of prediction tools for the field performance coarse-fine sands.

%A Ali Firat Cabalar
%A Abdulkadir Cevik
%T Genetic programming-based attenuation relationship: An application of recent earthquakes in Turkey
%J Computers \& Geosciences
%V 35
%N 9
%D 2009
%P 1884--1896
%I 
%K genetic algorithms, genetic programming, Attenuation relationship
%U http://www.sciencedirect.com/science/article/B6V7D-4W99W08-1/2/aa19b6639659945b1d4e78c6209fe435
%X This study investigates an application of genetic programming (GP) for the prediction of peak ground acceleration (PGA) using strong-ground-motion data from Turkey. The
   input variables in the developed GP model are the average shear-wave velocity, earthquake source to site distance and earthquake magnitude, and the output is the PGA
   values. The proposed GP model is based on the most reliable database compiled for earthquakes in Turkey. The results show that the consistency between the observed PGA
   values and the predicted ones by the GP model yields relatively high correlation coefficients (R2=0.75). The proposed model is also compared with an existing attenuation
   relationship and found to be more accurate.

%A Ali Firat Cabalar
%A Abdulkadir Cevik
%T Triaxial behavior of sand-mica mixtures using genetic programming
%J Expert Systems with Applications
%V 38
%N 8
%D 2011
%P 10358--10367
%I 
%K genetic algorithms, genetic programming, Leighton Buzzard Sand, Mica, Triaxial testing, Modelling
%U http://www.sciencedirect.com/science/article/B6V03-524FSB9-M/2/eb83d6182c4d3c0b1271b301c5a04e15
%X This study investigates an application of genetic programming (GP) for modelling of coarse rotund sand-mica mixtures. An empirical model equation is developed by means of
   GP technique. The experimental database used for GP modeling is based on a laboratory study of the properties of saturated coarse rotund sand and mica mixtures with various
   mix ratios under a 100 kPa effective stresses, because of its unusual behaviour. In the tests, deviatoric stress, and pore pressure generation, and strain have been
   measured in a 100 mm diameter conventional triaxial testing apparatus. The input variables in the developed GP models are the mica content, and strain, and the outputs are
   deviatoric stress, pore water pressure generation. The performance of accuracies of proposed GP based equations is observed to be quite satisfactory.

%A C. Cabrita
%A J. Botzheim
%A A. E. Ruano
%A L. T. Koczy
%T Design of B-spline Neural Networks using a Bacterial Programming Approach
%B Proceedings of the International Joint Conference on Neural Networks, IJCNN 2004
%D 2004
%P 2313--2318
%I 
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%X The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be
   dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the
   microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.
%8 July
%Z This is the first paper on Bacterial Programming.

%A Cristian Cadar
%A Peter Pietzuch
%A Alexander L. Wolf
%T Multiplicity computing: a vision of software engineering for next-generation computing platform applications
%B Proceedings of the FSE/SDP workshop on Future of software engineering research
%S FoSER '10
%E Kevin Sullivan
%D 2010
%P 81--86
%I ACM New York, NY, USA
%I ACM sigsoft
%C Santa Fe, New Mexico, USA
%K cloud computing, data centers, multicore, virtualization, Design, Experimentation, Measurement, Performance, Reliability, Security
%U http://www.doc.ic.ac.uk/~cristic/papers/multicomp-foser-10.pdf
%X New technologies have recently emerged to challenge the very nature of computing: multicore processors, virtualised operating systems and networks, and data-centre clouds.
   One can view these technologies as forming levels within a new, global computing platform. We aim to open a new area of research, called multiplicity computing, that takes
   a radically different approach to the engineering of applications for this platform. Unlike other efforts, which are largely focused on innovations within specific levels,
   multiplicity computing embraces the platform as a virtually unlimited space of essentially redundant resources. This space is formed as a whole from the cross product of
   resources available at each level in the platform, offering a multiplicity of end-to-end resources. We seek to discover fundamentally new ways of exploiting the
   combinatorial multiplicity of computational, communication, and storage resources to obtain scalable applications exhibiting improved quality, dependability, and security
   that are both predictable and measurable.
%8 7-11  November
%Z Not on GP but does refer to GP work \citeDBLP:conf/gecco/ForrestNWG09. Also known as \citeCadar:2010:MCV:1882362.1882380

%A S. Cagnoni
%T GECCO2004 Workshop Proceedings: Preface
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/
%8 26-30  June
%Z GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A Stefano Cagnoni
%A Federico Bergenti
%A Monica Mordonini
%A Giovanni Adorni
%T Evolving Binary Classifiers Through Parallel Computation of Multiple Fitness Cases
%J IEEE Transactions on Systems, Man and Cybernetics - Part B
%V 35
%N 3
%D 2005
%P 548--555
%I 
%K genetic algorithms, genetic programming, cellular programming, sub-machine code genetic programming, multiple classifiers, pattern recognition
%X We describe two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic
   programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimised by allowing multiple solutions to be
   computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly, taking advantage of the
   intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem
   and compared to a reference classifier.
%8 June
%Z PMID: 15971922 [PubMed - indexed for MEDLINE]

%A S. Cagnoni
%A R. Poli
%T Genetic and evolutionary Computation
%J Intelligenza Artificiale
%V 3
%N 1/2
%D 2006
%P 94--101
%I 
%K genetic algorithms, genetic programming, gec, gas, es, gsice, italian GEC, human-competitive
%8 Marzo-Giugno
%Z In English. Tutorial. http://ia.di.uniba.it/ Periodico trimestrale dell'Associazione Italiana per l'Intelligenza Artificiale by 2012 daily invention machine

%A S. Cagnoni
%A E. Lutton
%A G. Olague
%T Editorial Introduction to the Special Issue on Evolutionary Computer Vision
%J Evolutionary Computation
%V 16
%N 4
%D 2008
%P 437--438
%I 
%K genetic algorithms, genetic programming
%8 Winter

%A Weihua Cai
%A Mihir Sen
%A K. T. Yang
%A Arturo Pacheco-Vega
%T Genetic-Programming-Based Symbolic Regression for Heat Transfer Correlations of a Compact Heat Exchanger
%B ASME Summer Heat Transfer Conference (HT2005)
%V 4
%D 2005
%P 367--374
%I ASME
%C San Francisco, California, USA
%K genetic algorithms, genetic programming
%X We describe a symbolic regression methodology based on genetic programming to find correlations that can be used to estimate the performance of compact heat exchangers.
   Genetic programming is an evolutionary search technique in which functions represented as parse trees evolve as the search proceeds. An advantage of this approach is that
   functional forms of the correlation need not be assumed. The algorithm performs symbolic regression by seeking both the functional structure of the correlation and the
   coefficients therein that enable the closest fit to experimental data. This search is conducted within a functional domain constructed from sets of operators and terminals
   that are used to build tree-structures representing functions. A penalty function is used to prevent large correlations. The methodology is tested using first artificial
   data from a one-dimensional function and later a set of published heat exchanger experiments. Comparison with published results from the same data show that
   symbolic-regression correlations are as good or better. The effect of the penalty parameters on the best function is also analysed.
%8 July  17-22
%Z collocated with the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems (HT2005) University of
   Notre Dame, Notre Dame, IN
%@ 0-7918-4734-9

%A Weihua Cai
%A Arturo Pacheco-Vega
%A Mihir Sen
%A K. T. Yang
%T Heat transfer correlations by symbolic regression
%J International Journal of Heat and Mass Transfer
%V 49
%N 23-24
%D 2006
%P 4352--4359
%I 
%K genetic algorithms, genetic programming, Heat transfer, Correlations, Symbolic regression, Heat exchanger
%X We describe a methodology that uses symbolic regression to extract correlations from heat transfer measurements by searching for both the form of the correlation equation
   and the constants in it that enable the closest fit to experimental data. For this purpose we use genetic programming modified by a penalty procedure to prevent large
   correlation functions. The advantage of using this technique is that no initial assumption on the form of the correlation is needed. The procedure is tested using two sets
   of published experimental data, one for a compact heat exchanger and the other for liquid flow in a circular pipe. In both situations, predictive errors from correlations
   found from symbolic regression are smaller than their published counterparts. A parametric analysis of the penalty function is also carried out.
%8 November

%A Xinye Cai
%A Stephen L. Smith
%A Andrew M. Tyrrell
%T Benefits of Employing an Implicit Context Representation on Hardware Geometry of CGP
%B Evolvable Systems: From Biology to Hardware, 6th International Conference, ICES 2005, Proceedings
%S Lecture Notes in Computer Science
%E Juan Manuel Moreno and Jordi Madrenas and Jordi Cosp
%V 3637
%D 2005
%P 143--154
%I Springer
%C Sitges, Spain
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%X Cartesian Genetic Programming (CGP) has successfully been applied to the evolution of simple image processing filters and implemented in intrinsic evolvable hardware by the
   authors. However, conventional CGP exhibits the undesirable characteristic of positional dependence in which the specific location of genes within the chromosome has a
   direct or indirect influence on the phenotype. An implicit context representation of CGP (IRCGP) has been implemented by the authors which is positionally independent and
   outperforms conventional CGP in this application. This paper describes the additional benefits of IRCGP when considering alternative geometries for the hardware components.
   Results presented show that smaller hardware arrays under IRCGP are more robust and outperform equivalent arrays implemented in conventional CGP.
%8 September  12-14
%@ 3-540-28736-1

%A Xinye Cai
%A Stephen L. Smith
%A Andy M. Tyrrell
%T Positional Independence and Recombination in Cartesian Genetic Programming
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 351--360
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050351.pdf
%X Previously, recombination (or crossover) has proved to be unbeneficial in Cartesian Genetic Programming (CGP). This paper describes the implementation of an implicit
   context representation for CGP in which the specific location of genes within the chromosome has no direct or indirect influence on the phenotype. Consequently,
   recombination has a beneficial effect and is shown to outperform conventional CGP in the even-3 parity problem.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Xinye Cai
%A Stephen M. Welch
%A Praveen Koduru
%A Sanjoy Das
%T Discovering structures in gene regulatory networks using genetic programming and particle swarms
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1750--1750
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming: Poster, bioinformatics, gene regulatory network, Particle Swarm Optimisation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1750.pdf
%X GP + PSO for gene network discovery
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071. Synthetic data. PSO used to set 'parameters' in each network (evolved by GP). See also ICAI'07: The 2007 World Congress in Computer Science, Computer
   Engineering, & Applied Computing, Las Vegas, Nevada, USA. June 27 Discovering Structures in Gene Regulatory Networks Using Genetic Programming and Particle Swarms Xinye
   Cai, Stephen Welch, Praveen Koduru, and Sanjoy Das Kansas State University, Manhattan, Kansas, USA http://www.world-academy-of-science.org/worldcomp07/ws/program/ica27

%A Xinye Cai
%A Praveen Koduru
%A Sanjoy Das
%A Stephen M. Welch
%T Simultaneous structure discovery and parameter estimation in gene networks using a multi-objective GP-PSO hybrid approach
%J International Journal of Bioinformatics Research and Applications
%V 5
%N 3
%D 2009
%P 254--268
%I Inderscience Publishers
%K genetic algorithms, genetic programming, gene regulatory networks, PSO, particle swarm optimisation, multi-objective optimisation, Bioinformatics, structure discovery,
   parameter estimation, gene networks, plant genes, plant flowering times, gene expressions
%U http://www.inderscience.com/link.php?id=26418
%X This paper presents a hybrid algorithm based on Genetic Programming (GP) and Particle Swarm Optimisation (PSO) for the automated recovery of gene network structure. It uses
   gene expression time series data as well as phenotypic data pertaining to plant flowering time as its input data. The algorithm then attempts to discover simple structures
   to approximate the plant gene regulatory networks that produce model gene expressions and flowering times that closely resemble the input data. To show the efficacy of the
   proposed approach, simulation results applied to flowering time control in Arabidopsis thaliana are demonstrated and discussed.
%8 11  June

%A Yu-Dong Cai
%T Genetic programming for prediction of earthquake sequence type
%J Acta Seismologica Sinica
%V 9
%D 1996
%P 53--57
%I Seismological Society of China
%K genetic algorithms, genetic programming, earthquake sequence, prediction
%X The genetic programming for the prediction of earthquake sequence type was studied, and the reliability for a group of samples was tested. The results show that the
   performance of the genetic programming is good, and therefore it might be referred as an effective technique for the prediction of earthquake sequence type.
%8 February
%Z Journal now called Earthquake Science (2009-2011)

%A Stephane Calderoni
%A Pierre Marcenac
%T Genetic Programming For Automatic Design Of Self-Adaptive Robots
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 163--177
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/267374.html
%X The general framework tackled in this paper is the automatic generation of intelligent collective behaviors using genetic programming and reinforcement learning. We define
   a behavior-based system relying on automatic design process using artificial evolution to synthesize high level behaviors for autonomous agents. Behavioral strategies are
   described by tree-based structures, and manipulated by genetic evolving processes. Each strategy is dynamically evaluated during simulation, and weighted by an adaptative
   value. This value is a quality factor that reflects the relevance of a strategy as a good solution for the learning task. It is computed using heterogeneous reinforcement
   techniques associating immediate and delayed reinforcements as dynamic progress estimators. This work has been tested upon a canonical experimentation framework: the
   foraging robots problem. Simulations have been conducted and have produced some promising results.
%8 14-15  April
%Z EuroGP'98
%@ 3-540-64360-5

%A Stephane Calderoni
%A Pierre Marcenac
%A Remy Courdier
%T Genetic Encoding of Agent Behavioral Strategy
%B Proceedings of the 3rd International Conference on Multi Agent Systems
%D 1998
%P 403
%I IEEE Computer Society
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/185735.html
%X The general framework tackled in this paper is the automatic generation of intelligent collective behaviors using genetic programming and reinforcement learning. We define
   a behavior-based system relying on automatic design process using artificial evolution to synthesize high level behaviors for autonomous agents. Behavioral strategies are
   described by tree-based structures, and manipulated by genetic evolving processes. Each strategy is dynamically evaluated during simulation, and is weighted by an
   adaptation function as a quality factor that reflects its relevance as good solution for the learning task. It is computed using heterogeneous reinforcement techniques
   associating immediate reinforcements and delayed reinforcements as dynamic progress estimators.
%O The Pennsylvania State University CiteSeer Archives
%@ 0-8186-8500-X

%A Stephane Calderoni
%T Behavior-Based Control System in MultiAgent Domain
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1439
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, artificial life, adaptive behavior and agents, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-048.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Stephane Calderoni
%T Generic Control Ssystem in MultiAgent Domain
%B World Multiconference on Systemics, Cybernetics and Informatics SCI-99
%V 7
%D 1999
%I 
%K genetic algorithms, genetic programming, Multiagent Systems, Control Systems, Reinforcement Learning
%U http://citeseer.ist.psu.edu/247844.html
%X This paper reports on-going works dealing with collective learning in autonomous agents context. We propose a methodology to design robust and flexible adaptive behavior
   with both genetic and reinforcement learning techniques.The originality of this contribution relies on the ability of the agents to manage themselves their learning task.
   Indeed, rather than coming from the environment, as it is implemented in many programs, we consider that the reinforcement must be intrinsically deduced by the agent
   itself, from satisfaction and disapointment indicators. We show that in such a way, the agents are capable of robustness facing with unexpected situations. A collective
   regulation problem is presented to help in clarify the different issues tackled in this paper. A software toolkit has been developped as a support for these works.
%O The Pennsylvania State University CiteSeer Archives

%A Carlos Ivan {Camargo Bareno}
%A Cesar Augusto {Pedraza Bonilla}
%A Luis Fernado Nino
%A Jose Ignacio {Martinez Torre}
%T Intrinsic evolvable hardware for combinatorial synthesis based on SoC+FPGA and GPU platforms
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 189--190
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, GPU: Poster
%X This paper presents a novel a parallel genetic programming (PGP) Boolean synthesis implementation on a low cost cluster of an embedded open platform called SIE. Some tasks
   of the PGP have been accelerated through a hardware coprocessor called FCU, that allows to evaluate individuals onchip as intrinsic evolution. Results have been compared
   with GPU and HPC implementations, resulting in speedup values up to approximately 2 and 180 respectively.
%8 12-16  July
%Z Also known as \cite2001964 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Elliott Campbell
%T Evaluation of Genetic Programming for Determining Reservoir Operating Rules
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 54--59
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Paul J. Campbell
%T Reviews
%J Mathematics Magazine
%V 66
%N 2
%D 1993
%P 136--137
%I 
%K genetic algorithms, genetic programming
%U http://links.jstor.org/sici?sici=0025-570X%28199304%2966%3A2%3C136%3AR%3E2.0.CO%3B2-4
%Z review of \citekoza:book

%A Birkan Can
%A Cathal Heavey
%T Sequential metamodelling with genetic programming and particle swarms
%B Proceedings of the 2009 Winter Simulation Conference (WSC)
%D 2009
%P 3150--3157
%I 
%K genetic algorithms, genetic programming, PSO, buffer allocation, design of experiment, discrete event simulation, evolutionary algorithm, global metamodelling,
   manufacturing lines, particle swarm algorithm, sampling data, sequential metamodelling, simulation-based metamodelling, symbolic function, symbolic regression, design of
   experiments, discrete event simulation, manufacturing systems, particle swarm optimisation, regression analysis, sampling methods
%X This article presents an application of two main component methodologies of evolutionary algorithms in simulation-based metamodelling. We present an evolutionary framework
   for constructing analytical metamodels and apply it to simulations of manufacturing lines with buffer allocation problem. In this framework, a particle swarm algorithm is
   integrated to genetic programming to perform symbolic regression of the problem. The sampling data is sequentially generated by the particle swarm algorithm, while genetic
   programming evolves symbolic functions of the domain. The results are promising in terms of efficiency in design of experiments and accuracy in global metamodelling.
%8 13-16  Decemeber
%Z Also known as \cite5429276

%A Birkan Can
%T Evolutionary Modelling of Industrial Systems with Genetic Programming
%R Ph.D. Thesis
%D 2011
%I 
%I University of Limerick
%C Ireland
%K genetic algorithms, genetic programming
%Z 2011 Supervisor Dr. Cathal Heavey. 'thesis is available in the University Library'

%A Birkan Can
%A Cathal Heavey
%T Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems
%J Computer \& Industrial Engineering
%D 2011
%I 
%K genetic algorithms, genetic programming, Meta-modelling, Design of experiments, Discrete-event simulation, Decision support
%U http://www.sciencedirect.com/science/article/B6V27-52JDFD9-1/2/207e7db7ff221a11f1a808666cba277d
%X In this article, an empirical analysis of experimental design approaches in simulation-based metamodelling of manufacturing systems with genetic programming (GP) is
   presented. An advantage of using GP is that prior assumptions on the structure of the metamodels are not required. On the other hand, having an unknown structure
   necessitates an analysis of the experimental design techniques used to sample the problem domain and capture its characteristics. Therefore, the study presents an empirical
   analysis of experimental design methods while developing GP metamodels to predict throughput rates in a common industrial system, serial production lines. The objective is
   to identify a robust sampling approach suitable for GP in simulation-based meta-modelling. Experiments on different sizes of production lines are presented to demonstrate
   the effects of the experimental designs on the complexity and quality of approximations as well as their variance. The analysis showed that GP delivered system-wide
   meta-models with good predictive characteristics even with the limited sample data.
%O In Press, Corrected Proof

%A Hanifi Canakci
%A Adil Baykasoglu
%A Hamza Gullu
%T Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming
%J Neural Computing and Applications
%V 18
%N 8
%D 2009
%P 1031--1041
%I 
%K genetic algorithms, genetic programming, gene expression programming, Artificial neural networks, Basalt, Tensile and compressive strength
%X In this paper, two soft computing approaches, which are known as artificial neural networks and Gene Expression Programming (GEP) are used in strength prediction of basalts
   which are collected from Gaziantep region in Turkey. The collected basalts samples are tested in the geotechnical engineering laboratory of the University of Gaziantep. The
   parameters, ultrasound pulse velocity, water absorption, dry density, saturated density, and bulk density which are experimentally determined based on the procedures given
   in ISRM (Rock characterisation testing and monitoring. Pergamon Press, Oxford, 1981) are used to predict uniaxial compressive strength and tensile strength of Gaziantep
   basalts. It is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts. The
   results obtained are also useful in characterizing the Gaziantep basalts for practical applications.
%Z Department of Civil Engineering, University of Gaziantep, Gaziantep, Turkey (2) Department of Industrial Engineering, Faculty of Engineering, University of Gaziantep, 27310
   Gaziantep, Turkey

%A Angelo Cangelosi
%T Heterochrony and Adaptation in Developing Neural Networks
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1241--1248
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-008.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Richard O. Canham
%A Andy M. Tyrrell
%T A Hardware Artificial Immune System and Embryonic Array for Fault Tolerant Systems
%J Genetic Programming and Evolvable Machines
%V 4
%N 4
%D 2003
%P 359--382
%I 
%K artificial immune systems, embryonic array, fault tolerance
%X Nature demonstrates amazing levels of fault tolerance; animals can survive injury, damage, wear and tear, and are under continual attack from infectious pathogens. This
   paper details inspiration from biology to provide fault tolerant electronic circuits. An artificial immune system (AIS) is used to detect faults and an embryonic array to
   quickly reconfigure around them. The AIS makes use of a negative selection algorithm to detect abnormal behaviour. The embryonic array takes its inspiration from the
   development of multi-cellular organisms; each cell contains all the information necessary to describe the complete individual. Should an electronic cell fail, its
   neighbours have the configuration data to take over the failed cell's functionality. Two demonstration robot control systems have been implemented to provide a Khepera
   robot with fault tolerance. The first is very simple and is implemented on an embryonic array within a Virtex FPGA. An AIS is also implemented within the array which learns
   normal behaviour. Injected stuck-at faults were detected and accommodated. The second system uses fuzzy rules (implemented in software) to provide a more graceful
   functionality. A small AIS has been implemented to provide fault detection; it detected all faults that produced an error greater than 15% (or 23% off straight).
%8 Decemeber
%Z Special issue on artificial immune systems Article ID: 5144848

%A Alberto Cano
%A Amelia Zafra
%A Sebastian Ventura
%T Solving Classification Problems Using Genetic Programming Algorithms on GPUs
%B Hybrid Artificial Intelligence Systems
%S Lecture Notes in Computer Science
%E Emilio Corchado and Manuel Grana Romay and Alexandre Manhaes Savio
%V 6077
%D 2010
%P 17--26
%I Springer
%C San Sebastian, Spain
%K genetic algorithms, genetic programming, gpu, gpgpu, gpgpgpu
%X Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper
   proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelise the evaluation phase and reduce
   computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental
   results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results
   show that our proposal is much more efficient.
%8 June  23-25
%Z JCLEC. No absolute measure of speed given. confusion matrix calculated on two GTX 285. big multi-class training sets from UCI (poker and shuttle) comparison with Java and
   Intel i7 multi-core. Three GP fitness functions. RPN interpreter

%A Alberto Cano
%A Amelia Zafra
%A Sebastian Ventura
%T Speeding up the evaluation phase of GP classification algorithms on GPUs
%J Soft Computing - A Fusion of Foundations, Methodologies and Applications
%D 2011
%I Springer Berlin / Heidelberg
%K genetic algorithms, genetic programming, GPU, Computer Science
%X The efficiency of evolutionary algorithms has become a studied problem since it is one of the major weaknesses in these algorithms. Specifically, when these algorithms are
   employed for the classification task, the computational time required by them grows excessively as the problem complexity increases. This paper proposes an efficient
   scalable and massively parallel evaluation model using the NVIDIA CUDA GPU programming model to speed up the fitness calculation phase and greatly reduce the computational
   time. Experimental results show that our model significantly reduces the computational time compared to the sequential approach, reaching a speedup of up to 820×.
   Moreover, the model is able to scale to multiple GPU devices and can be easily extended to any evolutionary algorithm.
%Z No absolute measure of speed given. UCI: Iris, New-thyroid, Ecoli, Contraceptive, Thyroid, Penbased, Shuttle, Connect-4, KDDcup, Poker. GTX 285, two GTX 480. 64-bit Linux
   Ubuntu. execution time was reduced from 30 hours to 2 minutes.

%A Uwe Cantner
%A Bernd Ebersberger
%A Horst Hanusch
%A Jens J. Kruger
%A Andreas Pyka
%T Empirically Based Simulation: The Case of Twin Peaks in National Income
%J The Journal of Artificial Societies and Social Simulation
%D 2001
%I 
%K genetic algorithms, genetic programming, bimodal productivity structure, master equation approach
%U http://jasss.soc.surrey.ac.uk/4/3/9.html
%X Only recently a new stylised fact of economic growth has been introduced, the bimodal shape of the distribution of per capita income or the twin-peaked nature of that
   distribution. Drawing on the Summers/Hestons Penn World Table 5.6 (1991) we determine kernel density distributions which are able to detect the aforementioned twin peaked
   structure and show that the world income distribution starting with an unimodal structure in 1960 evolves subsequently to a bimodal or twin-peak structure. This empirical
   results can be explained theoretically by a synergetic model based on the master equation approach as in Pyka/Kruger/Cantner (1999). This paper attempts to extend this
   discussion by taking the reverse procedure, that is to find empirical evidence for the working mechanism of the theoretical model. We determine empirically the transition
   rates used in the synergetic approach by applying alternatively NLS to chosen functional forms and genetic programming in order to determine the functional forms and the
   parameters simultaneously. Using the so determined transition rates in the synergetic model leads in both cases to the emergence of the bimodal distribution, which,
   however, is only in the latter case a persistent phenomenon.
%8 30-  June
%Z JASSS

%A Erick Cantu-Paz
%A David E. Goldberg
%T Modeling Idealized Bounding Cases of Parallel Genetic Algorithms
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 353--361
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Genetic Algorithms
%8 13-16  July
%Z GP-97

%A Erick Cantu-Paz
%T Designing Efficient Master-Slave Parallel Genetic Algorithms
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 455
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Erick Cantu-Paz
%T Using Markov Chains to Analyze a Bounding Case of Parallel Genetic Algorithms
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 456--462
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Erick Cantu-Paz
%T Migration Policies and Takeover Times in Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 775
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://dangermouse.brynmawr.edu/ec/gecco99-migpolicy.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Erick Cantu-Paz
%T Topologies, Migration Rates, and Multi-Population Parallel Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 91--98
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://dangermouse.brynmawr.edu/ec/gecco99-topologies.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Erick Cantu-Paz
%T Migration policies, selection pressure, and parallel evolutionary algorithms
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 65--73
%I 
%C Orlando, Florida, USA
%8 13  July
%Z GECCO-99LB

%T Late Breaking papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming, Evolvable Network Architecture , Dynamic Neural Net, Pattern Recognition , Evolutionary Computation, Automated Sensor, Multiagent
   Systems, Optimisation, Evolvable Hardware , Genetic Multi-Agent Planning, Evolutionary Testing, Evolving Neural Network Architectures, Evolving Software, Airline Fleet
   Assignment, Ant Colony Algorithm, Artificial Immune System , Artificial Life, Evolving Cellular Automata
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002)

%T Genetic and Evolutionary Computation -- GECCO 2003, Part I
%S Lecture Notes in Computer Science
%E Erick Cant\'u-Paz and James A. Foster and Kalyanmoy Deb and Lawrence Davis and Rajkumar Roy and Una-May O'Reilly and Hans-Georg Beyer and Russell K. Standish and Graham
   Kendall and Stewart W. Wilson and Mark Harman and Joachim Wegener and Dipankar Dasgupta and Mitchell A. Potter and Alan C. Schultz and Kathryn A. Dowsland and Natasha
   Jonoska and Julian F. Miller
%V 2723
%D 2003
%I Springer
%C Chicago, IL, USA
%K genetic algorithms, genetic programming, A-Life, Adaptive Behaviour, Agents, Artificial Immune Systems, Coevolution, DNA computing, Evolution Strategies, Evolutionary
   Programming, Evolutionary Robotics, Evolutionary Scheduling Routing, Evolvable Hardware, Genetic Algorithms, Learning Classifier Systems, Molecular computing, Quantum
   Computing, Real World Applications, Search Based Software Engineering, Ant Colony Optimization, grammatical evolution
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40602-6

%T Genetic and Evolutionary Computation -- GECCO 2003, Part II
%S Lecture Notes in Computer Science
%E Erick Cant\'u-Paz and James A. Foster and Kalyanmoy Deb and Lawrence Davis and Rajkumar Roy and Una-May O'Reilly and Hans-Georg Beyer and Russell K. Standish and Graham
   Kendall and Stewart W. Wilson and Mark Harman and Joachim Wegener and Dipankar Dasgupta and Mitchell A. Potter and Alan C. Schultz and Kathryn A. Dowsland and Natasha
   Jonoska and Julian F. Miller
%V 2724
%D 2003
%I Springer
%K genetic algorithms, genetic programming, A-Life, Adaptive Behavior, Agents, Artificial Immune Systems, Coevolution, DNA computing, Evolution Strategies, Evolutionary
   Programming, Evolutionary Robotics, Evolutionary Scheduling Routing, Evolvable Hardware, Genetic Algorithms, Learning Classifier Systems, Molecular computing, Quantum
   Computing, Real World Applications, Search Based Software Engineering, Ant Colony Optimization, grammatical evolution
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Hongqing Cao
%A Lishan Kang
%A Zbigniew Michalewicz
%A Yuping Chen
%T A Hybrid Evolutionary Modeling Algorithm for System of Ordinary Differential Equations
%J Neural, Parallel \& Scientific Computations
%V 6
%N 2
%D 1998
%P 171--188
%I Dynamic Publishers
%C Atlanta, USA
%K genetic algorithms, genetic programming
%8 June

%A Hongqing Cao
%A Lishan Kang
%A Zbigniew Michalewicz
%A Yuping Chen
%T A Two-level Evolutionary Algorithm for Modeling System of Ordinary Differential Equations
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 17--22
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Hongqing Cao
%A Jingxian Yu
%A Lishan Kang
%A Yuping Chen
%A Yongyan Chen
%T The Kinetic Evolutionary Modeling of Complex Systems of Chemical Reactions
%J Computers \& Chemistry
%V 23
%N 2
%D 1999
%P 143--152
%I 
%K genetic algorithms, genetic programming, kinetic analysis, Complex systems of chemical reactions, Evolutionary modeling
%X To overcome the drawbacks of most available methods for kinetic analysis, this paper proposes a hybrid evolutionary modelling algorithm called HEMA to build kinetic models
   of systems of ordinary differential equations (ODEs) automatically for complex systems of chemical reactions. The main idea of the algorithm is to embed a genetic algorithm
   (GA) into genetic programming (GP) where GP is employed to optimise the structure of a model, while a GA is employed to optimize its parameters. The experimental results of
   two chemical reaction systems show that by running the HEMA, the computer can discover the kinetic models automatically which are appropriate for describing the kinetic
   characteristics of the reacting systems. Those models can not only fit the kinetic data very well, but also give good predictions.
%8 30  March

%A Hongqing Cao
%A Lishan Kang
%A Yuping Chen
%T Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 959--965
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-401.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Hongqing Cao
%A Lishan Kang
%A Yuping Chen
%A Jingxian Yu
%T Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 1
%N 4
%D 2000
%P 309--337
%I 
%K genetic algorithms, genetic programming, evolutionary modeling, system of ordinary differential equations, higher-order ordinary differential equation
%U http://www.ees.adelaide.edu.au/people/enviro/cao/2000-05.pdf
%X This paper describes an approach to the evolutionary modeling problem of ordinary differential equations including systems of ordinary differential equations and
   higher-order differential equations. Hybrid evolutionary modeling algorithms are presented to implement the automatic modeling of one- and multi-dimensional dynamic systems
   respectively. The main idea of the method is to embed a genetic algorithm in genetic programming where the latter is employed to discover and optimize the structure of a
   model, while the former is employed to optimize its parameters. A number of practical examples are used to demonstrate the effectiveness of the approach. Experimental
   results show that the algorithm has some advantages over most available modeling methods.
%8 October
%Z Article ID: 273810

%A Hong-Qing Cao
%A Li-Shan Kang
%A Tao Guo
%A Yu-Ping Chen
%A Hugo {de Garis}
%T A two-level hybrid evolutionary algorithm for modeling one-dimensional dynamic systems by higher-order ODE models
%J IEEE Transactions on Systems, Man and Cybernetics -- Part B: Cybernetics
%V 40
%N 2
%D 2000
%P 351--357
%I 
%K genetic algorithms, genetic programming, evolutionary computation, evolutionary algorithm, ODE models, one-dimensional dynamic systems, ordinary differential equation,
   two-level hybrid evolutionary modeling algorithm, THEMA, crossover operator
%U http://ieeexplore.ieee.org/iel5/3477/18067/00836383.pdf
%X This paper presents a new algorithm for modeling one-dimensional (1-D) dynamic systems by higher-order ordinary differential equation (HODE) models instead of the ARMA
   models as used in traditional time series analysis. A two-level hybrid evolutionary modeling algorithm (THEMA) is used to approach the modeling problem of HODE's for
   dynamic systems. The main idea of this modeling algorithm is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure
   of a model (the upper level), while a GA is employed to optimize the parameters of the model (the lower level). In the GA, we use a novel crossover operator based on a
   nonconvex linear combination of multiple parents which works efficiently and quickly in parameter optimization tasks. Two practical examples of time series are used to
   demonstrate the THEMA's effectiveness and advantages.
%8 April

%A Hongqing Cao
%A Jingxian Yu
%A Lishan Kang
%A Hanxi Yang
%A Xinping Ai
%T Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms
%J Computers \& Chemistry
%V 25
%N 3
%D 2001
%P 251--259
%I 
%K genetic algorithms, genetic programming, Discharge lifetime of battery systems, Lithium-ion battery, Hybrid evolutionary modelling
%X A hybrid evolutionary modeling algorithm (HEMA) is proposed to build the discharge lifetime models with multiple impact factors for battery systems as well as make
   predictions. The main idea of the HEMA is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimise the structure of a model, while
   a GA is employed to optimize its parameters. The experimental results on lithium-ion batteries show that the HEMA works effectively, automatically and quickly in modelling
   the discharge lifetime of battery systems. The algorithm has some advantages compared with most existing modelling methods and can be applied widely to solving the
   automatic modelling problems in many fields.
%8 May
%Z http://www.elsevier.com/wps/find/journaldescription.cws_home/627320/description#description

%A Hongqing Cao
%A Lishan Kang
%A Jingxian Yu
%T Parallel Implementations of Modeling Dynamical Systems by Using System of Ordinary Differential Equations
%J Wuhan University Journal of Natural Sciences
%V 8
%N IB
%D 2003
%P 229--233
%I 
%K genetic algorithms, genetic programming

%A Hongqing Cao
%A Jingxian Yu
%A Lishan Kang
%A R I Bob McKay
%T An Experimental Study of Some Control Parameters in Parallel Genetic Programming
%J Neural, Parallel and Scientific Computation
%V 11
%N 4
%D 2003
%P 377--393
%I 
%K genetic algorithms, genetic programming

%A Hongqing Cao
%A Lishan Kang
%A Yuping Chen
%A Tao Guo
%T The Dynamic Evolutionary Modeling of HODEs for Time Series Prediction
%J Computers \& Mathematics with Applications
%V 46
%N 8-9
%D 2003
%P 1397--1411
%I 
%K genetic algorithms, genetic programming, Time series, Differential equation
%U http://www.sciencedirect.com/science/article/B6TYJ-4BRR761-P/2/4d226ed6e682798de2e1d83d01cebd95
%X The prediction of future values of a time series generated by a chaotic dynamic system is an extremely challenging task. Besides some methods used in traditional time
   series analysis, a number of nonlinear prediction methods have been developed for time series prediction, especially the evolutionary algorithms. Many researchers have
   built various models by using different evolutionary techniques. Different from those available models, this paper presents a new idea for modelling time series using
   higher-order ordinary differential equations (HODEs) models. Accordingly, a dynamic hybrid evolutionary modeling algorithm called DHEMA is proposed to approach this task.
   Its main idea is to embed a genetic algorithm (GA) into genetic programming (GP) where GP is employed to optimise the structure of a model, while a GA is employed to
   optimize its parameters. By running the DHEMA, the modeling and predicting processes can be carried on successively and dynamically with the renewing of observed data. Two
   practical examples are used to examine the effectiveness of the algorithm in performing the prediction task of time series whose experimental results are compared with
   those of standard GP.

%A Hongqing Cao
%A Jingxian Yu
%A Lishan Kang
%T An evolutionary approach for modeling the equivalent circuit for electrochemical impedance spectroscopy
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 1819--1825
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming, Gene Expression Programming, GEP, HEMA
%U http://www.ees.adelaide.edu.au/people/enviro/cao/2003-05.pdf
%X This paper proposes an evolutionary approach to build the equivalent circuit model for electrochemical impedance spectroscopy. It works by using a hybrid evolutionary
   modelling algorithm (HEMA) whose main idea is to embed a genetic algorithm (GA) in gene expression programming (GEP) where GEP is employed to discover and optimise the
   structure of a circuit, while the GA is employed to optimize the parameters of all the electric components contained in the circuit. By running the HEMA, the computer can
   automatically find suitable circuit structures as well as optimise the component parameters simultaneously. Compared with most available methods, it has the advantages of
   automation of modeling process, great diversity of model structures, high stability and efficiency of parameter optimisation.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Hongqing Cao
%A Friedrich Recknagel
%A Gea-Jae Joo
%A Dong-Kyun Kim
%T Discovery of Predictive Rule Sets for Chlorophyll-a Dynamics in the Nakdong River (Korea) by Means of the Hybrid Evolutionary Algorithm HEA
%J Ecological Informatics
%V 1
%N 1
%D 2006
%P 43--53
%I 
%K genetic algorithms, genetic programming, Hybrid evolutionary algorithm, Rule sets, Chl.a, Sensitivity analysis, Nakdong River
%X We present a hybrid evolutionary algorithm (HEA) to discover complex rule sets predicting the concentration of chlorophyll-a (Chl.a) based on the measured meteorological,
   hydrological and limnological variables in the hypertrophic Nakdong River. The HEA is designed: (1) to evolve the structure of rule sets by using genetic programming and
   (2) to optimise the random parameters in the rule sets by means of a genetic algorithm. Time-series of input-output data from 1995 to 1998 without and with time lags up to
   7 days were used for training HEA. Independent input output data for 1994 were used for testing HEA. HEA successfully discovered rule sets for multiple nonlinear
   relationships between physical, chemical variables and Chl.a, which proved to be predictive for unseen data as well as explanatory. The comparison of results by HEA and
   previously applied recurrent artificial neural networks to the same data with input--output time lags of 3 days revealed similar good performances of both methods. The
   sensitivity analysis for the best performing predictive rule set revealed relationships between seasons, specific input variables and Chl.a which to some degree correspond
   with known properties of the Nakdong River. The statistics of numerous random runs of the HEA also allowed determining most relevant input variables without a priori
   knowledge.
%8 January
%Z http://www.elsevier.com/wps/find/journaldescription.cws_home/705192/description#description

%A Hongqing Cao
%A Friedrich Recknagel
%A Bomchul Kim
%A Noriko Takamura
%T Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and
   Eutrophication
%B Ecological Informatics: Scope, Techniques and Applications
%E Friedrich Recknagel
%D 2006
%P 347--367
%I Springer-Verlag
%C Berlin, Heidelberg, New York
%K genetic algorithms, genetic programming
%O 17
%Z http://www.springer.com/sgw/cda/frontpage/0,11855,5-10031-22-68637391-0,00.html
%@ 3-540-28383-8

%A Hongqing Cao
%A Friedrich Recknagel
%A Lydia Cetin
%A Byron Zhang
%T Process-based simulation library SALMO-OO for lake ecosystems. Part 2: Multi-objective parameter optimization by evolutionary algorithms
%J Ecological Informatics
%V 3
%N 2
%D 2008
%P 181--190
%I 
%K genetic algorithms, genetic programming, Multi-objective parameter optimization, SALMO-OO, Lake categories, Evolutionary algorithms
%U http://www.sciencedirect.com/science/article/B7W63-4S69SG8-1/2/95e920ec339c554888f67696a93f2f37
%X SALMO-OO represents an object-oriented simulation library for lake ecosystems that allows to determine generic model structures for certain lake categories. It is based on
   complex ordinary differential equations that can be assembled by alternative process equations for algal growth and grazing as well as zooplankton growth and mortality. It
   requires 128 constant parameters that are causally related to the metabolic, chemical and transport processes in lakes either estimated from laboratory and field
   experiments or adopted from the literature. An evolutionary algorithm (EA) was integrated into SALMO-OO in order to facilitate multi-objective optimization for selected
   parameters and to substitute them by optimum temperature and phosphate functions. The parameters were related to photosynthesis, respiration and grazing of the three algal
   groups diatoms, green algae and blue-green algae. The EA determined specific temperature and phosphate functions for same parameters for 3 lake categories that were
   validated by ecological data of six lakes from Germany and South Africa. The results of this study have demonstrated that: (1) the hybridization of ordinary differential
   equations by EA provide a sophisticated approach to fine-tune crucial parameters of complex ecological models, and (2) the multi-objective parameter optimization of
   SALMO-OO by EA has significantly improved the accuracy of simulation results for three different lake categories.

%A Lijuan Cao
%A Tay Eng Hock (Francis)
%A Ma Lawrence
%A Wai Cheong Yeong
%T Classification of the Market States Using Neural Network
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 776
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Lijuan Cao
%A Tay Eng Hock (Francis)
%T Neuro-Genetic Based Method to the Classification of Acupuncture Needle: A Case Study
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 99--105
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Mathieu S. Capcarrece
%T An evolving ontogenetic cellular system for better adaptiveness
%J Biosystems
%V 76
%N 1-3
%D 2004
%P 177--189
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6T2K-4D1R6V6-2/2/ceb26b0139eed613393486f88bc2ac23
%X we present an original cellular system named Phuon. The main motivation behind this project is to go beyond classical cellular systems, such as cellular automata (CA). CA
   often lack adaptability and turn out to be very brittle in uncertain environment. The idea here is to add ontogeny to cellularity, growth and development being means of
   adaptation and thus robustness. However, we do not wish to develop yet another cellular system for the sake of it. What we are seeking in the long term is a developmental
   system for problem solving. This global aim enticed us into finding a way to map a desired global behaviour of the system to the local behaviour of a cell. Quite naturally
   a peculiar brand of genetic programming was used for that purpose. The results are still preliminary but in our view they already validate some of the hypotheses behind
   this work.
%Z Papers presented at the Fifth International Workshop on Information Processing in Cells and Tissues PMID: 15351141 [PubMed - indexed for MEDLINE]

%T 8th European Conference on Advances in Artificial Life, ECAL 2005
%S Lecture Notes in Computer Science
%E Mathieu S. Capcarrere and Alex Alves Freitas and Peter J. Bentley and Colin G. Johnson and Jon Timmis
%V 3630
%D 2005
%I Springer
%C Canterbury, UK
%8 September  5-9
%@ 3-540-28848-1

%A Michael Caplan
%A Ying Becker
%T Lessons Learned Using Genetic Programming in a Stock Picking Context
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 87--102
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, stock selection, data mining, fitness functions, quantitative portfolio management
%X This is a narrative describing the implementation of a genetic programming technique for stock picking in a quantitatively driven, risk-controlled, US equity portfolio. It
   describes, in general, the problems that the authors faced in their portfolio context when using genetic programming techniques and in gaining acceptance of the technique
   by a skeptical audience. We discuss in some detail the construction of the fitness function, the genetic programming systemÇs parametrisation (including data selection and
   internal function choice), and the interpretation and modification of the generated programs for eventual implementation.
%O 6
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A Santiago Garcia
%A Fermin Gonzalez
%A Luciano Sanchez
%T Evolving Fuzzy Rule Based Classifiers with GA-P: A Grammatical Approach
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 203--210
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=203
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP Combination of grammar based GP and GA-P with fuzzy rules. UCI machine learning databases First author is Santiago Garcia Carbajal
%@ 3-540-65899-8

%A Santiago {Garcia Carbajal}
%A Fermin Gonzalez Martinez
%T Evolutive Introns: A Non-Costly Method of Using Introns in GP
%J Genetic Programming and Evolvable Machines
%V 2
%N 2
%D 2001
%P 111--122
%I 
%K genetic algorithms, genetic programming, bloating, introns, intertwined spirals
%X We proposed a new strategy to explicitly define introns that increases the probability of selecting good crossover points as evolution goes on. Our approach differs from
   existing methods in the procedure followed to adapt the probabilities of groups of code being protected. We also provide some experimental results in symbolic regression
   and classification that reinforced our belief in the usefulness of this procedure. Collateral effects of Evolutive Introns (EIs) are also studied to determine possible
   modifications in the behavior of a classical Genetic Programming (GP) system.
%8 June
%Z Article ID: 335711

%A Santiago {Garcia Carbajal}
%T Automatic Identification of Partial Goals with Grammar-Directed Genetic Programming
%R Ph.D. Thesis
%D 2002
%I 
%I Faculty of Informatics. GIJON
%K genetic algorithms, genetic programming, algorithms, grammar directed GP
%X Automatic Defined Functions (ADFs) concept is expanded with the use of Grammar Directed Genetic Programming. The approach is applied to classical regression problems and
   control systems.
%Z In spanish. Available by email

%A Santiago Garcia
%A John Levine
%A Fermin Gonzalez
%T Multi Niche Parallel GP with a Junk-code Migration Model
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 327--334
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=327
%X We describe in this paper a parallel implementation of Multi Niche Genetic Programming that we use to test the performance of a modified migration model. Evolutive introns
   is a technique developed to accelerate the convergence of GP in classification and symbolic regression problems. Here, we will copy into a differentiated subpopulation the
   individuals that due to the evolution process contain longer Evolutive Introns. Additionally, the multi island model is parallelised in order to speed up convergence. These
   results are also analysed. Our results prove that the multi island model achieves faster convergence in the three different symbolic regression problems tested, and that
   the junk-coded subpopulation is not significantly worse than the others, which reinforces our belief in that the important thing is not only fitness but keeping good
   genetic diversity along all the evolution process. The overhead introduced in the process by the existence of various island, and the migration model is reduced using a
   multi-thread approach.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Santiago {Garcia Carbajal}
%A Martin Bosque Moran
%A Fermin Gonzales Martinez
%T EvolGL: Life in a Pond
%B Artificial Life XI Ninth International Conference on the Simulation and Synthesis of Living Systems
%E Jordan Pollack and Mark Bedau and Phil Husbands and Takashi Ikegami and Richard A. Watson
%D 2004
%P 75--80
%I The MIT Press
%C Boston, Massachusetts
%K genetic algorithms, genetic programming, GA-P, artificial Life
%8 12-15  September
%Z http://www.alife9.org/ ALIFE9 3D artificial life forms. Evolworm. Promotion of introns (junk code). Genetic definition (GA-P \citehoward:1995:GA-P ) of texture of moving
   aquatic creatures. Brain finite state automata. Colour of worms. Kill by hitting head with your tail. GP stereo Vision. 24 node MOSIX redhat 7.2 linux cluster. Separate
   visualisation (microsoft) PC. Pop > 600.
%@ 0-262-66183-7

%A Santiago {Garcia Carbajal}
%A Nouhad J. Rizk
%T Hierarchical Reinforcement Learning with Grammar-Directed GA-P
%J International Journal of Soft Computing
%V 1
%N 1
%D 2006
%P 52--60
%I 
%K genetic algorithms, genetic programming, reinforcement learning, grammar, knowledge
%X We propose a grammatical approach to hierarchical reinforcement learning. It is based on the grammatical description of a problem, a complex task, or objective. The use of
   a grammar to control the learning process,constraining the structure of the solutions generated with standard GP, permits the inclusion of knowledge about the problem in a
   straightforward manner, if this knowledge exists. When the problem to be solved involves the use of fuzzy concepts,the membership functions can be evolved simultaneously
   within the learning process using the advantages of the GA-P paradigm. Additionally,the inclusion of penalty factors in the evaluation function allows us to try to bias the
   search toward solutions that are optimal in safety or economical terms,not only taking into account control matters. We tested this approach with a real problem, obtaining
   three different control policies as a consequence of the different fitness functions employed. So,we conclude that the manipulation of fitness function and the use of a
   grammar to introduce as much knowledge as possible into the search process are useful tools when applying evolutionary techniques in industrial environments. The modified
   fitness functions and genetic operators are also discussed.
%8 March
%Z http://www.medwellonline.net/ijcs/

%A Santiago Garcia Carbajal
%A David Corne
%A Alejandro Conty
%T Parallelizing Automatic Induction of Langton Parameter with Genetic Programming
%B Science and Supercomputing in Europe
%E Giovanni erbacci
%V 2006
%D 2007
%P 540--544
%I Cineca, Italy
%K genetic algorithms, genetic programming, cellular automata, parallel programming
%U http://www.hpc-europa.org
%X Many classifications for Cellular Automata have been proposed durng time. One of them is based on Langton Parameter. Depending on the probability of a cell of being active
   at one moment, Cellular Automata are divided into four types. We use Genetic Programming to obtain transition rules with any desired value of Langton Parameter, in our
   search of Cellular Automata capable of Universal Computation.

%A Santiago {Garcia Carbajal}
%T Parallelizing Three Dimensional Cellular Automata With OpenMP
%J Parallel Processing Letters
%V 17
%N 4
%D 2007
%P 349--361
%I 
%K genetic algorithms, genetic programming, cellular automata, Parallel Programming
%U http://www.worldscinet.com/ppl/ppl.shtml
%X This paper describes our research on using Genetic Programming to obtain transition rules for Cellular Automata, which are one type of massively parallel computing system.
   Our purpose is to determine the existence of a limit of chaos for three dimensional Cellular Automata, empirically demonstrated for the two dimensional case. To do so, we
   must study statistical properties of 3D Cellular Automata over long simulation periods. When dealing with big three dimensional meshes, applying the transition rule to the
   whole structure can become a extremely slow task. In this work we decompose the Automata into pieces and use OpenMp to parallelise the process. Results show that using a
   decomposition procedure, and distributing the mesh between a set of processors, 3D Cellular Automata can be studied without having long execution times.
%8 Decemeber
%Z PPL

%A Stuart Card
%T Genetic Programming of Wavelet Networks for Time Series Prediction
%B GECCO-99 Student Workshop
%E Una-May O'Reilly
%D 1999
%P 341--342
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, neural-nets, wavelets, time, scale, frequency, prediction, stochastic, nonlinear
%U http://www.borg.com/~stu/GECCO99.html
%X A hybrid genetic programming / neural network / wavelet technique for time series prediction is proposed. Iterative software development and experimentation are ongoing.
%8 13  July
%Z GECCO-99WKS Part of wu:1999:GECCOWKS

%A Stuart W. Card
%T Time Series Prediction by Genetic Programming with Relaxed Assumptions in Mathematica
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WGSW002.pdf
%8 26-30  June
%Z GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A Stuart W. Card
%A Chilukuri K. Mohan
%T Information Theoretic Indicators of Fitness, Relevant Diversity \& Pairing Potential in Genetic Programming
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2545--2552
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X Commonly used fitness measures, such as mean squared error, often fail to reward individuals whose presence in the population is necessary to explain substantial portions
   of the data variance. Diversity indicators are often arbitrary, may reflect diversity irrelevant to solving the problem, and are incommensurate with fitness measures. By
   contrast, information theoretic functionals are computable general indicators of fitness and diversity without these typical failings. We propose normalised mutual
   information, redundancy and synergy measures for genetic programming. We also propose selection for recombination and survival by "pairing potential" and "pair potential"
   estimation, and offer numerical examples as empirical support for theoretical claims.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. Syracuse University 7417 S. Main St. P.O. Box 61 Newport, NY 13416
%@ 0-7803-9363-5

%A Stuart W. Card
%A Chilukuri K. Mohan
%T Ensemble selection for evolutionary learning using information theory and price's theorem
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 2
%D 2006
%P 1587--1588
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Learning Classifier Systems and other Genetics-Based Machine Learning: Poster, evolutionary computation, ensemble models, group
   selection, mate selection, measurement, Price's equation, theory
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1587.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Stuart W. Card
%A Chilukuri K. Mohan
%T Towards an Information Theoretic Framework for Genetic programming
%B Genetic Programming Theory and Practice V
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2007
%P 87--106
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%X An information-theoretic framework is presented for the development and analysis of the ensemble learning approach of genetic programming. As evolution proceeds, this
   approach suggests that the mutual information between the target and models should: (i) not decrease in the population; (ii) concentrate in fewer individuals; and (iii) be
   distilled from the inputs, eliminating excess entropy. Normalised information theoretic indices are developed to measure fitness and diversity of ensembles, without a
   priori knowledge of how the multiple constituent models might be composed into a single model. With the use of these indexes for reproductive and survival selection,
   building blocks are less likely to be lost and more likely to be recombined. Price's Theorem is generalised to pair selection, from which it follows that the heritability
   of information should be stronger than the heritability of error, improving evolvability. We support these arguments with simulations using a logic function benchmark and a
   time series application. For a chaotic time series prediction problem, for instance, the proposed approach avoids familiar difficulties (premature convergence, deception,
   poor scaling, and early loss of needed building blocks) with standard GP symbolic regression systems; information-based fitness functions showed strong intergenerational
   correlations as required by Price's Theorem.
%O 6
%8 17-19  May
%Z http://www.cscs.umich.edu/events/gptp2007/ Card-Mohan-draft-2007-4-4.pdf part of \citeRiolo:2007:GPTP To be published after workshop Jan 2008?

%A Stuart W. Card
%A Chilukuri K. Mohan
%T An Application of Information Theoretic Selection to Evolution of Models with Continuous-valued Inputs
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 29--43
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%O 3
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008

%A Stuart W. Card
%T Information distance based fitness and diversity metrics
%B GECCO 2010 Entropy, information and complexity
%E Stuart William Card and Yossi Borenstein
%D 2010
%P 1851--1854
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%X Commensurate indicators of diversity and fitness with desirable metric properties are derived from information distances based on Shannon entropy and Kolmogorov complexity.
   These metrics measure various useful distances: from an information theoretic characterisation of the phenotypic behaviour of a candidate model in the population to that of
   an ideal model of the target system's input-output relationship (fitness); from behavior of one candidate model to that of another (total information diversity); from the
   information about the target provided by one model to that provided by another (target relevant information diversity); from the code of one model to that of another
   (genotypic representation diversity); etc. Algorithms are cited for calculating the Shannon entropy based metrics from discrete data and estimating analogs thereof from
   heuristically binned continuous data; references are cited to methods for estimating the Kolmogorov complexity based metric. Not in the paper, but at the workshop, results
   will be shown of applying these algorithms to several synthetic and real world data sets: the simplest known deterministic chaotic flow; symbolic regression test functions;
   industrial process monitoring and control variables; and international political leadership data. Ongoing work is outlined.
%8 7-11  July
%Z Also known as \cite1830815 Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.

%A Luigi Cardamone
%A Andrea Mocci
%A Carlo Ghezzi
%T Dynamic Synthesis of Program Invariants using Genetic Programming
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 617--624
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, SBSE
%X In the field of software engineering, invariant detection techniques have been proposed to overcome the problem of software behaviour comprehension. If the code of a
   program is available, combining symbolic and concrete execution has been shown to provide an effective method to derive logic formulae that describe a program's behavior.
   However, symbolic execution does not work very well with loops, and thus such methods are not able to derive useful descriptions of programs containing loops. In this
   paper, we present a preliminary approach that aims to integrate genetic programming to synthesise a logic formula that describes the behaviour of a loop. Such formula could
   be integrated in a symbolic execution based approach for invariant detection to synthesize a complex program behaviour. We present a specific representation of formulae
   that works well with loops manipulating arrays. The technique has been validated with a set of relevant examples with increasing complexity. The preliminary results are
   promising and show the feasibility of our approach.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Jonas Carlsson
%A Carlos Paiz
%A Krister Wolff
%A Peter Nordin
%T Interactive Evolution of Speech using VoiceXML Speaking to you GP System.
%B Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics
%E Nagib Callaos and Alexander Pisarchik and Mitsuyoshi Ueda
%V VI
%D 2002
%P 58--62
%I IIIS
%K genetic algorithms, genetic programming, voice XML
%X we describe and discuss experiments in which we try to evolve meaningful sentences in English using Genetic Programming with interactive evolution. We use VoiceXML as the
   user interface, through which the user hears each individual, acts as the fitness function and tells the system what individuals to select. This is the first GP-system that
   accepts voice as guidance for fitness calculations. We use context free grammars to define the individuals and the genetic operators make sure that the grammar is followed,
   avoiding destructive mutation and crossover. The results show that it is possible to evolve meaningful phrases with our approach but improvements to the system are required
   in order to fully achieve the goal. The wide availability of voice terminals, such as phones, enables powerful learning of, for example, natural language grammar with
   possible feedback even from the general public. The described work also constitutes the first GP-system written in JavaScript (ECMAScript) enabling easy distributed GP-run
   over the Web without any installation.
%@ 980-07-8150-1

%A Alexander P. Carobus
%T Evolution of Game Playing Behavior: Using Genetic Programming to Create Players for Net Hack
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 60--69
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Emiliano Carreno
%A Guillermo Leguizamon
%A Neal Wagner
%T Evolution of Classification Rules for Comprehensible Knowledge Discovery
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 1261--1268
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X This article, which lies within the data mining framework, proposes a method to build classifiers based on the evolution of rules. The method, named REC (Rule Evolution for
   Classifiers), has three main features: it applies genetic programming to perform a search in the space of potential solutions; a procedure allows biasing the search towards
   regions of comprehensible hypothesis with high predictive quality and it includes a strategy for the selection of an optimum subset of rules (classifier) from the rules
   obtained as the result of the evolutionary process. A comparative study between this method and the rule induction algorithm C5.0 is carried out for two application
   problems (data sets). Experimental results show the advantages of using the method proposed.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Emiliano {Carreno Jara}
%T Long memory time series forecasting by using genetic programming
%J Genetic Programming and Evolvable Machines
%V 12
%N 4
%D 2012
%P 429--456
%I 
%K genetic algorithms, genetic programming, Long memory, Time series forecasting, Multi-objective search, ARFIMA models
%X Real-world time series have certain properties, such as stationarity, seasonality, linearity, among others, which determine their underlying behaviour. There is a
   particular class of time series called long-memory processes, characterised by a persistent temporal dependence between distant observations, that is, the time series
   values depend not only on recent past values but also on observations of much prior time periods. The main purpose of this research is the development, application, and
   evaluation of a computational intelligence method specifically tailored for long memory time series forecasting, with emphasis on many-step-ahead prediction. The method
   proposed here is a hybrid combining genetic programming and the fractionally integrated (long-memory) component of autoregressive fractionally integrated moving average
   (ARFIMA) models. Another objective of this study is the discovery of useful comprehensible novel knowledge, represented as time series predictive models. In this respect, a
   new evolutionary multi-objective search method is proposed to limit complexity of evolved solutions and to improve predictive quality. Using these methods allows for
   obtaining lower complexity (and possibly more comprehensible) models with high predictive quality, keeping run time and memory requirements low, and avoiding bloat and
   over-fitting. The methods are assessed on five real-world long memory time series and their performance is compared to that of statistical models reported in the
   literature. Experimental results show the proposed methods' advantages in long memory time series forecasting.
%8 Decemeber
%Z River Nile flow, Radial basis function, finance UK inflation rate. FI-GP. Long-memory variables. RBF-GP. fractional Gaussian Model encapsulation, lags. GPC++ version 0.40

%A Iacopo Carreras
%A David Linner
%T Self-evolving applications over opportunistic communication systems
%B 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops, 2010)
%D 2010
%P 153--158
%I 
%K genetic algorithms, genetic programming, BioNets, P2P, mobile devices, opportunistic communication systems, self-evolving applications, mobile radio
%X In this work, we focus on an application scenario in which services running over users mobile devices exploit opportunistic communications in order to evolve over time, as
   the result of a distributed and collaborative process. We propose a framework which is based on genetic programming and supports an asynchronous and distributed evolution
   of composite services. We implement the framework over off-the-shelf components and evaluate it through field trials in the case of a gaming scenario. Results show the
   ability of the proposed system to evolve over time in order to adapt to varying contexts.
%8 March  29-  April  2
%Z Mashup. telephone. GUI assumed. bionets. Ten people played unsolvable quiz. Evolve workflow graph (task schedule) and data flow graph (port connections). Fitness based on
   resources consumed (memory, CPU, network, electrical power) and fitting user needs against 'optimal execution profile' which gives 'optimal output values'. Deadlock
   prevention. W3C widget. Also known as \cite5470677

%A Brian Carse
%A Anthony G. Pipe
%T A Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming
%B Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
%E Ingrid Russell and John F. Kolen
%D 2001
%P 465--469
%I AAAI Press
%C Key West, Florida, USA
%K genetic algorithms, genetic programming
%X A fuzzy classifier system framework is proposed which employs a tree-based representation for fuzzy rule (classifier) antecedents and genetic programming for fuzzy rule
   discovery. Such a rule representation is employed because of the expressive power and generality it endows to individual rules. The framework proposes accuracy-based
   fitness for individual fuzzy classifiers and employs evolutionary competition between simultaneously matched classifiers. The evolutionary algorithm (GP) is therefore
   searching for compact fuzzy rule bases which are simultaneously general, accurate and co-adapted. Additional extensions to the proposed framework are suggested
%8 May  21-23
%@ 1-57735-133-9

%A D. R. Carvalho
%A A. A. Freitas
%T A genetic algorithm for discovering small disjunct rules in data mining
%J Applied Soft Computing
%V 2
%N 2
%D 2002
%P 75--88
%I 
%K genetic algorithms, data mining, classification, Rule discovery, Small disjuncts
%U http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html
%X This paper addresses the well-known classification task of data mining, where the goal is to discover rules predicting the class of examples (records of a given dataset).
   In the context of data mining, small disjuncts are rules covering a small number of examples. Hence, these rules are usually error-prone, which contributes to a decrease in
   predictive accuracy. At first glance, this is not a serious problem, since the impact on predictive accuracy should be small. However, although each small-disjunct covers
   few examples, the set of all small disjuncts can cover a large number of examples. This paper presents evidence that this is the case in several datasets. This paper also
   addresses the problem of small disjuncts by using a hybrid decision-tree/genetic-algorithm approach. In essence, examples belonging to large disjuncts are classified by
   rules produced by a decision-tree algorithm (C4.5), while examples belonging to small disjuncts are classified by a genetic-algorithm specifically designed for discovering
   small-disjunct rules. We present results comparing the predictive accuracy of this hybrid system with the prediction accuracy of three versions of C4.5 alone in eight
   public domain datasets. Overall, the results show that our hybrid system achieves better predictive accuracy than all three versions of C4.5 alone.
%8 Decemeber

%A Isidoro J. Casanova
%T Tradinnova-LCS: Dynamic stock portfolio decision-making assistance model with genetic based machine learning
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X This paper describes a decision system based on rules for the management of a stock portfolio using a mechanism of dynamic learning to select the stocks to be incorporated.
   This system simulates the intelligent behaviour of an investor, carrying out the buying and selling of stocks, such that during each day the best stocks will be selected to
   be incorporated in the portfolio by reinforcement learning with genetic programming. The system has been tested in 3 time periods (1 year, 3 years and 5 years), simulating
   the purchase/sale of stocks in the Spanish continuous market and the results have been compared with the revaluations obtained by the best investment funds operating in
   Spain.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586067

%A Mauro Castelli
%A Luca Manzoni
%A Sara Silva
%A Leonardo Vanneschi
%T A comparison of the generalization ability of different genetic programming frameworks
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Generalisation is an important issue in machine learning. In fact, in several applications good results over training data are not as important as good results over unseen
   data. While this problem was deeply studied in other machine learning techniques, it has become an important issue for genetic programming only in the last few years. In
   this paper we compare the generalization ability of several different genetic programming frameworks, including some variants of multi-objective genetic programming and
   operator equalisation, a recently defined bloat free genetic programming system. The test problem used is a hard regression real-life application in the field of drug
   discovery and development, characterised by a high number of features and where the generalisation ability of the proposed solutions is a crucial issue. The results we
   obtained show that, at least for the considered problem, multi-optimization is effective in improving genetic programming generalization ability, outperforming all the
   other methods on test data.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5585925

%A Mauro Castelli
%A Luca Manzoni
%A Sara Silva
%A Leonardo Vanneschi
%T A Quantitative Study of Learning and Generalization in Genetic Programming
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 25--36
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X The relationship between generalisation and solutions functional complexity in genetic programming (GP) has been recently investigated. Three main contributions are
   contained in this paper: (1) a new measure of functional complexity for GP solutions, called Graph Based Complexity (GBC) is defined and we show that it has a higher
   correlation with GP performance on out-of-sample data than another complexity measure introduced in a recent publication. (2) A new measure is presented, called Graph Based
   Learning Ability (GBLA). It is inspired by the GBC and its goal is to quantify the ability of GP to learn difficult training points; we show that GBLA is negatively
   correlated with the performance of GP on out-of-sample data. (3) Finally, we use the ideas that have inspired the definition of GBC and GBLA to define a new fitness
   function, whose suitability is empirically demonstrated. The experimental results reported in this paper have been obtained using three real-life multidimensional
   regression problems.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Flor A. Castillo
%A Ken A. Marshall
%A James L. Green
%A Arthur K. Kordon
%T Symbolic Regression In Design Of Experiments: A Case Study With Linearizing Transformations
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 1043--1047
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, real world applications, design of experiment (DoE), lack of fit, linearizing transformations, symbolic regression
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Flor Castillo
%A Kenric Marshall
%A James Green
%A Arthur Kordon
%T A Methodology for Combining Symbolic Regression and Design of Experiments to Improve Empirical Model Building
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1975--1985
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, symbolic regression, design of experiments, Real World Applications
%X A novel methodology for empirical model building using GP-generated symbolic regression in combination with statistical design of experiments as well as undesigned data is
   proposed. The main advantage of this methodology is the maximum data usage when extrapolation is necessary. The methodology offers alternative non-linear models that can
   either linearize the response in the presence of Lack or Fit or challenge and confirm the results from the linear regression in a cost effective and time efficient fashion.
   The economic benefit is the reduced number of additional experiments in the presence of Lack of Fit.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eights Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Flor Castillo
%A Arthur Kordon
%A Jeff Sweeney
%A Wayne Zirk
%T Using Genetic Programming in Industrial Statistical Model Building
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 31--48
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, statistical model building, symbolic regression, undesigned data
%X The chapter summarises the practical experience of integrating genetic programming and statistical modelling at The Dow Chemical Company. A unique methodology for using
   Genetic Programming in statistical modeling of designed and undesigned data is described and illustrated with successful industrial applications. As a result of the
   synergistic efforts, the building technique has been improved and the model development cost and time can be significantly reduced. In case of designed data Genetic
   Programming reduced costs by suggesting transformations as an alternative to doing additional experimentation. In case of undesigned data Genetic Programming was
   instrumental in reducing the model building costs by providing alternative models for consideration.
%O 3
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A P. A. Castillo
%A V. Rivas
%A J. J. Merelo
%A J. Gonzalez
%A A. Prieto
%A G. Romero
%T G-Prop-III: Global Optimization of Multilayer Perceptrons using an Evolutionary Algorithm
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 942
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming, poster papers
%U http://geneura.ugr.es/~pedro/gprop/G-Prop-III_poster.ps.gz
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Pedro A. Castillo
%A Maribel G. Arenas
%A J. J. Merelo
%A Gustavo Romero
%A Fatima Rateb
%A Alberto Prieto
%T Comparing hybrid systems to design and optimize artificial neural networks
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 240--249
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=240
%X We conduct a comparative study between hybrid methods to optimise multi-layer perceptrons: a model that optimises the architecture and initial weights of multi layer
   perceptrons; a parallel approach to optimise the architecture and initial weights of multilayer perceptrons; a method that searches for the parameters of the training
   algorithm, and an approach for cooperative co-evolutionary optimisation of multi layer perceptrons. Obtained results show that a co-evolutionary model obtains similar or
   better results than specialised approaches, needing much less training epochs and thus using much less simulation time.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Flor Castillo
%A Jeff Sweeney
%A Wayne Zirk
%T Using Evolutionary Algorithms to Suggest Variable Transformations in Linear Model Lack-of-Fit Situations
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 556--560
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Evolutionary Computing in the Process Industry
%X When significant model lack of fit (LOF) is present in a second-order linear regression model, it is often difficult to propose the appropriate parameter transformation
   that will make model LOF insignificant. This paper presents the potential of genetic programming (GP) symbolic regression for reducing or eliminating significant
   second-order linear model LOF. A case study in an industrial setting at The Dow Chemical Company is presented to illustrate this methodology.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Flor Castillo
%A Arthur Kordon
%A Guido Smits
%T Robust Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data
%B Genetic Programming Theory and Practice IV
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%V 5
%D 2006
%P -
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, symbolic regression, industrial applications, design of experiments, parameter selection
%X Symbolic regression based on Pareto front GP is a very effective approach for generating high-performance parsimonious empirical models acceptable for industrial
   applications. The chapter addresses the issue of finding the optimal parameter settings of Pareto front GP which direct the simulated evolution toward simple models with
   acceptable prediction error. A generic methodology based on statistical design of experiments is proposed. It includes determination of the number of replicates by
   half-width confidence intervals, determination of the significant factors by fractional factorial design of experiments, approaching the optimum by steepest ascent/descent,
   and local exploration around the optimum by Box Behnken design of experiments. The results from implementing the proposed methodology to different types of industrial data
   sets show that the statistically significant factors are the number of cascades, the number of generations, and the population size. The optimal values for the three
   parameters have been defined based on second order regression models with R2 higher than 0.97 for small, medium, and large-sized data sets. The robustness of the optimal
   parameters toward the types of data sets was explored and a robust setting for the three significant parameters was obtained. It reduces the calculation time by 30per cent
   to 50per cent without statistically significant reduction in the mean response.
%O 2
%8 11-13  May
%Z part of \citeRiolo:2006:GPTP Published Jan 2007 after the workshop
%@ 0-387-33375-4

%A Flor Castillo
%A Arthur Kordon
%A Guido Smits
%A Ben Christenson
%A Dee Dickerson
%T Pareto front genetic programming parameter selection based on design of experiments and industrial data
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 2
%D 2006
%P 1613--1620
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Real-World Applications, industrial applications, Pareto front, statistical design of experiments, symbolic regression
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1613.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Flor Castillo
%A Arthur Kordon
%A Carlos Villa
%T Genetic Programming Transforms in Linear Regression Situations
%B Genetic Programming Theory and Practice VIII
%S Genetic and Evolutionary Computation
%E Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva
%V 8
%D 2010
%P 175--194
%I Springer
%C Ann Arbor, USA
%K genetic algorithms, genetic programming
%U http://www.springer.com/computer/ai/book/978-1-4419-7746-5
%O 11
%8 20-22  May
%Z part of \citeRiolo:2010:GPTP

%A Tom Castle
%A Colin G. Johnson
%T Positional Effect of Crossover and Mutation in Grammatical Evolution
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 26--37
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming, Grammatical Evolution, crossover, mutation, position, bias
%X An often-mentioned issue with Grammatical Evolution is that a small change in the genotype, through mutation or crossover, may completely change the meaning of all of the
   following genes. This paper analyses the crossover and mutation operations in GE, in particular examining the constructive or destructive nature of these operations when
   occurring at points throughout a genotype. The results we present show some strong support for the idea that events occurring at the first positions of a genotype are
   indeed more destructive, but also indicate that they may be the most constructive crossover and mutation points too. We also demonstrate the sensitivity of this work to the
   precise definition of what is constructive/destructive.
%8 7-9  April
%Z 5-parity, Santa Fe trail, 6-mux, symbolic regression Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and
   EvoApplications2010

%A Tom Castle
%A Colin G. Johnson
%T Evolving High-Level Imperative Program Trees with Strongly Formed Genetic Programming
%B Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012
%S LNCS
%E Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta
%V 7244
%D 2012
%P 1--12
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Imperative programming, Loops
%U http://www.cs.kent.ac.uk/pubs/2012/3202/content.pdf
%X We present a set of extensions to Montana's popular Strongly Typed Genetic Programming system that introduce constraints on the structure of program trees. It is
   demonstrated that these constraints can be used to evolve programs with a naturally imperative structure, using common high-level imperative language constructs such as
   loops. A set of three problems including factorial and the general even-n-parity problem are used to test the system. Experimental results are presented which show success
   rates and required computational effort that compare favourably against other systems on these problems, while providing support for this imperative structure.
%8 11-13  April
%Z EpochX Part of \citeMoraglio:2012:GP EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012

%A G. A. Casula
%A G. Mazzarella
%A N. Sirena
%T Genetic Programming design of wire antennas
%B IEEE Antennas and Propagation Society International Symposium, APSURSI '09
%D 2009
%P 1--4
%I 
%K genetic algorithms, genetic programming, genetic programming design, wire antennas
%X Genetic optimization has been used in the last years for solving different electromagnetic problems. However, this technique assumes, and binary-codes, a fixed structure
   from the beginning, so it has a limited use in antenna design. On the other hand, Genetic Programming is able to determine the antenna shape as an outcome of the procedure.
   This work describes how to use genetic programming to design wire antennas. The performances of each antenna generated by the genetic programming during the optimization
   process are evaluated by a standard method of moments code, NEC-2.
%8 June
%Z VSWR, SWR, gain, 800MHz Also known as \cite5171505

%A Phil T. Cattani
%A Colin G. Johnson
%T ME-CGP: Multi Expression Cartesian Genetic Programming
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%X Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. In this paper we propose a way of structuring a CGP
   algorithm to make use of the multiple phenotypes which are implicitly encoded in a genome string. We show that this leads to a large increase in efficiency compared with
   standard CGP where genomes are translated into only one phenotype. We call this method Multi Expression CGP (ME-CGP), based on Mihai Oltean's work on Multi Expression
   Programming using linear GP.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586478

%A Robert Cattral
%A Franz Oppacher
%A Dwight Deugo
%T Rule Acquisition with a Genetic Algorithm
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 778
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, classifier systems, poster papers
%X Data mining, applied to poisonous mushroom machine learning benchmark
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Michael J. Cavaretta
%A Kumar Chellapilla
%T Data Mining using Genetic Programming: The Implications of Parsimony on Generalization Error
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 2
%D 1999
%P 1330--1337
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, data mining
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143
%@ 0-7803-5537-7 (Microfiche)

%A James B. Caverlee
%T A Genetic Algorithm Approach to Discovering an Optimal Blackjack Strategy
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 70--79
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Rachel Cavill
%A Steve Smith
%A Andy Tyrrell
%T Multi-chromosomal genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1753--1759
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, design, performance, representations, team evolution
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1753.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Rachel Cavill
%A Stephen L. Smith
%A Andy Tyrrell
%T The performance of polyploid evolutionary algorithms is improved both by having many chromosomes and by having many copies of each chromosome on symbolic regression
   problems
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Bob McKay and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Gunther Raidl and Kay Chen Tan and Ali
   Zalzala
%V 1
%D 2005
%P 935--941
%I IEEE Press
%C Edinburgh, Scotland, UK
%K genetic algorithms, genetic programming, biology, cellular biophysics, evolutionary computation, regression analysis, multiple chromosomes, polyploid evolutionary
   algorithm, symbolic regression problem
%U http://ieeexplore.ieee.org/servlet/opac?punumber=10417
%X This paper presents important new findings for a new method for evolving individual programs with multiple chromosomes. Previous results have shown that evolving
   individuals with multiple chromosomes produced improved results over evolving individuals with a single chromosome. The multiple chromosomes are organised along two axes;
   there are a number of different chromosomes and a number of copies of each chromosome. This paper investigates the effects which these two axes have on the performance of
   the algorithm; whether the improvement in performance comes from just one of these features or whether it is a combination of them both
%8 2-5  September
%Z Last author is NOT Terrell
%@ 0-7803-9363-5

%A Rachel Cavill
%A Stephen L Smith
%A Andy M Tyrrell
%T Variable length genetic algorithms with multiple chromosomes on a variant of the Onemax problem
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 2
%D 2006
%P 1405--1406
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K Genetic Algorithms: Poster, algorithms performance design, representation(s), size
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1405.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Rachel Cavill
%T Multi-Chromosomal Genetic Programming
%R Ph.D. Thesis PhD Dissertation
%D 2006
%I 
%I Department of Electronics, University of York
%C UK
%K genetic algorithms, genetic programming

%A Rachel Cavill
%A Hector C. Keun
%A Elaine Holmes
%A John C. Lindon
%A Jeremy K. Nicholson
%A Timothy M. D. Ebbels
%T Genetic algorithms for simultaneous variable and sample selection in metabonomics
%J Bioinformatics
%V 25
%N 1
%D 2009
%P 112--118
%I 

%A Seamus Cawley
%A Fearghal Morgan
%A Brian McGinley
%A Sandeep Pande
%A Liam McDaid
%A Snaider Carrillo
%A Jim Harkin
%T Hardware spiking neural network prototyping and application
%J Genetic Programming and Evolvable Machines
%V 12
%N 3
%D 2011
%P 257--280
%I 
%K genetic algorithms, evolvable hardware, EMBRACE, Spiking neural networks, Network on chip, Intrinsic evolution, FPGA
%X EMBRACE has been proposed as a scalable, reconfigurable, mixed signal, embedded hardware Spiking Neural Network (SNN) device. EMBRACE, which is yet to be realised, targets
   the issues of area, power and scalability through the use of a low area, low power analogue neuron/synapse cell, and a digital packet-based Network on Chip (NoC)
   communication architecture. The paper describes the implementation and testing of EMBRACE-FPGA, an FPGA-based hardware SNN prototype. The operation of the NoC inter-neuron
   communication approach and its ability to support large scale, reconfigurable, highly interconnected SNNs is illustrated. The paper describes an integrated training and
   configuration platform and an on-chip fitness function, which supports GA-based evolution of SNN parameters. The practicalities of using the SNN development platform and
   SNN configuration toolset are described. The paper considers the impact of latency jitter noise introduced by the NoC router and the EMBRACE-FPGA processor-based
   neuron/synapse model on SNN accuracy and evolution time. Benchmark SNN applications are described and results demonstrate the evolution of high quality and robust solutions
   in the presence of noise. The reconfigurable EMBRACE architecture enables future investigation of adaptive hardware applications and self repair in evolvable hardware.
%O Special Issue Title: Evolvable Hardware Challenges
%8 September

%A Manuel Cebrian
%A Alfonso Ortega {de la Puente}
%A Manuel Alfonseca
%T Acceleration of a procedure to generate fractal curves of a given dimension through the probabilistic analysis of execution time
%B Intelligent Engineering Systems Through Artificial Neural Networks
%E C. H. Dagli and A. L. Buczak and D. L. Enke and M. J. Embrecht
%V 14
%D 2004
%P 265--270
%I ASME Press
%C New York
%K genetic algorithms, genetic programming
%U http://www.ii.uam.es/~alfonsec/docs/annie.pdf
%Z Presented at ANNIE 2004: Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Data Mining, San Louis, Missouri, Nov.
   7-10, 2004
%@ 0-7918-0228-0

%A Manuel Cebrian
%A Manuel Alfonseca
%A Alfonso Ortega
%T Automatic generation of benchmarks for plagiarism detection tools using grammatical evolution
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 2253--2253
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, grammatical evolution, Real-World Applications: Poster, human factors, reliability, source code plagiarism detection tool
   assessment
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2253.pdf
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Manuel {Cebrian Ramos}
%T Using Algorithmic Information Theory and Stochastic Modeling to Improve Classification and Evolutionary Computation
%R Ph.D. Thesis Sobresaliente Cum Laude
%D 2007
%I 
%I Department of Computer Science, Universidad Autonoma de Madrid
%C Spain
%K genetic algorithms, genetic programming, grammatical evolution
%U digitool-uam.greendata.es:1801/webclient/DeliveryManager?pid=3411
%X Esta tesis presenta contribuciones teoricas y practicas de la Teoria de Informacion Algoritmica y del Modelado Estocastico (Algoritmico). La Teoria de Informacion
   Algoritmica es la teoria concerniente a la obtencion de una medida absoluta de la cantidad informacion contenida en un objeto. El Modelado Estocastico es una metodologia
   para la mejora del rendimiento de algoritmos mediante la introduccion de elementos aleatorios en su logica. Una de las mas interesantes aportaciones de la Teoria de
   Informacion Algoritmica es el desarrollo de una medida absoluta de similitud entre objetos. Esta medida solo puede ser estimada, al ser no computable por definicion. La
   estimacion tipica se basa en el uso de algoritmos de compresion de datos, siendo esta estimacion conocida como la distancia de compresion. Las dos aportaciones teoricas de
   esta tesis analizan la calidad de esta estimacion. La primera cuantifica la robustez de la estimacion cuando la informacion contenida en los objetos ha sido alterada por
   ruido externo, concluyendo que esta es considerablemente resistente al mismo. La segunda, estudia el impacto de la implementacion del algoritmo de compresion sobre la
   estimacion, obteniendose algunas recetas practicas para realizar dicha eleccion. Usamos variantes de la distancia de compresion para desarrollar dos aplicaciones para
   clasificacion y una para computacion evolutiva. La primera aplicacion considera el problema de la deteccion de similitudes entre documentos que han sido generados por una
   fuente comun predecesora, independientemente de si estos usan o no la misma codificacion: esto incluye la deteccion de traducciones de documentos y la reconstruccion de
   arboles filogeneticos a partir de material genetico. Hacemos uso de la ya demostrada utilidad de las distancias de similitud basadas en compresion en la deteccion de plagio
   (en el ambito educacional) para desarrollar nuestra segunda aplicacion: AC, un entorno integrado de deteccion de plagio en codigo fuente. La tercera aplicacion hace uso de
   esta distancia como una funcion de fitness, que es usada por algoritmos evolutivos para generar de forma automatica musica con un estilo predefinido. Otras tres nuevas
   aplicaciones derivan del uso de Modelado Estocastico, dos para computacion evolutiva y una para clasificacion. Dos de ellas estan intimamente relacionadas y hacen uso de la
   presencia de distribuciones de probabilidad de Cola Pesada en los procesos de optimizacion involucrados en la generacion de fractales mediante un algoritmo evolutivo, y en
   el proceso de entrenamiento de un perceptron multicapa. Este descubrimiento se usa para mejorar el rendimiento de ambos algoritmos mediante el uso de estrategias de
   recomienzo. La ultima aplicacion presentada en esta tesis es una historia exitosa del uso de una heuristica aleatoria especial en un algoritmo genetico simple, obteniendose
   un algoritmo que equivale al estado del arte para la resolucion de Problemas de Satisfaccion de Restricciones (CSPs).
%8 13  July
%Z In english. Supervised by Manuel Alfonseca Moreno / Alfonso Ortega de la Puente

%A Manuel Cebrian
%A Manuel Alfonseca
%A Alfonso Ortega
%T Towards the Validation of Plagiarism Detection Tools by Means of Grammar Evolution
%J IEEE Transactions on Evolutionary Computation
%V 13
%N 3
%D 2009
%P 477--485
%I 
%K genetic algorithms, genetic programming, Grammar Evolution, Automatic programming, Benchmark testing, Data mining, Distance measurement, Evolution (biology), Genetics,
   Plagiarism, Probability density function, computer science education, educational technology
%X Student plagiarism is a major problem in universities worldwide. In this paper, we focus on plagiarism in answers to computer programming assignments, where students mix
   and/or modify one or more original solutions to obtain counterfeits. Although several software tools have been developed to help the tedious and time consuming task of
   detecting plagiarism, little has been done to assess their quality, because determining the real authorship of the whole submission corpus is practically impossible for
   markers. In this paper, we present a Grammar Evolution technique which generates benchmarks for testing plagiarism detection tools. Given a programming language, our
   technique generates a set of original solutions to an assignment, together with a set of plagiarisms of the former set which mimic the basic plagiarism techniques performed
   by students. The authorship of the submission corpus is predefined by the user, providing a base for the assessment and further comparison of copy-catching tools. We give
   empirical evidence of the suitability of our approach by studying the behavior of one advanced plagiarism detection tool (AC) on four benchmarks coded in APL2, generated
   with our technique.
%8 June
%Z also known as \cite4781609 Not GP

%A Scott Cederberg
%T The evolution of Cooperation: The Genetic Algorithm Applied to Three Normal-Form Games
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 45--51
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2002/Cederberg.pdf
%8 June
%Z part of \citekoza:2002:gagp

%A F. Cellini
%A A. Chesson
%A I. Colquhoun
%A A. Constable
%A H. V. Davies
%A K. H. Engel
%A A. M. R. Gatehouse
%A S. Karenlampi
%A E. J. Kok
%A J. -J. Leguay
%T Unintended effects and their detection in genetically modified crops
%J Food and Chemical Toxicology
%V 42
%N 7
%D 2004
%P 1089--1125
%I 
%K genetic algorithms, genetic programming
%U http://www.entransfood.com/products/publications/WG2_paper_rev1_19jan2004_unmarked.pdf
%X The commercialisation of GM crops in Europe is practically non-existent at the present time. The European Commission has instigated changes to the regulatory process to
   address the concerns of consumers and member states and to pave the way for removing the current moratorium. With regard to the safety of GM crops and products, the current
   risk assessment process pays particular attention to potential adverse effects on human and animal health and the environment. This document deals with the concept of
   unintended effects in GM crops and products, i.e. effects that go beyond that of the original modification and that might impact primarily on health. The document first
   deals with the potential for unintended effects caused by the processes of transgene insertion (DNA rearrangements) and makes comparisons with genetic recombination events
   and DNA rearrangements in traditional breeding. The document then focuses on the potential value of evolving "profiling" or "omics" technologies as non-targeted, unbiased
   approaches, to detect unintended effects. These technologies include metabolomics (parallel analysis of a range of primary and secondary metabolites), proteomics (analysis
   of polypeptide complement) and transcriptomics (parallel analysis of gene expression). The technologies are described, together with their current limitations. Importantly,
   the significance of unintended effects on consumer health are discussed and conclusions and recommendations presented on the various approaches outlined.
%Z Metapontum Agrobios, SS Jonica Km 448.2, I-75010 Metaponto Matera, Italy. Publication Types: * Multicenter Study * Review PMID: 15123383 [PubMed - indexed for MEDLINE]
   Brief mention of (Helen Johnson et al., 2000) \citeJohnson:2000:eamGPsir

%A Brian M. Cerny
%A Peter C. Nelson
%A Chi Zhou
%T Using differential evolution for symbolic regression and numerical constant creation
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1195--1202
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, combinatorial search, constant creation, differential evolution, gene expression programming, genetic algorithms (GA), neutral
   mutations, optimisation, prefix gene expression programming, Redundant representations, symbolic regression
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1195.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389331

%A Ahmet Cetinkaya
%T Regular expression generation through grammatical evolution
%B Genetic and Evolutionary Computation Conference (GECCO2007) workshop program
%E Tina Yu
%D 2007
%P 2643--2646
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, grammatical evolution, regular expressions
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2643.pdf
%X This study investigates automatic regular expression generation using Grammatical Evolution. The software implementation is based on a subset of POSIX regular expression
   rules. For fitness calculation, a multiline text file is supplied. Lines which are required to match with generated regular expressions are specified beforehand. Fitness is
   evaluated according to the successful match results. Using this fitness evaluation strategy, preliminary tests have been performed on different files. Results indicate that
   the Grammatical Evolution approach to automatic generation of regular expressions is promising.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071 Haskell, match HTML anchor tags in one file (266 lines), subset of POSIX, Pop=100.

%A Abdulkadir Cevik
%A Ibrahim H. Guzelbey
%T A soft computing based approach for the prediction of ultimate strength of metal plates in compression
%J Engineering Structures
%V 29
%N 3
%D 2007
%P 383--394
%I 
%K genetic algorithms, genetic programming, Soft computing, Neural networks, Buckling, Plates
%X This paper presents two plate strength formulations applicable to metals with nonlinear stress-strain curves, such as aluminium and stainless steel alloys, obtained by soft
   computing techniques, namely Neural Networks (ANN) and Genetic Programming (GP). The proposed soft computing formulations are based on well-defined FE results available in
   the literature. The proposed formulations enable determination of the buckling strength of rectangular plates in terms of RambergOsgood parameters. The strength curves
   obtained by the proposed soft computing formulations show perfect agreement with FE results. The formulations are later compared with related codes and results are found to
   be quite satisfactory.
%8 March

%A Abdulkadir Cevik
%T A new formulation for web crippling strength of cold-formed steel sheeting using genetic programming
%J Journal of Constructional Steel Research
%V 63
%N 7
%D 2007
%P 867--883
%I 
%K genetic algorithms, genetic programming, gene expression programming, Web crippling, Cold-formed steel decks, Formulation
%X This study presents Genetic programming (GP) as a new tool for the formulation of web crippling strength of cold-formed steel decks for various loading cases. There is no
   well established analytical solution of the problem due to complex plastic behaviour. The objective of this study is to provide an alternative robust formulation to related
   design codes and to verify the robustness of GP for the formulation of such structural engineering problems. The training and testing patterns of the proposed GP
   formulation are based on well established experimental results from the literature. The GP based formulation results are compared with experimental results and current
   design codes and found to be more accurate.
%8 July
%Z Karva

%A Abdulkadir Cevik
%T Genetic programming based formulation of rotation capacity of wide flange beams
%J Journal of Constructional Steel Research
%V 63
%N 7
%D 2007
%P 884--893
%I 
%K genetic algorithms, genetic programming, Rotation capacity, Beams, Formulation
%X This study is a pioneer work that proposes genetic programming (GP) as a new approach for the explicit formulation of available rotation capacity of wide-flange beams which
   is an important phenomenon that determines the plastic behaviour of steel structures. The database for the GP formulation is based on extensive experimental results from
   literature. The results of the GP-based formulation are compared with numerical results obtained by a specialised computer program and existing analytical equations. The
   results indicate that the proposed GP formulation performs quite well compared to numerical results and existing analytical equations and is quite practical for use.
%8 July

%A A. Cevik
%T A new formulation for longitudinally stiffened webs subjected to patch loading
%J Journal of Constructional Steel Research
%V 63
%D 2007
%P 1328--1340
%I 
%K genetic algorithms, genetic programming, Patch loading, Formulation, Girders, Webs, Longitudinal stiffeners
%X This study proposes a new formulation for patch loading longitudinally stiffened webs using genetic programming (GP) for the first time in the literature. The database for
   the GP formulation is based on extensive experimental results from the literature. The results of the GP based formulation are compared with existing models and design
   codes. The results indicate that the proposed GP formulation performs quite well compared to existing models and design codes.

%A Abdulkadir Cevik
%T Unified formulation for ultimate capacity of shear failure of arc spot welding using genetic programming
%J Journal of Materials Processing Technology
%V 204
%N 1-3
%D 2008
%P 117--124
%I 
%K genetic algorithms, genetic programming, Arc spot welding, Ultimate capacity, Shear failure
%U http://www.sciencedirect.com/science/article/B6TGJ-4R2H7VY-3/2/b16ece537522603ec7cc693ad17fd283
%X This study addresses genetic programming (GP) for the formulation of ultimate capacity of shear failure of arc spot welding. The proposed GP formulation is based on
   experimental results. The ultimate shear capacity of arc spot welding is formulated in terms of tensile strength, average welding thickness and diameter. The results of the
   proposed GP model are later compared with results of existing codes and are found to more accurate. In existing design codes four different equations are used whereas the
   proposed GP model is a unified formulation valid for all governing shear failures at the same time

%A Abdulkadir Cevik
%A Ali Firat Cabalar
%T Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming
%J Expert Systems with Applications
%V 36
%N 4
%D 2009
%P 7749--7757
%I 
%K genetic algorithms, genetic programming, Leighton Buzzard sand, Mica, Resonant column testing
%U http://www.sciencedirect.com/science/article/B6V03-4TGHN90-2/2/78164c859cf3127425aedcca7e6f7d21
%X This study presents two Genetic Programming (GP) models for damping ratio and shear modulus of sand-mica mixtures based on experimental results. The experimental database
   used for GP modeling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different
   effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe resonant
   column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear
   modulus. The performance of accuracies of proposed NN models are quite satisfactory (R2=0.95 for damping ratio and R2=0.98 for shear modulus).
%8 May

%A Abdulkadir Cevik
%A Nihat Atmaca
%A Talha Ekmekyapar
%A Ibrahim H. Guzelbey
%T Flexural buckling load prediction of aluminium alloy columns using soft computing techniques
%J Expert Systems with Applications
%V 36
%N 3, Part 2
%D 2009
%P 6332--6342
%I 
%K genetic algorithms, genetic programming, Gene-expression programming, Soft computing, Neural networks, Flexural buckling, Aluminium alloy columns
%U http://www.sciencedirect.com/science/article/B6V03-4TB6X28-1/2/3f64ccc54bc41be648922dc688ccad4a
%X This paper presents the application of soft computing techniques for strength prediction of heat-treated extruded aluminium alloy columns failing by flexural buckling.
   Neural networks (NN) and genetic programming (GP) are presented as soft computing techniques used in the study. Gene-expression programming (GEP) which is an extension to
   GP is used. The training and test sets for soft computing models are obtained from experimental results available in literature. An algorithm is also developed for the
   optimal NN model selection process. The proposed NN and GEP models are presented in explicit form to be used in practical applications. The accuracy of the proposed soft
   computing models are compared with existing codes and are found to be more accurate.
%8 April

%A Abdulkadir Cevik
%A M. Tolga Gogus
%A Ibrahim H. Guzelbey
%A Huzeyin Filiz
%T Soft computing based formulation for strength enhancement of CFRP confined concrete cylinders
%J Advances in Engineering Software
%V 41
%N 4
%D 2010
%P 527--536
%I 
%K genetic algorithms, genetic programming, Soft computing, Stepwise regression, FRP confinement, Concrete cylinder, Strength enhancement
%U http://www.sciencedirect.com/science/article/B6V1P-4XPBSMR-1/2/fce8b7ee023873cc437bf1c86ee3eb19
%X This study presents the application of soft computing techniques namely as genetic programming (GP) and stepwise regression (SR) for formulation of strength enhancement of
   carbon-fiber-reinforced polymer (CFRP) confined concrete cylinders. The proposed soft computing based formulations are based on experimental results collected from
   literature. The accuracy of the proposed GP and SR formulations are quite satisfactory as compared to experimental results. Moreover, the results of proposed soft computing
   based formulations are compared with 15 existing models proposed by various researchers so far and are found to be more accurate.

%A Abdulkadir Cevik
%A Ebru {Akcapinar Sezer}
%A Ali Firat Cabalar
%A Candan Gokceoglu
%T Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network
%J Applied Soft Computing
%V 11
%N 2
%D 2011
%P 2587--2594
%I 
%K genetic algorithms, genetic programming, Clay-bearing rock, Uniaxial compressive strength, Neural network, Slake durability index
%U http://www.sciencedirect.com/science/article/B6W86-51F7PJN-1/2/29835a31bf86c4e457cfa3e0ae15bae5
%X Uniaxial compressive strength of intact rock is significantly important for engineering geology and geotechnics, because it is an important design parameter for tunnels,
   rock slopes rock foundations, and it is also used as input parameter in some rock mass classification systems. This paper documents the results of laboratory experiments
   and numerical simulations (i.e. neural network) conducted to estimate the uniaxial compressive strength of some clay-bearing rocks selected from Turkey. Emphasis was placed
   on assessing the role of slake durability indices and clay contents. The input variables in developed neural network (NN) model are the origin of rocks, two/four-cycle
   slake durability indices and clay contents, and the output is uniaxial compressive strength. It is shown that the performance of capacities of proposed NN model is quite
   satisfactory. However, the NN model including four cycle slake durability index yielded slightly more precise results than that including two cycle slake durability index
   as input parameter. The paper also presents a comparative study on the accuracy of NN model and genetic programming (GP) in the results.
%O The Impact of Soft Computing for the Progress of Artificial Intelligence

%A Abdulkadir Cevik
%T Neuro-fuzzy modeling of rotation capacity of wide flange beams
%J Expert Systems with Applications
%V 38
%N 5
%D 2011
%P 5650--5661
%I 
%K genetic algorithms, genetic programming, Rotation capacity, Beams, Neuro-fuzzy, Modelling
%U http://www.sciencedirect.com/science/article/B6V03-51CJ387-K/2/ce5fff4acc0b21a9cd4c1ac3c5afe7df
%X This study is a pioneer work that investigates the feasibility of neuro-fuzzy (NF) approach for the modeling of rotation capacity of wide flange beams. The database for the
   NF modeling is based on experimental studies from literature. The results of the NF model are compared with numerical results obtained by a specialised computer programme
   and existing analytical and genetic programming based equations. The results indicate that the proposed NF model performs better. By using the proposed NF model, a wide
   range of parametric studies are also performed to evaluate the main effects of each variable on rotation capacity.

%A Abdulkadir Cevik
%T Modeling strength enhancement of FRP confined concrete cylinders using soft computing
%J Expert Systems with Applications
%V 38
%N 5
%D 2011
%P 5662--5673
%I 
%K genetic algorithms, genetic programming, Soft computing, Neural networks, Neuro-fuzzy, Stepwise regression, FRP confinement, Concrete cylinder, Strength enhancement
%U http://www.sciencedirect.com/science/article/B6V03-51CJ387-J/2/4b0e7942a4c46980f638964d442e332a
%X This study presents the application of soft computing techniques namely as genetic programming (GP) and stepwise regression (SR), neuro-fuzzy (NF) and neural networks (NN)
   for modelling of strength enhancement of FRP (fibre-reinforced polymer) confined concrete cylinders. The proposed soft computing models are based on experimental results
   collected from literature. The accuracy of the proposed soft computing models are quite satisfactory as compared to experimental results. Moreover the results of proposed
   soft computing formulations are compared with 10 models existing in the literature proposed by various researchers so far and are found to be by far more accurate.

%A Daniel Chai
%T Development of a Computer Controller Players for Daleks using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 80--89
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Roungsan Chaisricharoen
%A Boonruk Chipipop
%T Practical tuning of an OTA-C bandpass biquad via recurrent geometric programming
%B IEEE 8th International Conference on ASIC, ASICON '09
%D 2009
%P 1193--1196
%I 
%K geometric programming, HSPICE simulations, OTA-C bandpass biquad tuning, evolutionary algorithms, heuristic algorithms, operational amplifiers, recurrent geometric
   programming, second-order bandpass requirement, band-pass filters, biquadratic filters, operational amplifiers
%X The geometric programming which can be globally solved special cases of nonlinear problems is operated recurrently with calibrated
%8 20-23  October
%Z not on GP. Sch. of Inf. Technol., Mae Fah Luang Univ., Chiang Rai, Thailand Also known as \cite5351182

%A C. Chakraborti
%A K. K. N. Sastry
%T The Genetic Algorithms Approach for Proving Logical Arguments in Natural Language
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 463--470
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Uday K. Chakraborty
%T Genetic and evolutionary computing
%J Information Sciences
%V 178
%N 23
%D 2008
%P 4419--4420
%I 
%K genetic algorithms, genetic programming
%O Introduction to special section on Genetic and Evolutionary Computing
%8 1  Decemeber

%A Uday K. Chakraborty
%T Genetic programming model of solid oxide fuel cell stack: first results
%J International Journal of Information and Communication Technology (IJICT)
%V 1
%N 3/4
%D 2008
%P 453--461
%I Inderscience Publishers
%K genetic algorithms, genetic programming, solid oxide fuel cells, SOFC stack, modelling, nonlinear dynamics, simulation
%U http://www.inderscience.com/link.php?id=24015
%X Models that predict performance are important tools in understanding and designing solid oxide fuel cells (SOFCs). Modelling of SOFC stack-based systems is a powerful
   approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the
   electrochemical and thermal properties. Several algorithmic approaches have already been reported for the modelling of solid oxide fuel cell stack-based systems. This paper
   presents a new, genetic programming approach to SOFC modelling. Initial simulation results obtained with the proposed approach outperform the state-of-the-art radial basis
   function neural network method for this task.

%A Uday K. Chakraborty
%T An Evolutionary Computation Approach to Predicting Output Voltage from Fuel Utilization in SOFC Stacks
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 2165--2171
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, RBFANN
%X Modeling of solid oxide fuel cell (SOFC) stack based systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the
   need for formulating complicated systems of equations describing the electrochemical and thermal properties. This paper presents an efficient genetic programming approach
   for modeling and simulation of SOFC output voltage versus fuel burn behavior. This method is shown to outperform the state-of-the-art radial basis function neural network
   approach for SOFC modeling.
%8 18-21  May
%Z Fuel cell hydrogen + oxygen = steam + 1.18volts at 1000Centigrade and 1bar. DSS \citega94aGathercole Discipulus. NeuroSolutions. CEC 2009 - A joint meeting of the IEEE, the
   EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Uday Kumar Chakraborty
%T Static and dynamic modeling of solid oxide fuel cell using genetic programming
%J Energy
%V 34
%N 6
%D 2009
%P 740--751
%I 
%K genetic algorithms, genetic programming, Solid oxide fuel cell, SOFC stack, Dynamic model, Transient response, Neural network
%U http://www.sciencedirect.com/science/article/B6V2S-4W32975-1/2/c334dcacd8fee2c381ecd788e82d33fc
%X Modeling of solid oxide fuel cell (SOFC) systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for
   formulating complicated systems of equations describing the electrochemical and thermal properties. Several algorithmic approaches have in the past been reported for the
   modeling of solid oxide fuel cell stacks. However, all of these models have their limitations. This paper presents an efficient genetic programming approach to SOFC
   modeling and simulation. This method, belonging to the computational intelligence paradigm, is shown to outperform the state-of-the-art radial basis function neural network
   approach for SOFC modeling. Both static (fixed load) and dynamic (load transient) analyses are provided. Statistical tests of significance are used to validate the
   improvement in solution quality.

%A Lance D. Chambers
%T Book Review: Genetic Programming and Data Structures: Genetic Programming+Data Structures=Automatic Programming
%J Genetic Programming and Evolvable Machines
%V 2
%N 3
%D 2001
%P 301--303
%I 
%K genetic algorithms, genetic programming
%8 September
%Z Review of \citelangdon:book Article ID: 357598

%A Malik Chami
%A Denis Robilliard
%T Inversion of oceanic constituents in case I and II waters with genetic programming algorithms
%J Applied Optics
%V 41
%N 30
%D 2002
%P 6260--6275
%I 
%K genetic algorithms, genetic programming, ARTIFICIAL SATELLITES, ATMOSPHERIC OPTICS, COLOUR, INFRARED SPECTROSCOPY, LIGHT TRANSMISSION, OPTICAL PROPERTIES, RADIATIVE
   TRANSFER, REFLECTANCE, REMOTE SENSING, SEA WATER, SPECTROSCOPIC ANALYSIS, STOCHASTIC PROCESSES, WAVE PROPAGATION
%U http://ao.osa.org/ViewMedia.cfm?id=70258&seq=0
%X A stochastic inverse technique based on a genetic programming (GP) algorithm was developed to invert oceanic constituents from simulated data for case I and case II water
   applications. The simulations were carried out with the Ordre Successifs Ocean Atmosphere (OSOA) radiative transfer model. They include the effects of oceanic substances
   such as algal-related chlorophyll, nonchlorophyllous suspended matter, and dissolved organic matter. The synthetic data set also takes into account the directional effects
   of particles through a variation of their phase function that makes the simulated data realistic. It is shown that GP can be successfully applied to the inverse problem
   with acceptable stability in the presence of realistic noise in the data. GP is compared with neural network methodology for case I waters; GP exhibits similar retrieval
   accuracy, which is greater than for traditional techniques such as band ratio algorithms. The application of GP to real satellite data [a Sea-viewing Wide Field-of-view
   Sensor (SeaWiFS)] was also carried out for case I waters as a validation. Good agreement was obtained when GP results were compared with the SeaWiFS empirical algorithm.
   For case II waters the accuracy of GP is less than 33%, which remains satisfactory, at the present time, for remote-sensing purposes.
%8 October
%Z http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2002ApOpt..41.6260C&data_type=BIBTEX&db_key=INST%26amp;nocookieset=1

%A Wai Sum Chan
%A Friedrich Recknagel
%A Hongqing Cao
%A Ho-Dong Park
%T Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms
%J Water Research
%V 41
%N 10
%D 2007
%P 2247--2255
%I 
%K genetic algorithms, genetic programming, Lake Suwa, Microcystis, Microcystin, Ordination, Clustering, Forecasting, Explanation
%X Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the
   shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species
   abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in
   typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the
   toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast
   the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of
   microcystin concentrations based on limnological and meteorological input data, achieving an r2=0.74 for testing.
%8 May
%Z a School of Earth and Environmental Sciences, University of Adelaide, Adelaide 5005, Australia b Cooperative Research Centre for Water Quality and Treatment, Salisbury
   5108, Australia c Department of Environmental Sciences, Shinshu University, Matsumoto 390-8621, Japan

%A Zeke S. H. Chan
%A H. W. Ngan
%A A. B. Rad
%T Minimum-Allele-Reserve-Keeper (MARK): A Fast and Effective Mutation Scheme for Genetic Algorithm (GA)
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 106--113
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Zeke S. H. Chan
%A H. W. Ngan
%A A. B. Rad
%T A new method to resist premature convergence: Synchonising gene-convergence with correlated recombination
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 74--79
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms
%8 13  July
%Z GECCO-99LB

%A King Choi Chan
%T Valid English Word Classifier Using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 39--48
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A David Michael Chan
%T Automatic Generation of Prime Factorization Algorithms using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 52--57
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2002/Chan.pdf
%8 June
%Z part of \citekoza:2002:gagp "GP hard" p57

%A Kit Yan Chan
%A M. Emin Aydin
%A Terence C. Fogarty
%T New Factorial Design Theoretic Crossover Operator for Parametrical Problem
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 22--33
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=22
%X Recent research shows that factorial design methods improve the performance of the crossover operator in evolutionary computation. However the methods employed so far
   ignore the effects of interaction between genes on fitness, i.e. ``epistasis''. Here we propose the application of a systematic method for interaction effect analysis to
   enhance the performance of the crossover operator. It is shown empirically that the proposed method significantly outperforms existing crossover operators on benchmark
   problems with high interaction between the variables.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Kit Yan Chan
%A Terence C. Fogarty
%T Experimental design based multi-parent crossover operator
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 297--306
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=297
%X Recently, the methodologies of multi-parent crossover have been developed by performing the crossover operation with multi-parent. Some studies have indicated the high
   performance of multi-parent crossover on some numerical optimization problems. Here a new crossover operator has been proposed by integrating multi-parent crossover with
   the approach of experimental design. It is based on experimental design method in exploring the solution space that compensates the random search as in traditional genetic
   algorithm. By replacing the inbuilt randomness of crossover operator with a more systematical method, the proposed method outperforms the classical GA strategy on several
   GA benchmark problems.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Kit Yan Chan
%A Terence C. Fogarty
%T An Evolutionary Algorithm for the Input-Output Block Assignment Problem
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 250--258
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=250
%X A procedure for system decomposition is developed for decentralised multi-variable systems. Optimal input-output pairing techniques are used to rearrange a large multi
   variable system into a structure that is closer to the block-diagonal decentralised form. The problem is transformed into a block assignment problem. An evolutionary
   algorithm is developed to solve this hard IP problem. The result shows that the proposed algorithm is simple to implement and efficient to find the reasonable solution.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Kit Yan Chan
%A C. K. Kwong
%A T. C. Wong
%T Modelling customer satisfaction for product development using genetic programming
%J Journal of Engineering Design
%V 22
%N 1
%D 2009
%P 55--68
%I Taylor \& Francis
%K genetic algorithms, genetic programming
%U http://www.informaworld.com/smpp/title~content=t713429619
%X Product development involves several processes in which product planning is the first one. Several tasks normally are required to be conducted in the product-planning
   process and one of them is to determine settings of design attributes for products. Facing with fierce competition in marketplaces, companies try to determine the settings
   such that the best customer satisfaction of products could be obtained.To achieve this, models that relate customer satisfaction to design attributes need to be developed
   first. Previous research has adopted various modelling techniques to develop the models, but those models are not able to address interaction terms or higher-order terms in
   relating customer satisfaction to design attributes, or they are the black-box type models. In this paper, a method based on genetic programming (GP) is presented to
   generate models for relating customer satisfaction to design attributes. The GP is first used to construct branches of a tree representing structures of a model where
   interaction terms and higher-order terms can be addressed. Then an orthogonal least-squares algorithm is used to determine the coefficients of the model. The models thus
   developed are explicit and consist of interaction terms and higher-order terms in relating customer satisfaction to design attributes. A case study of a digital camera
   design is used to illustrate the proposed method.
%Z Matlab a Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

%A K. Y. Chan
%A C. K. Kwong
%A T. C. Fogarty
%T Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers
%J Information Sciences
%V 180
%N 4
%D 2010
%P 506--518
%I 
%K genetic algorithms, genetic programming, Fuzzy regression, Outlier detection, Epoxy dispensing process
%U http://www.sciencedirect.com/science/article/B6V0C-4XFPR3M-3/2/1f27ff77e40dc7d917de59d3555abf36
%X Fuzzy regression (FR) been demonstrated as a promising technique for modeling manufacturing processes where availability of data is limited. FR can only yield linear type
   FR models which have a higher degree of fuzziness, but FR ignores higher order or interaction terms and the influence of outliers, all of which usually exist in the
   manufacturing process data. Genetic programming (GP), on the other hand, can be used to generate models with higher order and interaction terms but it cannot address the
   fuzziness of the manufacturing process data. In this paper, genetic programming-based fuzzy regression (GP-FR), which combines the advantages of the two approaches to
   overcome the deficiencies of the commonly used existing modeling methods, is proposed in order to model manufacturing processes. GP-FR uses GP to generate model structures
   based on tree representation which can represent interaction and higher order terms of models, and it uses an FR generator based on fuzzy regression to determine outliers
   in experimental data sets. It determines the contribution and fuzziness of each term in the model by using experimental data excluding the outliers. To evaluate the
   effectiveness of GP-FR in modeling manufacturing processes, it was used to model a non-linear system and an epoxy dispensing process. The results were compared with those
   based on two commonly used FR methods, Tanka's FR and Peters' FR. The prediction accuracy of the models developed based on GP-FR was shown to be better than that of models
   based on the other two FR methods.

%A K. Y. Chan
%A T. S. Dillon
%A C. K. Kwong
%T Using an evolutionary fuzzy regression for affective product design
%B IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X In affective product design, one of the main goals is to maximise customers' affective satisfaction by optimising design variables of a new product. To achieve this, a
   model in relating customers' affective responses and design variables of a new product is required to be developed based on customers' survey data. However, previous
   research on modelling the relationship between affective response and design variables cannot address the development of explicit models either involving nonlinearity or
   fuzziness, which exist in customers' survey data. In this paper, an evolutionary fuzzy regression approach is proposed to generate explicit models to represent this
   nonlinear and fuzzy relationship between affective responses and design variables. In the approach, genetic programming is used to construct branches of a tree representing
   structures of a model where the nonlinearity of the model can be addressed. Fuzzy coefficients of the model, which is represented by the tree, are determined based on a
   fuzzy regression algorithm. As a result, the fuzzy nonlinear regression model can be obtained to relate affective responses and design variables.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5584493

%A Kit Yan Chan
%A Sing Ho Ling
%A Tharam Singh Dillon
%A Hung Nguyen
%T Classification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridising the
   approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed classification unit is built and is
   validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed
   neural network based classification unit can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the
   commonly used classification methods for medical diagnosis including statistical regression, fuzzy regression and genetic programming.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586320

%A Kit Yan Chan
%A Tharam Singh Dillon
%A Che Kit Kwong
%T Polynomial modeling for manufacturing processes using a backward elimination based genetic programming
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms
   which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on untrained data sets. In this paper, a
   mechanism which aims at avoiding overfitting is proposed based on a statistical method, backward elimination, which intends to eliminate insignificant terms in polynomial
   models. By modeling a solder paste dispenser for electronic manufacturing, results show that the insignificant terms in the polynomial model can be eliminated by the
   proposed mechanism. Results also show that the polynomial model generated by the proposed GP can achieve better predictions than the existing methods.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586309

%A Kit Yan Chan
%A Tharam S. Dillon
%A C. K. Kwong
%T Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm
%J Information Sciences
%V 181
%N 9
%D 2011
%P 1623--1640
%I 
%K genetic algorithms, genetic programming, PSO, Particle swarm optimisation, Time-varying systems, Polynomial modelling
%U http://www.sciencedirect.com/science/article/B6V0C-51X1VSV-7/2/12b12f977248967cf70b6cfd1dc37507
%X In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are
   similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying models. The performance of the
   proposed PSO is evaluated by polynomial modelling based on various sets of time-invariant and time-varying data. Results of polynomial modeling in time-varying systems show
   that the proposed PSO outperforms commonly used modelling methods which have been developed for solving dynamic optimisation problems including genetic programming (GP) and
   dynamic GP. An analysis of the diversity of individuals of populations in the proposed PSO and GP reveals why the proposed PSO obtains better results than those obtained by
   GP.

%A K. Y. Chan
%A C. K. Kwong
%A T. S. Dillon
%A Y. C. Tsim
%T Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming
%J Applied Soft Computing
%V 11
%N 2
%D 2011
%P 1648--1656
%I 
%K genetic algorithms, genetic programming, Process modelling, Polynomial modelling, Overfitting
%U http://www.sciencedirect.com/science/article/B6W86-501FPF7-6/2/4bf5179fccc0bf3772b121aef439e062
%X Genetic programming (GP) has demonstrated as an effective approach in polynomial modelling of manufacturing processes. However, polynomial models with redundant terms
   generated by GP may depict over fitting, while the developed models have good accuracy on trained data sets but relatively poor accuracy on testing data sets. In the
   literature, approaches of avoiding overfitting in GP are handled by limiting the number of terms in polynomial models. However, those approaches cannot guarantee terms in
   polynomial models produced by GP are statistically significant to manufacturing processes. In this paper, a statistical method, backward elimination (BE), is proposed to
   incorporate with GP, in order to eliminate insignificant terms in polynomial models. The performance of the proposed GP has been evaluated by modeling three real-world
   manufacturing processes, epoxy dispenser for electronic packaging, solder paste dispenser for electronic manufacturing, and punch press system for leadframe downset in IC
   packaging. Empirical results show that insignificant terms in the polynomial models can be eliminated by the proposed GP and also the polynomial models generated by the
   proposed GP can achieve results with better predictions than the other commonly used existent methods, which are commonly used in GP for avoiding overfitting in polynomial
   modeling.
%O The Impact of Soft Computing for the Progress of Artificial Intelligence

%A K. Y. Chan
%A S. H. Ling
%A T. S. Dillon
%A H. T. Nguyen
%T Diagnosis of hypoglycemic episodes using a neural network based rule discovery system
%J Expert Systems with Applications
%V 38
%N 8
%D 2011
%P 9799--9808
%I 
%K genetic algorithms, genetic programming, Neural networks, Hypoglycemic episodes, Medical diagnosis, Type 1 diabetes mellitus
%U http://www.sciencedirect.com/science/article/B6V03-524WF2N-4/2/d9f5c30581fa33cc25387714abbbc4b6
%X Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM
   patients' physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have
   developed a neural network based rule discovery system with hybridising the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic
   episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients' data sets collected from
   Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate
   results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis,
   statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery
   system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a
   clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince
   medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients.

%A Bill C. H. Chang
%A Asanga Ratnaweera
%A Saman K. Halgamuge
%A Harry C. Watson
%T Particle Swarm Optimisation for Protein Motif Discovery
%J Genetic Programming and Evolvable Machines
%V 5
%N 2
%D 2004
%P 203--214
%I 
%K PSO, particle swarm optimisation, protein sequence motif, motif discovery, symbolic data optimisation, HPSO-TVAC
%X a modified particle swarm optimisation algorithm is proposed for protein sequence motif discovery. Protein sequences are represented as a chain of symbols and a protein
   sequence motif is a short sequence that exists in most of the protein sequence families. Protein sequence symbols are converted into numbers using a one to one amino acid
   translation table. The simulation uses EGF protein and C2H2 Zinc Finger protein families obtained from the PROSITE database. Simulation results show that the modified
   particle swarm optimisation algorithm is effective in obtaining global optimum sequence patterns, achieving 96.9 and 99.5 classification accuracy respectively in EGF and
   C2H2 Zinc Finger protein families. A better true positive hit result is achieved when compared to the motifs published in PROSITE database.
%8 June
%Z Title: Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster (1) Mechatronics Research Group,
   Mechanical and Manufacturing Engineering, University of Melbourne, Australia (2) Thermofluids Research Group, Mechanical and Manufacturing Engineering, University of
   Melbourne, Australia time varying c1 and c2

%A Chia-Lan Chang
%T Dynamic Proportion Portfolio Insurance with Genetic Programming and Market Volatility Factors Analysis
%R M.S. Thesis
%D 2005
%I 
%I National Central University, Jungli
%C Taiwan
%K genetic algorithms, genetic programming, DPPI, CPPI, market volatility, principal component analysis, PCA
%U http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/view_etd_e?URN=92423002
%X This thesis proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant
   multiplier in CPPI is generally regarded as the risk multiplier. It helps investor easily to understand how to allocate the capital among risky and risk-free assets and
   straightforward to implement. The risk multiplier in CPPI is predetermined by the investor's view-point and fixed to the end of investment duration. However, since the
   market changes constantly, we think that the risk multiplier should change accordingly. When the market becomes volatile, the predetermined large risk multiplier will lead
   to loss of insurance and DPPI may solve this kind of problem. This research identifies factors relating to market volatility. These factors are built into equation trees by
   genetic programming. We collected five stocks of American companies' financial data and the market information of New York Stock Exchange as input data feeding genetic
   programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. Because the equation trees are all different, there is no
   method to analyse the factor contributions to the results of the risk multiplier. We use principal component analysis to see the effect of factors, and the experimental
   results show that among the market volatility factors, risk-free rate influences the variances of risk multiplier most.
%8 30  June

%A Hsueh-Hsien Chang
%A Ching-Lung Lin
%T A New Method for Load Identification of Nonintrusive Energy Management System in Smart Home
%B 2010 IEEE 7th International Conference on e-Business Engineering (ICEBE)
%D 2010
%P 351--357
%I 
%K genetic algorithms, genetic programming, GP, NIEM system, electric demand management, electric equipments, energy sources, governmental policy, load demands, load
   identification, neural network, non-intrusive energy management system, non-intrusive energy management techniques, non-intrusive energy-managing results, nonintrusive
   energy management system, power signatures, recognition accuracy, smart home, turn-on transient energy analysis, turn-on transient energy signature, demand side management,
   home automation, neural nets, power engineering computing, power system transients
%X In response to the governmental policy of saving energy sources and reducing CO2, and carry out the resident quality of local; this paper proposes a new method for a
   non-intrusive energy management (NIEM) system in smart home to implement the load identification of electric equipments and establish the electric demand management.
   Non-intrusive energy management techniques were often based on power signatures in the past, these techniques are necessary to be improved for the results of reliability
   and accuracy of recognition. By using neural network (NN) in combination with genetic programming (GP) and turn-on transient energy analysis, this study attempts to
   identify load demands and improve recognition accuracy of non-intrusive energy-managing results. The turn-on transient energy signature can improve the efficiency of load
   identification and computational time under multiple operations.
%8 10-12  November
%Z Also known as \cite5704339

%A Jia-Ruey Chang
%A Shun-Hsing Chen
%A Dar-Hao Chen
%A Yao-Bin Liu
%T Rutting Prediction Model Developed by Genetic Programming Method Through Full Scale Accelerated Pavement Testing
%B Fourth International Conference on Natural Computation, ICNC '08
%V 6
%D 2008
%P 326--330
%I 
%K genetic algorithms, genetic programming, accelerated pavement testing, load repetitions, model evaluation, pavement performance evaluation, pavement rutting, pavement
   structural number, rutting prediction model, test pavements, wheel load, structural engineering computing
%X The application of genetic programming (GP) to pavement performance evaluation is relatively new. This paper both describes and demonstrates how to develop a model to
   predict the pavement rutting by using GP method. Results from closely controlled full-scale Accelerated Pavement Testing (APT) - 7 test pavements (264 records) from CRREL's
   HVS and 1 test pavement (8 records) from TxDOT's MLS - were employed to establish a rutting prediction model. For model evaluation purposes, additional test pavements (94
   records) from both CRREL's HVS and TxDOT's MLS were used. GP was applied successfully to develop a rutting prediction model that uses wheel load, load repetitions and the
   pavement Structural Number (SN) as inputs. The overall R2 for 272 records is 0.8140. The model and algorithms proposed in this study provide a good foundation for further
   refinement when additional data is available.
%8 October
%Z Discipulus Also known as \cite4667854

%A Jia-Ruey Chang
%A Sao-Jeng Chao
%T Pavement maintenance and rehabilitation decisions derived by genetic programming
%B Sixth International Conference on Natural Computation (ICNC), 2010
%V 5
%D 2010
%P 2439--2443
%I 
%C Yantai, Shandong, China
%K genetic algorithms, genetic programming, Darwinian principle, GP-based M amp, R decision model, Taiwan highway bureau, evolutionary computation technique, pavement distress
   surveys, pavement knowledge extraction process, pavement maintenance, pavement performance evaluation, problem-solving method, rehabilitation decisions, stochastic search
   method, maintenance engineering, road building, search problems, stochastic processes
%X The application of genetic programming (GP) to pavement performance evaluation is relatively new. GP was first proposed by John R. Koza as an evolutionary computation
   technique: a stochastic search method based on the Darwinian principle of `survival of the fittest', whereby intelligible relationships in a system are automatically
   extracted and used to generate mathematical expressions or `programs'. Nowadays, GP has been used as an important problem-solving method for function fitting and
   classification. In this paper, an empirical study is performed to develop a pavement maintenance and rehabilitation (M and R) decision model by using GP. As part of the
   research, experienced pavement engineers from the Taiwan Highway Bureau (THB) conducted pavement distress surveys on seven county roads. For each road section, the severity
   and coverage of existing distresses that required M and R treatments were separately identified and collated into an analytical database containing 2,340 records. These
   records were then used to train, validate, and apply the M and R decision model. The finding shows that the total accuracy of the evolved M and R decision model was 0.903,
   0.877, and 0.878 for the training, validation, and application data set, respectively. It proves that the GP-based M and R decision model process makes the pavement
   knowledge extraction process more systematic, easier to use and solvable with a higher probability of success - even for complex M and R decision problems.
%8 10-12  August
%Z Dept. of Civil Eng. & Environ. Inf., MingHsin Univ. of Sci. & Technol., Hsinchu, Taiwan Also known as \cite5583502

%A Shoou-Jinn Chang
%A Hao-Sheng Hou
%A Yan-Kuin Su
%T Automated synthesis of passive filter circuits including parasitic effects by genetic programming
%J Microelectronics Journal
%V 37
%N 8
%D 2006
%P 792--799
%I 
%K genetic algorithms, genetic programming, Parasitic effects, Passive filter synthesis
%X In this paper, we propose a genetic programming method to synthesise passive filter circuits including parasitic effects, which are very common in high-frequency
   application. This approach allows circuit topology and component values to be evolved simultaneously; therefore, novel circuits different from those generated by
   traditional methods can be explored. Experimental results show the proposed method can effectively generate not only compliant but also efficient solutions of such problems
   where the traditional approaches fail.
%8 August

%A Yun Seok Chang
%A Kwang Suk Park
%A Bo Yeon Kim
%T Nonlinear model for ECG R-R interval variation using genetic programming approach
%J Future Generation Computer Systems
%V 21
%N 7
%D 2005
%P 1117--1123
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V06-4CVX0RT-1/2/111fea795562435e39023c448749d96a
%X We propose a nonlinear system modelling method, which predicts characteristics of the ECG R-R interval variation. For determining model equation, we adopted a genetic
   programming method in which the chromosome represents the model equation consisting of time-delayed variables, constants, and four arithmetic operators, and determines the
   fitness function. By genetic programming, sequences of regressive nonlinear equations are produced and evolved until the finding of the optimal model equation, which could
   simulate the spectral, statistical and nonlinear behaviour of the given R-R interval dynamics. Experimental results showed that the evolutionary approach could find the
   equation which simulates the spectral and chaotic dynamics of the given signal. Therefore, the proposed evolutionary approach is useful for the system identification of the
   nonlinear biological system.
%8 July

%A Shoou-Jinn Chang
%A Hao-Sheng Hou
%A Yan-Kuin Su
%T Automated passive filter synthesis using a novel tree representation and genetic programming
%J IEEE Transactions on Evolutionary Computation
%V 10
%N 1
%D 2006
%P 93--100
%I 
%K genetic algorithms, genetic programming, RLC circuits, circuit optimisation, network topology, passive filters, GP-evolved circuits, RLC circuit analysis, automated passive
   filter synthesis, circuit topology, tree representation, Circuit analysis, circuit representation, passive filter synthesis
%X This paper proposes a novel tree representation which is suitable for the analysis of RLC (i.e., resistor, inductor, and capacitor) circuits. Genetic programming (GP) based
   on the tree representation is applied to passive filter synthesis problems. The GP is optimised and then incorporated into an algorithm which can automatically find
   parsimonious solutions without predetermining the number of the required circuit components. The experimental results show the proposed method is efficient in three
   aspects. First, the GP-evolved circuits are more parsimonious than those resulting from traditional design methods in many cases. Second, the proposed method is faster than
   previous work and can effectively generate parsimonious filters of very high order where conventional methods fail. Third, when the component values are restricted to a set
   of preferred values, the GP method can generate compliant solutions by means of novel circuit topology.
%8 February
%Z INSPEC Accession Number:8753451 Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan

%A Alastair D. Channon
%T The Evolutionary Emergence route to Artificial Intelligence
%R M.S. Thesis
%D 1996
%I 
%I School of Cognitive and Computing Sciences, University of Sussex
%C UK
%K genetic algorithms, genetic programming, Artificial Intelligence, Emergence, Artificial Life, Neural Networks, Development, Modularity, Fractals, Lindenmayer Systems,
   Recurrence
%U http://www.channon.net/alastair/msc/adc_msc.pdf
%X The artificial evolution of intelligence is discussed with respect to current methods. An argument for withdrawal of the traditional fitness function in genetic algorithms
   is given on the grounds that this would better enable the emergence of intelligence, necessary because we cannot specify what intelligence is. A modular developmental
   system is constructed to aid the evolution of neural structures and a simple virtual world with many of the properties believed beneficial is set up to test these ideas.
   Resulting emergent properties are given, along with a brief discussion.

%A A. D. Channon
%A R. I. Damper
%T The Artificial Evolution of Real Intelligence by Natural Selection
%D 1997
%I 
%K genetic algorithms, genetic programming
%O Published on the web site of and poster presented at the Fourth European Conference on Artificial Life (ECAL97), Brighton

%A A. D. Channon
%A R. I. Damper
%T Evolving Novel Behaviors via Natural Selection
%B Proceedings of the 6th International Conference on Artificial Life (ALIFE-98)
%E Christoph Adami and Richard K. Belew and Hiroaki Kitano and Charles Taylor
%D 1998
%P 384--388
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming, natural selection
%U http://www.channon.net/alastair/geb/alife6/channon_ad_alife6.pdf
%8 June ~27--29
%@ 0-262-51099-5

%A A. D. Channon
%A R. I. Damper
%T Perpetuating evolutionary emergence
%B From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior
%E Rolf Pfeifer and Bruce Blumberg and Jean-Arcady Meyer and Stewart W. Wilson
%D 1998
%P 534--539
%I MIT Press
%C Zurich, Switzerland
%K genetic algorithms, genetic programming, natural selection
%U http://www.channon.net/alastair/geb/sab98/channon_ad_sab98_nc.pdf
%8 August  17-21
%Z http://www.isab.org.uk/confs/sab98.php included in google books May 2008
%@ 0-262-66144-6

%A A. D. Channon
%A R. I. Damper
%T Towards the evolutionary emergence of increasingly complex advantageous behaviours
%J International Journal of Systems Science
%V 31
%N 7
%D 2000
%P 843--860
%I 
%K genetic algorithms, genetic programming
%U http://www.channon.net/alastair/geb/ijssepcs/channon_ad_ijssepcs.pdf
%O Special issue on Emergent Properties of Complex Systems

%A Alastair Channon
%T Evolutionary Emergence: The Struggle for Existence in Artificial Biota
%R Ph.D. Thesis
%D 2001
%I 
%I University of Southampton
%C UK
%K genetic algorithms, genetic programming, natural selection
%U http://www.channon.net/alastair/geb/phdthesis/channon_ad_phdthesis.pdf
%X The generation of complex entities with advantageous behaviours beyond our manual design capability requires long-term incremental evolution with continuing emergence. This
   thesis presents the argument that artificial selection models, such as traditional genetic algorithms, are fundamentally inadequate for this goal. Existing natural
   selection systems are evaluated, revealing both significant achievements and pitfalls. Thus, some requirements for the perpetuation of evolutionary emergence are
   established. An (artificial) environment containing simple virtual autonomous organisms with neural controllers has been created to satisfy these requirements and to aid in
   the development of an accompanying theory of evolutionary emergence. Resulting behaviours are reported alongside their neural correlates. In one example, the collective
   behaviour of one species provides a selective force which is overcome by another species, demonstrating the incremental evolutionary emergence of advantageous behaviours
   via naturally-arising coevolution. Further behavioural or neural analysis is infeasible in this environment, so evolutionary statistical methods are employed and extended
   in order to classify the evolutionary dynamics. This qualitative analysis indicates that evolution is unbounded in the system. As well as validating the theory behind it,
   work with the system has provided some useful lessons and directions towards the evolution of increasingly complex advantageous behaviours.
%8 November

%A Alastair Channon
%T Passing the ALife Test: Activity Statistics Classify Evolution in Geb as Unbounded
%B Advances in Artificial Life: Proceedings of the Sixth European Conference on Artificial Life (ECAL2001)
%S Lecture Notes in Computer Science
%E Jozef Kelemen and Petr Sosik
%V 2159
%D 2001
%P 417--426
%I Springer-Verlag
%K genetic algorithms, genetic programming, natural selection
%U http://link.springer-ny.com/link/service/series/0558/bibs/2159/21590417.htm; http://link.springer-ny.com/link/service/series/0558/papers/2159/21590417.pdf

%A Alastair Channon
%T Improving and still passing the ALife test: Component-normalised activity statistics classify evolution in Geb as unbounded
%B Proceedings of Artificial Life VIII, the 8th International Conference on the Simulation and Synthesis of Living Systems
%E Russell K. Standish and Mark A. Bedau and Hussein A. Abbass
%D 2002
%P 173--181
%I The MIT Press Cambridge, MA, USA
%C University of New South Wales, Sydney, NSW, Australia
%K genetic algorithms, genetic programming, natural selection
%U http://www.alife.org/alife8/proceedings/sub2118.pdf
%X Bedau's (1998a) classification system for long-term evolutionary dynamics provides a test for open-ended evolution. Making this ALife test more rigorous, and passing it,
   are two of the most important open problems in the field. Previously (Channon 2001) I presented the result that Geb, a system designed to verify and extend theories behind
   the generation of evolutionary emergent systems (Channon & Damper 2000), has passed this test. However I also criticised the test, most significantly with regard to its
   normalisation method for artificial systems. This paper details a modified normalisation method, based on component activity normalisation, that overcomes these criticisms.
   It then presents the results of the revised test when applied to Geb, which indicate that this system does indeed exhibit open-ended evolution.
%8 9th-13th  Decemeber
%Z Author claims this is a GP but "genetic programming" appears nowhere in it

%A Alastair Channon
%T Unbounded evolutionary dynamics in a system of agents that actively process and transform their environment
%J Genetic Programming and Evolvable Machines
%V 7
%N 3
%D 2006
%P 253--281
%I 
%K artificial life, Evolutionary dynamics, Variable-size genomes, Coevolution, Biotic selection, Emergence
%U http://www.channon.net/alastair/papers/channon_ad_gpem.pdf
%X Bedau et al.'s statistical classification system for long-term evolutionary dynamics provides a test for open-ended evolution. Making this test more rigorous, and passing
   it, are two of the most important open problems for research into systems of agents that actively process and transform their environment. This paper presents a detailed
   description of the application of this test to Geb, a system designed to verify and extend theories behind the generation of evolutionarily emergent systems. The result is
   that, according to these statistics, Geb exhibits unbounded evolutionary dynamics, making it the first autonomous artificial system to pass this test. However, having
   passed it, the most prudent course of action is to look for weaknesses in the test. The test is criticised, most significantly with regard to its normalisation method for
   artificial systems. Furthermore, this paper presents a modified normalisation method, based on component activity normalisation, that overcomes these criticisms. The
   results of the revised test, when applied to Geb, indicate that this system does indeed exhibit open-ended evolution.
%8 October

%A Dennis L. Chao
%A Stephanie Forrest
%T Information Immune Systems
%J Genetic Programming and Evolvable Machines
%V 4
%N 4
%D 2003
%P 311--331
%I 
%K artificial immune systems, collaborative design, collaborative filtering, evolutionary art, information filtering, biomorphs, sonomorphs, muzak
%X The concept of an information immune system (IIS) is introduced, in which undesirable information is eliminated before it can reach the user. The IIS is inspired by the
   natural immune systems that protect us from pathogens. IISs from multiple individuals can be combined to form a group IIS which filters out information undesirable to any
   of the members. The relationship between our proposed IIS architecture and the natural immune system is outlined, and potential applications, including information
   filtering, interactive design, and collaborative design, are discussed.
%8 Decemeber
%Z Special issue on artificial immune systems Article ID: 5144846 MusicFX, PolyLens, Arrow's paradox, p315 G. L. 'Nelson (1993) found that listening to a population ... "taxes
   the memory"'. "evaluate many things at once visually; p325 "failure of Biomorph group IIS to scale beyond three users." Adaptive Radio

%A Frederic Chapelle
%A O. Chocron
%A Philippe Bidaud
%T Genetic programming for inverse kinematics approximation
%B International Symposium on Robotics (ISR'00)
%D 2000
%P 5--11
%I 
%I International Federation of Robotics
%C Montreal, Canada
%K genetic algorithms, genetic programming
%8 14-17  May

%A Frederic Chapelle
%A G. Dumont
%A O. Chocron
%T Prototypage virtuel de micro-endoscopes par algorithmes evolutionnaires
%B Journees Jeunes Chercheurs en Robotique (JJCR 13)
%D 2000
%I 
%C Rennes, France
%K genetic algorithms
%U http://www.irisa.fr/manifestations/2000/jjcr/Papiers/chapelle.pdf
%O in french
%8 September

%A Frederic Chapelle
%A Philippe Bidaud
%T A closed form for inverse kinematics approximation of general 6R manipulators using genetic programming
%B IEEE International Conference on Robotics and Automation (ICRA'01)
%V 4
%D 2001
%P 3364--3369
%I IEEE
%C Seoul, Korea
%K genetic algorithms, genetic programming, industrial manipulators, manipulator kinematics, symbol manipulation, 6R manipulators, approximation, evolutionary algorithms,
   industrial manipulators, inverse kinematics, joint variables, symbolic regression, steady state, demes, ADF, parsimony preasure, subsample training data, learning base
%X We present an original use of evolutionary algorithms in order to approximate by a closed form the inverse kinematic model of analytical (non-analytical) and general
   manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively evaluated in the design processes of
   manipulators. A mathematical function is evolved through genetic programming according to the known direct kinematic model to determine an analytical expression which
   approximates the joint variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression process, the inverse kinematic
   models of the PUMA and GMF Arc Mate are approximated before applying the algorithm to general 6R manipulators.
%8 21-28  May
%Z INSPEC Accession Number:7018142 p2266 using integer constants. No sign of ADF?? 50 gens in 30 mins (pop size?) on Silicon Graphics O2. p3368 "a tournament of 200
   individuals" -- translation error? Cites A.P.Fraser's gpc++ and Thomas Weinbrenner's GP kernel 0.5.2 cf. \citeweinbrenner:1997:diploma

%A Frederic Chapelle
%T Evaluation de systemes robotiques et comportements complexes par algorithmes evolutionnaires
%R Ph.D. Thesis
%D 2002
%I 
%I University Pierre et Marie Curie, Paris VI
%C France
%K genetic algorithms, genetic programming, Computer-aided design, robotic systems, simultaneous structure/control evaluation, symbolic regression, inverse models, inverse
   kinematic problem, programming, control, simulation, medical devices, minimally invasive surgery
%X Evaluation of robotic systems and complex behaviours using evolutionary algorithms : in this thesis, an original approach for evaluation of robotic systems in the context
   of simultaneous structure/control design is presented. It relies on the evolutionary algorithms. The initial procedures for evaluation are usually difficult to implement
   and expensive in computing time. The developed method uses genetic programming within an evolutionary symbolic regression algorithm, to generate expressions with various
   levels of refinement which are intended to approximate the original evaluations (according to the concept of metamodels). The interest of this approach is illustrated by
   various applications of gradual complexity where the initial evaluation methods can be simple functions, algorithms or a value drawn from a simulation considering the
   globality of the system to be designed, its interactions with the environment and its tasks. Reliable and fast generic models, which are solutions of the inverse kinematic
   problem for any 6R manipulator geometry (analytical or not), have been produced via approximating functions. The application of these techniques to a problem with dynamics
   resulted in fixing restrictions to the use of our method for direct approximation of constrained behaviours. Evolutionary symbolic regression is then applied within the
   framework of optimisations by genetic algorithms (GA), for simple cases like when a GA seeks a solution of the 2D inverse kinematic problem, or more complex like
   preliminary design of smart active endoscopes for minimally invasive surgery. Additionally, an extension allowing to increase the evolutionarity of GA is deduced.
%O in french
%8 September

%A Frederic Chapelle
%A Philippe Bidaud
%A G. Dumont
%T Conception et evaluation de micro-endoscopes basees sur les algorithmes evolutionnaires
%B Journees du Reseau Thematique Pluri-disciplinaire Micro-robotique CNRS
%D 2002
%I 
%C Rennes, France
%K genetic algorithms, genetic programming
%O in french
%8 November

%A Frederic Chapelle
%A Philippe Bidaud
%T Closed form solutions for inverse kinematics approximation of general 6R manipulators
%J Mechanism and Machine Theory
%V 39
%N 3
%D 2004
%P 323--338
%I 
%K genetic algorithms, genetic programming, Inverse kinematics, Mechanical design, Manipulators, Genetic programming, Symbolic regression
%U http://www.sciencedirect.com/science/article/B6V46-4B1XNXT-1/2/2bf40af1f930c87f19d6fcc130f2f57a
%X This paper presents an original use of Evolutionary Algorithms in order to approximate by a closed form the inverse kinematic model (IKM) of analytical, non-analytical and
   general (i.e. with an arbitrary geometry) manipulators. The objective is to provide a fast and general solution to the inverse kinematic problem when it is extensively
   evaluated as in design processes of manipulators. A mathematical function is evolved through Genetic Programming according to the known direct kinematic model to determine
   an analytical expression which approximates the joint variable solution for a given end-effector configuration. As an illustration of this evolutionary symbolic regression
   process, the inverse kinematic models of the PUMA and the GMF Arc Mate are approximated before to apply the algorithm to general 6R manipulators.
%8 March

%A Frederic Chapelle
%A Philippe Bidaud
%T Evaluation functions synthesis for optimal design of hyper-redundant robotic systems
%J Mechanism and Machine Theory
%V 41
%N 10
%D 2006
%P 1196--1212
%I 
%K genetic algorithms, genetic programming, Mechanical design, Simultaneous structure/control evaluation, Functions synthesis, Hyper-redundant micro-robotics, Minimally
   invasive surgery
%X Simultaneous structure/control optimisation in a robotic system design is addressed through Genetic Algorithms. Both aspects are here evolved in the same algorithm through
   simulations for task oriented evaluations. Moreover, a technique based on Genetic Programming is proposed to generate approximated evaluation functions. Its aim is to
   significantly speed the design process up, while leading to robust evaluation. A specific adaptation of these principles is investigated for the design of hyper-redundant
   robotic systems such as smart active endoscopes intended for minimally invasive surgery. The design of these micro-robots is based on a serial arrangement of articulated
   rings with associated antagonist SMA micro-actuators, whose configuration has to be adapted to the surgical operation constraints. The control strategies for an adaptation
   of the system geometry to the environment are based on a multi-agent approach to minimise the inter-module communication requirements. The results obtained for the
   particular application of colonoscopy show the consistency of the solutions.
%8 October

%A K. Govinda Char
%T Constructivist AI with GP
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 28--34
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A K. Govinda Char
%T Evolution of Learning with Genetic Programming - Constructivist AI with Genetic Programming
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 289
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A K. Govinda Char
%A Walter Alden Tackett
%T Pattern recognition
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%I Oxford University Press
%K genetic algorithms, genetic programming
%U http://www.crcnetbase.com/isbn/9780750308953
%O section F1.6.2.5
%@ 0-7503-0392-1

%A K. Govinda Char
%T Constructive Learning with Genetic Programming
%B Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming
%E Riccardo Poli and W. B. Langdon and Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf
%D 1998
%P 1--5
%I CSRP-98-10, The University of Birmingham, UK School of Computer Science
%C Paris, France
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-10.ps.gz
%8 14-15  April
%Z EuroGP'98LB part of \citePoli:1998:egplb

%A K. Govinda Char
%T Constructivist AI with Genetic Programming
%R Ph.D. Thesis
%D 1998
%I 
%I Department of Electronics and Electrical Engineering, University of Glasgow
%C Rankine Building, Oakfield Avenue, Glasgow G12 8LT, Scotland, UK
%K genetic algorithms, genetic programming

%A S. B. Charhate
%A M. C. Deo
%A V. Sanil Kumar
%T Soft and hard computing approaches for real-time prediction of currents in a tide-dominated coastal area
%J Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment
%V 221
%N 4
%D 2007
%P 147--163
%I 
%K genetic algorithms, genetic programming, tidal currents, neural networks, harmonic analysis, current measurements
%X The prediction of tidal currents in the coastal region on a real-time or online basis is useful in taking operation- and planning-related decisions such as towing of
   vessels and monitoring of oil slick movements. Currently, however, this is done in offline mode on the basis of the statistical method of harmonic analysis involving
   fitting of harmonic functions to measured data. Alternatively, numerical solutions of hydrodynamic models can also provide spatial and temporal information on currents.
   Owing to the complex real sea conditions, such methods may not always yield satisfactory results. This paper discusses a few alternative approaches based on the soft
   computing tools of artificial neural networks (ANNs) and genetic programming (GP), as well as the hard mathematical approaches of stochastic and statistical methods. The
   suggested schemes use only a univariate time series of currents to forecast their future values. The measurements of coastal currents made at two locations in the Gulf of
   Khambhat along the west coast of India have been analysed. The current predictions over a time step of 20 min, a few hours, and a day at the specified locations were
   carried out. It was found that the soft computing schemes of GP and ANN performed better than the traditional hard technique of harmonic analysis in the present
   application. This work should initiate more application of GP in coastal engineering. Addressing the problem of current predictions in real-time mode based on analysis of
   observed time series of ocean currents is a specialty of this work.
%Z ARIMA, ANN, GP. Department of Civil Engineering, Indian Institute of Technology, Bombay

%A S. B. Charhate
%A M. C. Deo
%A S. N. Londhe
%T Inverse modeling to derive wind parameters from wave measurements
%J Applied Ocean Research
%V 30
%N 2
%D 2008
%P 120--129
%I 
%K genetic algorithms, genetic programming, Wave buoy, Wave data, Wind data, Neural networks
%U http://www.sciencedirect.com/science/article/B6V1V-4TCGM50-1/2/69dcf477c9fc85235d0cc5df25e6a54a
%X The problem of deriving wind parameters from measured waves is discussed in this paper. Such a need reportedly arises in the field when the wind sensor attached to a wave
   rider buoy at high elevation from the sea level gets disconnected during rough weather, or otherwise needs repairs. This task is viewed as an inverse modeling approach as
   against the direct and common one of evaluating the wind-wave relationship. Two purely nonlinear approaches of soft computing, namely genetic programming (GP) and
   artificial neural network (ANN) have been used. The study is oriented towards measurements made at five different offshore locations in the Arabian Sea and around the
   western Indian coastline. It is found that although the results of both soft approaches rival each other, GP has a tendency to produce more accurate results than the
   adopted ANN. It was also noticed that the equation-based GP model could be equally useful as the one based on computer programs, and hence for the sake of simplicity in
   implementation, the former can be adopted. In case the entire wave rider buoy does not function for some period, a common regional GP model prescribed in this work can
   still produce the desired wind parameters with the help of wave observations available from anywhere in the region. A graphical user interface is developed that puts the
   derived models to their actual use in the field.

%A Shrikant Bhauraoji Charhate
%T Applications of soft computing techniques to solve coastal and ocean problems
%R Ph.D. Thesis
%D 2008
%I 
%I Department of Civil Engineering, Indian Institute of Technology, Bombay
%C India
%K genetic algorithms, genetic programming
%U http://www.civil.iitb.ac.in/~mcdeo/thesis.html
%Z Supervised by Dr. M. C. Deo

%A S. B. Charhate
%A M. C. Deo
%A S. N. Londhe
%T Genetic programming for real-time prediction of offshore wind
%J Ships and Offshore Structures
%V 4
%N 1
%D 2009
%P 77--88
%I 
%K genetic algorithms, genetic programming, artificial neural networks, wind speed, wind direction, wind prediction
%X Wind speed and its direction at two offshore locations along the west coast of India are predicted over future time-steps of 3 to 24 hrs based on a sequence past wind
   measurements made by floating buoys. This is done based on a relatively new soft computing tool using genetic programming. The attention of investigators has recently been
   drawn to the application of this approach that differs from the well-known technique of genetic algorithms in basic coding and application of genetic operators. Unlike most
   of the past works dealing with causative modelling or spatial correlations, this study explores the usefulness of genetic programming to carry out temporal regression. It
   is found that the resulting predictions of wind movements rival those made by an equivalent and more traditional artificial neural network and sometimes appear more
   attractive when multiple-error criteria were applied. The success of genetic programming as a modelling tool reported in this study may inspire similar applications in
   future in the problem domain of offshore engineering, and more research in the computing domain as well.
%8 March
%Z Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India

%A Edmund Chattoe
%T Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Processes?
%J The Journal of Artificial Societies and Social Simulation
%V 1
%N 3
%D 1998
%I 
%K genetic algorithms, genetic programming, evolutionary algorithms, social evolution, selectionist paradigm
%U http://www.soc.surrey.ac.uk/JASSS/1/3/2.html
%X This paper attempts to illustrate the importance of a coherent behavioural interpretation in applying evolutionary algorithms like Genetic Algorithms and Genetic
   Programming to the modelling of social processes. It summarises and draws out the implications of the Neo-Darwinian Synthesis for processes of social evolution and then
   discusses the extent to which evolutionary algorithms capture the aspects of biological evolution which are relevant to social processes. The paper uses several recent
   papers in the field as case studies, discussing more and less successful uses of evolutionary algorithms in social science. The key aspects of evolution discussed in the
   paper are that it is dependent on relative rather than absolute fitness, it does not require global knowledge or a system level teleology, it avoids the credit assignment
   problem, it does not exclude Lamarckian inheritance and it is both progressive and open ended.
%8 30  June
%Z JASSS

%A Edmund Chattoe
%T Genetic Algorithms and Genetic Programming in Computational Finance, Chen, Shu-Heng (ed.)
%J Journal of Artificial Societies and Social Simulation
%V 7
%N 4
%D 2004
%I 
%K genetic algorithms, genetic programming
%U http://jasss.soc.surrey.ac.uk/7/4/reviews/chattoe.html
%O Book review
%8 31-  October
%Z review of \citechen:2002:gagpcf

%A Narendra S. Chaudhari
%A Anuradha Purohit
%A Aruna Tiwari
%T A multiclass classifier using Genetic Programming
%B 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
%D 2008
%P 1884--1887
%I IEEE
%C Hanoi, Vietnam
%K genetic algorithms, genetic programming
%X his paper presents an approach for designing classifiers for a multiclass problem using Genetic Programming (GP). The proposed approach takes an integrated view of all
   classes when GP evolves. An individual of the population will be represented using multiple trees. The GP is trained with a set of N training samples in steps. A concept of
   unfitness of a tree is used in order to improve genetic evolution. Weak trees having poor performance are given more chance to participate in the genetic operations, and
   thus improve themselves. In this context, a new mutation operation called nondestructive directed point mutation is used, which reduces the destructive nature of mutation
   operation. The approach is being demonstrated by experimenting on some datasets.
%8 17-20  Decemeber

%A U. K. Chaudhary
%A M. Iqbal
%T Determination of optimum genetic parameters for symbolic non-linear regression-like problems in genetic programming
%B IEEE 13th International Multitopic Conference, INMIC 2009
%D 2009
%P 1--5
%I 
%K genetic algorithms, genetic programming, Matlab, elitism, halfelitism-roulette, keepbest-doubletour, optimum genetic parameters, replace-doubletour, replace-lexictour,
   replace-tournament, symbolic non-linear regression-like problems, mathematics computing, regression analysis
%X Parametric studies have been carried out for the quartic-polynomial regression problem demonstrated in the Genetic Programming (GP) v3 toolbox of Matlab. Many
   classification schemes and modeling issues are polynomial based. Every possible combination originating from all available options between the two genetic parameters namely
   'elitism' and 'sampling' has been analyzed while keeping all other parameters as fixed. Three performance parameters namely, execution time of a given GP run, quickness of
   convergence to reach the required fitness and the most important, fitness improvement factor per generation have been studied. In terms of the last mentioned performance
   parameter, being an indicative of diversity, it is shown that the best particular combination is 'halfelitism-sus' if naming in the general format of 'elitism-sampling' is
   used. On the average, this combination went on improving the fitness value (of the best so far individual) in more than 78percent of generations as the GP simulations
   progressed towards the required solution. Secondly, halfelitism-roulette took, on the average, as less as 6.8 generations to complete a GP run outperforming other
   combinations in terms of quickness of convergence with again, halfelitism-sus as second best consuming 7.4 generations to reach at the desired quartic genre. In spite of
   its promising average values, this combination showed a contrasting behavior depending upon the auto-evolution process at the start of a given GP run. Either it took on a
   right track and converged to the solution efficiently or it de-tracked in the very beginning and lost its performance regarding the three aforementioned parameters.
   Furthermore, it was found that for the combinations replace-doubletour and keepbest-doubletour giving the best two execution times (in seconds) to complete a given GP run,
   their results were least encouraging regarding the other performance parameters. Also, in contrast to some combinations such as, replace-tournament and replace-lexictour,
   other combinations worked satisfactorily well in at least one of the three performances studied.
%8 Decemeber
%Z Also known as \cite5383162

%A Omer A. Chaudhri
%A Jason M. Daida
%A Jonathan C. Khoo
%A Wendell S. Richardson
%A Rachel B. Harrison
%A William J. Sloat
%T Characterizing a Tunably Difficult Problem in Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 395--402
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP206.pdf
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Asmatullah Chaudhry
%T Image Restoration using Machine Learning
%R Ph.D. Thesis
%D 2007
%I 
%I Ghulam Ishaq Khan Institute of Engineering Sciences \& Technology
%C Topi, NWFP, Pakistan
%K genetic algorithms, genetic programming, Image Restoration
%U http://prr.hec.gov.pk/thesis/2056.pdf
%X Restoration of degraded images has become an important and effective tool for many technological applications like space imaging, medical imaging and many other
   post-processing techniques. Most of the image restoration techniques model the degradation phenomena, usually blur and noise, and then obtain an approximation of the image.
   Whereas, in realistic situation, one has to estimate both the true image and the blur from the degraded image characteristics in the absence of any a priori information
   about the blurring system. The objective of this thesis is to develop a new punctual kriging based image restoration approach using machine-learning techniques. To achieve
   this objective, the research concentrates on the restoration of images corrupted with Gaussian noise by making good tradeoffs between two contradicting properties;
   smoothness versus edge preservation. This thesis makes the following contributions: (1) Quantitative analysis of the at hand punctual kriging based image restoration
   technique is carried out, (2) Fuzzy logic, punctual kriging and fuzzy averaging are used intelligently to develop a better image restoration technique, (3) A new image
   quality measure is proposed in terms of the semi-variograms to judge the performance of image restoration techniques, (4) Analysis of the effect of neighbourhood size on
   negative weights and the subsequent improvement in punctual kriging based image restoration is performed, (5) To avoid both the problems of matrix inversion failure and the
   negative weights in punctual kriging, artificial neural network is used to develop a neuro-fuzzy filter for image denoising, (6) Further, using genetic programming, a
   hybrid technique for image restoration based on fuzzy punctual kriging is developed, the developed machine learning technique uses local statistical measures along with
   kriged information for subsequent pixel estimation. Main parameters considered for evaluation of the proposed technique are image quality measure and computational cost.
   The image quality measures used for evaluation and comparison include MSE, PSNR, SSIM, wPSNR, VMSE and VPSNR. A series of empirical investigations have been made to
   evaluate the performance of the proposed techniques using database of standard images that show the effectiveness of our methodology.
%8 March
%Z Author also given as Ullah, Asmat eg by http://prr.hec.gov.pk/thesis/2056.pdf Subjects: Engineering & Technology (e) > Engineering(e1) > Computer Sciences & related
   disciplines(e1.9) ID Code: 2139

%A Asmatullah Chaudhry
%A Asifullah Khan
%A Asad Ali
%A Anwar M. Mirza
%T A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming
%J International Journal of Imaging Systems and Technology
%V 17
%N 4
%D 2007
%P 224--231
%I 
%K genetic algorithms, genetic programming, image restoration, fuzzy logic, punctual kriging, structure similarity index measure, SSIM, adaptive spatial filtering
%X We present an intelligent technique for image denoising problem of gray level images degraded with Gaussian white noise in spatial domain. The proposed technique consists
   of using fuzzy logic as a mapping function to decide whether a pixel needs to be krigged or not. Genetic programming is then used to evolve an optimal pixel
   intensity-estimation function for restoring degraded images. The proposed system has shown considerable improvement when compared both qualitatively and quantitatively with
   the adaptive Wiener filter, methods based on fuzzy kriging, and a fuzzy-based averaging technique. Experimental results conducted using an image database confirms that the
   proposed technique offers superior performance in terms of image quality measures. This also validates the use of hybrid techniques for image restoration.

%A Asmatullah Chaudhry
%A Anwar M. Mirza
%A Nisar Ahmed Memon
%T Fusion of Linear and Non-Linear Image Restoration Filters Using Genetic Programming
%J Mehran university Research Journal of Engineering and Technology
%V 28
%N 4
%D 2009
%P 429--436
%I Mehran University of Engineering and Technology
%C Pakistan
%K genetic algorithms, genetic programming, Image restoration, E-median filter, Adaptive Wiener filter (AWF)
%X In this paper, we present an intelligent technique for image de-noising of gray level still images degraded with Gaussian white noise. The proposed technique consists of
   using E-median filter in wavelet domain and adaptive Wiener filter to restore the noisy image. Genetic programming is then used to evolve an optimal pixel intensity
   estimation function used to restore the degraded images. The proposed method has shown considerable improvement in the image quality as compared to the adaptive Wiener and
   E-median filter approaches. Experimental results carried out on several standard images and a database consisting of 450 images confirm the superiority of the proposed
   technique in terms of image quality. This also validates the use of hybrid techniques for image restoration.
%8 October
%Z Unique item number RN257688172 Shelfmark 5536.314400

%A F. Chavez
%A J. L. Guisado
%A D. Lombrana
%A F. Fernandez
%T Una Herramienta de Programacion Genetica Paralela que Aprovecha Recursos Publicos de Computacion
%B MAEB'2007, V Congreso Espanol sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados
%D 2007
%I 
%C Puerto de la Cruz, Spain
%K genetic algorithms, genetic programming, Palabras clave, Algoritmos Paralelos, Programacion Genetica
%U http://icaro.eii.us.es/~jlguisado/publicaciones/MAEB2007_preprint.pdf
%X Eeste articulo presenta una primera implementacion de una herramienta generica de programacion genetica capaz de aprovechar recursos publicos de computacion. Dadas las
   altas necesidades de recursos de computacion requeridos por los algoritmos evolutivos, la aplicacion del paralelismo ha sido habitual recientemente, aunque las herramientas
   paralelas requieren infraestructuras costosas para su aprovechamiento. El modelo que se presenta en este articulo, permite utilizar computadores distribuidos en Internet,
   cuyos usuarios ceden altruistamente para colaborar en proyectos de investigacion. El proceso de donacion de recursos es simple e inmediato por parte del usuario, afectando
   solamente a los ciclos de CPU que no son consumidos por el propio usuario. Se estudia la mejora de las prestaciones obtenidas gracias al uso de estos recursos en
   Programacion Genetica Distribuida.
%Z in Spanish, BOINC

%A Sin Man Cheang
%A Kin Hong Lee
%A Kwong Sak Leung
%T Data Classification Using Genetic Parallel Programming
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1918--1919
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, Learning Classifier Systems, poster
%X A novel Linear Genetic Programming (LGP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. It
   is found that GPP can evolve parallel programs for Data Classification problems. In this paper, five binary-class UCI Machine Learning Repository databases are used to test
   the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware
   fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good
   generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Sin Man Cheang
%A Kin Hong Lee
%A Kwong Sak Leung
%T Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1802--1803
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, poster
%X sample weighting effect on Genetic Parallel Programming (GPP) that evolves parallel programs to solve the training samples captured directly from a real-world system. The
   distribution of these samples can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples
   dominate the fitness evaluation and cause premature convergence. This paper compares the performance of four sample weighting (SW) methods, namely, Equal SW (ESW),
   Class-equal SW (CSW), Static SW (SSW) and Dynamic SW (DSW) on five training sets. Experimental results show that DSW is superior in performance on tested problems.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Sin Man Cheang
%A Kin Hong Lee
%A Kwong Sak Leung
%T An Empirical Study of the Accelerating Phenomenon in Genetic Parallel Programming
%B Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Bart Rylander
%D 2003
%P 54--61
%I 
%C Chicago, USA
%K genetic algorithms, genetic programming
%8 12--16  July
%Z GECCO-2003LB

%A Sin Man Cheang
%A Kin Hong Lee
%A Kwong Sak Leung
%T Evolving data classification programs using genetic parallel programming
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 248--255
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%X A novel Linear Genetic Programming (Linear GP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU
   Processor. The GPP Accelerating Phenomenon, i.e. parallel programs are easier to be evolved than sequential programs, opens up a new two-step approach: 1) evolves a
   parallel program solution; and 2) serialises the parallel program to a equivalent sequential program. In this paper, five two-class UCI Machine Learning Repository
   databases are used to investigate the effectiveness of GPP. The main advantages to employ GPP for data classification are: 1) speeding up evolutionary process by parallel
   hardware fitness evaluation; 2) discovering parallel algorithms automatically; and 3) boosting evolutionary performance by the GPP Accelerating Phenomenon. Experimental
   results show that GPP evolves simple classification programs with good generalisation performance. The accuracies of these evolved classification programs are comparable to
   other existing classification algorithms.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Sin Man Cheang
%A Kin Hong Lee
%A Kwong Sak Leung
%T Applying sample weighting methods to genetic parallel programming
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 928--935
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%X We investigate the sample weighting effect on Genetic Parallel Programming (GPP). GPP evolves parallel programs to solve the training samples in a training set. Usually,
   the samples are captured directly from a real-world system. The distribution of samples in a training set can be extremely biased. Standard GPP assigns equal weights to all
   samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation causing premature convergence. This paper presents 4 sample weighting
   (SW) methods, i.e. Equal SW, Class-equal SW, Static SW (SSW) and Dynamic SW (DSW). We evaluate the 4 methods on 7 training sets (3 Boolean functions and 4 UCI medical data
   classification databases). Experimental results show that DSW is superior in performance on all tested problems. In the 5-input Symmetry Boolean function experiment, SSW
   and DSW boost the evolutionary performance by 465 and 745 times respectively. Due to the simplicity and effectiveness of SSW and DSW, they can also be applied to different
   population-based evolutionary algorithms.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Sin Man Cheang
%T An Empirical Study of the GPP Accelerating Phenomenon
%B Proceedings of the second International Conference on Computational Intelligence, Robotics and Autonomous Systems -- CIRAS-2003
%E P. Vadakkepat and T. W. Wan and T. K. Chen and L. A. Poh
%D 2003
%P PS04--4--03
%I National Univ. of Singapore
%I Centre for Intelligent Control, National Univ. of Singapore
%C Singapore
%K genetic algorithms, genetic programming
%X The Genetic Parallel Programming (GPP) is a novel Linear-structure Genetic Programming paradigm that learns parallel programs. We discover the GPP Accelerating Phenomenon,
   i.e. parallel programs are evolved faster than their counterpart sequential programs of identical functions. This paper presents an empirical study of Boolean function
   regression based on a Multi-ALU Processor that results in the phenomenon. We performed a series of random search experiments using different numbers of ALUs (w) and
   instructions (l). We identify that w (the degree of parallelism of the program) is the dominant factor that affects the searching performance. In a 3-input Boolean function
   experiment, searching a single-ALU program requires 875 times on average of the computational effort of an 8-ALU program. An investigation on the probabilities of finding
   solutions to different problem instances shows that parallel representation of programs can increase the probabilities of finding solutions to hard problems.
%8 15-18  Decemeber

%A Sin Man Cheang
%A Kin Hong Lee
%A Kwong Sak Leung
%T Designing Optimal Combinational Digital Circuits Using a Multiple Logic Unit Processor
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 23--34
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=23
%X Genetic Parallel Programming (GPP) is a novel Genetic Programming paradigm. The GPP Accelerating Phenomenon, i.e. parallel programs are easier to be evolved than sequential
   programs, opens up a new approach to evolve solution programs in parallel forms. Based on the GPP paradigm, we developed a combinational digital circuit learning system,
   the GPP+MLP system. An optimal Multiple Logic Unit Processor (MLP) is designed to evaluate genetic parallel programs. To show the effectiveness of the proposed GPP+MLP
   system, four multi-output Binary arithmetic circuits are used. Experimental results show that both the gate counts and the propagation gate delays of the evolved circuits
   are less than conventional designs. For example, in a 3-bit multiplier experiment, we obtained a combinational digital circuit with 26 two-input logic gates in 6 gate
   levels. It uses 4 gates less than a conventional design.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Sin Man Cheang
%A Kwong Sak Leung
%A Kin Hong Lee
%T Genetic Parallel Programming: Design and Implementation
%J Evolutionary Computation
%V 14
%N 2
%D 2006
%P 129--156
%I 
%K genetic algorithms, genetic programming, linear genetic programming, parallel processor architecture, MIMD, ALU MAP, GPP
%X This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP).
   The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated
   Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs.
   However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach
   to evolving a feasible problem solution in parallel program form and then serialises it into a sequential program if required. The effectiveness and efficiency of GPP are
   investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.
%8 Summer

%A Sin Man Cheang
%A Kin Hong Lee
%A Kwong Sak Leung
%T Applying Genetic Parallel Programming to Synthesize Combinational Logic Circuits
%J IEEE Transactions on Evolutionary Computation
%V 11
%N 4
%D 2007
%P 503--520
%I 
%K genetic algorithms, genetic programming, FPGA, Circuit design, digital circuits, evolvable hardware, parallel programming
%X Experimental results show that parallel programs can be evolved more easily than sequential programs in genetic parallel programming (GPP). GPP is a novel genetic
   programming paradigm which evolves parallel program solutions. With the rapid development of lookup-table-based (LUT-based) field programmable gate arrays (FPGAs),
   traditional circuit design and optimisation techniques cannot fully exploit the LUTs in LUT-based FPGAs. Based on the GPP paradigm, we have developed a combinational logic
   circuit learning system, called GPP logic circuit synthesiser (GPPLCS), in which a multilogic-unit processor is used to evaluate LUT circuits. To show the effectiveness of
   the GPPLCS, we have performed a series of experiments to evolve combinational logic circuits with two- and four-input LUTs. In this paper, we present eleven multi-output
   Boolean problems and their evolved circuits. The results show that the GPPLCS can evolve more compact four-input LUT circuits than the well-known LUT-based FPGA synthesis
   algorithms.
%8 August

%A Jitender Jit Singh Cheema
%A Narendra V. Sankpal
%A Sanjeev S. Tambe
%A Bhaskar D. Kulkarni
%T Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation
%J Biotechnology Progress
%V 18
%N 6
%D 2002
%P 1356--1365
%I 
%K genetic algorithms, genetic programming
%U http://www3.interscience.wiley.com/journal/121399381/abstract
%X This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel
   artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next
   step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic
   algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based
   optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the
   process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and
   optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two
   other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given
   by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in
   nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.
%Z PMID: 12467472 [PubMed - indexed for MEDLINE] S8756-7938(01)05509-6 ACS Publications Division, American Chemical Society and American Institute of Chemical Engineers
   Chemical Engineering Division, National Chemical Laboratory, Pune 411008, India

%A Kumar Chellapilla
%T Evolutionary Programming with Tree Mutations: Evolving Computer Programs without Crossover
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 431--438
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K evolutionary programming and evolution strategies
%8 13-16  July
%Z GP-97 non-standard initialisation of initial pop, 6 mutation operators, no crossover 6-bit multiplexor, simple symbolic regression x+x**2+x**3+x**4, artificial ant (Santa
   Fe Trail), cart centering

%A Kumar Chellapilla
%T Evolving Nonlinear Controllers for Backing up a Truck-and-Trialer Using Evolutionary Programming
%B Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%E V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben
%V 1447
%D 1998
%P 417--426
%I Springer-Verlag Berlin
%C Mission Valley Marriott, San Diego, California, USA
%K evolutionary programming
%8 25-27  March
%Z EP-98.
%@ 3-540-64891-7

%A Kumar Chellapilla
%T Automatic Generation of Nonlinear Optimal Control Laws for Broom Balancing using Evolutionary Programming
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%P 195--200
%I IEEE Press
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming
%X This paper explores the use of mutation operators with evolutionary programming (EP) to automatically generate time optimal "bang-bang" type control laws for the three
   dimensional broom balancing (inverted pendulum) problem. EP produces a time optimal nonlinear control strategy that takes the state variables as input and determines the
   direction of the "bang-bang" force to be applied. Preliminary results indicate that the control laws generated are capable of generalizing over previously unseen input
   states and compare well with nonlinear control laws that were generated using other evolutionary computation methods.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence. Comparison with \citekoza:book results
%@ 0-7803-4869-9

%A Kumar Chellapilla
%T A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 23--31
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Kumar Chellapilla
%A Hemanth Birru
%A Rao Sathyanarayan
%T Effectivenss of Local Search Operators in Evolutionary Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 753--761
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K evolutionary programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Kumar Chellapilla
%T Evolving Computer Programs without Subtree Crossover
%J IEEE Transactions on Evolutionary Computation
%V 1
%N 3
%D 1997
%P 209--216
%I 
%K genetic algorithms, genetic programming, symbolic expressions, Evolutionary Programming, variation operators
%X An evolutionary programming procedure is used for optimising computer programs in the form of symbolic expressions. Six tree mutation operators are proposed. Recombination
   operators such as crossover are not included. The viability and efficiency of the method is extensively investigated on a set of well-studied problems. The evidence
   indicates that the technique is not only viable but is indeed capable of evolving good computer programs. The results compare well with other evolutionary methods that rely
   on crossover to solve the same problems
%8 September
%Z negative results on building block hypothesis, C++ code available, Compares use of EP using 6 types of tree mutation with GP on: 6-mux, 3, 4, 5, 6 parity, symbolic
   regression, two box, two spirals, Santa Fe trail artificial ant, cart centering, 4 variation on broom balancing. In general EP wins in terms of Effort to find the solution.
   Gives algorithm used to create initial random trees

%A Bing-Rui Chen
%A Xia-Ting Feng
%A Cheng-Xiang Yang
%T A Self-adapting Algorithm for Identifying Rheology Model and Its Parameters of Rock Mass
%B International Conference on Computational Intelligence and Natural Computing, CINC '09
%V 2
%D 2009
%P 478--481
%I 
%K genetic algorithms, genetic programming, Jinping-2 hydropower station, chaos-genetic algorithm, hybrid genetic programming, optimal rheological model, rheology model
   identification, rock mass parameters, self-adapting system identification method, tentative model, identification, natural resources, rheology
%X As it is difficult to previously determine rockmass rheology constitutive model using phenomena methods of mechanics, so a new self-adapting system identification method, a
   hybrid genetic programming (GP) with the chaos-genetic algorithm (CGA) based on self-rheological characteristic of rock mass, is proposed. Genetic programming is used for
   exploring the model's structure and the chaos-genetic algorithm is produced to identify parameters (coefficients) in the tentative model. The optimal rheological model is
   determined by mechanical and rheological characteristic, important expertise etc and can describe rheological behavior of identified rock mass perfectly. The assistant
   tunnel B of Jinping-2 hydropower station is used as an example for verifying the proposed method. The results show that the algorithm is feasible and has great potential in
   finding new rheological models.
%8 June
%Z Also known as \cite5230917

%A Carla Chia-Ming Chen
%T Bayesian methodology for genetics of complex diseases
%R Ph.D. Thesis
%D 2010
%I 
%I Past, QUT Faculties \& Divisions, Faculty of Science and Technology, Queensland University of Technology
%C Australia
%K genetic algorithms, genetic programming, gene expression programming, Bayesian, statistics, genetics, phenotype analysis, complex diseases, complex etiology, model
   comparison, latent class analysis, grade of membership, fuzzy clustering, item response theory, migraine, twin study, heritability, genome-wide linkage analysis, deviance
   information criteria, model averaging, MCMC, genomewide association studies, epistasis, logistic regression, stochastic search algorithm, case-control studies, Type I
   diabetes, single nucleotide polymorphism, logic tree, logicFS, Monte Carlo logic regression, genetic programming for association study, random forest, GENICA
%U http://eprints.qut.edu.au/43357/1/Carla_Chen_Thesis.pdf
%X Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of
   the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases.
   In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying
   potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly
   investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the
   estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting
   for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of
   Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control
   studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic
   Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.
%Z ID Code: 43357 Supervisors: Mengersen, Kerrie and Keith, Jonathan

%A Carla Chia-Ming Chen
%A Holger Schwender
%A Jonathan Keith
%A Robin Nunkesser
%A Kerrie Mengersen
%A Paula Macrossan
%T Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression
%J IEEE/ACM Transactions on Computational Biology and Bioinformatics
%V 8
%N 6
%D 2011
%P 1580--1591
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming, Logic regressions, Genetic Programming for Association Studies, Modified Logic Regression-Gene
   Expression Programming, Random Forest, Bayesian logistic regression with stochastic search algorithm, candidate gene search
%X Due to advancements in computational ability, enhanced technology and a reduction i the price of genotyping, more data are being generated for understanding genetic
   associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and
   modelling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic
   epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to
   improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming
   for Association Studies and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We
   contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.
%8 November -  Decemeber
%Z Also known as \cite5728791

%A Ci Chen
%A Shingo Mabu
%A Chuan Yue
%A Kaoru Shimada
%A Kotaro Hirasawa
%T Network intrusion detection using fuzzy class association rule mining based on genetic network programming
%B IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
%D 2009
%P 60--67
%I 
%C San Antonio, Texas, USA
%K genetic algorithms, genetic programming, Internet, anomaly detection, computer systems, directed graph structure, evolutionary optimization, fuzzy class association rule
   mining, fuzzy set theory, genetic network programming, machine learning, network intrusion detection, Internet, data mining, security of data
%X Computer systems are exposed to an increasing number and type of security threats due to the expanding of Internet in recent years. How to detect network intrusions
   effectively becomes an important techniques. This paper presents a novel fuzzy class association rule mining method based on Genetic Network Programming (GNP) for detecting
   network intrusions. GNP is an evolutionary optimization techniques, which uses directed graph structures as genes instead of strings (Genetic Algorithm) or trees (Genetic
   Programming), leading to creating compact programs and implicitly memorizing past action sequences. By combining fuzzy set theory with GNP, the proposed method can deal
   with the mixed database which contains both discrete and continuous attributes. And it can be flexibly applied to both misuse and anomaly detection in Network Intrusion
   Detection Problem. Experimental results with KDD99Cup and DAPRA98 databases from MIT Lincoln Laboratory show that the proposed method provides a competitively high
   detection rate compared with other machine learning techniques.
%8 October
%Z Also known as \cite5346328

%A Chih-Yung Chen
%T The Studies of Artificial Intelligent Technology and Its Applications
%R Ph.D. Thesis
%D 2007
%I 
%I Graduate School of Electrical Engineering, I-Shou University
%C Kaohsiung, Taiwan
%K genetic algorithms, EHW, Image Processing, Fuzzy Control, Neural Network, Evolutionary Hardware, Artificial Intelligence
%U http://ethesys.isu.edu.tw/ETD-db/ETD-search/getfile?URN=etd-0114108-184337&filename=etd-0114108-184337.pdf
%X This thesis focuses on the concept of system design by using artificial intelligent (AI) techniques. Four different research topics were studied. For each topic, in order
   to achieve the condition and goal of the system's request, all the problems were firstly modeled and then solved based on the AI techniques. The proposed approaches could
   sufficiently evidence the importance of AI design methodology in modern system design area. Firstly, in the research of the evolutionary hardware design, a new digital
   circuit genetic coding method based on genetic algorithm was proposed. Such a coding method is more flexible in the real application. Its variable structure can make it
   express the floor plan and routing of digital components easier. In the studies of image processing and computer vision, the first part is about a new face detection method
   which consists of the fast ellipse detect algorithm and the probabilistic neural network based color classifier. Cooperated with the servo motor controllers designed by
   fuzzy theory, the proposed face tracking system can reach the goal for the real-time using. The second part is about the study of automatic white balancing. In this part, a
   hybrid neural model was developed for estimating the illuminate of an image and then performing the automatic white balancing procedure according to estimated illuminate.
   The third part is about the digital camera auto-focusing system. In this part, the developed passive auto-focusing system could measure the sharpness value of a capture
   scene, and then predict the best focused position by a self-organized map based lens controller. Such a focusing system not only can move the adjustable lens to the best
   position, but also can save the time of focus searching. Through the examples of real work design we proposed, AI techniques in each application could be clearly described
   and easily understood. These researches not only show the feasibility and superiority of AI algorithm in the real system design, but also make a great improvement in
   comparison with the traditional design approaches. In our experiments, all studies were implemented by the software, firmware or hardware. In addition, they were also
   carried out by several ways, including simulation, embedded system or integrated circuit, respectively.
%8 8  Decemeber

%A Chih-Yung Chen
%A Rey-Chue Hwang
%T A new variable topology for evolutionary hardware design
%J Expert Systems with Applications
%V 36
%N 1
%D 2009
%P 634--642
%I 
%K genetic algorithms, genetic programming, evolvable hardware, Evolutionary hardware design, Slicing structure, Routing graph
%U http://www.sciencedirect.com/science/article/B6V03-4PV2RVX-6/2/6aa751f84c76e323ab6ddab36f70e63d
%X In this paper, a novel variable topology for evolutionary hardware design is proposed. The slicing structure and routing graph are integrated into the design of
   evolutionary hardware. With off-line gate-level samples, simulation results clearly demonstrate the validity of this new approach performed as superior as existing methods
   in the logic circuit optimization. Compare with the random circuit matrix method, our approach uses less code length for evolutionary hardware description. The method we
   proposed could be taken as an alternative way for possible evolutionary hardware applications in the future.
%Z EHW, GP, graph based GA

%A Guang Chen
%A Mengjie Zhang
%T Evolving While-Loop Structures in Genetic Programming for Factorial and Ant Problems
%B AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Proceedings
%S Lecture Notes in Computer Science
%E Shichao Zhang and Ray Jarvis
%V 3809
%D 2005
%P 1079--1085
%I Springer
%C Sydney, Australia
%K genetic algorithms, genetic programming, STGP
%X Loop is an important structure in human written programs. However, it is seldom used in the evolved programs in genetic programming (GP). use of while-loop structure in GP
   for the factorial and the artificial ant problems. Two different forms of the while-loop structure, count-controlled loop and event-controlled loop, are investigated. The
   results suggest that both forms of the while-loop structure can be successfully evolved in GP, the system with the while-loop structure is more effective and more efficient
   than the standard GP system for the two problems, and the evolved genetic programs with the loop-structure are much easier to interpret.
%8 Decemeber  5-9
%Z easy (non-Santa Fe) Ant. Factorial. Proportional Selection. Ramped half and half tree mutation. For loop, limits on number of iterations. p1081 'perfect solution' in half
   runs.
%@ 3-540-30462-2

%A Jiah-Shing Chen
%A Ping-Chen Lin
%T Multi-Valued Stock Valuation Based on Fuzzy Genetic Programming Approach
%B Procceedings of the Sixth International Conference on Computational Intelligence and Natural Computing
%D 2003
%I 
%C Embassy Suites Hotel and Conference Center, Cary, North Carolina USA
%K genetic algorithms, genetic programming
%8 September  26-30
%Z http://axon.cs.byu.edu/CINC/ http://www.ee.duke.edu/JCIS/ CIEF3-39 (1) Dept. of Information Management, National Central University, Jungli, Taiwan 320, R.O.C. (2) Dept. of
   Information Management, Van Nung Institute of Technology, Jungli, Taiwan 320, R.O.C.

%A Jiah-Shing Chen
%A Chia-Lan Chang
%T Dynamical Proportion Portfolio Insurance with Genetic Programming
%B Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part II
%S Lecture Notes in Computer Science
%E Lipo Wang and Ke Chen and Yew-Soon Ong
%V 3611
%D 2005
%P 735--743
%I Springer
%C Changsha, China
%K genetic algorithms, genetic programming
%X a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is
   generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change accordingly. This research identifies
   factors relating to market volatility. These factors are built into equation trees by genetic programming. Experimental results show that our DPPI strategy is more
   profitable than traditional CPPI strategy.
%8 August  27-29
%@ 3-540-28325-0

%A J. S. Chen
%A Benjamin Penyang Liao
%T Piecewise nonlinear goal-directed CPPI strategy
%J Expert Systems with Applications
%V 33
%N 4
%D 2007
%P 857--869
%I 
%K genetic algorithms, genetic programming, Portfolio insurance strategy, Goal-directed strategy, Piecewise linear GDCPPI strategy, Piecewise nonlinear GDCPPI strategy
%X Traditional portfolio insurance (PI) strategy, such as constant proportion portfolio insurance (CPPI), only considers the floor constraint but not the goal aspect. This
   paper proposes a goal-directed (GD) strategy to express an investor's goal-directed trading behaviour and combines this floor-less GD strategy with the goal-less CPPI
   strategy to form a piecewise linear goal-directed CPPI (GDCPPI) strategy. The piecewise linear GDCPPI strategy shows that there is a wealth position M at the intersection
   of the GD and CPPI strategies. This M position guides investors to apply the CPPI strategy or the GD strategy depending on whether current wealth is less than or greater
   than M, respectively. In addition, we extend the piecewise linear GDCPPI strategy to a piecewise nonlinear GDCPPI strategy. This paper applies genetic algorithm (GA)
   technique to find better piecewise linear GDCPPI strategy parameters than those under the Brownian motion assumption. This paper also applies forest genetic programming
   (GP) technique to generate the piecewise nonlinear GDCPPI strategy. The statistical tests show that the GP strategy outperforms the GA strategy which in turn outperforms
   the Brownian strategy.
%8 November

%A Jiah-Shing Chen
%A Chia-Lan Chang
%A Jia-Li Hou
%A Yao-Tang Lin
%T Dynamic proportion portfolio insurance using genetic programming with principal component analysis
%J Expert Systems with Applications
%V 35
%N 1-2
%D 2008
%P 273--278
%I 
%K genetic algorithms, genetic programming, Dynamic proportion portfolio insurance (DPPI), Constant proportion portfolio insurance (CPPI), Principal component analysis (PCA)
%U http://www.sciencedirect.com/science/article/B6V03-4P40KHS-4/2/0bbb6228d04a3a1a4d59108b17c37664
%X This paper proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant
   multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change according to market
   conditions. This research identifies risk variables relating to market conditions. These risk variables are used to build the equation tree for the risk multiplier by
   genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. In addition, principal component analysis of the
   risk variables in equation trees indicates that among all the risk variables, risk-free interest rate influences the risk multiplier most.

%A Jing Chen
%A Zeng-zhi Li
%A Yun-lan Wang
%T Distributed Service Management Based on Genetic Programming
%B Advances in Web Intelligence Third International Atlantic Web Intelligence Conference, AWIC 2005, Proceedings
%S Lecture Notes in Computer Science
%E Piotr S. Szczepaniak and Janusz Kacprzyk and Adam Niewiadomski
%V 3528
%D 2005
%P 83--88
%I Springer
%C Lodz, Poland
%K genetic algorithms, genetic programming
%X An architecture for online discovery quantitative model of distributed service management based on genetic programming (GP) was proposed. The GP system was capable of
   constructing the quantitative models online without prior knowledge of the managed elements. The model can be updated continuously in response to the changes made in
   provider configurations and the evolution of business demands. The GP system chose a particular subset from the numerous metrics as the explanatory variables of the model.
   In order to evaluate the system, a prototype is implemented to estimate the online response times for Oracle Universal Database under a TPC-W workload. Of more than 500
   Oracle performance metrics, the system model choose three most influential metrics that weight 76percent of the variability of response time, illustrating the effectiveness
   of quantitative model constructing system and model constructing algorithms.
%8 6-9  June
%@ 3-540-26219-9

%A Jing Chen
%A Zeng-Zhi Li
%A Zhi-Gang Liao
%A Yun-Lan Wang
%T Distributed Service Performance Management Based on Linear Regression and Genetic Programming
%B Proceedings of 2005 International Conference on Machine Learning and Cybernetics
%V 1
%D 2005
%P 560--563
%I 
%K genetic algorithms, genetic programming
%X An architecture for online discovery quantitative models system of service performance management was proposed. The system was capable of constructing the quantitative
   models without prior knowledge of the managed elements. The model can be updated continuously in response to the changes made in provider configurations and the evolution
   of business demands. Due to the existence of strong correlation between the distributed service metrics and response times, a linear and a hyper-linear quantitative models
   are constructed, which respectively use the stepwise multiple linear regression and genetic programming algorithms. The simulation results show that the effectiveness of
   quantitative model constructing system and model constructing algorithms.
%8 18-21  August
%Z Telecommunication Engineering Institute, Air Force Engineering University, Xi'an 710077, China; Institute of Computer System Architecture & Network, Xi'an Jiaotong
   University, Xi'an 710049, China E-MAIL: jingchen@263.net

%A Li Chen
%T Study of Applying Macroevolutionary Genetic Programming to Concrete Strength Estimation
%J Journal of Computing in Civil Engineering
%V 17
%N 4
%D 2003
%P 290--294
%I ASCE
%K genetic algorithms, genetic programming, civil engineering computing, compressive strength, mixtures, concrete
%U http://link.aip.org/link/?QCP/17/290/1
%X This technical note is aimed at demonstrating a mixture-proportioning problem, which uses the macroevolutionary algorithm (MA) combined with genetic programming (GP) to
   estimate the compressive strength of high-performance concrete (HPC). GP provides system identification in a transparent and structured way; a fittest function type of
   experimental results will be obtained automatically from this method. MA is a new concept of species evolution at the higher level. It could improve the capability of
   searching global optima and avoid premature convergence during the selection process of GP. In the study, two appropriate functions have been found to represent the
   relationships between different ingredients and the compressive strength. The results show that this new model, MAGP, is better than the traditional proportional selection
   GP for HPC strength estimation.
%8 October
%Z Dept. of Civil Engineering, Chung Hua Univ., Hsin Chu, Taiwan 30067, Republic of China.

%A Li Chen
%A Chih-Hung Tan
%A Shuh-Ji Kao
%A Tai-Sheng Wang
%T Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery
%J Water Research
%V 42
%N 1-2
%D 2008
%P 296--306
%I 
%K genetic algorithms, genetic programming, Grammatical evolution, Parallel genetic algorithm, Water quality monitoring, Chlorophyll-a, Remote-sensed imagery
%U http://www.sciencedirect.com/science/article/B6V73-4P7FS78-1/2/1cc0a607d7b67fe51a5f0d27a2c9d0fc
%X Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on
   remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for
   Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an
   evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed
   imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the
   optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the
   traditional LMR model with lower estimating errors.

%A Mu-Yen Chen
%A Kuang-Ku Chen
%A Heien-Kun Chiang
%A Hwa-Shan Huang
%A Mu-Jung Huang
%T Comparing extended classifier system and genetic programming for financial forecasting: an empirical study
%J Soft Computing
%V 11
%N 12
%D 2007
%P 1173--1183
%I 
%K genetic algorithms, genetic programming, Learning classifier system, Extended classifier system, Machine learning
%X As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to learn. These methods
   such as fuzzy logic, neural networks, support vector machines, decision trees and Bayesian learning have been applied to learn meaningful rules; however, the only drawback
   of these methods is that it often gets trapped into a local optimal. In contrast with machine learning methods, a genetic algorithm (GA) is guaranteeing for acquiring
   better results based on its natural evolution and global searching. GA has given rise to two new fields of research where global optimization is of crucial importance:
   genetic based machine learning (GBML) and genetic programming (GP). This article adopts the GBML technique to provide a three-phase knowledge extraction methodology, which
   makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. Moreover, the proposed system and GP are both applied to the
   theoretical and empirical experiments. Results for both approaches are presented and compared. This paper makes two important contributions: (1) it uses three criteria
   (accuracy, coverage, and fitness) to apply the knowledge extraction process which is very effective in selecting an optimal set of rules from a large population; (2) the
   experiments prove that the rule sets derived by the proposed approach are more accurate than GP.

%A Peng Chen
%A Takashi Isoda
%A Shinichiro Mitutake
%A Shinji Koyama
%T Automatic Running Planning for Omni$\phi$Directional Mobile Robot By Genetic Programming
%B WSEAS SEPAD-AIKED-ISPRA-EHAC
%D 2003
%P 5
%I 
%I The World Scientific and Engineering Academy and Society (WSEAS)
%C Rethymno, Greece
%K genetic algorithms, genetic programming
%8 August ~11-13

%A Peng Chen
%A Masatoshi Taniguchi
%A Toshio Toyota
%A Zhengja He
%T Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming
%J Mechanical Systems and Signal Processing
%V 19
%N 1
%D 2005
%P 175--194
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6WN1-4BKPSGD-2/2/6c68916b11c23706a7fee9f780c0e637
%X This paper proposes a fault diagnosis method for plant machinery in an unsteady operating condition using instantaneous power spectrum (IPS) and genetic programming (GP).
   IPS is used to extract feature frequencies of each machine state from measured vibration signals for distinguishing faults by relative crossing information. Excellent
   symptom parameters for detecting faults are automatically generated by the GP. The excellent symptom parameters generated by GP can sensitively reflect the characteristics
   of signals for precise diagnosis. The method proposed is verified by applying it to the fault diagnosis of a rolling bearing.
%8 January

%A Qing-Shan Chen
%A De-Fu Zhang
%A Li-Jun Wei
%A Huo-Wang Chen
%T A Modified Genetic Programming for Behavior Scoring Problem
%B IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
%D 2007
%P 535--539
%I IEEE
%C Honolulu, HI, USA
%K genetic algorithms, genetic programming, Chinese commercial bank, backpropagation neural network, behavior scoring problem, financial institutions, future credit
   performance forecasting, real life credit data set, risk management, backpropagation, customer relationship management, financial data processing
%X Behavior scoring is an important part of risk management in financial institutions, which is used to help financial institutions make better decisions in managing existing
   customers by forecasting their future credit performance. In this paper, a modified genetic programming (MGP) is introduced to solve the behavior scoring problems. A real
   life credit data set in a Chinese commercial bank is selected as the experimental data to demonstrate the classification accuracy of this method. MGP is compared with
   back-propagation neural network (BPN), and another GP that uses normalized inputs (NGP), the experimental results show that the MGP has slight better classification
   accuracy rate than NGP, and outperforms BPN in dealing with behavior scoring problems because of less historical samples of credit data in Chinese commercial banks
%8 March  1-  April  5
%@ 1-4244-0705-2

%A Shih-Huang Chen
%A Jun-Nan Chen
%T Forecasting container throughputs at ports using genetic programming
%J Expert Systems with Applications
%V 37
%N 3
%D 2010
%P 2054--2058
%I 
%K genetic algorithms, genetic programming, Container throughput, Forecasting
%U http://www.sciencedirect.com/science/article/B6V03-4WNXTWY-M/2/1a5e0fe084ba3ea36303bd280acecc04
%X To accurately forecast container throughput is crucial to the success of any port operation policy. This study attempts to create an optimal predictive model of volumes of
   container throughput at ports by using genetic programming (GP), decomposition approach (X-11), and seasonal auto regression integrated moving average (SARIMA). Twenty-nine
   years of historical data from Taiwan's major ports were collected to establish and validate a forecasting model. The Mean Absolute Percent Error levels between forecast and
   actual data were within 4percent for all three approaches. The GP model predictions were about 32-36percent better than those of X-11 and SARIMA. These results suggest that
   GP is the optimal method for this case. GP predicted that container through puts at Taiwan's major ports would slowly increase in the year 2008. Since Taiwan's government
   opened direct transportation with China in July 2008, the issue of container throughput in Taiwan has become even more worthy of discussion.

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Predicting Stock Returns with Genetic Programming: Do the Short-Term Nonlinear Regularities Exist?
%B Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics
%E Doug Fisher
%D 1995
%P 95--101
%I 
%I Society for Artificial Intelligence and Statistics
%C Ft. Lauderdale, Florida, U.S.A.
%K genetic algorithms, genetic programming
%8 January  4-7
%Z http://web.archive.org/web/20011127035349/http://www.vuse.vanderbilt.edu/~dfisher/ai-stats/fifth-workshop/contents.html

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T On the Competitiveness of the Quantity Theory of Money: A Natural-Selection Test Based on Genetic Programming
%B 11th International Conference on Advanced Science and Technology
%D 1995
%I 
%C Chicago, Illinois, U.S.A
%K genetic algorithms, genetic programming
%8 25-27  March

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T On the Coordination and Adaptability of the Large Economy: An Application of Genetic Programming to the Cobweb Model
%B Proceedings of the First International Conference on Applications of Dynamic Models to Economics
%S The School of Management National Central University's International Conference Series
%N 3
%D 1995
%P 121--159
%I 
%C ChungLi, Taiwan, R.O.C.
%K genetic algorithms, genetic programming
%8 June  17-18

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Genetic Programming, Predictability and Stock Market Efficiency
%B Proceedings of 1995 IFAC/IFIP/IFORS/SEDC Symposium on Modelling and Control of National and Regional Economies
%V II
%D 1995
%I 
%I International Federation of Automatic Control
%C Gold Coast, Australia
%K genetic algorithms, genetic programming
%8 July  3-5

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Predicting Chaotic Dynamic Systems with Genetic Programming
%B Proceedings of the 50th International Statistical Institute Session
%D 1995
%I 
%C Beijing
%K genetic algorithms, genetic programming
%8 August  21-29

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Information Transmission, Market Efficiency and the Evolution of Information-Processing Technology
%B Proceedings of the 1995 National Conference on Management of Technology
%E C. Houng
%D 1995
%P 339--348
%I 
%I Chinese Society of Management of Technology
%K genetic algorithms, genetic programming

%A Shu-Heng Chen
%A John Duffy
%A Chia-Hsuan Yeh
%T Modelling Coordination Game as a Multi-Agent Adaptive System by Genetic Programming
%B Position Papers of the 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World (MAAMAW'96)
%E W. Van de Velde and J. W. Perram
%D 1996
%I 
%I Institute for Perception Research, Eindhoven, The Netherlands
%K genetic algorithms, genetic programming
%O Technical Report 96-1
%8 January  22-25

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Genetic Programming in Computable Financial Economics
%B Proceedings of the ISCA 11th Conference: Computers and Their Applications
%D 1996
%P 135--138
%I ISCA Press
%C San Francisco, California, U.S.A.
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/324902.html
%X From a computation-theoretic standpoint, this paper formalises the notion of unpredictability in the efficient market hypothesis (EMH) by a biological-based search program,
   i.e., genetic programming (GP). This formalisation differs from the traditional notion based on probabilistic independence in its treatment of search. Compared with the
   traditional notion, a GP-based search provided an explicit and efficient search program upon which an objective measure for predictability can be formalized...
%8 March  7-9
%@ 1-880843-15-3

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Bridging the Gap between Nonlinearity Tests and the Efficient Market Hypothesis by Genetic Programming
%B Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering
%D 1996
%P 34--39
%I IEEE Press
%C Crowne Plaza Manhattan, New York City
%K genetic algorithms, genetic programming
%8 March  24-26
%@ 0-7803-3236-9

%A Shu-Heng Chen
%T Genetic Programming, Predictability, and Stock Market Efficiency
%B Modelling and Control of National and Regional Economies 1995
%E L. Vlacic and T. Nguyen and D. Cecez-Kecmanovic
%D 1996
%P 283--288
%I Pergamon
%C Oxford, Great Britain
%K genetic algorithms, genetic programming
%@ 0-08-042376-0

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T On the Coordination and Adaptability of the Large Economy: An Application of Genetic Programming to the Cobweb Model
%B Preprints of 13th World Congress International Federation of Automatic Control
%V L
%D 1996
%P 279--284
%I 
%C San Francisco, CA, USA
%K genetic algorithms, genetic programming
%8 June  30-  July  5

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Genetic Programming Learning and the Cobweb Model
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 443--466
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/482
%X Using genetic programming to model the cobweb model as a multiagent system, this chapter generalises the work done by Arifovic (1994), which is based on genetic algorithms.
   We find that the rational expectations equilibrium price which can be discovered by genetic algorithms can also be discovered by genetic programming. Furthermore, genetic
   programming requires much less prior knowledge than genetic algorithms. The reasonable upper limit of the price and the characteristic of the equilibrium which is assumed
   as the prior knowledge in genetic algorithms can all be discovered by genetic programming. In addition, GP-based markets have a self-stabilising force which is capable of
   bringing any deviations caused by mutation back to the rational expectations equilibrium price. All of these features show that genetic programming can be a very useful
   tool for economists to model learning and adaptation in multiagent systems. In particular, with respect to the understanding of the dynamics of the market process, it
   provides us with a visible foundation for the 'invisible hand'.
%O 22
%@ 0-262-01158-1

%A Shu-Heng Chen
%A John Duffy
%A Chia-Hsuan Yeh
%T Genetic Programming in the Coordination Game with a Chaotic Best-Response Function
%B Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming
%E Lawrence J. Fogel and Peter J. Angeline and Thomas Baeck
%D 1996
%P 277--286
%I MIT Press Cambridge, MA, USA
%C San Diego
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/326396.html
%X By modelling the coordination game as GP (Genetic Programming)-based adaptive multiagent systems, this paper analyses the coordination experiments with human subjects
   conducted by (Van Huyck et al. 1994). In the model on which their experiments were based, the coordination pattern in the equilibrium crucially depends on the learning
   schemes adopted by the interactive agents in the society. While, in general, we cannot exclude the possibility of chaotic-like coordination, such a result did not...
%8 February  29-  March  3
%Z EP-96 http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383
%@ 0-262-06190-2

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Toward a Computable Approach to the Efficient Market Hypothesis: An Application of Genetic Programming
%J Journal of Economic Dynamics and Control
%V 21
%N 6
%D 1997
%P 1043--1063
%I 
%K genetic algorithms, genetic programming, Evolutionary computation, Minimum description length principle, Mean absolute percentage error, Efficient market hypothesis
%U http://www.sciencedirect.com/science/article/B6V85-3SWYBJD-P/2/d1bb80ffce780c45697f44001e20f672
%X From a computation-theoretic standpoint, this paper formalises the notion of unpredictability in the efficient market hypothesis (EMH) by a biological-based search program,
   i.e., genetic programming (GP). This formalization differs from the traditional notion based on probabilistic independence in its treatment of <I>search</I>. Compared with
   the traditional notion, a GP-based search provides an explicit and efficient search program upon which an objective measure for predictability can be formalized in terms of
   search intensity and chance of success in the search. This will be illustrated by an example of applying GP to predict chaotic time series. Then the EMH based on this
   notion will be exemplified by an application to the Taiwan and US stock market. A short-term sample of TAIEX and S&P 500 with the highest complexity defined by Rissanen's
   minimum description length principle (MDLP) is chosen and tested. It is found that, while linear models cannot predict better than the random walk, a GP-based search can
   beat random walk by 50%. It, therefore, confirms the belief that while the short-term nonlinear regularities might still exist, the search costs of discovering them might
   be too high to make the exploitation of these regularities profitable, hence the efficient market hypothesis is sustained.
%8 1  June
%Z Society of Computational Economics Conference JEL classification codes: C63; G14

%A Shu-Heng Chen
%A John Duffy
%A Chia-Hsuan Yeh
%T Equilibrium Selection Using Genetic Programming
%B Progress in Neural Information Precessing: Proceedings of the International Conference on Neural Information Processing (ICONIP'96)
%E S. Amari and L. Xu and L. Chan and I. King and K. Leung
%V 2
%D 1996
%P 1341--1346
%I Springer-Verlag Singapore
%C Hong Kong Convention and Exhibition Center, Hong Kong
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/323448.html
%X We use genetic programming techniques developed by Koza (1992) to model the behaviour of a population of heterogeneous agents playing a simple coordination game with
   multiple equilibria. We compare the results from our computational experiments with results obtained from a number of controlled laboratory experiments conducted by Van
   Huyck et al. (1994) where human subjects played the same coordination game. We nd that the behavior exhibited by our population of artificially intelligent...
%@ 981-3083-04-2

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Genetic Programming Learning in the Cobweb Model with Speculators
%B Proceedings of 3rd Conference on Business Education
%D 1996
%P 155--176
%I 
%C Department of Business Education, National Changhua University of Education, Chunghua, Taiwan
%K genetic algorithms, genetic programming
%8 Decemeber  5

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Genetic Programming Learning in the Cobweb Model with Speculators
%B International Computer Symposium (ICS'96). Proceedings of International Conference on Artificial Intelligence
%D 1996
%P 39--46
%I 
%C National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C.
%K genetic algorithms, genetic programming
%8 Decemeber  19-21

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Information Transmission, Market Efficiency and the Evolution of Information-Processing Technology
%J Journal of Technology Management
%V 1
%N 1
%D 1996
%P 23--41
%I 
%K genetic algorithms, genetic programming

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T A Comparison of Forcast Accuracy between Genetic Programming and Other Forcasters: A loss-Differential Approach
%B The First International Workshop on Machine Learning, Forecasting, and Optimization (MALFO96)
%E Daniel Borrajo and Pedro Isasi
%D 1996
%P 39--51
%I 
%I Universidad Carlos III de Madrid
%C Gatafe, Spain
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/chen_1996_cfaGPothers.pdf
%8 10--12  July
%@ 84-89315-04-3

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Genetic Programming and the Efficient Market Hypothesis
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 45--53
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X While search plays an important role in the efficient market hypothesis (EMH), the traditional formalisation of the EMH, based on probabilistic independence, fails to
   capture it. Due to this failure, recent findings of nonlinear tests misled us into concluding that the EMH is rejected. Even though most economists are reluctant to make
   this conclusion, the traditional formalization leaves us no other choice. This paper reformalizes the EMH with a biologically-based search program, i.e., genetic...
%8 28--31  July
%Z GP-96

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Speculative Trades and Financial Regulations: Simulations Based on Genetic Programming
%B Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr'97)
%D 1997
%P 123--129
%I IEEE Press
%C New York City, U.S.A.
%K genetic algorithms, genetic programming, 2D parameter space, cobweb markets, financial regulations, market efficiency, price volatility reduction, simulations, speculative
   trades, unstable economy, economics, financial data processing, mathematical programming, simulation, stock markets
%X By exploring a two-dimensional parameter space, the paper pinpoints the area where speculative trades can contribute to the reduction of price volatility and are hence
   imperative for market efficiency. This area is delimited by a rather restrictive financial regulations imposed on an inherently unstable economy. Specifically, depending on
   the associated financial regulations, the authors' GP-based simulations of cobweb markets show that speculative trades may reduce price volatility by 20percent to 50percent
   in an inherently unstable economy; on the other hand they may also increase price volatility by 300percent to 3000percent. The paper generalises the earlier finding by Chen
   and Yeh (1997), which basically shows that in an inherently stable economy, speculative trades can only be destabilising
%8 March  24-25

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Simulating Economic Transition Processes by Genetic Programming
%B Proceedings of the International Conference on Transition to Advanced Market Institutions and Economies: Systems and Operations Research Challenges (Transition'97)
%E R. Kulikowski and Z. Nahorski and J. W. Owsinski
%D 1997
%P 87--93
%I 
%I System Research Institute and Polish Academy of Sciences
%C Warsaw, Poland
%K genetic algorithms, genetic programming
%8 June  18-21
%@ 83-85847-81-2

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Trading Restrictions, Speculative Trades and Price Volatility: An Application of Genetic Programming
%B Proceedings of the 3rd International Mendel Conference on Genetic Algorithms, Optimization Problems, Fuzzy Logic, Neural Networks, Rough Sets (Mendel'97)
%D 1997
%P 31--37.
%I PC-DIR Brno
%C Brno, Czech Republic
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/chen97trading.html
%X n this paper, genetic programming is employed to explore the significance of speculative activities in economic theory. Unlike most previous studies, this paper explicitly
   take interaction of speculators into account. Through genetic programming, this interaction processes is modelled as a competitive process which applies the
   survival-of-the-fittest principle to the selection of trading strategies. There are two interesting findings which make this paper distinctive. Firstly, while markets...
%8 June  25-27
%@ 80-214-0884-7

%A Shu-Heng Chen
%A Chih-Chi Ni
%T Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data
%B Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97
%E George D. Smith and Nigel C. Steele and Rudolf F. Albrecht
%D 1997
%P 397--400
%I Springer-Verlag
%C University of East Anglia, Norwich, UK
%K genetic algorithms, genetic programming
%O published in 1998
%Z http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html Opps duplicates chen:1997:eANNGP Note: 22 Aug 2004 chen:1997:eANNGP combined with
   \citechen:1997:eannGPfd
%@ 3-211-83087-1

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Modeling Speculators with Genetic Programming
%B Proceedings of the Sixth Conference on Evolutionary Programming
%S Lecture Notes in Computer Science
%E Peter J. Angeline and Robert G. Reynolds and John R. McDonnell and Russ Eberhart
%V 1213
%D 1997
%P 137--147
%I Springer-Verlag Berlin
%C Indianapolis, Indiana, USA
%K genetic algorithms, genetic programming, no-trade theorems
%U http://citeseer.ist.psu.edu/chen96modeling.html
%X In spirit of the earlier works done by Arthur (1992) and Palmer et al. (1993), this paper models speculators with genetic programming (GP) in a production economy (Muthian
   Economy). Through genetic programming, we approximate the consequences of speculating about the speculations of others, including the price volatility and the resulting
   welfare loss. Some of the patterns observed in our simulations are consistent with findings in experimental markets with human subjects. For example,...
%Z EP-97

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Using Genetic Programming to Model Volatility in Financial Time Series
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 58--63
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/chen_1997_GPmvfts.pdf
%X RGP tested by using Nikkei 255 and S&P 500 as an example
%8 13-16  July
%Z GP-97 Fixed size sliding window of the original time series. BGP used to learn first window, then whole pop used with second window (ie as population seed). Fitness = sum
   of errors squared also serves to give estimate of volatility.
%@ 1-55860-483-9

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Using Genetic Programming to Model Volatility in Financial Time Series: The Case of Nikkei 225 and S\&P 500
%B Proceedings of the 4th JAFEE International Conference on Investments and Derivatives (JIC'97)
%D 1997
%P 288--306
%I 
%C Aoyoma Gakuin University, Tokyo, Japan
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/322892.html
%X In this paper we propose a time-variant and non-parametric approach to estimating volatility. This approach is based on recursive genetic programming (RGP). Here,
   volatility is estimated by a class of non-parametric models which are generated through a recursive competitive process. The essential feature of this approach is that it
   can estimate volatility by simultaneously detecting and adapting to structural changes. Thus, volatility is estimated by taking possible structural changes into...
%8 July  29-31

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Speculative Trades and Financial Regulations: Simulation Bassed on Genetic Programming
%B Working Notes of The IJCAI-97: Workshop on Business Applications of AI. Fifteenth International Joint Conference on Artificial Intelligence (IJCAI'97)
%E A. Ghose
%D 1997
%P 1--8
%I 
%C Nagoya, Japan
%K genetic algorithms, genetic programming
%8 August  23-29

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Modelling Structural Changes with Genetic Programming: An Outline
%B Proceedings of 15th IMACS World Congress on Scientific Computation, Moldelling and Applied Mathematics
%E A. Sydow
%V 2
%D 1997
%P 621--626
%I Numerical Mathematics, Wissenschaft \& Technik Verlag
%C Berlin
%K genetic algorithms, genetic programming
%8 August  24-29
%@ 3-89685-552-2

%A Shuheng Chen
%A Jiaxuan Ye
%T Competition in "Quantity theory of money" : Genetic Programming Application in Knowledge Discovery
%B Development(s) and Application(s) of Measurement Method(s) in Social Science
%S Literature of Sun Yat-Sen Institute for Social Sciences and Philosophy
%E Wenshan Yang
%N 41
%D 1997
%P 139--183
%I Sun Yat-Sen Institute for Social Sciences and Philosophy
%C Taipei, Taiwan
%K genetic algorithms, genetic programming
%U http://www.issp.sinica.edu.tw/chinese/book/ebook/pdf1/bk41/charp-7.pdf
%O 7
%8 September
%Z In Chinese. Description of GP being used for economic modeling of GDP based on \citekoza:book. Tests GP's ability to "discover" money supply equation M2-GNP in USA and in
   Taiwanese datasets. Also known as \citebk41/charp-7

%A Shu-Heng Chen
%A C-H. Yeh
%T Genetic Programming in the Overlapping Generations Model: An Ilustration with the Dynamics of the Inflation Rate
%B Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%E V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben
%V 1447
%D 1998
%P 829--837
%I Springer-Verlag Berlin
%C Mission Valley Marriott, San Diego, California, USA
%K genetic algorithms, genetic programming
%8 25-27  March
%Z EP-98. National Chengchi University
%@ 3-540-64891-7

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%A Woh-Chiang Lee
%T Option Pricing with Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 32--37
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/324313.html
%X One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulas. Earlier studies take the Black-Scholes model as the
   true model and use the artificial data generated by it to train and to test GP. This paper may be regarded as the first attempt to provide some initial evidence of the
   empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test two styles of GP, one-stage GP which does not...
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Shu-Heng Chen
%A W.-C. Lee
%A C.-H. Yeh
%T Hedging Derivative Securities with Genetic Programming
%B Application of Machine Learning and Data Mining in Finance: Workshop at ECML-98
%E G. Nakhaeizadeh and E. Steurer
%D 1998
%P 140--151
%I 
%C Dorint-Parkhotel, Chemnitz, Germany
%K genetic algorithms, genetic programming
%8 24  April
%Z ECML-98 workshop 6 http://www.tu-chemnitz.de/informatik/ecml98/ws6_ag.txt

%A Shu-Heng Chen
%A Hung-Shuo Wang
%A Byoung-Tak Zhang
%T Forecasting High-Frequency Financial Time Series with Evolutionary Neural Trees: The Case of Hang-Seng Stock Index
%B Proceedings of the International Conference on Artificial Intelligence, IC-AI '99
%E Hamid R. Arabnia
%V 2
%D 1999
%P 437--443
%I CSREA Press
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, Evolutionary Artificial Neural Networks, Neural Trees, Sigma-Pi Neural Trees, Breeder Genetic Algorithm
%U http://citeseer.ist.psu.edu/454950.html
%X In this paper, the evolutionary neural trees (ENT) are applied to forecasing the highfrequency stock returns of Heng-Sheng stock index on December, 1998. To understand what
   may consistute an effective implementation, six experiments are conducted. These experiments are different in data-preprocessing procedures, sample sizes, search intensity
   and complexity regularization. Our results shows that ENT can perform more efficiently if we can associate ENT with a linear filter so that it can concentrate on searching
   in the space of nonlinear signals. Also, as well demonstarted in this study, the infrequent bursts (outliers) appearing in the high-frequency data can be very disturbing
   for the normal operation of ENT.
%O The Pennsylvania State University CiteSeer Archives
%8 28  June -1  July
%Z http://www.sigmod.org/sigmod/dblp/db/conf/icai/icai1999-2.html This dataset (HSIX.HF) was downloaded from the Bridge company. The Hong Kong stock market opens 4 hours a day
   and five days a week. The specific period considered by us has 22 working days and 4586 observations.
%@ 1-892512-17-3

%A Shu-Heng Chen
%A Wei-Yuan Lin
%A Chueh-Iong Tsao
%T Genetic Algorithms, Trading Strategies and Stochastic Processes: Some New Evidence from Monte Carlo Simulations
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 114--121
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-397.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Genetic Programming in the Agent-Based Modeling of Stock Markets
%B Fifth International Conference: Computing in Economics and Finance
%E David A. Belsley and Christopher F. Baum
%D 1999
%P 77
%I 
%C Boston College, MA, USA
%K genetic algorithms, genetic programming, Agent-Based Computational Economics, Social Learning, Business School, Artificial Stock Markets, Simulated Annealing, Peer Pressure
%U http://fmwww.bc.edu/cef99/papers/ChenYeh.pdf
%X In this paper, we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called school which is a procedure to map the
   phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of school, and consider school as an evolving population
   driven by single-population GP (SGP). The architecture also takes into consideration traders' search behavior. By simulated annealing, traders' search density can be
   connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard
   artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid),
   and hence supports the efficient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all.
   In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived.
%O Book of Abstracts
%8 24-26  June
%Z PDF and abstract on paper differ in detail. Using PDF info

%A Shu-Heng Chen
%A Tzu-Wen Kuo
%T Towards an Agent-Based Foundation of Financial Econometrics: An Approach Based on Genetic-Programming Artificial Markets
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 966
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-425c.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Genetic Programming in the Agent-Based Artificial Stock Market
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 2
%D 1999
%P 834--841
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, algorithms
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143
%@ 0-7803-5537-7 (Microfiche)

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Modeling the expectations of inflation in the OLG model with genetic programming
%J Soft Computing - A Fusion of Foundations, Methodologies and Applications
%V 3
%N 3
%D 1999
%P 53--62
%I 
%K genetic algorithms, genetic programming, overlapping generations models, bounded rationality, agent-based computational economics, Pareto-superior equilibrium
%X genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model
   of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior
   equilibrium (the low-inflation steady state) rather than the Pareto inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with
   different initial conditions, economic parameters, GP control parameters, and the selection mechanism. We find that as long as the survival-of-the-fittest principle is
   maintained, the evolutionary operators are only secondarily important. However, once the survival-of-the-fittest principle is absent, the well-coordinated economy is also
   gone and the inflation rate can jump quite wildly. To some extent, these results shed light on the biological foundations of economics.
%8 September

%A Shu-Heng Chen
%A Chung-Chi Liao
%A Chi-Hsuan Yeh
%T On The Emergent Properties Of Artificial Stock Markets: Some Initial Evidences
%B Computing in Economics and Finance
%D 2000
%I 
%C Universitat Pompeu Fabra, Barcelona, Spain
%K genetic algorithms, genetic programming
%X Using the framework of agent-based artificial stock markets, this paper addresses the two well-known properties frequently observed in financial markets, namely,
   price-volume relation and sunspots, from a bottom-up perspective. In spirit of ``bottom-up'', these two phenomena are pursued in a more fundamental level, i.e., we are
   asking: is it possible to observed the emergence of these phenomena without explicit references to the assumptions frequently used by the studies in a ``top-down'' style?
   Posing it slightly different, would it be enough to generate these phenomena once we model the market as an evolving decentralised system of autonomous interacting agents?
   Or, can these two phenomenon be coined as ``emergent phenomena'', a terminology from complex adaptive systems.To do so, simulation based on AIE-ASM Version 3 (Chen and Yeh,
   2000) are conducted for multiple runs. Within the genetic programming framework, we include trading volume and some irrelevant exogenous variables into the terminal sets.
   This make it possible that trader can choose to believe that trading volume or sunspots can help forecast the future movement of stock returns if they are convinced so from
   the market behaviour endogenously generated by themselves. To have a further examination on the emergence of sunspot effects, sunspots are generated by deterministic cyclic
   processes, such as sin curve, and the purely iid random processes. We then test the emergent of these two phenomena by using a new version of the Granger causality test,
   which does not require an ad-hoc procedure of filtering.
%8 6-8  July
%Z http://ideas.repec.org/p/sce/scecf0/328.html CEF 2000 number 328

%A Shu-Heng Chen
%T On Bargaining Strategies in the SFI Double Auction Tournaments: Is Genetic Programming the Answer?
%B Computing in Economics and Finance
%D 2000
%I 
%C Universitat Pompeu Fabra, Barcelona, Spain
%K genetic algorithms, genetic programming
%X While early computational studies of bargaining strategies, such as Rust, Miller and Palmer (1993, 1994) and Andrew and Prager (1996) all indicates the significance of
   agent-based modeling in the follow-up research, a real agent-based model of bargaining strategies in DA markets has never been taken. This paper attempts to take the fisrt
   step toward it. In this paper, genetic programming is employed to evolve bargaining strategies within the context of SFI double auction tournaments. We are interested in
   knowing that given a set of traders, each with a fixed trading strategies, can the automated trader driven by genetic programming eventually develop bargaining strategies
   which can outperform its competitors' strategies? To see how GP trader can survive in various environments, different sets of traders characterized by different
   compositions of bargaining strategies are chosen to compete with the single GP trader. To give a measure of the difficult level of the DA auction markets facing the GP
   trader, the program length is used to define the intelligence of chosen traders. In one experiment, the chosen traders are all naive; in another experiment, the traders are
   all sophisticated. Other experiments are placed in the middle of these two extremes.
%8 6-8  July
%Z http://enginy.upf.es/SCE/index2.html 22 Aug 2004 http://ideas.repec.org/p/sce/scecf0/329.html

%A Shu-Heng Chen
%T Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming
%B Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents
%S Lecture Notes in Computer Science
%E Kwong Sak Leung and Lai-Wan Chan and Helen Meng
%V 1983
%D 2000
%P 517--531
%I Springer-Verlag
%C Shatin, N.T., Hong Kong, China
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/1983/19830517.pdf", acknowledgement = ack-nhfb
%X Using genetic programming, this paper proposes an agent- based computational modelling of double auction (DA) markets in the sense that a DA market is modeled as an
   evolving market of autonomous interacting traders (automated software agents). The specific DA market on which our modeling is based is the Santa Fe DA market ([12], [13]),
   which in structure, is a discrete-time version of the Arizona continuous- time experimental DA market ([14], [15]).
%8 13-15  Decemeber
%@ 3-540-41450-9

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Simulating economic transition processes by genetic programming
%J Annals of Operations Research
%V 97
%N 1-4
%D 2000
%P 265--286
%I 
%K genetic algorithms, genetic programming, Kolmogorov complexity, minimum description length principle, bounded rationality, short selling
%X Recently, genetic programming has been proposed to model agents' adaptive behaviour in a complex transition process where uncertainty cannot be formalised within the usual
   probabilistic framework. However, this approach has not been widely accepted by economists. One of the main reasons is the lack of the theoretical foundation of using
   genetic programming to model transition dynamics. Therefore, the purpose of this paper is two-fold. First, motivated by the recent applications of algorithmic information
   theory in economics, we would like to show the relevance of genetic programming to transition dynamics given this background. Second, we would like to supply two concrete
   applications to transition dynamics. The first application, which is designed for the pedagogic purpose, shows that genetic programming can simulate the non-smooth
   transition, which is difficult to be captured by conventional toolkits, such as differential equations and difference equations. In the second application, genetic
   programming is applied to simulate the adaptive behavior of speculators. This simulation shows that genetic programming can generate artificial time series with the
   statistical properties frequently observed in real financial time series.
%8 Decemeber

%A Shu-Heng Chen
%A Bin-Tzong Chie
%T The Schema Analysis of Emergent Bargaining Strategies in Agent-Based Double Auction Markets
%B Fourth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'01)
%D 2001
%P 61
%I 
%C Yokusike City, Japan
%K genetic algorithms, genetic programming, Double Auctions, Bargaining Strategies, Predatory Pricing, Truth-Tellers
%U http://citeseer.ist.psu.edu/475338.html
%X In this paper, we simulate the double auction markets with AIE-DA Ver.2. Given that all traders are truth tellers and non-adaptive, we find that the GP trader can always
   find the most profitable trading strategies. Furthermore, the analysis shows that the trading strategies discovered by GP are very market-specific, which makes our
   artificial bargaining agent behave quite intelligently.
%O The Pennsylvania State University CiteSeer Archives
%8 30  October -1  November

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market
%J Journal of Economic Dynamics and Control
%V 25
%N 3-4
%D 2001
%P 363--393
%I 
%K genetic algorithms, genetic programming, Agent-based computational economics, Social learning, Business school, Artificial stock markets
%X we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called `school' which is a procedure to map the phenotype to the
   genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of `school', and consider school as an evolving population driven by
   single-population GP (SGP). The architecture also takes into consideration traders' search behavior. By simulated annealing, traders' search density can be connected to
   psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial
   stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence
   supports the efficient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact,
   our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived.
%8 March
%Z JEL classification codes: G12; G14; D83 a AI-ECON Research Group, Department of Economics, National Chengchi University, Taipei, 11623 Taiwan b AI-ECON Research Group,
   Department of Finance I-Shou University, Kaohsiung County, 84008 Taiwan

%A Shu-Heng Chen
%A John Duffy
%A Chia-Hsuan Yeh
%T Equilibrium Selection via Adaptation: Using Genetic Programming to Model Learning in a Coordination Game
%J The Electronic Journal of Evolutionary Modeling and Economic Dynamics
%D 2002
%I 
%K genetic algorithms, genetic programming, Adaptation, Coordination Game, Equilibrium Selection, Survival of the Fittest
%U http://beagle.montesquieu.u-bordeaux.fr/jemed/1002/
%X This paper models adaptive learning behavior in a simple coordination game that Van Huyck, Cook and Battalio (1994) have investigated in a controlled laboratory setting
   with human subjects. We consider how populations of artificially intelligent players behave when playing the same game. We use the genetic programming paradigm, as
   developed by Koza (1992, 1994), to model how a population of players might learn over time. In genetic programming one seeks to breed and evolve highly fit computer
   programs that are capable of solving a given problem. In our application, each computer program in the population can be viewed as an individual agent's forecast rule. The
   various forecast rules (programs) then repeatedly take part in the coordination game evolving and adapting over time according to principles of natural selection and
   population genetics. We argue that the genetic programming paradigm that we use has certain advantages over other models of adaptive learning behavior in the context of the
   coordination game that we consider. We find that the pattern of behavior generated by our population of artificially intelligent players is remarkably similar to that
   followed by the human subjects who played the same game. In particular, we find that a steady state that is theoretically unstable under a myopic, bestresponse learning
   dynamic turns out to be stable under our genetic programming based learning system, in accordance with Van Huyck et al.'s (1994) finding using human subjects. We conclude
   that genetic programming techniques may serve as a plausible mechanism for modelling human behavior, and may also serve as a useful selection criterion in environments with
   multiple equilibria.
%8 15  January
%Z RePEc:jem:ejemed:1002

%A Shu-Heng Chen
%A Chia-Hsuan Yeh
%T On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis
%J Journal of Economic Behavior \& Organization
%V 49
%N 2
%D 2002
%P 217--239
%I 
%K genetic algorithms, genetic programming, Artificial stock markets, Emergent properties, Efficient market hypothesis, Rational expectations hypothesis
%U http://www.sciencedirect.com/science/article/B6V8F-45F900X-8/2/c034ae35c111ca061a11cae1df4b2cd5
%X By studying two well known hypotheses in economics, this paper illustrates how emergent properties can be shown in an agent-based artificial stock market. The two
   hypotheses considered are the efficient market hypothesis and the rational expectations hypothesis. We inquire whether the macrobehavior depicted by these two hypotheses is
   consistent with our understanding of the micro-behaviour. In this agent-based model, genetic programming is applied to evolving a population of traders learning over time.
   We first apply a series of econometric tests to show that the EMH and the REH can be satisfied with some portions of the artificial time series. Then, by analysing traders'
   behavior, we show that these aggregate results cannot be interpreted as a simple scaling-up of individual behaviour. A conjecture based on sunspot-like signals is proposed
   to explain why macrobehavior can be very different from micro-behaviour. We assert that the huge search space attributable to genetic programming can induce sunspot-like
   signals, and we use simulated evolved complexity of forecasting rules and Granger causality tests to examine this assertion.

%T Genetic Algorithms and Genetic Programming in Computational Finance
%E Shu-Heng Chen
%D 2002
%I Kluwer Academic Publishers
%C Dordrecht
%K genetic algorithms, genetic programming
%U http://www.springer.com/west/home/business?SGWID=4-40517-22-33195998-detailsPage=ppmmedia|toc
%8 July
%Z Sometimes refered to as Genetic Algorithms and Programming in Computational Finance
%@ 0-7923-7601-3

%A Shu-Heng Chen
%T An Overview
%B Genetic Algorithms and Genetic Programming in Computational Finance
%E Shu-Heng Chen
%D 2002
%P 1--28?
%I Kluwer Academic Press
%K genetic algorithms, genetic programming
%O 1
%Z part of \citechen:2002:gagpcf
%@ 0-7923-7601-3

%A Shu-Heng Chen
%A Tzu-Wen Kuo
%A Yuh-Pyng Shieh
%T Genetic Programming: A Tutorial
%B Genetic Algorithms and Genetic Programming in Computational Finance
%E Shu-Heng Chen
%D 2002
%P 55--80?
%I Kluwer Academic Press
%K genetic algorithms, genetic programming
%O 3
%Z part of \citechen:2002:gagpcf
%@ 0-7923-7601-3

%A Shu-Heng Chen
%A Chung-Chih Liao
%T Price Discovery in Agent-Based Computational Modeling of the Artificial Stock Market
%B Genetic Algorithms and Genetic Programming in Computational Finance
%E Shu-Heng Chen
%D 2002
%P 335--356?
%I Kluwer Academic Press
%K genetic algorithms, genetic programming, Price Discovery, Homogeneous Rational Expectation Equilibrium, Agent-Based Computational Finance, Excessive Volatility
%U http://www.aiecon.org/staff/shc/pdf/apga002.pdf
%X the behaviour of price discovery within a context of an agent based stock market, in which the twin assumptions, namely, rational expectations and the representative agents
   normally made in mainstream economics, are removed. In this model, traders stochastically update their forecasts by searching the business school whose evolution is driven
   by genetic programming. Via these agent based simulations, it is found that, except for some extreme cases, the mean prices generated from these artificial markets deviate
   from the homogeneous rational expectation equilibrium (HREE) prices no more than by 20per cent. This figure provides us a rough idea on how different we can possibly be
   when the twin assumptions are not taken. Furthermore, while the HREE price should be a deterministic constant in all of our simulations, the artificial price series
   generated exhibit quite wild fluctuation, which may be coined as the well-known excessive volatility in finance.
%O 16
%Z part of \citechen:2002:gagpcf
%@ 0-7923-7601-3

%A Shu-Heng Chen
%A Chung-Ching Tai
%A Bin-Tzong Chie
%T Individual Rationality as a Partial Impediment to Market Efficiency: Allocative Efficiency of Markets with Smart Traders
%B Genetic Algorithms and Genetic Programming in Computational Finance
%E Shu-Heng Chen
%D 2002
%P 379--396?
%I Kluwer Academic Press
%K genetic algorithms, genetic programming, Agent-Based Double Auction Markets, Quote Limits, Alpha Value, Allocative Efficiency
%U http://www.econ.iastate.edu/tesfatsi/shusmart.ps
%O 17
%Z part of \citechen:2002:gagpcf
%@ 0-7923-7601-3

%A Shu-Heng Chen
%A Tzu-Wen Kuo
%T Overfitting or Poor Learning: A Critique of Current Financial Applications of GP
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 34--46
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=34
%X Motivated by a measure of predictability, this paper uses the extracted signal ratio as a measure of the degree of overfitting. With this measure, we examine the
   performance of one type of overfitting-avoidance design frequently used in financial applications of GP. Based on the simulation results run with the software Simple GP, we
   find that this design is not effective in avoiding overfitting. Furthermore, within the range of search intensity typically considered by these applications, we find that
   underfitting, instead of overfitting, is the more prevalent problem. This problem becomes more serious when the data is generated by a process that has a high degree of
   algorithmic complexity. This paper, therefore, casts doubt on the conclusions made by those early applications regarding the poor performance of GP, and recommends that
   changes be made to ensure progress.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Shu-Heng Chen
%A Tzu-Wen Kuo
%T Modeling International Short-Term Capital Flow with Genetic Programming
%B Procceedings of the Sixth International Conference on Computational Intelligence and Natural Computing
%D 2003
%I 
%C Embassy Suites Hotel and Conference Center, Cary, North Carolina USA
%K genetic algorithms, genetic programming
%8 September  26-30
%Z http://axon.cs.byu.edu/CINC/ http://www.ee.duke.edu/JCIS/ National Chengchi University, Taiwan

%A Shu-Heng Chen
%A Bin-Tzong Chie
%T Functional Modularity in the Test Bed of Economic Theory -- Using Genetic Programming
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP062.pdf
%X In this paper, we follow the model of Chen and Chie (2004), but start with the primeval setup. The implementation of computer simulations show mutation did play an
   important role in the technology evolution. In a well define simulation world, the producer will exert all of his effort to make the life get better. The parameter of
   mutation rate is just like the frequency of innovation in the real world. Different mutation rate will shift the model to the different path of history. The path of real
   world might be represented by one of the mutation rate, but it must be emergent from the different behaviours of the bottom actors.
%8 26  July
%Z Part of keijzer:2004:GECCO:lbp

%A Shu-Heng Chen
%A Bin-Tzong Chie
%T Functional Modularity in the Fundamentals of Economic Theory: Toward an Agent-Based Economic Modeling of the Evolution of Technology
%J International Journal of Modern Physics B
%V 18
%N 17-19
%D 2004
%P 2376--2386
%I 
%K genetic algorithms, genetic programming, Agent-based computational economics, innovation, functional modularity
%X No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues
   that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation.
%8 July  30
%Z A1 AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, 116, Taiwan ROC

%A Shu-Heng Chen
%A Chung-Chih Liao
%T Agent-based computational modeling of the stock price-volume relation
%J Information Sciences
%V 170
%N 1
%D 2005
%P 75--100
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V0C-4B3JHTS-6/2/9e023835b1c70f176d1903dd3a8b638e
%X From the perspective of the agent-based model of stock markets, this paper examines the possible explanations for the presence of the causal relation between stock returns
   and trading volume. Using the agent-based approach, we find that the explanation for the presence of the stock price-volume relation may be more fundamental. Conventional
   devices such as information asymmetry, reaction asymmetry, noise traders or tax motives are not explicitly required. In fact, our simulation results show that the stock
   price-volume relation may be regarded as a generic property of a financial market, when it is correctly represented as an evolving decentralised system of autonomous
   interacting agents. One striking feature of agent-based models is the rich profile of agents' behaviour. This paper makes use of the advantage and investigates the
   micro-macro relations within the market. In particular, we trace the evolution of agents' beliefs and examine their consistency with the observed aggregate market behavior.
   We argue that a full understanding of the price-volume relation cannot be accomplished unless the feedback relation between individual behaviour at the bottom and aggregate
   phenomena at the top is well understood.
%8 18  February

%A Shu-Heng Chen
%A Ya-Chi Huang
%T On the Role of Risk Preference in Survivability
%B Advances in Natural Computation, Proceedings of First International Conference, ICNC 2005, Part III
%S Lecture Notes in Computer Science
%E Lipo Wang and Ke Chen and Yew-Soon Ong
%V 3612
%D 2005
%P 612--621
%I Springer
%C Changsha, China
%K genetic algorithms
%U http://www4.nccu.edu.tw/ezkm11/ezcatfiles/cust/img/img/29.pdf
%X Using an agent-based multi-asset artificial stock market, we simulate the survival dynamics of investors with different risk preferences. It is found that the survivability
   of investors is closely related to their risk preferences. Among the eight types of investors considered in this paper, only the CRRA investors with RRA coefficients close
   to one can survive in the long run. Other types of agents are eventually driven out of the market, including the famous CARA agents and agents who base their decision on
   the capital asset pricing model.
%8 August  27-29
%Z ICNC (3)
%@ 3-540-28320-X

%A Shu-Heng Chen
%A Bin-Tzong Chie
%T A Functional Modularity Approach to Agent-based Modeling of the Evolution of Technology
%B The Complex Networks of Economic Interactions: Essays in Agent-Based Economics and Econophysics
%S Lecture Notes in Economics and Mathematical Systems
%E Akira Namatame and Yuuji Aruka and Taisei Kaizouji
%V 567
%D 2006
%P 165--178
%I Springer
%K genetic algorithms, genetic programming, agent-based computational economics, innovation, functional modularity
%X No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues
   that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation.
%8 January
%@ 3-540-28726-4

%A Shu-Heng Chen
%T Computationally intelligent agents in economics and Finance
%J Information Sciences
%V 177
%N 5
%D 2007
%P 1153--1168
%I 
%K genetic algorithms, genetic programming, Computational intelligence, Agent-based computational economics
%U http://www.aiecon.org/staff/shc/pdf/INS_7416.pdf
%X This paper is an editorial guide for the second special issue on Computational Intelligence in economics and finance, which is a continuation of the special issue of
   Information Sciences, Vol. 170, No. 1. This second issue appears as a part of the outcome from the 3rd International Workshop on Computational Intelligence in Economics and
   Finance, which was held in Cary, North Carolina, September 26-30, 2003. This paper offers some main highlights of this event, with a particular emphasis on some of the
   observed progress made in this research field, and a brief introduction to the papers included in this special issue.
%8 1  March
%Z The 3rd International Workshop on Computational Intelligence in Economics and Finance (CIEF'2003)

%A Shu-Heng Chen
%A Nicolas Navet
%T Pretests for Genetic-Programming Evolved Trading Programs: zero-intelligence Strategies and Lottery Trading
%B Neural Information Processing, 13th International Conference, ICONIP 2006, Proceedings, Part III
%S Lecture Notes in Computer Science
%E Irwin King and Jun Wang and Laiwan Chan and DeLiang L. Wang
%V 4234
%D 2006
%P 450--460
%I Springer
%C Hong Kong, China
%K genetic algorithms, genetic programming
%X Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but
   the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a
   series of pretests, based on several variants of random search, aiming at giving more clear-cut answers on whether a GP scheme, or any other machine-learning technique, can
   be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends.
%8 October  3-6
%@ 3-540-46484-0

%A Shu-heng Chen
%A Nicolas Navet
%T Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms
%B Computational Intelligence in Economics and Finance: Volume II
%E Shu-Heng Chen and Paul P. Wang and Tzu-Wen Kuo
%D 2007
%P 169--182
%I Springer
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.144.5068
%X Over the last decade, numerous papers have investigated the use of Genetic Programming (GP) for creating financial trading strategies. Typically, in the literature, the
   results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided.
   In this paper, we discuss a series of pretests aimed at giving more clear-cut answers as to whether GP can be effective with the training data at hand. Precisely,
   pretesting allows us to distinguish between a failure due to the market being efficient or due to GP being inefficient. The basic idea here is to compare GP with several
   variants of random searches and random trading behaviors having well-defined characteristics. In particular, if the outcomes of the pretests reveal no statistical evidence
   that GP possesses a predictive ability superior to a random search or a random trading behavior, then this suggests to us that there is no point in investing further
   resources in GP. The analysis is illustrated with GP-evolved strategies for nine markets exhibiting various trends.

%A Shu-Heng Chen
%A Ren-Jie Zeng
%A Tina Yu
%T Co-Evolving Trading Strategies to Analyze Bounded Rationality in Double Auction Markets
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 195--215
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%O 13
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008

%A Shu-Heng Chen
%A Chung-Ching Tai
%T Modeling intelligence of learning agents in an artificial double auction market
%B IEEE Symposium on Computational Intelligence for Financial Engineering, CIFEr '09
%D 2009
%P 36--42
%I 
%K genetic algorithms, genetic programming, artificial double auction market, individual intelligence modeling, learning agents, psychological, socioeconomic, software agents,
   commerce, psychology, socio-economic effects, software agents
%X In psychological as well as socioeconomic studies, individual intelligence has been found decisive in many domains. In this paper, we employ genetic programming as the
   algorithm of our learning agents who compete with other designed strategies extracted from the literature.We then discuss the possibility of using population size as a
   proxy parameter of individual intelligence of software agents. By modeling individual intelligence in this way, we demonstrate not only a nearly positive relation between
   individual intelligence and performance, but more interestingly the effect of decreasing marginal contribution of IQ to performance found in psychological literature.
%8 30  March -  April  2
%Z Also known as \cite4937500

%A Shu-Heng Chen
%A Chung-Ching Tai
%T Modeling Intelligence of Learning Agents in An Artificial Double Auction Market
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 171--182
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%X Individual differences in intellectual abilities can be observed across time and everywhere in the world, and this fact has been well studied by psychologists for a long
   time. To capture the innate heterogeneity of human intellectual abilities, this paper employs genetic programming as the algorithm of the learning agents, and then proposes
   the possibility of using population size as a proxy parameter of individual intelligence. By modeling individual intelligence in this way, we demonstrate not only a nearly
   positive relation between individual intelligence and performance, but more interestingly the effect of decreasing marginal contribution of IQ to performance found in
   psychological literature.
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Shu-Heng Chen
%A Ren-Jie Zeng
%A Tina Yu
%T Analysis of micro-behavior and bounded rationality in double auction markets using co-evolutionary GP
%B GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
%E Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding
%D 2009
%P 807--810
%I ACM New York, NY, USA
%I SigEvo
%C Shanghai, China
%K genetic algorithms, genetic programming, Poster
%X We investigate the dynamics of trader behaviors using a co-evolutionary genetic programming system to simulate a double-auction market. The objective of this study is
   two-fold. First, we seek to evaluate how, if any, the difference in trader rationality/intelligence influences trading behavior. Second, besides rationality, we also
   analyze how, if any, the co-evolution between two learnable traders impacts their trading behaviors. We have found that traders with different degrees of rationality may
   exhibit different behavior depending on the type of market they are in. When the market has a profit zone to explore, the more intelligent trader demonstrate more
   intelligent behaviors. Also, when the market has two learnable buyers, their co-evolution produced more profitable transactions than when there was only one learnable buyer
   in the market. We have analyzed the learnable traders' strategies and found their behavior are very similar to humans in decision making. We will conduct human subject
   experiments to validate these results in the near future.
%8 June  12-14
%Z Also known as \citeDBLP:conf/gecco/ChenZY09 part of \citeDBLP:conf/gec/2009

%A Shu-Heng Chen
%A Chung-Ching Tai
%A Shu G. Wang
%T Does Cognitive Capacity Matter When Learning Using Genetic Programming in Double Auction Markets?
%B MABS
%S Lecture Notes in Computer Science
%E Gennaro di Tosto and H. Van Dyke Parunak
%V 5683
%D 2009
%P 37--48
%I Springer
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.1007/978-3-642-13553-8

%A Stephen Chen
%A Stephen F. Smith
%T Experiments on Commonality in Sequencing Operators
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 471--478
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Stephen Chen
%A Stephen F. Smith
%T Improving Genetic Algorithms by Search Space Reductions (with Applications to Flow Shop Scheduling)
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 135--140
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-829.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Stephen Chen
%A Stephen F. Smith
%T Introducing a New Advantage of Crossover: Commonality-Based Selection
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 122--128
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-827.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Stephen Chen
%A Cesar Guerra-Salcedo
%A Stephen F. Smith
%T Non-Standard Crossover for a Standard Representation -- Commonality-Based Feature Subset Selection
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 129--134
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-828.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Xi Chen
%A Ye Pang
%A Guihuan Zheng
%T Macroeconomic Forecasting Using Genetic Programming Based Vector Error Correction Model
%B Buisness Intelligence in Economic Forcasting
%E Jue Wang and Shouyang Wang
%D 2010
%P 1--15
%I IGI Global
%K genetic algorithms, genetic programming
%X Vector autoregressions are widely used in macroeconomic forecasting since they became known in the 1970s. Extensions including vector error correction models,
   co-integration and dynamic factor models are all rooted in the framework of vector autoregression. The three important extensions are demonstrated to have formal
   equivalence between each other. Above all, they all emphasise the importance of common trends or common factors. Many researches, including a series of work of Stock and
   Watson, find that common factor models significantly improve accuracy in forecasting macroeconomic time series. This study follows the work of Stock and Watson. The authors
   propose a hybrid framework called genetic programming based vector error correction model (GPVECM), introducing genetic programming to traditional econometric models. This
   new method could construct common factors directly from nonstationary data set, avoiding differencing the original data and thus preserving more information. The authors'
   model guarantees that the constructed common factors satisfy the requirements of econometric models such as co-integration, in contrast to the traditional approach. Finally
   but not trivially, their model could save lots of time and energy from repeated work of unit root tests and differencing, which they believe is convenient for
   practitioners. An empirical study of forecasting US import from China is reported. The results of the new method dominates those of the plain vector error correction model
   and the ARIMA model.
%O 1
%Z http://www.igi-global.com/bookstore/titledetails.aspx?titleid=37325&detailstype=chapters Xi Chen (Deloitte Financial Advisory Services, China), Ye Pang (The People's
   Insurance Company (Group) of China Limited, China), and Guihuan Zheng (The People's Bank of China, China)

%A Xiaofang Chen
%A Weihua Gui
%A Yalin Wang
%A Lihui Cen
%T Multi-step optimal control of complex process: a genetic programming strategy and its application
%J Engineering Applications of Artificial Intelligence
%V 17
%N 5
%D 2004
%P 491--500
%I 
%K genetic algorithms, genetic programming, Multi-step comprehensive evaluation, Fitness function, Process optimal control
%U http://www.sciencedirect.com/science/article/B6V2M-4CMHSNB-1/2/5c02b126719099d090f4dba0eaaa5cea
%X In many industrial processes, especially chemistry and metallurgy industry, the plant is slow for feedback and data test because of complex and varying factors. Considering
   the multi-objective feature and the complex problem of production stability in optimal control, this paper proposed an optimal control strategy based on genetic programming
   (GP), used as a multi-step state transferring procedure. The fitness function is computed by multi-step comprehensive evaluation algorithm, which provides a synthetic
   evaluation of multi-objective in process state based on single objective models. The punishment to process state variance is also introduced for the balance between optimal
   performance and stability of production. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution in
   GP with compact individuals. The optimal solution gained by evolution is a multi-step command program of process control, which not only ensures the optimisation tendency
   but also avoids violent process variation by adjusting control parameters step by step. An optimal control system for operation direction is developed based on this
   strategy for imperial smelting process in Shaoguan. The simulation and application results showed its effectiveness for production objects optimisation in complex process
   control.

%A Xiao-nan Chen
%A Hai-tao Chen
%A Lin Qiu
%A Chun-qing Duan
%T Model of Water Production Function with Genetic Programming
%B Fourth International Conference on Natural Computation, ICNC '08
%V 6
%D 2008
%P 311--314
%I 
%K genetic algorithms, genetic programming, evolution calculation, optimal structure searching, water production function, water stress, irrigation, search problems
%X A new model for analyzing the relation between production and water stress is proposed. A model of genetic programming is established to describe water production function
   with evolution calculation, which can find the optimal model structure by samples. Simulation and experimental results indicated that water production function based on
   genetic programming is good at searching optimal structure automatically, and intelligent, accurate.
%8 October
%Z Also known as \cite4667851

%A Yan Chen
%A Shingo Mabu
%A Kotaro Hirasawa
%A Jinglu Hu
%T Genetic Network Programming with Sarsa Learning and Its Application to Creating Stock Trading Rules
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 220--227
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X In this paper, trading rules on stock market using the Genetic Network Programming (GNP) with Sarsa learning is described. GNP is an evolutionary computation, which
   represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since
   GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important
   points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs
   after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be
   created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the timing of the buying and selling
   stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price
   information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalisation
   ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than
   Buy&amp;Hold method and its effectiveness has been confirmed.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Yan Chen
%A Shingo Mabu
%A Kaoru Shimada
%A Kotaro Hirasawa
%T Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X The key in stock trading model is to take the right actions for trading at the right time, primarily based on accurate forecast of future stock trends. Since an effective
   trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to create a stock trading
   model. In this paper, we present a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are two
   important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the change
   of stock prices according to the real time updating. Second, we combine RTU-GNP with a reinforcement learning algorithm to create the programs efficiently. The experimental
   results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without time updating method. It yielded
   significantly higher profits than the traditional trading model without time updating. We also compare the experimental results using the proposed method with Buy&amp;Hold
   method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Yan Chen
%A Shingo Mabu
%A Kaoru Shimada
%A Kotaro Hirasawa
%T Construction of portfolio optimization system using genetic network programming with control nodes
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1693--1694
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, control node, genetic network programming, portfolio optimisation, reinforcement learning, Real-World application: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1693.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389413

%A Yan Chen
%A Shingo Mabu
%A Etsushi Ohkawa
%A Kotaro Hirasawa
%T Constructing Portfolio Investment Strategy Based on Time Adapting Genetic Network Programming
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 2379--2386
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, genetic network programming, Japanese stock market, candlestick chart, evolutionary method, investment advice, portfolio investment
   strategy, portfolio model, portfolio optimisation problem, portfolio problem, stock prices, technical analysis rules, technical indices, time adapting genetic network
   programming, investment, stock markets
%X The classical portfolio problem is a problem of distributing capital to a set of stocks. By adapting to the change of stock prices, this study proposes an portfolio
   investment strategy based on an evolutionary method named "Genetic Network Programming" (GNP). This method makes use of the information from Technical Indices and
   Candlestick Chart. The proposed portfolio model, consisting of technical analysis rules, are trained to generate investment advice. Experimental results on the Japanese
   stock market show that the proposed investment strategy using Time Adapting GNP (TA-GNP) method outperforms other traditional models in terms of both accuracy and
   efficiency. We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy&amp;Hold method to confirm its
   effectiveness, and it is clarified that the proposed investment strategy is effective on the portfolio optimization problem.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as \cite4983238

%A Yan Chen
%A Kotaro Hirasawa
%A Shingo Mabu
%T A portfolio selection model using genetic relation algorithm and genetic network programming
%B IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
%D 2009
%P 4378--4383
%I 
%K genetic algorithms, genetic programming, genetic network programming, correlation coefficient, evolutionary method, genetic relation algorithm, portfolio selection model,
   stock market, stock markets
%X In this paper, a new evolutionary method named genetic relation algorithm (GRA) has been proposed and applied to the portfolio selection problem. The number of brands in
   the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick
   up a fixed number of the most efficient portfolio, the proposed model considers the correlation coefficient between stocks as strength, which indicates the relationship
   between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final
   generation. The efficiency of GRA method is confirmed by the stock trading model using genetic network programming (GNP) that has been proposed in the previous study. We
   present the experimental results obtained by GRA and compare them with those obtained by traditional method, and it is clarified that the proposed model can obtain much
   higher profits than the traditional one.
%8 11-14  October
%Z Also known as \cite5346940

%A Yan Chen
%A Etsushi Ohkawa
%A Shingo Mabu
%A Kaoru Shimada
%A Kotaro Hirasawa
%T A portfolio optimization model using Genetic Network Programming with control nodes
%J Expert Systems with Applications
%V 36
%N 7
%D 2009
%P 10735--10745
%I 
%K genetic algorithms, genetic programming, Portfolio optimization, Genetic Network Programming, Control node, Reinforcement learning
%U http://www.sciencedirect.com/science/article/B6V03-4VPD6KS-2/2/3cf6750a5518ab6e7d6cf817197d96bd
%X Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named 'Genetic Network Programming' (GNP) has been
   developed and applied to the stock market recently. The efficient trading rules created by GNP has been confirmed in our previous research. In this paper a multi-brands
   portfolio optimisation model based on Genetic Network Programming with control nodes is presented. This method makes use of the information from technical indices and
   candlestick chart. The proposed optimization model, consisting of technical analysis rules, are trained to generate trading advice. The experimental results on the Japanese
   stock market show that the proposed optimization system using GNP with control nodes method outperforms other traditional models in terms of both accuracy and efficiency.
   We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy&Hold method to confirm its effectiveness, and it is
   clarified that the proposed trading model can obtain much higher profits than these methods.

%A Yan Chen
%A Shingo Mabu
%A Kaoru Shimada
%A Kotaro Hirasawa
%T A genetic network programming with learning approach for enhanced stock trading model
%J Expert Systems with Applications
%V 36
%N 10
%D 2009
%P 12537--12546
%I 
%K genetic algorithms, genetic programming, Genetic Network Programming, Sarsa Learning, Stock trading model, Technical Index, Candlestick Chart
%U http://www.sciencedirect.com/science/article/B6V03-4WC113D-2/2/a6c6277183e3b22cc3cc50ba71d7062f
%X In this paper, an enhancement of stock trading model using Genetic Network Programming (GNP) with Sarsa Learning is described. There are three important points in this
   paper: First, we use GNP with Sarsa Learning as the basic algorithm while both Technical Indices and Candlestick Charts are introduced for efficient stock trading
   decision-making. In order to create more efficient judgement functions to judge the current stock price appropriately, Importance Index (IMX) has been proposed to tell GNP
   the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we proposed a new method that can learn the appropriate
   function describing the relation between the value of each technical index and the value of the IMX. This is an important point that devotes to the enhancement of the
   GNP-Sarsa algorithm. The third point is that in order to create more efficient judgment functions, sub-nodes are introduced in each node to select appropriate stock price
   information depending on the situations and to determine appropriate actions (buying/selling). To confirm the effectiveness of the proposed method, we carried out the
   simulation and compared the results of GNP-Sarsa with other methods like GNP with Actor Critic, GNP with Candlestick Chart, GA and Buy&Hold method. The results shows that
   the stock trading model using GNP-Sarsa outperforms all the other methods.

%A Yan Chen
%A Shingo Mabu
%A Kotaro Hirasawa
%T A model of portfolio optimization using time adapting genetic network programming
%J Computers \& Operations Research
%V 37
%N 10
%D 2010
%P 1697--1707
%I 
%K genetic algorithms, genetic programming, Genetic network programming, Portfolio optimisation, Reinforcement learning, Technical indices, Candlestick chart
%U http://www.sciencedirect.com/science/article/B6VC5-4Y0D6CX-1/2/2b2154c00eb0c11cef64666b20be06e1
%X This paper describes a decision-making model of dynamic portfolio optimisation for adapting to the change of stock prices based on an evolutionary computation method named
   genetic network programming (GNP). The proposed model, making use of the information from technical indices and candlestick chart, is trained to generate portfolio
   investment advice. Experimental results on the Japanese stock market show that the decision-making model using time adapting genetic network programming (TA-GNP) method
   outperforms other traditional models in terms of both accuracy and efficiency. A comprehensive analysis of the results is provided, and it is clarified that the TA-GNP
   method is effective on the portfolio optimization problem.
%8 October

%A Yan Chen
%A Shingo Mabu
%A Kotaro Hirasawa
%T A portfolio selection strategy using Genetic Relation Algorithm
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X This paper proposes a new strategy #x03B2;-GRA for portfolio selection in which the return and risk are considered as measures of strength in Genetic Relation Algorithm
   (GRA). Since the portfolio beta #x03B2; efficiently measures the volatility relative to the benchmark index or the capital market, #x03B2; is usually employed for portfolio
   evaluation or prediction, but scarcely for portfolio construction process. The main objective of this paper is to propose an integrated portfolio selection strategy, which
   selects stocks based on #x03B2; using GRA. GRA is a new evolutionary algorithm designed to solve the optimisation problem due to its special structure. We illustrate the
   proposed strategy by experiments and compare the results with those derived from the traditional models.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586430

%A Yuehui Chen
%T Hybrid Soft Computing Approach to Identification and Control of Nonlinear Systems
%R Ph.D. Thesis
%D 2001
%I 
%I Department of Computer Science, Kumamoto University
%C Japan
%K genetic algorithms, genetic programming, PIPE Algorithm
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/yuehui.chen/YuehuiChenThesis.pdf
%X In chapter 5, the soft computing based identification and control schemes developed in chapter 3 and 4 are applied to the drilling system. In order to control thrust force
   (cutting torque) in the process of drill, a number of thrust force (cutting torque) identification methods are developed, and then thrust force (cutting torque) soft model
   based neural control scheme are presented. Real time implementations show that the soft computing approaches based control schemes are efficient and effective. Finally in
   chapter 6, the results obtained in previous chapter are summarized, and some topics for future research in this direction are given. In this research, the applicability of
   PIPE algorithm to identification and control of nonlinear systems is confirmed. Based on the MPIPE and some parameter tuning strategies, a unified framework of hybrid soft
   computing models is constructed. Simulation and experiments results for the identification and control of nonlinear systems show the effectiveness of the proposed methods.
   The key point of the research is that various soft computing based identification and control schemes can be re-evaluated in a unified framework and then it is valuable for
   the proposed approach in order to construct the unified soft computing theories and applications.
%8 March
%Z my PDF reader barfed 20 July 2001. url_2 ok

%A Yuehui Chen
%A Bo Yang
%A Ajith Abraham
%T Optimal design of hierarchical wavelet networks for time-series forecasting
%B 14th European Symposium on Artificial Neural Networks (ESANN 2006)
%D 2006
%P 155--160
%I 
%C Bruges, Belgium
%K genetic algorithms, genetic programming, ECGP
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.9044
%X The purpose of this study is to identify the Hierarchical Wavelet Neural Networks (HWNN) and select important input features for each sub-wavelet neural network
   automatically. Based on the predefined instruction/operator sets, a HWNN is created and evolved using tree-structure based Extended Compact Genetic Programming (ECGP), and
   the parameters are optimised by Differential Evolution (DE) algorithm. This framework also allows input variables selection. Empirical results on benchmark time-series
   approximation problems indicate that the proposed method is effective and efficient.
%8 April  26-28

%A Yuehui Chen
%A Ajith Abraham
%A Bo Yang
%T Feature selection and classification using flexible neural tree
%J Neurocomputing
%V 70
%N 1-3
%D 2006
%P 305--313
%I 
%K genetic algorithms, genetic programming, Flexible neural tree model, Memetic algorithm, Intrusion detection system, Breast cancer classification
%X The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined
   instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different
   activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimised by a memetic algorithm
   (MA). The proposed approach was applied for two real-world problems involving designing intrusion detection system (IDS) and for breast cancer classification. The IDS data
   has 41 inputs/features and the breast cancer classification problem has 30 inputs/features. Empirical results indicate that the proposed method is efficient for both input
   feature selection and improved classification rate.
%O Selected Papers from the 7th Brazilian Symposium on Neural Networks (SBRN '04), 7th Brazilian Symposium on Neural Networks
%8 Decemeber

%A Yuehui Chen
%A Bo Yang
%A Ajith Abraham
%T Flexible neural trees ensemble for stock index modeling
%J Neurocomputing
%V 70
%N 4-6
%D 2007
%P 697--703
%I 
%K genetic algorithms, genetic programming, Flexible neural tree, GP-like tree structure-based evolutionary algorithm, Particle swarm optimisation, Ensemble learning, Stock
   index
%X The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behaviour of stock markets
   could be well represented using flexible neural tree (FNT) ensemble technique. We considered the Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock
   index. We analysed 7-year Nasdaq-100 main index values and 4-year NIFTY index values. This paper investigates the development of novel reliable and efficient techniques to
   model the seemingly chaotic behaviour of stock markets. The structure and parameters of FNT are optimised using genetic programming (GP) like tree structure-based
   evolutionary algorithm and particle swarm optimization (PSO) algorithms, respectively. A good ensemble model is formulated by the local weighted polynomial regression
   (LWPR). This paper investigates whether the proposed method can provide the required level of performance, which is sufficiently good and robust so as to provide a reliable
   forecast model for stock market indices. Experimental results show that the model considered could represent the stock indexes behaviour very accurately.
%O Advanced Neurocomputing Theory and Methodology - Selected papers from the International Conference on Intelligent Computing 2005 (ICIC 2005), International Conference on
   Intelligent Computing 2005
%8 January

%A Yuehui Chen
%A Ajith Abraham
%T Tree-Structure based Hybrid Computational Intelligence
%S Intelligent Systems Reference Library
%V 2
%D 2010
%I Springer
%K genetic algorithms, genetic programming, Computational Intelligence, flexible neural trees, flexible neural trees networks, neural networks
%U http://www.springer.com/engineering/book/978-3-642-04738-1
%X Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence. As evident, the ultimate achievement
   in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. In this
   book, the authors illustrate an hybrid computational intelligence framework and it applications for various problem solving tasks. Based on tree-structure based encoding
   and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques. The main idea behind this
   model is the flexible neural tree, which is very adaptive, accurate and efficient. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be
   created and evolved. This volume comprises of 6 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important
   research challenges. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques and data mining
   will find the comprehensive coverage of this book invaluable.
%Z Theoretical Foundations and Applications ECGP, fuzzy, iris dataset

%A Yuehui Chen
%A Bin Yang
%A Qingfang Meng
%A Yaou Zhao
%A Ajith Abraham
%T Time-series forecasting using a system of ordinary differential equations
%J Information Sciences
%V 181
%N 1
%D 2011
%P 106--114
%I 
%K genetic algorithms, genetic programming, PSO, Hybrid evolutionary method, Network traffic, Small-time scale, The additive tree models, Ordinary differential equations,
   Particle swarm optimisation
%U http://www.sciencedirect.com/science/article/B6V0C-5100HS4-3/2/c9722759c9e35e7dba49e35c559ae617
%X This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) to predict the small-time scale traffic measurements
   data. We used the tree-structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization (PSO) algorithm to fine tune the parameters of
   the additive tree models for the system of ordinary differential equations. We also illustrate some experimental comparisons with genetic programming, gene expression
   programming and a feedforward neural network optimised using PSO algorithm. Experimental results reveal that the proposed method is feasible and efficient for forecasting
   the small-scale traffic measurements data.

%A Yuxin Chen
%T A Novel Hybrid Focused Crawling Algorithm to Build Domain-Specific Collections
%R Ph.D. Thesis
%D 2007
%I 
%I Virginia Polytechnic Institute and State University
%C Blacksburg, Virginia, USA
%K genetic algorithms, genetic programming, digital libraries, focused crawler, classification, meta-search
%U http://scholar.lib.vt.edu/theses/available/etd-02162007-005107/unrestricted/YuxinDissertation_etd_final1.pdf
%X The Web, containing a large amount of useful information and resources, is expanding rapidly. Collecting domain-specific documents/information from the Web is one of the
   most important methods to build digital libraries for the scientific community. Focused Crawlers can selectively retrieve Web documents relevant to a specific domain to
   build collections for domain-specific search engines or digital libraries. Traditional focused crawlers normally adopting the simple Vector Space Model and local Web search
   algorithms typically only find relevant Web pages with low precision. Recall also often is low, since they explore a limited sub-graph of the Web that surrounds the
   starting URL set, and will ignore relevant pages outside this sub-graph. In this work, we investigated how to apply an inductive machine learning algorithm and meta-search
   technique, to the traditional focused crawling process, to overcome the above mentioned problems and to improve performance. We proposed a novel hybrid focused crawling
   framework based on Genetic Programming (GP) and meta-search. We showed that our novel hybrid framework can be applied to traditional focused crawlers to accurately find
   more relevant Web documents for the use of digital libraries and domain-specific search engines. The framework is validated through experiments performed on test documents
   from the Open Directory Project. Our studies have shown that improvement can be achieved relative to the traditional focused crawler if genetic programming and meta-search
   methods are introduced into the focused crawling process.
%8 February  5

%A Zheng Chen
%A Siwei Lu
%T A Genetic Programming Approach for Classification of Textures Based on Wavelet Analysis
%B IEEE International Symposium on Intelligent Signal Processing, WISP 2007
%D 2007
%P 1--6
%I 
%K genetic algorithms, genetic programming, feature extraction, texture classification, wavelet analysis, wavelet decomposition, feature extraction, image classification,
   image texture, wavelet transforms
%X In this paper, we propose a method for classifying textures using Genetic Programming (GP). Texture features are extracted from the energy of subimages of the wavelet
   decomposition. The GP is then used to evolve rules, which are arithmetic combinations of energy features, to identify whether a texture image belongs to certain class.
   Instead of using only one rule to discriminate the samples, a set of rules are used to perform the prediction by applying the majority voting technique. In our experiment
   results based on Brodatz dataset, the proposed method has achieved 99.6percent test accuracy on an average. In addition, the experiment results also show that
   classification rules generated by this approach are robust to some noises on textures.
%8 October
%Z Also known as \cite4447575

%A Cleve Cheng
%T Recognizing Poker Hands with Genetic Programming and Restricted Iteration
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 18--27
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Huifang Cheng
%A Yongqiang Zhang
%A Jing Zhao
%T Improved Genetic Programming Model for Software Reliability
%B International Asia Symposium on Intelligent Interaction and Affective Computing, ASIA '09
%D 2009
%P 164--167
%I 
%K genetic algorithms, genetic programming, SBSE, IGP algorithm, improved genetic programming model, software failure mechanism, software reliability, software reliability
%X Many existing software reliability models are based on some subjective assumptions those could be easily impractical in reality. Genetic Programming(GP for short) does not
   need some subjective assumption due to the basic characteristic of the data. Also, this method doesn't require to understand the inherent processes for failures, but to
   create models based on the given data for a "true" process during the specific modeling course, which can describe the software failure mechanisms more effectually and
   predict for the next failure times more exactly. This paper adopts improved GP(IGP for short) algorithm to hunting model, which can possibly reflect system behaviors, in
   the function spaces are compoundly constituted by the authorized function operators. Meanwhile, we have proved that IGP can obtain the best solution for failure behavior's
   variation rules from the convergence character of itself. Moreover, this paper makes use of Orthogonal experimental to adjust the parameters.
%8 Decemeber
%Z Also known as \cite5376005

%A Huifang Cheng
%A Yongqiang Zhang
%A Fangping Li
%T Improved Genetic Programming Algorithm
%B International Asia Symposium on Intelligent Interaction and Affective Computing, ASIA '09
%D 2009
%P 168--171
%I 
%K genetic algorithms, genetic programming, canonical genetic programming algorithm, crossover operator, mutation operation, problem solving, reproduction operator, symbolic
   regression, regression analysis
%X The present study aims at improving the problem solving ability of the canonical genetic programming algorithm. The proposed method can be described as follows. The first
   investigates initialising population, the second investigates reproduction operator, the third investigates crossover operator, the fourth investigates mutation operation.
   This approach is examined on two experiments about symbolic regression. The results suggest that the new approach is more effective and more efficient than the canonical
   one.
%8 Decemeber
%Z Also known as \cite5376006

%A V. H. L. Cheng
%A L. S. Crawford
%A P. K. Menon
%T Air Traffic Control Using Genetic Search Techniques
%B 1999 IEEE International Conference on Control Applications
%D 1999
%I 
%C Hawai'i, HA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/521419.html
%X Genetic search techniques constitute an optimisation methodology effective for solving discontinuous, non-convex, nonlinear, or non-analytic problems. This paper explores
   the application of such techniques to a non-analytic event-related air traffic control problem, that of runway assignment, sequencing, and scheduling of arrival flights at
   an airport with multiple runways. Several genetic search formulations are developed and evaluated with a representative arrival traffic scenario. The results exemplify the
   importance of the selection of the chromosomal representation for a genetic-search problem.
%O The Pennsylvania State University CiteSeer Archives
%8 August  22-27

%A Henry Wai Kit Chia
%A Chew Lim Tan
%T Neural Logic Network Learning Using Genetic Programming
%J International Journal of Computational Intelligence and Applications
%V 1
%N 4
%D 2001
%P 357--368
%I 
%K genetic algorithms, genetic programming, Neural network, rule-based learning, data mining
%X Neural Logic Networks or Neulonets are hybrids of neural networks and expert systems capable of representing complex human logic in decision making. Each neulonet is
   composed of rudimentary net rules which themselves depict a wide variety of fundamental human logic rules. An early methodology employed in neulonet learning for pattern
   classification involved weight adjustments during back-propagation training which ultimately rendered the net rules incomprehensible. A new technique is now developed that
   allows the neulonet to learn by composing the net rules using genetic programming without the need to impose weight modifications, thereby maintaining the inherent logic of
   the net rules. Experimental results are presented to illustrate this new and exciting capability in capturing human decision logic from examples. The extraction and
   analysis of human logic net rules from an evolved neulonet will be discussed. These extracted net rules will be shown to provide an alternate perspective to the greater
   extent of knowledge that can be expressed and discovered. Comparisons will also be made to demonstrate the added advantage of using net rules, against the use of standard
   boolean logic of negation, disjunction and conjunction, in the realm of evolutionary computation.

%A Henry Wai-Kit Chia
%A Chew-Lim Tan
%T Association-Based Evolution of Comprehensible Neural Logic Networks
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP061.pdf
%X Neural Logic Network (Neulonet) learning has been successfully used in emulating complex human reasoning processes. One recent implementation generates a single large
   neulonet via genetic programming using an accuracy-based fitness measure. However, in terms of human comprehensibility and amenability during logic inference, evolving
   multiple compact neulonets are preferred. The present work realizes this by adopting associative-classification measures of confidence and support as part of the fitness
   computation. The evolved neulonets are combined together to form an eventual macro-classier. Empirical study shows that associative classification integrated with neulonet
   learning performs better than general association-based classifiers in terms of higher accuracies and smaller rule sets. This is primarily due to the richness in logic
   expression inherent in the neulonet learning paradigm.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Henry Wai-Kit Chia
%A Chew-Lim Tan
%T Confidence and Support Classification Using Genetically Programmed Neural Logic Networks
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 836--837
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming, Poster
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030836.htm
%X Typical learning classifier systems employ conjunctive logic rules for representing domain knowledge. The classifier XCS is an extension of LCS with the ability to learn
   boolean logic functions for data mining. However, most data mining problems cannot be expressed simply with boolean logic. Neural Logic Network (Neulonet) learning is a
   technique that emulates the complex human reasoning processes through the use of net rules. Each neulonet is analogous to a learning classifier that is rewarded using
   support and confidence measures which are often used in association-based classification. Empirical results shows promise in terms of generalisation ability and the
   comprehensibility of rules.
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Henry W. K. Chia
%A Chew Lim Tan
%A Sam Y. Sung
%T Enhancing Knowledge Discovery via Association-Based Evolution of Neural Logic Networks
%J IEEE Transactions on Knowledge and Data Engineering
%V 18
%N 7
%D 2006
%P 889--901
%I IEEE Computer Society
%C Los Alamitos, CA, USA
%K genetic algorithms, genetic programming, Data mining, knowledge acquisition, connectionism and neural nets, rule-based knowledge representation
%X The comprehensibility aspect of rule discovery is of emerging interest in the realm of knowledge discovery in databases. Of the many cognitive and psychological factors
   relating the comprehensibility of knowledge, we focus on the use of human amenable concepts as a representation language in expressing classification rules. Existing work
   in neural logic networks (or neulonets) provides impetus for our research; its strength lies in its ability to learn and represent complex human logic in decision-making
   using symbolic-interpretable net rules. A novel technique is developed for neulonet learning by composing net rules using genetic programming. Coupled with a sequential
   covering approach for generating a list of neulonets, the straightforward extraction of human-like logic rules from each neulonet provides an alternate perspective to the
   greater extent of knowledge that can potentially be expressed and discovered, while the entire list of neulonets together constitute an effective classifier. We show how
   the sequential covering approach is analogous to association-based classification, leading to the development of an association-based neulonet classifier. Empirical study
   shows that associative classification integrated with the genetic construction of neulonets performs better than general association-based classifiers in terms of higher
   accuracies and smaller rule sets. This is due to the richness in logic expression inherent in the neulonet learning paradigm.

%A Chia-Hsuan Yeh
%A Shu-Heng Chen
%T The Differences between Social and Individual Learning on the Time Series Properties: The Approach Based on Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 191
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster, Social Learning, Individual Learning, Artificial Stock Market, Agent-Based Modeling
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Cheng-Hsiung Chiang
%T A genetic programming based rule generation approach for intelligent control systems
%B 2010 International Symposium on Computer Communication Control and Automation (3CA)
%V 1
%D 2010
%P 104--107
%I 
%K genetic algorithms, genetic programming, genetic programming intelligent control system, percepter, radaptor, rule generation approach, symbolic rule controller,
   intelligent control, learning (artificial intelligence), path planning
%X This paper presents an intelligent control system (namely GPICS). The GPICS consists of a Symbolic Rule Controller, a Percepter and a rAdaptor. The Percepter judges whether
   the control system can adapt the environment. If the system is inadaptable, the rAdaptor will be activated to search the new rule to adapt the environment; otherwise, the
   controller will keeps on its controlling assignments. Once the rAdaptor is activated, the flexible genetic programming will be employed for searching the new rule.
   Simulation results of the robotic path planning showed that the GPICS method can successfully find a satisfactory path.
%8 May
%Z Also known as \cite5533882

%A N. K. Chidambaran
%A Chi-Wen {Jevons Lee}
%A Joaquin R. Trigueros
%T An Adaptive Evolutionary Approach to Option Pricing via Genetic Programming
%R Working paper FIN-98-086
%D 1998
%I 
%I Leonard N. Stern School of Buisness, New York University
%K genetic algorithms, genetic programming
%U http://www.stern.nyu.edu/fin/workpapers/wpa98086.pdf
%X We propose a methodology of Genetic Programming to approximate the relationship between the option price, its contract terms and the properties of the underlying stock
   price. An important advantage of the Genetic Programming approach is that we can incorporate currently known formulas, such as the Black-Scholes model, in the search for
   the best approximation to the true pricing formula. Using Monte Carlo simulations, we show that the Genetic Programming model approximates the true solution better than the
   Black-Scholes model when stock prices folow a jump-diffusion process. We also show that the Genetic Programming model outperforms various other models in many different
   settings. Other advantages of the Genetic Programming approach include its robustness to changing environment, its low demand for data, and its computational speed. Since
   genetic programs are flexible, self-learning and sefl-improving, they are an ideal tool for practitioners.
%8 November
%Z see also \citechidambaran:1998:aeaopGP and \citechidambaran:2002:ECEF

%A N. K. Chidambaran
%A C. H. Jevons Lee
%A Joaquin R. Trigueros
%T An Adaptive Evolutionary Approach to Option Pricing via Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 38--41
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98 See also \citewpa98086
%@ 1-55860-548-7

%A N. K. Chidambaran
%A Joaquin Triqueros
%A Chi-Wen Jevons Lee
%T Option Pricing via Genetic Programming
%B Evolutionary Computation in Economics and Finance
%S Studies in Fuzziness and Soft Computing
%E Shu-Heng Chen
%V 100
%D 2002
%P 383--398?
%I Physica Verlag
%K genetic algorithms, genetic programming
%O 20
%8 2002
%Z http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=452
   http://btobsearch.barnesandnoble.com/booksearch/isbnInquiry.asp?sourceid=00395996645644787198&btob=Y&endeca=1&isbn=3790814768&itm=2 See also \citewpa98086
%@ 3-7908-1476-8

%A N. K. Chidambaran
%T Genetic programming with Monte Carlo simulation for option pricing
%B Proceedings of the 2003 Winter Simulation Conference
%E S. Chick and P. J. Sanchez and D. Ferrin and D. J. Morrice
%V 1
%D 2003
%P 285--292
%I IEEE
%C New Orleans, USA
%K genetic algorithms, genetic programming
%U http://www.informs-sim.org/wsc03papers/035.pdf
%X I examine the role of programming parameters in determining the accuracy of genetic programming for option pricing. I use Monte Carlo simulations to generate stock and
   option price data needed to develop a genetic option pricing program. I simulate data for two different stock price processes - a geometric Brownian process and a
   jump-diffusion process. In the jump-diffusion setting, I seed the genetic program with the Black-Scholes equation as a starting approximation. I find that population size,
   fitness criteria, and the ability to seed the program with known analytical equations, are important determinants of the efficiency of genetic programming.
%8 7-10  Decemeber
%Z details from ieee
%@ 0-7803-8132-7

%A Bin-Tzong Chie
%A Chih-Chien Wang
%T Model for Evolutionary Technology - An Automatically Defined Terminal Approach
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006)
%E J\"orn Grahl
%D 2006
%I 
%C Seattle, WA, USA
%K genetic algorithms, genetic programming, Automatically Defined Terminal, Agent-Based Modeling
%X automatically defined terminal (ADT) to keep ready and stable building blocks growing into complex structure. The idea is originated from the functional modularity
   approach. ADT is tested in an agent-based innovation model to see how it works and whether there is any improvement in searching new commodities for commercialising in the
   market; hence the market represents an environment for nourishing the development during innovative process. This paper will not only show how the capable producers with
   ADT work, but also how market selection plays an important role in the evolution of innovation. In other word, the agent-based modelling approach will present the
   evolutionary dynamic of interaction between producers and consumers in a commodity market.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2006

%A Been-Chian Chien
%A Jung Yi Lin
%A Tzung-Pei Hong
%T Learning discriminant functions with fuzzy attributes for classification using genetic programming
%J Expert Systems with Applications
%V 23
%N 1
%D 2002
%P 31--37
%I 
%K genetic algorithms, genetic programming, Classification, Knowledge discovery, Fuzzy sets
%U http://www.sciencedirect.com/science/article/B6V03-45C00T2-1/2/e7d49cc18dd12961ac2e5c114c41f667
%X Classification is one of the important tasks in developing expert systems. Most of the previous approaches for classification problem are based on classification rules
   generated by decision trees. we propose a new learning approach based on genetic programming to generate discriminant functions for classifying data. An adaptable
   incremental learning strategy and a distance-based fitness function are developed to improve the efficiency of genetic programming-based learning process. We first
   transform attributes of objects into fuzzy attributes and then a set of discriminant functions is generated based on the proposed learning procedure. The set of derived
   functions with fuzzy attributes gives high accuracy of classification and presents a linear form. Hence, the functions can be transformed into inference rules easily and we
   can use the rules to provide the building of rule base in an expert system.

%A Been Chian Chien
%A Jung Yi Lin
%T A Classifier with the Function-based Decision Tree
%B Proceedings of KES'2002 the Sixth International Conference on Knowledge-Based Intelligent Information Engineering Systems
%S Frontiers in Artificial Intelligence and Applications
%E E. Damiani and L. C. Jain and R. J. Howlett and N. Ichalkaranje
%V 82
%D 2002
%P 648--652
%I IOS Press Amsterdam
%C Podere d'Ombriano, Crema, Italy
%K genetic algorithms, genetic programming, classification, decision tree
%8 19-19  September
%Z http://www.iospress.nl/loadtop/load.php?isbn=1586032801
%@ 1-58603-280-1

%A Been-Chian Chien
%A Jui-Hsiang Yang
%A Wen-Yang Lin
%T Generating Effective Classifiers with Supervised Learning of Genetic Programming
%B Data Warehousing and Knowledge Discovery: 5th International Conference, DaWaK 2003
%S Lecture Notes in Computer Science
%V 2737
%D 2003
%P 192--201
%I Springer-Verlag
%C Prague, Czech Republic
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2737&spage=192
%X A new approach of learning classifiers using genetic programming has been developed recently. Most of the previous researches generate classification rules to classify
   data. However, the generation of rules is time consuming and the recognition accuracy is limited. In this paper, an approach of learning classification functions by genetic
   programming is proposed for classification. Since a classification function deals with numerical attributes only, the proposed scheme first transforms the nominal data into
   numerical values by rough membership functions. Then, the learning technique of genetic programming is used to generate classification functions. For the purpose of
   improving the accuracy of classification, we proposed an adaptive interval fitness function. Combining the learned classification functions with training samples, an
   effective classification method is presented. Numbers of data sets selected from UCI Machine Learning repository are used to show the effectiveness of the proposed method
   and compare with other classifiers.
%8 3-5  September
%@ 3-540-40807-X

%A Been-Chian Chien
%A Jung-Yi Lin
%A Wei-Pang Yang
%T Learning effective classifiers with Z-value measure based on genetic programming
%J Pattern Recognition
%V 37
%N 10
%D 2004
%P 1957--1972
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V14-4CPVJFT-3/2/51f0ecbd7d198da15f4ae094e378c5d0
%X This paper presents a learning scheme for data classification based on genetic programming. The proposed learning approach consists of an adaptive incremental learning
   strategy and distance-based fitness functions for generating the discriminant functions using genetic programming. To classify data using the discriminant functions
   effectively, the mechanism called Z-value measure is developed. Based on the Z-value measure, we give two classification algorithms to resolve ambiguity among the
   discriminant functions. The experiments show that the proposed approach has less training time than previous GP learning methods. The learned classifiers also have high
   accuracy of classification in comparison with the previous classifiers.
%8 October

%A Been-Chian Chien
%A Jui-Hsiang Yang
%T Features Selection based on Rough Membership and Genetic Programming
%B IEEE International Conference on Systems, Man and Cybernetics, ICSMC '06
%V 5
%D 2006
%P 4124--4129
%I IEEE
%C Taipei, Taiwan
%K genetic algorithms, genetic programming
%X This paper discusses the feature selection problem upon supervised learning. A learning method based on rough sets and genetic programming is proposed to select significant
   features and classify numerical data. The proposed method uses rough membership to transform nominal data into numerical values, then selects important features and learns
   classification functions using genetic programming. We use several UCI data sets to show the performance of the proposed scheme and make comparisons with three different
   features selection approaches: distance measure, information measure and dependence measure. The results demonstrate that the proposed method is effective both in features
   selection and classification.
%8 8-11  October
%Z Member, IEEE, National University of Tainan, Tainan 700, Taiwan, R. O. C. Tel: +886-6-2606123 ext. 7707, fax:+886-6-2606125;
%@ 1-4244-0100-3

%A Been-Chian Chien
%A Jui-Hsiang Yang
%A Tzung-Pei Hong
%T Learning Discriminant Functions based on Genetic Programming and Rough Sets
%J Multiple-Valued Logic and Soft Computing
%V 17
%N 2-3
%D 2011
%P 135--155
%I 
%K genetic algorithms, genetic programming, Machine learning, discriminant function, classification, rough sets.
%U http://www.oldcitypublishing.com/MVLSC/MVLSCabstracts/MVLSC17.2-3abstracts/MVLSCv17n2-3p135-155Chien.html
%X Supervised learning based on genetic programming can find different classification models including decision trees, classification rules and discriminant functions. The
   previous researches have shown that the classifiers learnt by GP have high precision in many application domains. However, nominal data cannot be handled and calculated by
   the model of using discriminant functions. In this paper, we present a scheme based on rough set theory and genetic programming to learn discriminant functions from general
   data containing both nominal and numerical attributes. The proposed scheme first transforms the nominal data into numerical values by applying the technique of rough sets.
   Then, genetic programming is used to learn discriminant functions. The conflict problem among discriminant functions is solved by an effective conflict resolution method
   based on the distance-based fitness function. The experimental results show that the classifiers generated by the proposed scheme using GP are effective on nominal data in
   comparison with C4.5, CBA, and NB-based classifiers.

%A Edward K. Chien
%T Grid-Based Trace Routing Using Evolutionary Methods
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 90--97
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Maezawa Chikara
%A Atsumi Masayasu
%T Collaborative Learning Agents with Structural Classifier Systems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 777
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-859.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Barret Chin
%A Mengjie Zhang
%T Object Detection using Neural Networks and Genetic Programming
%R Technical report CS-TR-07-3
%D 2007
%I 
%I Computer Science, Victoria University of Wellington
%C New Zealand
%K genetic algorithms, genetic programming, object detection, neural networks, region refinement, feature selection
%U http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-07-3.abs.html
%X This paper describes a domain independent approach to the use of neural networks (NNs) and genetic programming (GP) for object detection problems. Instead of using high
   level features for a particular task, this approach uses domain independent pixel statistics for object detection. The paper first compares an NN method and a GP method on
   four image data sets providing object detection problems of increasing difficulty. The results show that the GP method performs better than the NN method on these problems
   but still produces a large number of false alarms on the difficult problem and computation cost is still high. To deal with these problems, we develop a new method called
   GP-refine that uses a two stage learning process. The results suggest that the new GP method further improves object detection performance on the difficult object detection
   task.
%8 November

%A Barret Chin
%A Mengjie Zhang
%T Object Detection Using Neural Networks and Genetic Programming
%B Proceedings of EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops
%S Lecture Notes in Computer Science
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Rolf Drechsler and Anik\'o Ek\'art and Anna Esparcia-Alc\'azar and Muddassar Farooq and
   Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang
%V 4974
%D 2008
%P 335--340
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March

%A Clement Chion
%A Luis E. {Da Costa}
%A Jacques-Andre Landry
%T Genetic programming for agricultural purposes
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 783--790
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, crop nitrogen content, GP, hyperspectral imagery, management, precision farming, remote sensing, site-specific management, spectral
   vegetation indices (SVI), vegetation indices
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p783.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Clement Chion
%A Jacques-Andre Landry
%A Luis {Da Costa}
%T A Genetic-Programming-Based Method for Hyperspectral Data Information Extraction: Agricultural Applications
%J IEEE Transactions on Geoscience and Remote Sensing
%V 46
%N 8
%D 2008
%P 2446--2457
%I 
%K genetic algorithms, genetic programming, CASI sensor, agricultural application, band selection, canopy nitrogen content, crop biophysical variable, feature selection,
   genetic programming-spectral vegetation index, hyperspectral data information extraction, hyperspectral remote sensing, pixel reflectance, precision farming, crops,
   farming, feature extraction, geophysical signal processing, vegetation mapping
%X A new method, called genetic programming-spectral vegetation index (GP-SVI), for the extraction of information from hyperspectral data is presented. This method is
   introduced in the context of precision farming. GP-SVI derives a regression model describing a specific crop biophysical variable from hyperspectral images (verified with
   in situ observations). GP-SVI performed better than other methods [multiple regression, tree-based modeling, and genetic algorithm-partial least squares (GA-PLS)] on the
   task of correlating canopy nitrogen content in a cornfield with pixel reflectance. It is also shown that the band selection performed by GP-SVI is comparable with the
   selection performed by GA-PLS, a method that is specifically designed to deal with hyperspectral data.
%8 August
%Z Also known as \cite4559746

%A Darren M. Chitty
%T A data parallel approach to genetic programming using programmable graphics hardware
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1566--1573
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, data parallelism, GPU, graphics cards
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1566.pdf
%X In recent years the computing power of graphics cards has increased significantly. Indeed, the growth in the computing power of these graphics cards is now several orders
   of magnitude greater than the growth in the power of computer processor units. Thus these graphics cards are now beginning to be used by the scientific community as low
   cost, high performance computing platforms. Traditional genetic programming is a highly computer intensive algorithm but due to its parallel nature it can be distributed
   over multiple processors to increase the speed of the algorithm considerably. This is not applicable for single processor architectures but graphics cards provide a
   mechanism for developing a data parallel implementation of genetic programming. In this paper we will describe the technique of general purpose computing using graphics
   cards and how to extend this technique to genetic programming. We will demonstrate the improvement in the performance of genetic programming on single processor
   architectures which can be achieved by harnessing the computing power of these next generation graphics cards.
%8 7-11  July
%Z NVidia GeForce 6400 GO. Fisher Iris, x^4+x^3+x^2+x, 11-mux. Cg. GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and
   the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071 Cg toolkit used to compile each GP tree individually before transferring each
   separately to nVidia GeForce 6400 GO GPU. p1571 claims "changing the GPU program ... is relatively fast". Iris, 11-Mux. Symbolic regression. Pop 500, 50 gens. Fig 11 10000
   to 15000 fitness cases GPU big win over CPU.

%A Chaochang Chiu
%A Jih-Tay Hsu
%A Chih-Yung Lin
%T The Application of Genetic Programming in Milk Yield Prediction for Dairy Cows
%B Rough Sets and Current Trends in Computing : Second International Conference, RSCTC 2000. Revised Papers
%S Lecture Notes in Computer Science
%E W. Ziarko and Y. Yao
%V 2005
%D 2001
%P 598--602
%I Springer-Verlag Heidelberg
%C Banff, Canada
%K genetic algorithms, genetic programming, dynamic mutation, milk yield prediction
%U http://link.springer-ny.com/link/service/series/0558/papers/2005/20050598.pdf", acknowledgement = ack-nhfb
%X Milk yield forecasting can help dairy farmers to deal with the continuously changing condition all year round and to reduce the unnecessary overheads. Several variables
   (somatic cell count, pariety, day in milk, milk protein content, milk fat content, season) related to milk yield are collected as the parameters of the forecasting model.
   The use of an improved Genetic Programming (GP) technique with dynamic learning operators is proposed and achieved with acceptable prediction results.
%8 October  16-19

%A Sung-Bae Cho
%A Katsunori Shimohara
%T Modular Neural Networks Evolved by Genetic Programming
%B Proceedings of the 1996 IEEE International Conference on Evolutionary Computation
%V 1
%D 1996
%P 681--684
%I 
%I IEEE Neural Network Council
%C Nagoya, Japan
%K genetic algorithms, genetic programming, Khepera, artificial life, artificial neural network, behavior based robots, control system design, evolutionary mechanism,
   evolvable model, genetic programming, handwritten digits, modular neural networks, network architectures, randomly connected networks, visual categorization task, genetic
   algorithms, intelligent control, neural net architecture, neurocontrollers, systems analysis
%X In this paper we present an evolvable model of modular neural networks which are rich in autonomy and creativity. In order to build an artificial neural network which is
   rich in autonomy and creativity, we have adopted the ideas and methodologies of Artificial Life. This paper describes the concepts and methodologies for the evolvable model
   of modular neural networks, which will be able not only to develop new functionality spontaneously but also to grow and evolve its own structure autonomously. Although the
   ultimate goal of this model is to design the control system for such behaviour-based robots as Khepera, we have attempted to apply the mechanism to a visual categorisation
   task with handwritten digits. The evolutionary mechanism has shown a strong possibility to generate useful network architectures from an initial set of randomly-connected
   networks.
%8 20-22  May
%Z ICEC-96 Evolves ANN network for recognising human written characters
%@ 0-7803-2902-3

%A Sung-Bae Cho
%A Katsunori Shimohara
%T Evolutionary Learning of Modular Neural Networks with Genetic Programming
%J Applied Intelligence
%V 9
%N 3
%D 1998
%P 191--200
%I 
%K genetic algorithms, genetic programming, neural networks, evolutionary computation, modules, emergence, handwritten digits, OCR
%8 November /  Decemeber
%Z Evolves ANN network for categorizing human written characters. USA Federal post office dataset online?

%T Proceedings of The First Asian-Pacific Workshop on Genetic Programming
%E Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan
%D 2003
%I 
%I School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University College, University of New South Wales, Australia
%C Rydges (lakeside) Hotel, Canberra, Australia
%K genetic algorithms, genetic programming
%U http://www.cs.adfa.edu.au/~cec_gp/
%8 8  Decemeber
%@ 0-9751724-0-9

%A D. Y. Cho
%A B. T. Zhang
%T Genetic programming of multi-agent cooperation strategies for table transport
%B The Third Asian Fuzzy Systems Symposium
%E K. C. Min
%D 1998
%P 170--175
%I 
%I Korea Fuzzy Logic and Intelligent Systems Society (KFIS)
%C Kyungnam University, Masan, Korea
%K genetic algorithms, genetic programming
%8 18-21  June
%Z AFSS'98

%A D. Y. Cho
%A B. T. Zhang
%T Genetic programming-based Alife techniques for evolving collective robotic intelligence
%B Proceedings 4th International Symposium on Artificial Life and Robotics
%E M. Sugisaka
%D 1999
%P 236--239
%I 
%C B-Con Plaza, Beppu, Oita, Japan
%K genetic algorithms, genetic programming, artificial life, multiagent learning, fitness switching, training data selection
%U http://citeseer.ist.psu.edu/455064.html
%X Control strategies for a multiple robot system should be adaptive and decentralized like those of social insects. To evolve this kind of control programs, we use genetic
   programming (GP). However, conventional GP methods are difficult to evolve complex coordinated behaviors and not powerful enough to solve the class of problems which
   require some emergent behaviors to be achieved in sequence. In a previous work, we presented a novel method called fitness switching. Here we extend the fitness switching
   method by introducing the concept of active data selection to further accelerate evolution speed of GP. Experimental results are reported on a table transport problem in
   which multiple autonomous mobile robots should cooperate to transport a large and heavy table.
%8 19-22  January
%Z AROB'99 Details from www site etc

%A Dong-Yeon Cho
%A Byoung-Tak Zhang
%T Bayesian Evolutionary Algorithms for Evolving Neural Tree Models of Time Series Data
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%V 2
%D 2000
%P 1451--1458
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K time series
%X Model induction plays an important role in many fields of science and engineering to analyse data. Specifically, the performance of time series prediction whose objectives
   are to find out the dynamics of the underlying process in given data is greatly affected by the model. Bayesian evolutionary algorithms have been proposed as a method for
   automatic model induction from data. We apply Bayesian evolutionary algorithms (BEAs) to evolving neural tree models of time series data. The performances of various BEAs
   are compared on two time series prediction problems by varying the population size and the type of variation operations. Our experimental results support that population
   based BEAs with unlimited crossover find good models more efficiently than single individual BEAs, parallelised individual based BEAs, and population based BEAs with
   limited crossover.
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Dong-Yeon Cho
%A Kwang-Hyun Cho
%A Byoung-Tak Zhang
%T Identification of biochemical networks by S-tree based genetic programming
%J Bioinformatics
%V 22
%N 13
%D 2006
%P 1631--1640
%I 
%K genetic algorithms, genetic programming
%X Motivation: Most previous approaches to model biochemical networks have focused either on the characterisation of a network structure with a number of components or on the
   estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions
   among the components and the dynamic behaviours of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data
   compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to
   exist in the real system. Results: We propose a new representation named S-trees for both the structural and dynamical modelling of a biochemical network within a unified
   scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the
   same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse
   primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings,
   we obtain the true structure and their relative squared errors are less than 5percent regardless of releasing constraints about structural sparseness. In addition, we
   confirm that the proposed algorithm is robust within 10percent noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation
   pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10**2 (??). To demonstrate the usefulness
   of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in
   Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation. Availability: The executable program and
   data are available from the authors upon request.
%8 July
%Z C The Author 2006

%A Sunil Choenni
%T On the Suitability of Genetic-Based Algorithms for Data Mining
%B Advances in Database Technologies
%S LNCS
%E Yahiko Kambayashi and Dik Lun Lee and Ee-Peng Lim and Mukesh Kumar Mohania and Yoshifumi Masunaga
%V 1552
%D 1999
%P 55--67
%I Springer-Verlag
%C Singapore
%K genetic algorithms
%8 19-20  November  1998
%Z DWDM98 ER'98 Workshop on Data Warehousing and Data Mining, Mobile Data Access, and Collaborative Work Support and Spatio-Temporal Data Management Also available as Dutch
   military "National Aerospace Laboratory" NLR tech report. \citechoenni:1998:SGADM NLR Technical Publications 98484-tp.pdf NLR-TP-98484 Also "University of Twente". Fixed
   length representation, one locus per database attribute. Attributes either 1) not used 2) actual value (categorical data) or 3) range, eg [3,34]. All attributes anded
   together to give query. Mutation and crossover a little bit smart. Microsoft Access interface. Interactive. User specifies initial topic to be mind and can interactively
   update this. http://wwwhome.cs.utwente.nl/~choenni/ http://www.nlr.nl/public/library/index.html#diagram
%@ 3-540-65690-1

%A Sunil Choenni
%T On the Suitability of Genetic-Based Algorithms for Data Mining
%R Technical Report NLR-TP-98484
%D 1998
%I 
%I National Aerospace Laboratory
%C Amsterdam
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/271039.html
%X Data mining has as goal to extract knowledge from large databases. To extract this knowledge, a database may be considered as a large search space, and a mining algorithm
   as a search strategy. In general, a search space consists of an enormous number of elements, making an exhaustive search infeasible. Therefore, efficient search strategies
   are of vital importance. Search strategies based on genetic-based algorithms have been applied successfully in a wide range of applications. In this paper, we discuss the
   suitability of genetic-based algorithms for data mining. We discuss the design and implementation of a genetic-based algorithm for data mining and illustrate its
   potentials.
%8 November
%Z shorter version published as \citeChoenni:1999:SGB page 22 "real-life database, FAA incident database, contains aircraft incident data 1978-95"

%A Sunil Choenni
%T Implementation and Evaluation of a Genetic-Based Data Mining Algorithm
%R Technical Report NLR-TR-99281
%D 1999
%I 
%I National Aerospace Laboratory
%C Amsterdam
%K genetic algorithms, genetic programming
%X GA can be rapidly implemented for DM yielding reasonable results. However, building an operational tool requires more effort
%8 July
%Z Jan 2000 not (yet) published. SQL queries generated. Implemented in Visual Basic. Individuals are conjenctions of predicates over database attributes implemented as binary
   tables. p8 DM specific limits on mutation of table rows. data mining of FAA Aircraft incident records (cleaned up, normalised) http://www.asy.faa.gov/asp/asy_fids.asp p9
   User must specify mining question, beta fraction coressponding to maximum fitness p10 individual must contain at least two elementary expressions ad-hoc rule no expression
   to cover more tha 10% of a domian. profiles of risky flights.

%A Andy Choi
%T Optimizing Local Area Networks Using Genetic Algorithms
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 467--472
%I MIT Press
%C Stanford University, CA, USA
%K Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 GA paper

%A Andy Choi
%T Optimizing Local Area Networks Using Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 49--58
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Seongim Choi
%T Speedups for Efficient Genetic Algorithms: Design Optimization of Low-Boom Supersonic Jet Using Parallel GA and Micro-GA with External Memory
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 21--30
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2003/Choi.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Sung-Soon Choi
%A Byung-Ro Moon
%T Polynomial Approximation of Survival Probabilities Under Multi-point Crossover
%B Genetic and Evolutionary Computation -- GECCO-2004, Part I
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3102
%D 2004
%P 994--1005
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3102/31020994.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22344-4

%A Wook-Jin Choi
%A Tae-Sun Choi
%T Computer-aided detection of pulmonary nodules using genetic programming
%B 17th IEEE International Conference on Image Processing (ICIP 2010 )
%D 2010
%P 4353--4356
%I 
%K genetic algorithms, genetic programming, CT image sequence, adaptive thresholding, computer-aided detection, false positive reduction, feature extraction, fitness function,
   lung imaging database consortium, lung region, nodule detection, pulmonary nodules, rule based classifier, voxel labelling, computerised tomography, feature extraction,
   image classification, image segmentation, image sequences, lung, medical image processing
%X This paper describes a novel nodule detection method that enhances false positive reduction. Lung region is extracted from CT image sequence using adaptive thresholding and
   18-connectedness voxel labelling. In the extracted lung region, nodule candidates are detected using adaptive multiple thresholding and rule based classifier. After that,
   we extract the 3D and 2D features from nodule candidates. The nodule candidates are then classified using genetic programming (GP) based classifier. In this work, a new
   fitness function is proposed to generate optimal adaptive classifier. We tested the proposed algorithm by using Lung Imaging Database Consortium (LIDC) database of National
   Cancer Institute (NCI). The classifier was trained and evaluated using two independent dataset and whole dataset. The proposed method reduced the false positives in nodule
   candidates and achieved 92percent detection rate with 6.5 false positives per scan.
%8 26-29  September
%Z Sch. of Inf. & Mechatron., Gwangju Inst. of Sci. & Technol. (GIST), Gwangju, South Korea. Also known as \cite5652369

%A Fuey Sian Chong
%T A Java based Distributed Approach to Genetic Programming on the Internet
%R M.S. Thesis
%D 1998
%I 
%I Computer Science, University of Birmingham
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/p.chong/p.chong.msc.25-sep-98.ps.gz
%X This paper presents a distributed approach to parallelise Genetic Programming on the Internet. The motivation for the approach is to harness the wealth of computing
   resources available on the Internet to provide the computing power required for solving difficult problems. A distributed genetic programming system termed DGP is developed
   in the Java programming language to demonstrate the feasibility of distributing genetic programming on the Internet. Unique features of the DGP system include the use of
   Java Servlets to handle the communication between DGP clients, the use of a population pool to neutralise differences in speeds of hosts, the interactive user interface and
   graphical displays of the evolution process. The DGP system has been implemented over the Internet and the results are favourable. Experiments were conducted to determine
   the performance of the DGP system. Results showed that the DGP system has a much higher probability of finding solutions as compared to the distributed approaches taken in
   our previous studies and the single population Genetic Programming.
%Z Code available at ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/gp-code/DGP Phyllis Chong Awarded a distinction in MSc in Advanced Computer Science

%A Fuey Sian Chong
%T Java based Distributed Genetic Programming on the Internet
%R Technical Report CSRP-99-7
%D 1999
%I 
%I University of Birmingham, School of Computer Science
%K genetic algorithms, genetic programming, DGP
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1999/CSRP-99-07.ps.gz
%X We proposed a distributed approach for parallelising Genetic Programming on the Internet. The approach harnesses the wealth of computing resources available on the Internet
   to provide the computing power required by Genetic Programming to solve hard problems. A distributed genetic programming system termed DGP is developed in the Java
   progamming language to demonstrate the feasibility of our approach. Features of the DGP system include the use of Java Servlets to handle communication between distributed
   machines and the use of a population pool to facilitate migrations. In addition, the DGP system has an interactive user interface for controlling the run and graphical
   displays of the evolution process. The DGP system has been implemented live over the Internet and the results prove that the approach is feasible. An experiment was
   conducted to determine the performance of the DGP system and results showed that the DGP system has a much higher probability of finding solutions than the distributed
   approaches taken in our previous work and the conventional single population Genetic Programming approach.
%8 April
%Z long version of \citechong:1999:jDGPi Phyllis Chong

%A Fuey Sian Chong
%A W. B. Langdon
%T Java based Distributed Genetic Programming on the Internet
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1229
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, DGP, Distributed Computing, Java Applet / Application, World Wide Computing, Internet, Servlets, poster
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/p.chong/DGPposter.ps.gz
%X A distributed approach for parallelising Genetic Programming (GP) on the Internet is proposed and its feasibility demonstrated with a distributed GP system termed DGP
   developed in Java. DGP is run successfully across the world over the Internet on heterogeneous platforms without any central co-ordination. The run results and the outcome
   of an experiment to determine DGP's performance are reported together with a description of DGP.
%O Full text in technical report CSRP-99-7
%8 13-17  July
%Z GECCO-99, part of \citebanzhaf:1999:gecco99, A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic
   programming conference (GP-99) see also \citechong:1999:jDGPis Phyllis Chong Note (2006) most URLs no longer working but hyperlinks in PDF should be ok. Code available at
   http://www.cs.bham.ac.uk/~wbl/ftp/gp-code/DGP/DGPsrc.tar.gz
%@ 1-55860-611-4

%A Fuey Sian Chong
%T Java based Distributed Genetic Programming on the Internet
%B Evolutionary computation and parallel processing
%E Erick Cantu-Paz and Bill Punch
%D 1999
%P 163--166
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/p.chong/GeccoWkShop.ps.gz
%8 13  July
%Z GECCO'99 WKSHOP Phyllis Chong

%A Fuey Sian Chong
%A W. B. Langdon
%T Java based Distributed Genetic Programming on the Internet
%B GECCO-99 Student Workshop
%E Una-May O'Reilly
%D 1999
%P 345
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, distributed, evolutionary programming, Internet, java, parallel
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/p.chong/DGPposter.ps.gz
%X GECCO'99 graduate WKSHOP Phyllis Chong
%8 13  July

%A Sanders Chong
%T Genetic Algorithms Applied to Computational Genomics
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 58--64
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2002/Chong.pdf
%8 June
%Z part of \citekoza:2002:gagp

%A Prabhas Chongstitvatana
%T Using Perturbation To Improve Robustness Of Solutions Generated By Genetic Programming For Robot Learning
%J Journal of Circuits, Systems and Computers
%V 9
%N 1-2
%D 1999
%P 133--143
%I World Scientific Publishing Company
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/421006.html
%X This paper proposes a method to improve robustness of the robot programs generated by genetic programming. The main idea is to inject perturbation into the simulation
   during the evolution of the solutions. The resulting robot programs are more robust because they have evolved to tolerate the changes in their environment. We set out to
   test this idea using the problem of navigating a mobile robot from a starting point to a target in an unknown cluttered environment. The result of the experiments shows the
   effectiveness of this scheme. The analysis of the result shows that the robustness depends on the "experience" that a robot program acquired during evolution. To improve
   robustness, the size of the set of "experience" should be increased and/or the amount of reusing the "experience" should be increased.
%O The Pennsylvania State University CiteSeer Archives
%Z discrete 2D 500x750 simulation, smellLeft,smellRight

%A Shou-yen Choo
%T Emergence of a Division of Labor in a Bee Colony
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 98--107
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A B. Chopard
%A Y. Baggi
%A P. Luthi
%A J. F. Wagen
%T Wave Propagation and Optimal Antenna Layout using a Genetic Algorithm
%J Speedup
%V 11
%N 2
%D 1997
%P 42--47
%I 
%O TelePar Conference, EPFL, 1997
%8 November
%Z SPEEDUP Journal speedup@cscs.ch

%A B. Chopard
%A O. Pictet
%A M. Tomassini
%T Parallel and distributed evolutionary computation for financial applications
%J Parallel Algorithms and Applications
%V 15
%D 2000
%P 15--36
%I 
%K genetic algorithms, genetic programming
%Z On Saturday, January 01, 2005 this journal was renamed International Journal of Parallel, Emergent and Distributed Systems.

%A I-Chun Chou
%A Eberhard O. Voit
%T Recent developments in parameter estimation and structure identification of biochemical and genomic systems
%J Mathematical Biosciences
%V 219
%N 2
%D 2009
%P 57--83
%I 
%K genetic algorithms, genetic programming, Parameter estimation, Network identification, Inverse modelling, Biochemical Systems Theory
%U http://www.sciencedirect.com/science/article/B6VHX-4VXDV4R-2/2/f7f1904f15cf7aa7404c664ae4658ce8
%X The organisation, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of
   mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems
   are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the
   underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterise the
   dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using
   [`]top-down' or [`]inverse' approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and
   regulatory information is technically difficult. The challenges can generally be categorised into four areas, namely, issues related to the data, the model, the
   mathematical structure of the system, and the optimisation and support algorithms. Many recent articles have addressed inverse problems within the modelling framework of
   Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological
   system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on
   support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data,
   estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data
   preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their
   applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic
   options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and
   proposes an operational [`]work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based
   on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open
   questions.
%Z GP included in Survey

%A Li-Der Chou
%A Shao-Chi Wang
%T Channel assignment using genetic programming in wireless networks
%B Global Telecommunications Conference, 1998. GLOBECOM 98. The Bridge to Global Integration. IEEE
%V 5
%D 1998
%P 2664--2668
%I IEEE
%C Sydney, NSW, Australia
%K genetic algorithms, genetic programming
%X It has become an important issue to design a control scheme to assign efficiently channel resources, according to the changes in network environment, in wireless networks.
   In the paper, a control scheme based on genetic programming is proposed and applied to assign channels in wireless networks. Compared to traditional schemes, simulation
   results demonstrate the superiority of the proposed control scheme
%8 8-12  November
%Z INSPEC Accession Number: 6430014 Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-Li;
%@ 0-7803-4984-9

%A Mariano Chouza
%A Claudio Rancan
%A Osvaldo Clua
%A and Ramon Garcia-Martinez
%T Passive Analog Filter Design Using GP Population Control Strategies
%B Opportunities and Challenges for Next-Generation Applied Intelligence: Proceedings of the International Conference on Industrial, Engineering \& Other Applications of
   Applied Intelligent Systems (IEA-AIE) 2009
%S Studies in Computational Intelligence
%E Been-Chian Chien and Tzung-Pei Hong
%V 214
%D 2009
%P 153--158
%I Springer-Verlag

%A Steffen Christensen
%A Franz Oppacher
%T An Analysis of Koza's Computational Effort Statistic for Genetic Programming
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 182--191
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780182.pdf
%X As research into the theory of genetic programming progresses, more effort is being placed on systematically comparing results to give an indication of the effectiveness of
   sundry modifications to traditional GP. The statistic that is commonly used to report the amount of computational effort to solve a particular problem with 99% probability
   is Koza's I(M, i, z) statistic. This paper analyzes this measure from a statistical perspective. In particular, Koza's I tends to underestimate the true computational
   effort, by 25% or more for commonly used GP parameters and run sizes. The magnitude of this underestimate is nonlinearly decreasing with increasing run count, leading to
   the possibility that published results based on few runs may in fact be unmatchable when replicated at higher resolution. Additional analysis shows that this statistic also
   under reports the generation at which optimal results are achieved.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Steffen Christensen
%A Franz Oppacher
%T The Y-Test: Fairly Comparing Experimental Setups with Unequal Effort
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 1060--1065
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X Evolutionary Computation has been dogged by a central statistical issue: how does one fairly compare the performance of two techniques which differ in the amount of work
   required? While Koza's computational effort statistic attempts to answer this problem, it is a point statistic and has other statistical problems. We present the y-test, a
   statistical test which takes as input a set of outcomes from the observed runs of two processes A and B. The y-test synthetically performs a work-balanced comparison
   between k runs of A and l runs of B. We show that by choosing k and l appropriately, we can compensate for the fact that one of the processes is computationally more
   efficient than the other.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = "356--361",
%@ 0-7803-9487-9

%A Steffen Christensen
%A Franz Oppacher
%T Solving the artificial ant on the Santa Fe trail problem in 20,696 fitness evaluations
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1574--1579
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, representation, runtime analysis, speedup technique
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1574.pdf
%X In this paper, we provide an algorithm that systematically considers all small trees in the search space of genetic programming. These small trees are used to generate
   useful subroutines for genetic programming. This algorithm is tested on the Artificial Ant on the Santa Fe Trail problem, a venerable problem for genetic programming
   systems. When four levels of iteration are used, the algorithm presented here generates better results than any known published result by a factor of 7.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Steffen Moffatt Christensen
%T Towards scalable genetic programming
%R Ph.D. Thesis
%D 2007
%I 
%I Carleton University
%C Ottawa, Canada
%K genetic algorithms, genetic programming
%X Genetic programming (GP) is a technique for automatically solving optimisation problems where candidate solutions are expressible as trees with no human intervention. We
   propose an extension of GP, termed scalable genetic programming, which solves problems parametrised by a scalable difficulty parameter. We first define a taxonomy of
   evolutionary computation (EC) systems that identifies variability dimensions and levels for EC systems. We define an algorithm, the scientist algorithm, which uses genetic
   programming as a subroutine to reliably make progress on scalable problems. The scientist algorithm uses a toolkit of provided routines to progress, by carrying out
   experiments to determine the value of different methods. We define several of the tools for this toolkit. We define and implement an algorithm for systematically
   considering all small trees for a problem. We then use these small trees in an iterative algorithm to define subroutines that improve performance on a problem under study.
   Using this algorithm, we beat the best known performance on the artificial ant on the Santa Fe trail problem by a factor of 7. As science depends on accurate hypothesis
   testing to make progress, we perform a comparison and evaluation of statistical techniques used to evaluate evolutionary computation systems. Finding many of these wanting,
   with the exception of computational effort, we introduce two additional techniques, effective mean best fitness and the y-test. We also perform an extensive analysis of the
   computational effort, and identify some statistical cautions around the use of this key statistic. We provide an algorithm that carefully uses computational effort to
   determine the best values of population size and generation number for an EC treatment. Finally, we identify several components that are of use with the scientist
   algorithm. We treat the use of multiobjective algorithms in GP, principal components analysis, and their combination. We demonstrate this by providing and testing an
   algorithm that makes evolved trees parsimonious. We introduce the notion of incremental evolution, and use it to make useful subroutines automatically from successful
   solutions to easy problems. We then use this to demonstrate scalable genetic programming on an integer sorting problem.
%Z http://portal.acm.org/citation.cfm?id=1292850 http://www.tamale.uottawa.ca/winter2007/300107.html Tuesday, Jan. 30, 2007 Also known as \cite1292850,

%A Chao-Hsien Chu
%A G. Premkumar
%A Carey Chou
%A Jianzhong Sun
%T Dynamic Degree Constrained Network Design: A Genetic Algorithm Approach
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 141--148
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-846.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Dominique Chu
%A Jonathan E. Rowe
%T Crossover Operators to Control Size Growth in Linear GP and Variable Length GAs
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X In various nuances of evolutionary algorithms it has been observed that variable sized genomes exhibit large degrees of redundancy and corresponding undue growth. This
   phenomenon is commonly referred to as ``bloat.'' The present contribution investigates the role of crossover operators as the cause for length changes in variable length
   genetic algorithms and linear GP. Three crossover operators are defined; each is tested with three different fitness functions. The aim of this article is to indicate
   suitable designs of crossover operators that allow efficient exploration of designs of solutions of a wide variety of sizes, while at the same time avoiding bloat.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Louise L. Chuang
%A Jeng-Yang Hwang
%A Been Chian Chien
%A Jung Yi Lin
%A Chiung Hsin Chang
%A Chen Hsiang Yu
%A Fong Ming Chang
%T Predicting fetal birth weight by ultrasound with the use of genetic programming
%J Ultrasound in Medicine \& Biology
%V 29
%N 5, Supplement 1
%D 2003
%P S163--S163
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6TD2-48KKXMV-R0/2/b03751f18c26cc039779c29a58106151
%8 May
%Z 1 Obstetrics and Gynecology, National Cheng Kung University Hospital, Tainan, Taiwan 2 Technology Research, ASN Technology Corp. (Taiwan), Tainan, Taiwan 3 Information
   Engineering, I, Shou University, Kaohsiung, Taiwan 4 Computer & Information Science, National Chiao Tung University, Hsinchu, Taiwan

%A Victor Ciesielski
%A Peter Wilson
%T Developing a team of soccer playing robots by genetic programming
%B Proceedings of The Third Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems
%E Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and Akira Namatame and Xin Yao and Mitsuo Gen
%D 1999
%P 101--108
%I 
%C School of Computer Science Australian Defence Force Academy, Canberra, Australia
%K genetic algorithms, genetic programming
%U http://www.cs.rmit.edu.au/~vc/papers/aus-jap-ec99.ps.gz
%8 22-25  November
%Z http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html

%A Vic Ciesielski
%A Dylan Mawhinney
%T Prevention of Early Convergence in Genetic Programming by Replacement of Similar Programs
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 67--72
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002) See also \citeoai:CiteSeerPSU:451316
%@ 0-7803-7278-6

%A Vic Ciesielski
%A Dylan Mawhinney
%A Peter Wilson
%T Genetic Programming for Robot Soccer
%B RoboCup 2001: Robot Soccer World Cup V
%S Lecture Notes in Computer Science
%E Andreas Birk and Silvia Coradeschi and Satoshi Tadokoro
%V 2377
%D 2002
%P 319--324
%I Springer
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2377/23770319.pdf", acknowledgement = ack-nhfb
%X RoboCup is a complex simulated environment in which a team of players must cooperate to overcome their opposition in a game of soccer. This paper describes three
   experiments in the use of genetic programming to develop teams for RoboCup. The experiments used different combinations of low level and high level functions. The teams
   generated in experiment 2 were clearly better than the teams in experiment 1, and reached the level of `school boy soccer' where the players follow the ball and try to kick
   it. The teams generated in experiment 3 were quite good, however they were not as good as the teams evolved in experiment 2. The results suggest that genetic programming
   could be used to develop viable teams for the competition, however, much more work is needed on the higher level functions, fitness measures and fitness evaluation.
%8 August  2001
%@ 3-540-43912-9

%A Vic Ciesielski
%A Xiang Li
%T Pyramid search: Finding solutions for deceptive problems quickly in genetic programming
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 936--943
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%X In deceptive problems many runs lead to suboptimal solutions and it can be difficult to escape from these local optima and find the global best solution. We propose a
   pyramid search strategy for these kinds of problems. In the pyramid strategy a number of populations are initialised and independently evolved for a number of generations
   at which point the worst performing populations are discarded. This evolve/discard process is continued until the problem is solved or one population remains. We show that
   for a number of deceptive problems the pyramid strategy results in a higher probability of success with fewer evaluations and a lower standard deviation of the number
   evaluations to success than the conventional approach of running to a maximum number of generations and then restarting.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Vic Ciesielski
%A Xiang Li
%T Experiments with Explicit For-loops in Genetic Programming
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 494--501
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Theory of evolutionary algorithms
%X Evolving programs with explicit loops presents major difficulties, primarily due to the massive increase in the size of the search space. Fitness evaluation becomes
   computationally expensive. We have investigated ways of dealing with these poblems by the evolution of for-loops of increasing semantic complexity. We have chosen two
   problems -- a modified Santa Fe ant problem and a sorting problem -- which have natural looping constructs in their solution and a solution without loops is not possible
   unless the tree depth is very large. We have shown that by conrolling the complexity of the loop structures it is possible to evolve smaller and more understandable
   programs for these problems.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Victor Ciesielski
%A Andrew Innes
%A Sabu John
%A John Mamutil
%T Understanding Evolved Genetic Programs for a Real World Object Detection Problem
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 351--360
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=351
%X We describe an approach to understanding evolved programs for a real world object detection problem, that of finding orthodontic landmarks in cranio-facial X-Rays. The
   approach involves modifying the fitness function to encourage the evolution of small programs, limiting the function set to a minimal number of operators and limiting the
   number of terminals (features). When this was done for two landmarks, an easy one and a difficult one, the evolved programs implemented a linear function of the features.
   Analysis of these linear functions revealed that underlying regularities were being captured and that successful evolutionary runs usually terminated with the best programs
   implementing one of a small number of underlying algorithms. Analysis of these algorithms revealed that they are a realistic solution to the object detection problem, given
   the features and operators available.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Vic Ciesielski
%A Gayan Wijesinghe
%A Andrew Innes
%A Sabu John
%T Analysis of the Superiority of Parameter Optimization over Genetic Programming for a Difficult Object Problem
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 4407--4414
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X We describe a progression of solutions to a difficult object detection problem, that of locating landmarks in X-Rays used in orthodontic treatment planning. In our first
   formulation an object detector was a genetic program whose inputs were a number of attributes computed from a scanning window. We used a rich function set comprising + -
   times divide min; max; ifthenelse. Experimentation with different function sets revealed that using the function set + - gave detectors that were almost as accurate. Such
   detectors are essentially a linear combination of attributes so we also implemented a parameter optimisation solution with a particle swarm optimiser. Contrary to
   expectation, the PSO detectors are more accurate and smaller than the GP ones. Our analysis of the reasons for this reveals that (1) the PSO approach involves a
   considerably smaller search space than the GP approach, (2) in the PSO approach there is a 1-1 mapping between genotype and phenotype while in the GP approach this mapping
   is many-1 and many semantically equivalent potential solutions are evaluated, (3) the fitness landscape for PSO is a good one for search in that solutions are distributed
   in areas of high fitness that are easy to locate while the GP landscape is much more difficult to characterise and areas of high fitness more difficult to find.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = "1264--1271",
%@ 0-7803-9487-9

%A Vic Ciesielski
%A Xiang Li
%T Data Mining of Genetic Programming Run Logs
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 281--290
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X We have applied a range of data mining techniques to a data base of log file records created from genetic programming runs on twelve different problems. We have looked for
   unexpected patterns, or golden nuggets in the data. Six were found. The main discoveries were a surprising amount of evaluation of duplicate programs across the twelve
   problems and one case of pathological behaviour which suggested a review of the genetic programming configuration. For problems with expensive fitness evaluation, the
   results suggest that there would be considerable speedup by caching evolved programs and fitness values. A data mining analysis performed routinely in a GP application
   could identify problems early and lead to more effective genetic programming applications.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Vic Ciesielski
%T Linear genetic programming, Springer Science+Business Media, Markus Brameier and Wolfgang Banzhaf, 2007, 315 pp, Book Series: Genetic Programming, Hard Cover, 62.95, ISBN
   0-387-31029-0
%J Genetic Programming and Evolvable Machines
%V 9
%N 1
%D 2008
%P 105--106
%I 
%K genetic algorithms, genetic programming
%8 March
%Z book review of \citeBrameier:2006:book

%A Ozan Nazim Ciftci
%A Sibel Fadiloglu
%A Fahrettin Gogus
%A Aytac Guven
%T Genetic programming approach to predict a model acidolysis system
%J Engineering Applications of Artificial Intelligence
%V 22
%N 4-5
%D 2009
%P 759--766
%I 
%K genetic algorithms, genetic programming, Gene-expression programming, Acidolysis
%U http://www.sciencedirect.com/science/article/B6V2M-4VTVJNC-2/2/5894a9c11ade2e94a1ff09a18b63a062
%X This paper models acidolysis of triolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase. A gene-expression programming (GEP), which is an
   extension to genetic programming (GP)-based model was developed for the prediction of the concentration of major reaction products of this reaction
   (1-palmitoyl-2,3-oleoyl-glycerol (POO), 1,3-dipalmitoyl-2-oleoyl-glycerol (POP) and triolein (OOO). Substrate ratio (SR), reaction temperature (T) and reaction time (t)
   were used as input parameters. The predicted models were able to predict the progress of the reactions with a mean standard error (MSE) of less than 1.0 and R of 0.978.
   Explicit formulation of proposed GEP models was also presented. Considerable good performance was achieved in modeling acidolysis reaction by using GEP. The predictions of
   proposed GEP models were compared to those of neural network (NN) modeling, and strictly good agreement was observed between the two predictions. Statistics and scatter
   plots indicate that the new GEP formulations can be an alternative to experimental models.

%A Rudi Cilibrasi
%A Paul Vitanyi
%A Ronald {de Wolf}
%T Algorithmic Clustering of Music Based on String Compression
%J Computer Music Journal
%V 28
%N 4
%D 2004
%P 49--67
%I 
%K genetic algorithms, genetic programming, complearn
%U http://homepages.cwi.nl/~paulv/papers/music.pdf
%X All musical pieces are similar, but some are more similar than others. Apart from serving as an infinite source of discussion (''Haydn is just like Mozart No, he's not!''),
   such similarities are also crucial for the design of efficient music information retrieval systems. The amount of digitised music available on the Internet has grown
   dramatically in recent years, both in the public domain and on commercial sites; Napster and its clones are prime examples.
%8 Winter
%Z C 2004 Massachusetts Institute of Technology Earlier version at cs.SD/0303025 http://arxiv.org/abs/cs.SD/0303025

%A Rudi Cilibrasi
%A Paul M. B. Vitanyi
%T Automatic Meaning Discovery Using Google
%N cs.CL/0412098
%D 2005
%I 
%K genetic algorithms, genetic programming, randomised hill-climbing, SVM, support vector machines, complearn, Computation and Language, Artificial Intelligence, Databases,
   Information Retrieval, Learning
%U http://homepages.cwi.nl/~paulv/papers/amdug.pdf
%X We have found a method to automatically extract the meaning of words and phrases from the world-wide-web using Google page counts. The approach is novel in its unrestricted
   problem domain, simplicity of implementation, and manifestly ontological underpinnings. The world-wide-web is the largest database on earth, and the latent semantic context
   information entered by millions of independent users averages out to provide automatic meaning of useful quality. We demonstrate positive correlations, evidencing an
   underlying semantic structure, in both numerical symbol notations and number-name words in a variety of natural languages and contexts. Next, we demonstrate the ability to
   distinguish between colours and numbers, and to distinguish between 17th century Dutch painters; the ability to understand electrical terms, religious terms, and emergency
   incidents; we conduct a massive experiment in understanding WordNet categories; and finally we demonstrate the ability to do a simple automatic English-Spanish translation.
%O v2
%8 15  March
%Z ACM-class: I.2.4; I.2.7 Date (v1): Tue, 21 Dec 2004 16:05:36 GMT (127kb,S) Date (revised v2): Tue, 15 Mar 2005 16:53:43 GMT (58kb) cited by \citegraham-rowe:2005:complearn
   Code http://www.complearn.org/

%A Rudi Cilibrasi
%A Paul M. B. Vitanyi
%T Clustering by Compression
%J IEEE Transactions on Information Theory
%V 51
%N 4
%D 2005
%P 1523--1545
%I 
%K genetic algorithms, genetic programming, complearn, universal dissimilarity distance, normalised compression distance, hierarchical unsupervised clustering, quartet tree
   method, parameter-free data-mining, heterogenous data analysis, Kolmogorov complexity
%U http://homepages.cwi.nl/~paulv/papers/cluster.pdf
%X We present a new method for clustering based on compression. The method doesn't use subject-specific features or background knowledge, and works as follows: First, we
   determine a parameter-free, universal, similarity distance, the normalized compression distance or NCD , computed from the lengths of compressed data files (singly and in
   pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is not restricted to a specific application area, and works across application area
   boundaries. A theoretical precursor, the normalised information distance, co-developed by one of the authors, is provably optimal. However, the optimality comes at the
   price of using the non-computable notion of Kolmogorov complexity. We propose axioms to capture the real-world setting, and show that the NCD approximates optimality. To
   extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (binary tree) by a new quartet method and a fast heuristic to implement it. The method
   is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report
   evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from
   completely different domains, using statistical, dictionary, and block sorting compressors. In genomics we presented new evidence for major questions in Mammalian
   evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis.
%8 April
%Z With respect to the version published in the IEEE Trans. Inform. Th., 51:4(2005), 1523?1545, we have changed Definition 2.1 of "admissible distance" making it more general
   and Definitions 2.4 and 2.5 of "normalized admissible distance," consequently adapted Lemma 2.6 (II.2) and in its proof (II.3) and the displayed inequalities. This left
   Theorem 6.3 unchanged except for changing "such that d(x; y)  e" to "such that d(x; y)  e and C(v)  C(x)."

%A Rudi Cilibrasi
%A Paul Vitanyi
%T A New Quartet Tree Heuristic for Hierarchical Clustering
%B Principled methods of trading exploration and exploitation Workshop
%D 2005
%I 
%I PASCAL
%C London
%K genetic algorithms, genetic programming, Computational, Information-Theoretic Learning with Statistics, Learning/Statistics, Optimisation, Theory, Algorithms
%U http://www.cwi.nl/~paulv/papers/quartet.pdf
%X We consider the problem of constructing an an optimal-weight tree from the 3Chose(n,4) weighted quartet topologies on n objects, where optimality means that the summed
   weight of the embedded quartet topologies is optimal (so it can be the case that the optimal tree embeds all quartets as non-optimal topologies). We present a heuristic for
   reconstructing the optimal-weight tree, and a canonical manner to derive the quartet-topology weights from a given distance matrix. The method repeatedly transforms a
   bifurcating tree, with all objects involved as leaves, achieving a monotonic approximation to the exact single globally optimal tree. This contrasts to other heuristic
   search methods from biological phylogeny, like DNAML or quartet puzzling, which, repeatedly, incrementally construct a solution from a random order of objects, and
   subsequently add agreement values. We do not assume that there exists a true bifurcating supertree that embeds each quartet in the optimal topology, or represents the
   distance matrix faithfully|not even under the assumption that the weights or distances are corrupted by a measuring process. Our aim is to hierarchically cluster the input
   data as faithfully as possible, both phylogenetic data and data of completely different types. In our experiments with natural data, like genomic data, texts or music, the
   global optimum appears to be reached. Our method is capable of handling over 100 objects, possibly up to 1000 objects, while no existing quartet heuristic can computionally
   approximate the exact optimal solution of a quartet tree of more than about 20-30 objects without running for years. The method is implemented and available as public
   software.
%8 6-7  July
%Z http://eprints.pascal-network.org/archive/00001821/ paper improved after workshop?

%A Rudi Cilibrasi
%A Paul M. B. Vitany
%T A New Quartet Tree Heuristic for Hierarchical Clustering
%B Theory of Evolutionary Algorithms
%S Dagstuhl Seminar Proceedings
%E Dirk V. Arnold and Thomas Jansen and Michael D. Vose and Jonathan E. Rowe
%N 06061
%D 2006
%I Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany
%C Dagstuhl, Germany
%K genetic algorithms, genetic programming, hierarchical clustering, quartet tree method
%U http://drops.dagstuhl.de/opus/volltexte/2006/598
%X We present a new quartet heuristic for hierarchical clustering from a given distance matrix. We determine a dendrogram (ternary tree) by a new quartet method and a fast
   heuristic to implement it. We do not assume that there is a true ternary tree that generated the distances and which we with to recover as closely as possible. Our aim is
   to model the distance matrix as faithfully as possible by the dendrogram. Our algorithm is essentially randomised hill-climbing, using parallelised Genetic Programming,
   where undirected trees evolve in a random walk driven by a prescribed fitness function. Our method is capable of handling up to 60--80 objects in a matter of hours, while
   no existing quartet heuristic can directly compute a quartet tree of more than about 20--30 objects without running for years. The method is implemented and available as
   public software at www.complearn.org. We present applications in many areas like music, literature, bird-flu (H5N1) virus clustering, and automatic meaning discovery using
   Google.
%O $<$http://drops.dagstuhl.de/opus/volltexte/2006/598$>$ [date of citation: 2006-01-01]
%8 5-10  February

%A Rudi Langston Cilibrasi
%T Statistical Inference Through Data Compression
%R Ph.D. Thesis
%D 2007
%I 
%I Institute for Logic, Language and Computation, Universiteit van Amsterdam
%C Plantage Muidergracht 24, 1018 TV, Amsterdam, Holland
%K genetic algorithms, genetic programming
%U http://www.lulu.com/shop/search.ep?contributorId=254359
%X This thesis provides a breadth-first tour of artificial intelligence techniques using ordinary data compression programs like zip. Using mathematical theory such as
   Kolmogorov Complexity and Shannon's Coding Theory, we arrive at a unique and generic perspective on universal learning with a plethora of real examples. Included are
   results from literature, astronomy, animal and virus evolution, linguistics, semantics, and music. An open source software package, CompLearn, is available for download so
   that interested readers may continue the research themselves in their own applications.
%8 23  February
%Z p51 'Our algorithm is essentially randomized hill-climbing, using parallellized Genetic Programming,' Promotor: Prof.dr.ir. P.M.B. Vitanyi (CWI)
%@ 90-6196-540-3

%A Luca Citi
%A Riccardo Poli
%A Caterina Cinel
%T High-significance Averages of Event-Related Potential via Genetic Programming
%B Genetic Programming Theory and Practice VII
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Una-May O'Reilly and Trent McConaghy
%D 2009
%P 135--157
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Event-related potentials, Register-based GP, Memory-with-Memory
%O 9
%8 14-16  May
%Z part of \citeRiolo:2009:GPTP

%A Chris Clack
%A Jonny Farringdon
%A Peter Lidwell
%A Tina Yu
%T An Adaptive Document Classification Agent
%R Research Note RN/96/45
%D 1996
%I 
%I University College London
%C Computer Science, Gower Street, London, WC1E 6BT, UK
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/research/rns/rns96.html
%X The development of an intelligent text classification application is discussed which uses genetic programming methods. Learning capabilities are used to effect a adaptive
   system in order to meet the needs of dynamic-information users. Deriving structure and priority from text, target environments are discussed where large volumes of
   (on-line) textual documents are manipulated.
%O Submitted to BCS-ES96
%8 21  June
%Z 3 figures as separte ps files in the same directory

%A Chris D. Clack
%A S. J. Gould
%A Peter R. Lidwell
%A Janet T. McDonnell
%T Advanced Technology Support for Information Management at Friends of the Earth
%R Research Note RN/96/48
%D 1996
%I 
%I University College London
%C Computer Science, Gower Street, London, WC1E 6BT, UK
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/92899.html
%X INTRODUCTION We report early results from a project to study the application of advanced technology to enhance information management in a medium sized enterprise where the
   collection, analysis and dissemination of information are key business processes. Our two-year TCD-funded project is a collaboration between University College London (UCL)
   and Friends of the Earth (FOE), a research and campaigning organisation with 65 full time employees and a turnover of about 3.5 million pounds. We explain our strategy for
   re-engineering information management at Foe and present three example projects which demonstrate the application of innovative IT solutions to problems associated with
   fundamental working practices.

%A Chris Clack
%A Jonny Farringdon
%A Peter Lidwell
%A Tina Yu
%T Autonomous Document Classification for Business
%R Research Note RN/96/48
%D 1996
%I 
%I University College London
%C Computer Science, Gower Street, London, WC1E 6BT, UK
%K genetic algorithms, genetic programming, Softbot, agent architecture, pattern recognition, long term adaptation and learning
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/clack_1997_adcb.pdf
%X With the continuing exponential growth of the Internet and the more recent growth of business Intranets, the commercial world is becoming increasingly aware of the problem
   of electronic information overload. This has encouraged interest in developing agents/softbots that can act as electronic personal assistants and can develop and adapt
   representations of users information needs, commonly known as profiles. As the result of collaborative research with Friends of the Earth, an environmental issues
   campaigning organisation, we have developed a general purpose information classification agent architecture and have applied it to the problem of document classification
   and routing. Collaboration with Friends of the Earth allows us to test our ideas in a non-academic context involving high volumes of documents. We use the technique of
   genetic programming (GP), (Koza and Rice 1992), to evolve classifying agents. This is a novel approach for document classification, where each agent evolves a parse-tree
   representation of a user's particular information need. The other unusual feature of our research is the longevity of our agents and the fact that they undergo a continual
   training process; feedback from the user enables the agent to adapt to the user's long-term information requirements.
%O Appears in Autonomous Agents '97
%8 June
%Z see also \citeclack:1997:adcb

%A Chris Clack
%A Jonny Farringdon
%A Peter Lidwell
%A Tina Yu
%T Autonomous Document Classification for Business
%B The First International Conference on Autonomous Agents (Agents '97)
%E W. Lewis Johnson
%D 1997
%P 201--208
%I ACM Press 1515 Broadway, New York, NY 10036, USA
%I ACM SIGART
%C Marina del Rey, California, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/clack_1997_adcb.pdf
%X With the continuing exponential growth of the Internet and the more recent growth of business Intranets, the commercial world is becoming increasingly aware of the problem
   of electronic information overload. This has encouraged interest in developing agents/softbots that can act as electronic personal assistants and can develop and adapt
   representations of users information needs, commonly known as profiles. As the result of collaborative research with Friends of the Earth, an environmental issues
   campaigning organisation, we have developed a general purpose information classification agent architecture and have applied it to the problem of document classification
   and routing. Collaboration with Friends of the Earth allows us to test our ideas in a non-academic context involving high volumes of documents. We use the technique of
   genetic programming (GP), (Koza and Rice 1992), to evolve classifying agents. This is a novel approach for document classification, where each agent evolves a parse-tree
   representation of a user's particular information need. The other unusual features of our research are the longevity of our agents and the fact that they undergo a
   continual training process; feedback from the user enables the agent to adapt to the user's long-term information requirements.
%8 February  5-8
%Z http://www.isi.edu/isd/AA97/info.html see also \citeclack:1996:adcb
%@ 0-89791-877-0

%A Chris Clack
%T Software -- The Next Generation: Evolving Document Classification
%R white paper
%D 1997
%P 55--67
%I 
%I UCL, Andersen Consulting
%C University College London, Gower Street, London
%K genetic algorithms, genetic programming
%8 April
%Z Part of "Emerging Technologies White Papers: Software -- The Next Generation" which reports the 1996 workshop on Emerging technologies held in UCL Computer Science dept.
   for Andersen Consulting's Emerging Technologies Group and others.

%A Adam Clark
%T Predator-Prey Interactions in a Simulated World
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 59--64
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Robert Cleary
%T Extending Grammatical Evolution with Attribute Grammars: An Application to Knapsack Problems
%R M.S. Thesis Master of Science in Computer Science
%D 2005
%I 
%I University of Limerick
%C University of Limerick, Ireland
%K genetic algorithms, genetic programming, grammatical evolution, grammatical swarm, attribute grammars
%U http://ncra.ucd.ie/downloads/pub/thesisExtGEwithAGs-CRC.pdf
%X Research extending the capabilities of the well-known evolutionary-algorithm (EA) of Grammatical Evolution (GE) is presented. GE essentially describes a software component
   for (potentially) any search algorithm (more prominently an EA) - whereby it serves to facilitate the generation of viable solutions to the problem at hand. In this way, GE
   can be thought of as a generally applicable, robust and pluggable component to any search-algorithm. Facilitating this plug- ability - is the ability to hand-describe the
   structure of solutions to a particular problem; this, under the guise of the concise and effective notation of a grammar definition. This grammar may be thought of, as the
   rules for the generation of solutions to a problem. Recent research has shown, that for static-problems - (problems whose optimum-solution resides within a
   finitely-describable set, for the set of all possible solutions), the ability to focus the search (for the optimum) on the more promising regions of this set, has provided
   the best-performing approaches to-date. As such, it is suggested that search be biased toward more promising areas of the set of all possible solutions. In it's use of a
   grammar, GE provides such a bias (as a language-bias), yet remains unable, to effectively bias the search for problems of constrained optimisation. As such, and as detailed
   in this thesis - the mechanism of an attribute grammar is proposed to maintain GE as a pluggable component for problems of this type also; thus extending it's robustness
   and general applicability. A family of academically recognised (hard) knapsack problems, are used as a testing-ground for the extended-system and the results presented are
   encouraging. As a side-effect of this study (and possibly more importantly) we observe some interesting behavioural findings about the GE system itself. The standard GE
   one-point crossover operator, emerges as exhibiting a mid evolutionary change-of-role from a constructive to destructive operator; GE's ripple-crossover is found to be
   heavily dependent on the presence of a GE-tail (of residual-introns) in order to function effectively; and the propagation of individuals - characterised by
   large-proportions of such residual-introns - is found to be an evolutionary self- adaptive response to the destructive change of role found in the one-point crossover: all
   of these findings are found with respect to the problems examined.

%A Robert Cleary
%A Michael O'Neill
%T An Attribute Grammar Decoder for the 01 MultiConstrained Knapsack Problem
%B Evolutionary Computation in Combinatorial Optimization -- EvoCOP~2005
%S LNCS
%E G\"unther R. Raidl and Jens Gottlieb
%V 3448
%D 2005
%P 34--45
%I Springer Verlag Berlin
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming, grammatical evolution, evolutionary computation, attribute grammar
%X We describe how the standard genotype-phenotype mapping process of Grammatical Evolution (GE) can be enhanced with an attribute grammar to allow GE to operate as a
   decoder-based Evolutionary Algorithm (EA). Use of an attribute grammar allows GE to maintain context-sensitive and semantic information pertinent to the capacity
   constraints of the 01 Multi-constrained Knapsack Problem (MKP). An attribute grammar specification is used to perform decoding similar to a first-fit heuristic. The results
   presented are encouraging, demonstrating that GE in conjunction with attribute grammars can provide an improvement over the standard context-free mapping process for
   problems in this domain.
%8 30  March -1  April
%Z EvoCOP2005 Also known as \citecleary:evocop05

%A Janet Clegg
%A James Alfred Walker
%A Julian Francis Miller
%T A new crossover technique for Cartesian genetic programming
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1580--1587
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Cartesian Genetic Programming, crossover techniques, optimisation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1580.pdf
%X Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents' trees are
   swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree
   structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not use
   crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based
   on an adaptation of the Cartesian Genetic Programming representation and is tested on two simple regression problems. It is shown that by implementing the new crossover
   technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Janet Clegg
%T Combining cartesian genetic programming with an estimation of distribution algorithm
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1333--1334
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, cartesian genetic programming, crossover techniques, optimisation: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1333.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389350

%A Kester Clegg
%A Susan Stepney
%T Analogue Circuit Control through Gene Expression
%B Proceedings of EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops
%S Lecture Notes in Computer Science
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Rolf Drechsler and Anik\'o Ek\'art and Anna Esparcia-Alc\'azar and Muddassar Farooq and
   Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang
%V 4974
%D 2008
%P 154--163
%I Springer
%C Naples
%K genetic algorithms, genetic programming, Cartesian genetic programming
%X Software configurable analogue arrays offer an intriguing platform for automated design by evolutionary algorithms. Like previous evolvable hardware experiments, these
   platforms are subject to noise during physical interaction with their environment. We report preliminary results of an evolutionary system that uses concepts from gene
   expression to both discover and decide when to deploy analogue circuits. The output of a circuit is used to trigger its reconfiguration to meet changing conditions. We
   examine the issues of noise during our evolutionary runs, show how this was overcome and illustrate our system with a simple proof-of-concept task that shows how the same
   mechanism of control works for progressive developmental stages (canalisation) or adaptable control (homoeostasis).
%8 26-28  March

%A Kester Dean Clegg
%T Evolving gene expression to reconfigure analogue devices
%R Ph.D. Thesis
%D 2008
%I 
%I University of York
%K genetic algorithms, genetic programming, Cartesian genetic programming
%U http://www.cs.york.ac.uk/ftpdir/reports/2008/YCST/05/YCST-2008-05.pdf
%X Repeated, morphological functionality, from limbs to leaves, is widespread in nature. Pattern formation in early embryo development has shed light on how and why the same
   genes are expressed in different locations or at different times. Practitioners working in evolutionary computation have long regarded nature's reuse of modular
   functionality with admiration. But repeating nature's trick has proven difficult. To date, no one has managed to evolve the design for a car, a house or a plane. Or indeed
   anything where the number of interdependent parts exposed to random mutation is large. It seems that while we can use evolutionary algorithms for search-based optimisation
   with great success, we cannot use them to tackle large, complex designs where functional reuse is essential. This thesis argues that the modular functionality provided by
   gene reuse could play an important part in evolutionary computation being able to scale, and that by expressing subsets of genes in specific contexts, successive stages of
   phenotype configuration can be controlled by evolutionary search. We present a conceptual model of context-specific gene expression and show how a genome representation can
   hold many genes, only a few of which need be expressed in a solution. As genes are expressed in different contexts, their functional role in a solution changes. By allowing
   gene expression to discover phenotype solutions, evolutionary search can guide itself across multiple search domains. The work here describes the design and implementation
   of a prototype system to demonstrates the above features and evolve genomes that are able to use gene expression to find and deploy solutions, permitting mechanisms of
   dynamic control to be discovered by evolutionary computation.
%8 May

%A Eddie Clemente
%A Gustavo Olague
%A Leon Dozal
%A Martin Mancilla
%T Object Recognition with an Optimized Visual Cortex Model using Genetic Programming
%B Applications of Evolutionary Computing, EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC
%S LNCS
%E Cecilia Di Chio and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. Fernandez de Vega and Gianni A. Di Caro and Rolf Drechsler and Aniko Ekart and Anna I
   Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero
   and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis
%V 7248
%D 2011
%P 315--325
%I Springer Verlag Berlin
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming
%X Computational neuroscience is a discipline devoted to the study of brain function from an information processing standpoint. The ventral stream, also known as the 'what'
   pathway, is widely accepted as the model for processing the visual information related to object identification. This paper proposes to evolve a mathematical description of
   the ventral stream where key features are identified in order to simplify the whole information processing. The idea is to create an artificial ventral stream by evolving
   the structure through an evolutionary computing approach. In previous research, the 'what' pathway is described as being composed of two main stages: the interest region
   detection and feature description. For both stages a set of operations were identified with the aim of simplifying the total computational cost by avoiding a number of
   costly operations that are normally executed in the template matching and bag of feature approaches. Therefore, instead of applying a set of previously learnt patches,
   product of an off-line training process, the idea is to enforce a functional approach. Experiments were carried out with a standard database and the results show that
   instead of 1200 operations, the new model needs about 200 operations.
%8 11-13  April
%Z EvoIASP Part of \citeDiChio:2012:EvoApps EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012

%A Manuel Clergue
%A Philippe Collard
%A Marco Tomassini
%A Leonardo Vanneschi
%T Fitness Distance Correlation And Problem Difficulty For Genetic Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 724--732
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, distance between genotypes, fitness distance correlation, problem difficulty, royal trees, trap functions
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
   Nominated for best at GECCO award. Fitness landscape.
%@ 1-55860-878-8

%A T. Broughton
%A A. Tan
%A Paul S. Coates
%T The use of Genetic programing in Exploring 3D Design Worlds
%B CAAD Futures 97
%E Richard Junge
%D 1997
%P 885--917
%I Kluwer Academic Publishers
%C Technical University Munich, Germany
%K genetic algorithms, genetic programming
%U http://roar.uel.ac.uk/jspui/bitstream/10552/854/1/Broughton%2c%20T%20%281997%29%20CAAD%20Futures%20pp.%20885.pdf
%X Genetic algorithms are used to evolve rule systems for a generative process, in one case a shape grammar,which uses the 'Dawkins Biomorph' paradigm of user driven choices
   to perform artificial selection, in the other a CA/Lindenmeyer system using the Hausdorff dimension of the resultant configuration to drive natural selection. 1) Using
   Genetic Programming in an interactive 3d shape grammar (Amy Tan and P S Coates) A report of a generative system combining genetic programming(GP) and 3D shape grammars. The
   reasoning that backs up the basis for this work depends on the interpretation of design as search In this system, a 3D form is a computer program made up of functions
   (transformations and terminals (building blocks). Each program evaluates into a structure. Hence, in this instance a program is synonymous with form. Building blocks of
   form are platonic solids (box, cylinder....etc.). A Variety of combinations of the simple affine transformations of translation, scaling, rotation together with Boolean
   operations of union, subtraction and intersection performed on the building blocks generate different configurations of 3D forms. Using to the methodology of genetic
   programming, an initial population of such programs are randomly generated,subjected to a test for fitness (the eyeball test). Individual programs that have passed the test
   are selected to be parents for reproducing the next generation of programs via the process of recombination. 2) Using a GA to evolve rule sets to achieve a goal
   configuration (T.Broughton and P.Coates). The aim of these experiments was to build a framework in which a structure's form could be defined by a set of instructions
   encoded into its genetic make-up. This was achieved by combining a generative rule system commonly used to model biological growth with a genetic algorithm simulating the
   evolutionary process of selection to evolve an adaptive rule system capable of replicating any preselected 3-D shape. The generative modelling technique used is a string
   rewriting Lindenmayer system the genes of the emergent structures are the production rules of the L-system, and the spatial representation of the structures uses the
   geometry of iso-spatial dense-packed spheres.
%8 4-6  August
%Z University of East London, GB
%@ 0-7923-4726-9

%A Paul Coates
%A Dimitrios Makris
%T Genetic Programming and Spatial Morphogenesis
%B AISB Symposium on Creative Evolutionary Systems
%D 1999
%P 105--114
%I COGS, University of Sussex
%I AISB
%C Edinburgh College of Art and Division of Informatics, University of Edinburgh
%K genetic algorithms, genetic programming
%U http://www.aisb.org.uk/publications/proceedings/proc1999/aisb1999/AISB99_Evolutionary.pdf
%8 6-9  April
%Z PDF is scanned image. Some pictures poor. personal shape grammar, domino house. Abstract of Makris masters thesis? Dimitrios Makris Phd thesis at:
   http://www.unilim.fr/theses/2005/sciences/2005limo0037/markris_d.pdf
%@ 1-902956-03-6

%A Paul Coates
%T Programming.Architecture
%D 2010
%I Routledge
%K genetic algorithms, genetic programming
%U http://www.routledge.com/books/details/9780415451888/
%X Programming.Architecture is a simple and concise introduction to the history of computing and computational design, explaining the basics of algorithmic thinking and the
   use of the computer as a tool for design and architecture. Introduction 1. Falling Between Two Stools 2. Rethinking Representation 3. In the Beginning was the Word 4. The
   Mystery of the Machine that Invents Itself 5. Evolving the Text - Being even Lazier 6. The Text of the Vernacular. Epilogue. Glossary
%8 January  29th
%Z Reviewed by \citeMedjdoub:2011:GPEM

%A Andre L. V. Coelho
%A Everlandio Fernandes
%A Katti Faceli
%T Inducing multi-objective clustering ensembles with genetic programming
%J Neurocomputing
%V 74
%N 1-3
%D 2010
%P 494--498
%I 
%K genetic algorithms, genetic programming, Cluster analysis, Ensembles, Multi-objective optimization
%U http://www.sciencedirect.com/science/article/B6V10-517YN4X-P/2/7322b78e25061d5ecbaa12f058216cd0
%X The recent years have witnessed a growing interest in two advanced strategies to cope with the data clustering problem, namely, clustering ensembles and multi-objective
   clustering. In this paper, we present a genetic programming based approach that can be considered as a hybrid of these strategies, thereby allowing that different
   hierarchical clustering ensembles be simultaneously evolved taking into account complementary validity indices. Results of computational experiments conducted with
   artificial and real datasets indicate that, in most of the cases, at least one of the Pareto optimal partitions returned by the proposed approach compares favourably or go
   in par with the consensual partitions yielded by two well-known clustering ensemble methods in terms of clustering quality, as gauged by the corrected Rand index.
%O Artificial Brains

%A Andre L. V. Coelho
%A Everlandio Fernandes
%A Katti Faceli
%T Multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming
%J Decision Support Systems
%D 2011
%I 
%K genetic algorithms, genetic programming, Cluster analysis, Clustering ensembles, Multi-objective clustering, Hierarchical fusion, Partition selection
%U http://www.sciencedirect.com/science/article/B6V8S-5230PKR-5/2/797124c3ee3c0a2f623dd92203d4042a
%X This paper investigates a genetic programming (GP) approach aimed at the multi-objective design of hierarchical consensus functions for clustering ensembles. By this means,
   data partitions obtained via different clustering techniques can be continuously refined (via selection and merging) by a population of fusion hierarchies having
   complementary validation indices as objective functions. To assess the potential of the novel framework in terms of efficiency and effectiveness, a series of systematic
   experiments, involving eleven variants of the proposed GP-based algorithm and a comparison with basic as well as advanced clustering methods (of which some are clustering
   ensembles and/or multi-objective in nature), have been conducted on a number of artificial, benchmark and bioinformatics datasets. Overall, the results corroborate the
   perspective that having fusion hierarchies operating on well-chosen subsets of data partitions is a fine strategy that may yield significant gains in terms of clustering
   robustness.
%O In Press, Corrected Proof

%A Lucio Coelho
%A Ben Goertzel
%A Cassio Pennachin
%A Chris Heward
%T Classifier ensemble based analysis of a genome-wide SNP dataset concerning Late-Onset Alzheimer Disease
%B 8th IEEE International Conference on Cognitive Informatics, ICCI '09
%D 2009
%P 469--475
%I 
%K genetic algorithms, genetic programming, OpenBiomind toolkit, SLC6A15, brain genes, classifier ensemble based analysis, genome-wide SNP dataset, important features
   analysis, late-onset Alzheimer disease, local search methods, single-nucleotide polymorphisms, brain, diseases, genetic engineering, learning (artificial intelligence),
   medical administrative data processing, search problems
%X The OpenBiomind toolkit is used to apply GA, GP and local search methods to analyze a large SNP dataset concerning late-onset Alzheimer's disease (LOAD). Classification
   models identifying LOAD with statistically significant accuracy are identified, and ensemble-based important features analysis is used to identify brain genes related to
   LOAD, most notably the solute carrier gene SLC6A15. Ensemble analysis is used to identify potentially significant interactions between genes in the context of LOAD.
%8 June
%Z Also known as \cite5250695

%A Carlos A. {Coello Coello}
%A Nareli Cruz Cortes
%T Solving Multiobjective Optimization Problems Using an Artificial Immune System
%J Genetic Programming and Evolvable Machines
%V 6
%N 2
%D 2005
%P 163--190
%I 
%K AIS, artificial immune system, multiobjective optimization, clonal selection
%X we propose an algorithm based on the clonal selection principle to solve multiobjective optimisation problems (either constrained or unconstrained). The proposed approach
   uses Pareto dominance and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced
   and non-uniform mutation is applied to the 'not so good' antibodies (which are represented by binary strings that encode the decision variables of the problem to be
   solved). We also use a secondary (or external) population that stores the nondominated solutions found along the search process. Such secondary population constitutes the
   elitist mechanism of our approach and it allows it to move towards the true Pareto front of a problem over time. Our approach is compared with three other algorithms that
   are representative of the state-of-the-art in evolutionary multiobjective optimisation. For our comparative study, three metrics are adopted and graphical comparisons with
   respect to the true Pareto front of each problem are also included. Results indicate that the proposed approach is a viable alternative to solve multiobjective optimisation
   problems.
%8 June

%A Corrado Coia
%T Automatic Evolution of Conceptual Building Architectures
%R M.S. Thesis
%D 2011
%I 
%I Brock University
%K genetic algorithms, genetic programming

%A Corrado Coia
%A Brian Ross
%T Automatic Evolution of Conceptual Building Architectures
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 1145--1152
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Real-world applications, Art and music
%X An evolutionary approach to the automatic generation of 3D building topologies is presented. Genetic programming is used to evolve shape grammars. When interpreted, the
   shape grammars generate 3D models of buildings. Fitness evaluation considers user-specified criteria that evaluate different aspects of the model geometry. Such criteria
   might include maximising the number of unique normals, satisfying target height requirements, and conforming to supplied shape contours. Multi-objective evaluation is used
   to analyse and rank model fitness, based on the varied user-supplied criteria. A number of interesting models complying to given geometric specifications have been
   successfully evolved with the approach. A motivation for this research application is that it can be used as a generator of conceptual designs, to be used as inspirations
   for refinement or further exploration.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Oguz Colak
%A Cahit Kurbanoglu
%A M. Cengiz Kayacan
%T Milling surface roughness prediction using evolutionary programming methods
%J Materials \& Design
%V 28
%N 2
%D 2007
%P 657--666
%I 
%K genetic algorithms, genetic programming, gene expression programming, Surface roughness, CNC end milling, Genetic expression programming
%U http://www.sciencedirect.com/science/article/B6TX5-4GYNXVH-3/2/9f33fbb56f37b01600d2773bc207696f
%X CNC milling has become one of the most competent, productive and flexible manufacturing methods, for complicated or sculptured surfaces. In order to design, optimize, built
   up to sophisticated, multi-axis milling centers, their expected manufacturing output is at least beneficial. Therefore data, such as the surface roughness, cutting
   parameters and dynamic cutting behavior are very helpful, especially when they are computationally produced, by artificial intelligent techniques. Predicting of surface
   roughness is very difficult using mathematical equations. In this study gene expression programming method is used for predicting surface roughness of milling surface with
   related to cutting parameters. Cutting speed, feed and depth of cut of end milling operations are collected for predicting surface roughness. End of the study a linear
   equation is predicted for surface roughness related to experimental study.

%A Ron Coleman
%T Boosting Blackjack Returns with Machine Learned Betting Criteria
%B Third International Conference on Information Technology: New Generations (ITNG 2006)
%D 2006
%P 669--673
%I IEEE Computer Society
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%8 10-12  April
%@ 0-7695-2497-4

%A Mark Coletti
%A Thomas D. Lash
%A Ryszard Michalski
%A Craig Mandsager
%A Rida Moustafa
%T Comparing Performance of the Learnable Evolution Model and Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 779
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-386.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Andre Colin
%T Data-Mining and Genetic Programming
%J PC AI
%V 11
%N 5
%D 1997
%P 23
%I Knowledge Technology, Inc.
%C Phoenix, AZ, USA
%K genetic algorithms, genetic programming, data mining
%U http://www.pcai.com/web/issues/pcai_11_5_toc.html
%X To make intelligent real-world decisions, a data-mining package must often align with other technologies. One such technology is genetic programming, which derives rules by
   looking through a "space" of possibilities. Andrew Colin shows how data-mining can use genetic programming in important applications.
%8 September /  October
%Z easy going introduction to GP but little data mining information. Code available on line http://www.primenet.com/pcai/New_Home_Page/pcai_info/All_Lists.html broken Feb 2012

%A Pierre Collet
%A Evelyne Lutton
%A Frederic Raynal
%A Marc Schoenauer
%T Individual GP: an Alternative Viewpoint for the Resolution of Complex Problems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 974--981
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, IFS, fractals
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-467.ps
%X An unususal GP implementation is proposed, based on a more "economic" exploitation of the GP algorithm: the "individual" approach, where each individual of the population
   embodies a single function rather than a set of functions. The final solution is then a set of individuals. Examples are presented where results are obtained more rapidly
   than with the conventional approach, where all individuals of the final generation but one are discarded.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Pierre Collet
%A Evelyne Lutton
%A Frederic Raynal
%A Marc Schoenauer
%T Polar IFS + Individual Genetic Programming = Efficient IFS Inverse Problem Solving
%R Technical Report RR-3849
%D 1999
%I 
%I INRIA
%C Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le Chesnay Cedex France
%K genetic algorithms, genetic programming
%U http://minimum.inria.fr/evo-lab/Publications/RR-PolarIFS.ps.gz
%X Lorsque l'on s'interesse aux IFS (systemes de fonctions iterees) non affines, la resolution du probleme inverse (c'est-a-dire trouver l'IFS dont l'attracteur approxime au
   mieux une forme bidimensionnelle donnee) devient un probleme tres complexe. Ce probleme a deja ete resolu avec succes a l'aide de strategies de programmation genetique,
   fondees sur une representation des fonctions sous forme d'arbres. La principale difficulte de cette approche etant la gestion efficace des contraintes de contractance sur
   les fonctions, nous proposons ici l'emploi d'une representation polaire des IFS non affines, centree sur le point fixe de chaque fonction. Cette representation a deux
   principaux avantages : une contrainte simple sur la definition de la composante radiale de chaque fonction assure sa convergence vers un point fixe (le point central de sa
   representation polaire), l'acces au point fixe de chaque fonction est direct (il n'est plus necessaire de l'estimer comme dans l'approche en coordonnees cartesiennes). Nous
   presentons ensuite une strategie originale de programmation genetique, fondee sur une exploitation plus "economique" des strategies evolutionnaires : l'approche
   "individuelle", o\`u chaque individu de la population represente une seule fonction (au lieu d'un IFS complet). La solution au probleme etant fournie par un ensemble
   d'individus de la population finale, des resultats sont obtenus de fa\ccon plus rapide et plus efficace que dans la version classique o\`u tous les individus de la
   population finale sauf un (le meilleur) sont ecartes.
%8 Decemeber
%Z in english

%A Pierre Collet
%A Evelyne Lutton
%A Marc Schoenauer
%A Jean Louchet
%T Take it EASEA
%B Parallel Problem Solving from Nature - PPSN VI 6th International Conference
%S LNCS
%E Marc Schoenauer and Kalyanmoy Deb and G\"unter Rudolph and Evelyne Lutton Xin Yao and Juan Julian Merelo and Hans-Paul Schwefel
%V 1917
%D 2000
%P 891--901
%I Springer-Verlag
%C Paris, France
%K genetic algorithms, genetic programming
%U http://minimum.inria.fr/evo-lab/Publications/PPSNVI.ps.gz
%X Evolutionary algorithms are not straightforward to implement and the lack of any specialised language forces users to reinvent the wheel every time they want to write a new
   program. Over the last years, evolutionary libraries have appeared, trying to reduce the amount of work involved in writing such algorithms from scratch, by offering
   standard engines, strategies and tools. Unfortunately, most of these libraries are quite complex to use, and imply a deep knowledge of object programming and C++. To
   further reduce the amount of work needed to implement a new algorithm, without however throwing down the drain all the man-years already spent in the development of such
   libraries, we have designed EASEA (acronym for EAsy Specification of Evolutionary Algorithms): a new high-level language dedicated to the specification of evolutionary
   algorithms. EASEA compiles .ez files into C++ object files, containing function calls to a chosen existing library. The resulting C++ file is in turn compiled and linked
   with the library to produce an executable file implementing the evolutionary algorithm specified in the original .ez file.
%8 September  16-20
%Z online (http://minimum.inria.fr/evo-lab/Publications/PPSNVI.ps.gz) not identical format to published

%A Pierre Collet
%A Evelyne Lutton
%A Frederic Raynal
%A Marc Schoenauer
%T Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
%J Genetic Programming and Evolvable Machines
%V 1
%N 4
%D 2000
%P 339--361
%I 
%K genetic algorithms, genetic programming, fractals, Iterated Functions System, inverse problem for IFS, polar IFS
%U http://citeseer.ist.psu.edu/374242.html
%X This paper proposes a new method for treating the inverse problem for Iterated Functions Systems (IFS) using Genetic Programming. This method is based on two original
   aspects. On the fractal side, a new representation of the IFS functions, termed Polar Iterated Functions Systems, is designed, shrinking the search space to mostly
   contractive functions. Moreover, the Polar representation gives direct access to the fixed points of the functions. On the evolutionary side, a new variant of GP, the
   "Parisian" approach is presented. The paper explains its similarity to the "Michigan" approach of Classifier Systems: each individual of the population only represents a
   part of the global solution. The solution to the inverse problem for IFS is then built from a set of individuals. A local contribution to the global fitness of an IFS is
   carefully defined for each one of its member functions and plays a major role in the fitness of each individual. It is argued here that both proposals result in a large
   improvement in the algorithms. We observe a drastic cut-down on CPU-time, obtaining good results with small populations in few generations.
%8 October
%Z Article ID: 273811

%A Pierre Collet
%A Marc Schoenauer
%A Evelyne Lutton
%A Jean Louchet
%T EASEA : un langage de specification pour les algorithmes evolutionnaires
%R Technical Report RR4218
%D 2001
%I 
%I INRIA
%C Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le Chesnay Cedex France
%K genetic algorithms, genetic programming, EASEA, Java
%U ftp://ftp.inria.fr/INRIA/publication/publi-pdf/RR/RR-4218.pdf
%X Evolutionary algorithms are not straightforward to implement and the lack of any specialised language forces users to write their algorithms in C, C++ or JAVA. However,
   most evolutionary algorithms follow a similar design, and the only really specific part is the code representing the problem to be solved. Therefore, it seems that nothing,
   in theory, could prevent a user from being able to design and run his evolutionary algorithm from a Graphic User Interface, without any other programming effort than the
   function to be optimised. Writing such a GUI rapidly poses the problem of saving and reloading the evolutionary algorithm on which the user is working, and translating the
   information into compilable code. This very much sounds like a specifying language and its compiler. The EASEA software was created on this purpose, and to our knowledge,
   it is the first and only usable compiler of a language specific to evolutionary algorithms. This reprot describes how EASEA has been designed and the problems which needed
   to be solved to achieve its implementation.
%8 June
%Z in english

%A Pierre Collet
%A Jean Louchet
%A Evelyne Lutton
%T Issues on the Optimisation of Evolutionary Algorithms Code
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 1103--1108
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Pierre Collet
%A Marc Schoenauer
%T GUIDE: Unifying Evolutionary Engines through a Graphical User Interface
%B Evolution Artificielle, 6th International Conference
%S Lecture Notes in Computer Science
%E Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer
%V 2936
%D 2003
%P 203--215
%I Springer
%C Marseilles, France
%K genetic algorithms, genetic programming, Artificial Evolution
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2936&spage=229
%X Many kinds of Evolutionary Algorithms (EAs) have been described in the literature since the last 30 years. However, though most of them share a common structure, no
   existing software package allows the user to actually shift from one model to another by simply changing a few parameters, e.g. in a single window of a Graphical User
   Interface. GUIDE, a graphical user interface for DREAM experiments that, among other user-friendly features, unifies all kinds of EAs into a single panel, as far as
   evolution parameters are concerned. Such a window can be used either to ask for one of the well known ready-to-use algorithms, or to very easily explore new combinations
   that have not yet been studied. Another advantage of grouping all necessary elements to describe virtually all kinds of EAs is that it creates a fantastic pedagogic tool to
   teach EAs to students and newcomers to the field.
%O Revised Selected Papers
%8 27-30  October
%Z EA'03 general tool not specifically for GP
%@ 3-540-21523-9

%T Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=issue&issn=0302-9743&volume=3905
%8 10 - 12  April
%Z EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Pierre Collet
%T Genetic Programming
%B Handbook of Research on Nature-Inspired Computing for Economics and Management
%E Jean-Philippe Rennard
%V I
%D 2007
%P 59--73
%I Idea Group Inc.
%C 1200 E. Colton Ave
%K genetic algorithms, genetic programming, GP-std/same, homologous crossover, interval arithmetic, problem dependence, over fitting and bloat
%X GP is now mature and can routinely yeild results on par with or better than human intelligence
%O V
%@ 1-59140-984-5

%A Pierre Collet
%T Husbands, Holland, and Wheeler (eds): Review of the book "The Mechanical Mind in History" MIT Press, 2008, ISBN 978-0-262-08377-5
%J Genetic Programming and Evolvable Machines
%V 10
%N 1
%D 2009
%P 91--93
%I 
%K genetic algorithms, genetic programming
%8 March
%Z Book Review

%A Pierre Collet
%A Man Leung Wong
%T Evolutionary algorithms for data mining
%J Genetic Programming and Evolvable Machines
%V 13
%N 1
%D 2012
%P 69--70
%I 
%K genetic algorithms, genetic programming
%O Editorial Introduction to Special Section on Evolutionary Algorithms for Data Mining
%8 March

%A J. J. Collins
%T Modeling the Behaviour of Interacting Autonomous Intelligent Agents
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://www.csis.ul.ie/staff/jjcollins/gp98.html
%8 22-25  July
%Z GP-98LB, GP-98PhD Student Workshop

%A J. J. Collins
%A Lucia Sheehan
%A Conor Casey
%T Genetic Planner for a Mobile Robot Navigation System
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 782
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-399.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A J. J. Collins
%A Conor Ryan
%T Non-stationary Function Optimization using Polygenic Inheritance
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 781
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-398.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A M. Collins
%T Counting Solutions in Reduced Boolean Parity
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WNUE001.pdf
%8 26-30  June
%Z GECCO-2004WKS Distributed on CD-ROM at GECCO-2004 See also \citeCollins:2006:GPEM

%A M. Collins
%T Finding needles in haystacks is harder with neutrality
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1613--1618
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Cartesian genetic programming, reduced Boolean parity, search space
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1613.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052 See also \citeCollins:2006:GPEM
%@ 1-59593-010-8

%A Mark Collins
%T Finding needles in haystacks is harder with neutrality
%J Genetic Programming and Evolvable Machines
%V 7
%N 2
%D 2006
%P 131--144
%I 
%K genetic algorithms, genetic programming, Cartesian genetic programming, random sampling, solution density
%X an extended analysis of the reported successes of the Cartesian Genetic Programming method on a simplified form of the Boolean parity problem. We show the method of
   sampling used by the CGP is significantly less effective at locating solutions than the solution density of the corresponding formula space would warrant. We present
   results indicating that the loss of performance is caused by the sampling bias of the CGP, due to the neutrality friendly representation. We implement a simple intron free
   random sampling algorithm which performs considerably better on the same problem and then explain how such performance is possible.
%O Special Issue: Best of GECCO 2005
%8 August
%Z Reduced parity=given XOR and EQ only.

%A Mark Collins
%T An Algorithm for Evolving Protocol Constraints
%R Ph.D. Thesis
%D 2006
%I 
%I Artificial Intelligence Applications Institute, School of Informatics, University of Edinburgh
%K genetic algorithms
%U http://www.cisa.informatics.ed.ac.uk/ssp/pubs/collins_phd.pdf
%X We present an investigation into the design of an evolutionary mechanism for multiagent protocol constraint optimisation. Starting with a review of common population based
   mechanisms we discuss the properties of the mechanisms used by these search methods. We derive a novel algorithm for optimisation of vectors of real numbers and empirically
   validate the efficacy of the design by comparing against well known results from the literature. We discuss the application of an optimiser to a novel problem and remark
   upon the relevance of the no free lunch theorem. We show the relative performance of the optimiser is strong and publish details of a new best result for the Keane
   optimisation problem. We apply the final algorithm to the multi-agent protocol optimisation problem and show the design process was successful.

%A Trevor D. Collins
%T A Comparison of Search Space Visualization Techniques
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 780
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-395.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A J. Manuel Colmenar
%A Jose L. Risco-Martin
%A David Atienza
%A Oscar Garnica
%A J. Ignacio Hidalgo
%A Juan Lanchares
%T Improving reliability of embedded systems through dynamic memory manager optimization using grammatical evolution
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 1227--1234
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, grammatical evolution, SBSE
%X Technology scaling has offered advantages to embedded systems, such as increased performance, more available memory and reduced energy consumption. However, scaling also
   brings a number of problems like reliability degradation mechanisms. The intensive activity of devices and high operating temperatures are key factors for reliability
   degradation in latest technology nodes. Focusing on embedded systems, the memory is prone to suffer reliability problems due to the intensive use of dynamic memory on
   wireless and multimedia applications. In this work we present a new approach to automatically design dynamic memory managers considering reliability, and improving
   performance, memory footprint and energy consumption. Our approach, based on Grammatical Evolution, obtains a maximum improvement of 39percent in execution time, 38percent
   in memory usage and 50percent in energy consumption over state-of-the-art dynamic memory managers for several real-life applications. In addition, the resulting
   distributions of memory accesses improve reliability. To the best of our knowledge, this is the first proposal for automatic dynamic memory manager design that considers
   reliability. Categories and Subject
%8 7-11  July
%Z evolves garbage collector DMM, C++, p1230 six line BNF grammar given. Reliability fall assumed from increased temperature due to concentrated memory usage. Fitness =
   weighted sum of run time, bytes and energy used. GEVA applied offline to multi gigabyte profiling logs from VDrift and Physiscs3D. \citesDBLP:journals/todaes/AtienzaMMSC06
   Also known as \cite1830705 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A J. Manuel Colmenar
%A Jose L. Risco-Martin
%A David Atienza
%A J. Ignacio Hidalgo
%T Multi-objective optimization of dynamic memory managers using grammatical evolution
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1819--1826
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution, SBSE, Real world applications
%X The dynamic memory manager (DMM) is a key element whose customization for a target application reports great benefits in terms of execution time, memory usage and energy
   consumption. Previous works presented algorithms to automatically obtain custom DMMs for a given application. Nevertheless, those approaches are based on grammatical
   evolution where the fitness is built as an aggregate objective function, which does not completely exploit the search space, returning the designer the DMM solution with
   best fitness. However, this approach may not find solutions that could fit in a concrete hardware platform due to a very low value of one of the objectives while the others
   remain high, which may represent a high fitness. In this work we present the first multi-objective optimisation methodology applied to DMM optimisation where the Pareto
   dominance is considered, thus providing the designer with a set of non-dominated DMM implementations on each optimisation run. Our results show that the multi-objective
   optimisation provides Pareto-optimal alternatives due to a better exploitation of the search space obtaining better hypervolume values than the aggregate objective function
   approach.
%8 12-16  July
%Z Garbage collector. Energy consumption. NSGA-2. Pop=40. Also known as \cite2001820 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms
   (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)

%A Simon Colton
%A Cameron Browne
%T Evolving Simple Art-based Games
%B Applications of Evolutionary Computing, EvoWorkshops2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoPhD, EvoSTOC,
   EvoTRANSLOG
%S LNCS
%E Mario Giacobini and Ivanoe De Falco and Marc Ebner
%D 2009
%I Springer Verlag
%C Tubingen, Germany
%K genetic algorithms, genetic programming
%X Evolutionary art has a long and distinguished history, and genetic programming is one of only a handful of AI techniques which is used in graphic design and the visual
   arts. A recent trend in so-called 'new media' art is to design online pieces which are dynamic and have an element of interaction and sometimes simple game-playing aspects.
   This defines the challenge addressed here: to automatically evolve dynamic, interactive art pieces with game elements. We do this by extending the Avera user-driven
   evolutionary art system to produce programs which generate spirograph-style images by repeatedly placing, scaling, rotating and colouring geometric objects such as squares
   and circles. Such images are produced in an inherently causal way which provides the dynamic element to the pieces.We further extend the system to produce programs which
   react to mouse clicks, and to evolve sequential patterns of clicks for the user to uncover. We wrap the programs in a simple front end which provides the user with feedback
   on how close they are to uncovering the pattern, adding a lightweight gameplaying element to the pieces. The evolved interactive artworks are a preliminary step in the
   creation of more sophisticated multimedia pieces.
%8 15-17  April
%Z EvoWorkshops2009

%A F. Comellas
%A G. Gim{\'e}nez
%T Genetic Programming to Design Communication Algorithms for Parallel Architectures
%J Parallel Processing Letters
%V 8
%N 4
%D 1998
%P 549--560
%I 
%K genetic algorithms, genetic programming, broadcasting, networks, butterfly graph
%U http://www-mat.upc.es/~comellas/genprog/genprog.html", acknowledgement = ack-nhfb
%X Broadcasting is an information dissemination problem in which a message originating at one node of a communication network (modeled as a graph) is to be sent to all other
   nodes as quickly as possible. This paper describes a new way of producing broadcasting schemes using genetic programming. This technique has proven successful by easily
   finding optimal algorithms for several well-known families of networks (grids, hypercubes and cycle connected cubes) and has indeed generated a new scheme for butterflies
   that improves the known upper bound for the broadcasting time of these networks.
%Z GPQUICK. Tried on 4 problems (5x5 directed grid, torroidal, hypercube, cube connected cycles) finds known optima. "5.5 Butterfly graph For these graphs no optimal
   broadcasting algorithm is known... we improve the upper bound to BF_k \le 2k-2" for k=7,8...16

%A Francesc Comellas
%A Cristina Dalf\'o
%T Using Genetic Programming to Design Broadcasting Algorithms for Manhattan Street Networks
%B Applications of Evolutionary Computing, EvoWorkshops2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC
%S LNCS
%E Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori
   and Franz Rothlauf and George D. Smith and Giovanni Squillero
%V 3005
%D 2004
%P 170--177
%I Springer Verlag Berlin
%C Coimbra, Portugal
%K genetic algorithms, genetic programming, evolutionary computation
%X Broadcasting is the process of disseminating a message from a node of a communication network to all other nodes as quickly as possible. We consider Manhattan Street
   Networks (MSNs) which are mesh-structured, toroidal, directed, regular networks such that locally they resemble the geographical topology of the avenues and streets of
   Manhattan. With the use of genetic programming we have generated broadcasting algorithms for 2-dimensional and 3-dimensional MSNs.
%8 5-7  April
%Z EvoWorkshops2004
%@ 3-540-21378-3

%A Pascal Comte
%T Design \& Implementation of Parallel Linear GP for the IBM Cell Processor
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X We present two different single-core parallel SIMD linear genetic programming (LGP) systems for the IBM Cell Processor on the Playstation3. Our algorithms harness their
   computational power from the parallel capabilities of the Cell Processor. We implement two evolutionary algorithms and look at the classical problem of symbolic regression
   of functions. The first LGP generates a single offspring and selection from the population occurs randomly. The second algorithm generates two offspring and selection from
   the population is performed using k-tournament with k = 2. Mutation occurs at macro and micro levels. Both SIMD instructions and register operands are subject to mutation.
   We use a static population of 648 individuals due to memory and data transfer restrictions and, experiments are constrained to 300 seconds of computational time. Our
   results indicate that both EAs perform equally well though the first algorithm is faster and outperforms the 2nd algorithm in some cases. We speculate that the speed at
   which generations are iterated through is significantly greater than that of a typical tree-based GP and sequential linear GP.
%8 8-12  July
%Z Also known as \cite1596274. Omitted when CD was pressed. CIGPU-2009 GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009)
   and the fourteenth annual genetic programming conference (GP-2009).

%A Pascal Comte
%T Design \& Implementation of Real-time Parallel GA Operators on the IBM Cell Processor
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms
%X We present a set of single-core designed parallel SIMD Genetic Algorithm (GA) operators aimed at increasing computational speed of genetic algorithms. We use a
   discrete-valued chromosome representation. The explored operators include: single gene mutation, uniform crossover and a fitness evaluation function. We discuss their
   low-level hardware implementations on the Cell Processor. We use the Knapsack problem as a proof of concept, demonstrating performances of our operators. We measure the
   scalability in terms of generations per second. Using the architecture of the Cell Processor and a static population size of 648 individuals, we achieved 11.6 million
   generations per second on one Synergetic Processing Element (SPE) core for a problem size n = 8 and 9.5 million generations per second for a problem size n = 16. Generality
   for a problem size n multiple of 8 is also shown. Executing six independent concurrent GA runs, one per SPE core, allows for a rough overall estimate of 70 million
   generations per second and 57 million generations per second for problem sizes of n = 8 and n = 16 respectively.
%8 8-12  July
%Z Also known as \cite1596275. Omitted when CD was pressed. CIGPU-2009 GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009)
   and the fourteenth annual genetic programming conference (GP-2009).

%A John Cona
%T Developing a Genetic Programming System
%J AI Expert
%D 1995
%P 20--29
%I 
%K genetic algorithms, genetic programming, C++, Object Orientated
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ga95aCona_1995_dGPs.pdf
%X We can use an object-oriented C++ approach to develop gentic base classes. Discusses practical speed/memory tradeoffs for an (IBM) PC environment.
%8 February
%Z BLDSC shelfmark 0772.341000, UK Floor 6-1 'Exciting prospects of language and communication', 'memory', notes on more recent features of C++. Refers to Scott A. Kennedy, AI
   Expert, Five ways to a smarter genetic algorithm, AI Expert 8(12):35-38, December 1993.

%A Piero Conca
%A Giuseppe Nicosia
%A Giovanni Stracquadanio
%A Jon Timmis
%T Nominal-Yield-Area Tradeoff in Automatic Synthesis of Analog Circuits: A Genetic Programming Approach using Immune-Inspired Operators
%B NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2009)
%E Tughrul Arslan and Didier Keymeulen
%D 2009
%P 399--406
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, AIS, ElP, Pareto Front, analog circuit automatic synthesis, analog circuit design, circuit reliability, elitist immune programming,
   evolutionary algorithm, frequency response, genetic programming approach, immune-inspired operators, industrial components series, low-pass filter synthesis,
   nominal-yield-area tradeoff, Pareto optimisation, analogue circuits, circuit CAD, circuit reliability, frequency response, low-pass filters
%X The synthesis of analog circuits is a complex and expensive task; whilst there are various approaches for the synthesis of digital circuits, analog design is intrinsically
   more difficult since analog circuits process voltages in a continuous range. In the field of analog circuit design, the genetic programming approach has received great
   attention, affording the possibility to design and optimize a circuit at the same time. However, these algorithms have limited industrial relevance, since they work with
   ideal components. Starting from the well known results of Koza and co-authors, we introduce a new evolutionary algorithm, called elitist Immune Programming (EIP), that is
   able to synthesize an analog circuit using industrial components series in order to produce reliable and low cost circuits. The algorithm has been used for the synthesis of
   low-pass filters; the results were compared with the genetic programming, and the analysis shows that EIP is able to design better circuits in terms of frequency response
   and number of components. In addition we conduct a complete yield analysis of the discovered circuits, and discover that EIP circuits attain a higher yield than the
   circuits generated via a genetic programming approach, and, in particular, the algorithm discovers a Pareto Front which respects nominal performance (sizing), number of
   components (area) and yield (robustness).
%8 July  29-  August  1
%Z Co-located with Design Automation Conference (DAC-2009) http://www.see.ed.ac.uk/~ahs2009/ Also known as \cite5325428

%A Clare Bates Congdon
%A Emily F. Greenfest
%T Gaphyl: A genetic algorithm approach to cladistics
%B Data Mining with Evolutionary Algorithms
%E Alex A. Freitas and William Hart and Natalio Krasnogor and Jim Smith
%D 2000
%P 85--88
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/426598.html
%X his research investigates the use of genetic algorithms (GA's) to solve problems from cladistics --- a technique used by biologists to hypothesise the evolutionary
   relationships between organisms. Since exhaustive search is not practical in this domain, typical cladistics software packages use heuristic search methods to navigate
   through the space of possible trees in an attempt to find one or more "best" solutions. We have developed a system called GAphyl, which uses the GA...
%8 8  July
%Z GECCO-2000WKS Part of \citewu:2000:GECCOWKS

%A Congdon
%A Septor
%T Phylogenetic trees using evolutionary search: Initial progress in extending gaphyl to work with genetic data
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 320--326
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%X Gaphyl is an application of evolutionary algorithms to phylogenetics, an approach used by biologists to investigate evolutionary relationships among organisms. For datasets
   larger than 20-30 species, exhaustive search is not practical in this domain. Gaphyl uses an evolutionary search mechanism to search the space of possible phylogenetic
   trees, in an attempt to find the most plausible evolutionary hypotheses, while typical phylogenetic software packages use heuristic search methods. In previous work, Gaphyl
   has been shown to be a promising approach for searching for phylogentic trees using data with binary attributes and Wagner parsimony to evaluate the trees. In the work
   reported here, Gaphyl is extended to work with genetic data. Initial results with this extension further suggest that evolutionary search is a promising approach for
   phylogenetic work.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Brian Connolly
%T Genetic Algorithms Survival of the Fittest: Natural Selection with Windows Forms
%J MSDN Magazine
%V 19
%N 8
%D 2004
%I Microsoft
%K genetic algorithms, genetic programming
%U http://msdn.microsoft.com/msdnmag/issues/04/08/GeneticAlgorithms/default.aspx
%X This article discusses: * Genetic programming definition * Breeding new algorithm generations * Cross breeding * Mutations * Increasing fitness
%8 August
%Z Santa Fe ant trail. .net Reflection CodeDOM

%A Michael Conrad
%T The Price of Programmability
%B The Universal Turing Machine A Half-Century Survey
%E Rolf Herken
%D 1988
%P 285--307
%I Oxford University Press
%K genetic algorithms, genetic programming, cellular automata, evolvable hardware, quantum computing, DNA and molecular computing
%Z Essex Library Classmark Q 312 Evolution of programs. p287-288 "A real system is (effectively) programmable"...if "the user's manual" is "finite". "as programs become large
   it is inevitable that they will in some measure be incorrect (see Avizienis 1983)". p294 stuff about evolution which seems to have wrong mutation rates and ignore the
   possibility of neutral mutations or gene duplications. p296 fitness landscapes referred to as "adaptive surface". Redundancy..."opens up extradimensional bypasses to higher
   adaptive peaks (Conrad 1979). p303-304 "Quantum mechanical tunnelling...and electron diffusion...(Biological macromolecules, eg DNA) excellent type of dynamics for
   (computing) modules in an evolutionary architecture". p304 "Evolutionary programming". p304 "substrate is of such immense importance". p305 "Human intelligence (brain)...we
   cannot understand them in terms of a computer program and at the same time put our understanding to the test by running the program on a machine.
%@ 0-19-853741-7

%A Markus Conrads
%A Peter Nordin
%A Wolfgang Banzhaf
%T Speech Sound Discrimination With Genetic Programming
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 113--129
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%X The question that we investigate in this paper is, whether it is possible for Genetic Programming to extract certain regularities from raw time series data of human speech.
   We examine whether a genetic programming algorithm can find programs that are able to discriminate certain spoken vowels and consonan ts. We present evidence that this can
   indeed be achieved with a surprisingly simple approach that does not need preprocessing. The data we have collec ted on the system's behavior show that even
   speaker-independent discriminatio n is possible with GP.
%8 14-15  April
%Z EuroGP'98
%@ 3-540-64360-5

%A Henry Cook
%A Kevin Skadron
%T Predictive design space exploration using genetically programmed response surfaces
%B 45th ACM/IEEE Design Automation Conference, DAC 2008
%D 2008
%P 960--965
%I 
%K genetic algorithms, genetic programming, genetically programmed response surfaces, microarchitectural design space exploration, optimization process, predictive design
   space exploration, aircraft computers, computer architecture
%U http://www.cs.virginia.edu/~skadron/Papers/gprs_dac08.pdf
%X Exponential increases in architectural design complexity threaten to make traditional processor design optimization techniques intractable. Genetically programmed response
   surfaces (GPRS) address this challenge by transforming the optimization process from a lengthy series of detailed simulations into the tractable formulation and rapid
   evaluation of a predictive model. We validate GPRS methodology on realistic processor design spaces and compare it to recently proposed techniques for predictive
   microarchitectural design space exploration.
%8 June
%Z Also known as \cite4555958 \cite1391711

%A Michael Cook
%A Simon Colton
%T Multi-Faceted Evolution Of Simple Arcade Games
%B Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games
%D 2011
%P 289--296
%I IEEE
%C Seoul, South Korea
%K genetic algorithms
%U http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper64.pdf
%X We present a system for generating complete game designs by evolving rulesets, character layouts and terrain maps in an orchestrated way. In contrast to existing approaches
   to generate such game components in isolation, our ANGELINA system develops game components in unison with an appreciation for their interrelatedness. We describe this
   multi-faceted evolutionary approach, and give some results from a first round of experimentation.Y
%8 31  August  - 3  September
%Z fixed representation

%A Brett W. Coon
%T Circuit Synthesis through Genetic Programming
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 11--20
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 Decemeber
%Z This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html GP used to synthesis simple logic circuits.
   Able to simplify them. On some problems able to do as well as commercial tool "Synopsys".
%@ 0-18-187263-3

%A Jason Cooper
%A Chris Hinde
%T Comparison Of Evolving Against Peers And Fixed Opponents Using Corewars
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 887
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, poster paper, Corewars
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Luigi Pietro Cordella
%A Claudio {De Stefano}
%A Francesco Fontanella
%A Angelo Marcelli
%T EvoGeneS, a New Evolutionary Approach to Graph Generation
%B Evolutionary Computation in Combinatorial Optimization -- EvoCOP~2005
%S LNCS
%E G\"unther R. Raidl and Jens Gottlieb
%V 3448
%D 2005
%P 46--57
%I Springer Verlag Berlin
%C Lausanne, Switzerland
%K evolutionary computation
%X Graphs are powerful and versatile data structures, useful to represent complex and structured information of interest in various fields of science and engineering. We
   present a system, called EvoGeneS, based on an evolutionary approach, for generating undirected graphs whose number of nodes is not a priori known. The method is based on a
   special data structure, called multilist, which encodes undirected attributed relational graphs. Two novel crossover and mutation operators are defined in order to evolve
   such structure. The developed system has been tested on a wireless network configuration and the results compared with those obtained by a genetic programming based
   approach recently proposed in the literature.
%8 30  March -1  April
%Z EvoCOP2005 Claims to be significantly better than \citehu:2004:wapcbgp

%A L. P. Cordella
%A C. {De Stefano}
%A F. Fontanella
%A A. Marcelli
%T Genetic Programming for Generating Prototypes in Classification Problems
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 2
%D 2005
%P 1149--1155
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X We propose a genetic programming based approach for generating prototypes in a classification problem. In this context, the set of prototypes to which the samples of a data
   set can be traced back is coded by a multitree, i.e. a set of trees, which represents the chromosome. Differently from other approaches, our chromosomes are of variable
   length. This allows coping with those classification problems in which one or more classes consist of subclasses. The devised approach has been tested on several problems
   and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Luigi P. Cordella
%A Claudio {De Stefano}
%A Francesco Fontanella
%A Angelo Marcelli
%T A Novel Genetic Programming Based Approach for Classification Problems
%B Proceedings 13th International Conference Image Analysis and Processing - ICIAP 2005
%S Lecture Notes in Computer Science
%E Fabio Roli and Sergio Vitulano
%V 3617
%D 2005
%P 727--734
%I Springer
%C Cagliari, Italy
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3617&spage=727
%X A new genetic programming based approach to classification problems is proposed. Differently from other approaches, the number of prototypes in the classifier is not a
   priori fixed, but automatically found by the system. In fact, in many problems a single class may contain a variable number of subclasses. Hence, a single prototype, may be
   inadequate to represent all the members of the class. The devised approach has been tested on several problems and the results compared with those obtained by a different
   genetic programming based approach recently proposed in the literature.
%8 September  6-8
%@ 3-540-28869-4

%A Luigi P. Cordella
%A Claudio {De Stefano}
%A Francesco Fontanella
%A Angelo Marcelli
%T Looking for Prototypes by Genetic Programming
%B Advances in Machine Vision, Image Processing, and Pattern Analysis, International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, IWICPAS 2006, Proceedings
%S Lecture Notes in Computer Science
%E Nanning Zheng and Xiaoyi Jiang and Xuguang Lan
%V 4153
%D 2006
%P 152--159
%I Springer
%C Xi'an, China
%K genetic algorithms, genetic programming
%X In this paper we propose a new genetic programming based approach for prototype generation in Pattern Recognition problems. Prototypes consist of mathematical expressions
   and are encoded as derivation trees. The devised system is able to cope with classification problems in which the number of prototypes is not a priori known. The approach
   has been tested on several problems and the results compared with those obtained by other genetic programming based approaches previously proposed.
%8 August  26-27
%@ 3-540-37597-X

%A Oscar Cordon
%A Francisco Herrera
%A Luciano Sanchez
%T Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques
%J Applied Intelligence
%V 10
%N 1
%D 1999
%P 5--24
%I 
%K genetic algorithms, genetic programming, electrical engineering, data analysis, evolutionary algorithms, genetic algorithm program, genetic fuzzy rule-based systems
%U ftp://decsai.ugr.es/pub/arai/tech_rep/ga-fl/tr-98106.ps.Z
%8 January
%Z Two modeling problems from the Spanish electrical system are solved. In each a comparison of statistical regression, GA-P, genetic fuzzy rule based and artificial neural
   networks is made. Uses modification of \citehoward:1995:GA-P tr-98106.ps.Z PScript preliminary version

%A Oscar Cordon
%A Felix {de Moya Anegon}
%A Carmen Zarco
%T Learning Queries for a Fuzzy Information Retrieval System by means of GA-P Techniques
%B Proceedings of the EUSFLAT-ESTYLF Joint Conference
%E Gaspar Mayor and Jaume Su\~ner
%D 1999
%P 335--338
%I Universitat de les Illes Balears Palma de Mallorca, Spain
%I European Society for Fuzzy Logica and Technology
%C Palma de Mallorca, Spain
%K genetic algorithms, genetic programming
%U http://www.eusflat.org/publications/proceedings/EUSFLAT-ESTYLF_1999/papers/335-cordon.pdf
%8 September  22-25
%Z http://www.eusflat.org/publications/proceedings/EUSFLAT-ESTYLF_1999/

%A O. Cordon
%A F. {de Moya}
%A C. Zarco
%T A GA-P Algorithm to Automatically Formulate Extended Boolean Queries for a Fuzzy Information Retrieval System
%J Mathware \& Soft Computing
%V 7
%N 2-3
%D 2000
%P 309--322
%I 
%I European Society for Fuzzy Logic and Technology (EUSFLAT)
%K genetic algorithms, genetic programming
%U http://ic.ugr.es/Mathware/index.php/Mathware/article/viewFile/145/124
%X Although the fuzzy retrieval model constitutes a powerful extension of the boolean one, being able to deal with the imprecision and subjectivity existing in the Information
   Retrieval process, users are not usually able to express their query requirements in the form of an extended boolean query including weights. To solve this problem,
   different tools to assist the user in the query formulation have been proposed. In this paper, the genetic algorithm-programming technique is considered to build an
   algorithm of this kind that will be able to automatically learn weighted queries -modeling the user's needs- for a fuzzy information retrieval system by applying an
   off-line adaptive process starting from a set of relevant documents.
%Z http://ic.ugr.es/Mathware/index.php/Mathware

%A O. Cordon
%A E. Herrera-Viedma
%A Maria Luque
%A Felix Moya
%A Carmen Zarco
%T An Inductive Query by Example Technique for Extended Boolean Queries Based on Simulated-Annealing Programming
%B Challenges in Knowledge Representation and Organization for the 21st Century. Integration of Knowledge across Boundaries. Proceedings of the 7th International ISKO
   Conference (ISKO'2002)
%S Advances in knowledge organization
%E M. J. Lopez-Huertas
%V 8
%D 2002
%P 429--436
%I Ergon Wuerzburg, Germany
%C Granada, Spain
%K genetic algorithms, genetic programming
%U http://www.ergon-verlag.de/en/start.htm?information-_library_sciences_advances_in_knowledge_organization.htm
%8 July  10-13
%Z http://www.isko.org/events.html

%A O. Cordon
%A F. Moya
%A C. Zarco
%T A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems
%J Soft Computing - A Fusion of Foundations, Methodologies and Applications
%V 6
%N 5
%D 2002
%P 308--319
%I 
%K genetic algorithms, genetic programming, Fuzzy information retrieval, Relevance feedback, Evolutionary algorithms, Simulated annealing
%X Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the
   world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved
   documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval
   process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them
   have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In
   this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining
   simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this
   task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how
   the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.
%8 August

%A Oscar Cordon
%A Enrique Herrera-Viedma
%A Maria Luque
%T Evolutionary Learning of Boolean Queries by Multiobjective Genetic Programming
%B Parallel Problem Solving from Nature - PPSN VII
%S Lecture Notes in Computer Science, LNCS
%E Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel
%N 2439
%D 2002
%P 710--719
%I Springer-Verlag
%C Granada, Spain
%K genetic algorithms, genetic programming, MOGA, Pattern recognition and classification/datamining,Web services, Multi-objective
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=710
%X The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimisation of any of its
   components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of
   these applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme. In this paper, we will tackle with a usual
   information retrieval problem, the automatic derivation of Boolean queries, by incorporating a well known Pareto-based multiobjective evolutionary approach, MOGA, into a
   previous proposal of a genetic programming technique for this task.
%O Available from http://link.springer.de/link/service/series/0558/papers/2439/243900710.pdf
%8 7-11  September
%@ 3-540-44139-5

%A Oscar Cordon
%A Enrique Herrera-Viedma
%A Maria Luque
%A Felix {de Moya Anegon}
%A Carmen Zarco
%T Analyzing the Performance of a Multiobjective GA-P Algorithm for Learning Fuzzy Queries in a Machine Learning Environment
%B Proceedings of the 10th International Fuzzy Systems Association World Congress, Fuzzy Sets and Systems - IFSA 2003
%S Lecture Notes in Computer Science
%E Taner Bilgi\cc and Bernard De Baets and Okyay Kaynak
%V 2715
%D 2003
%P 611--619
%I Springer
%C Istanbul, Turkey
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article\&issn=0302-9743\&volume=2715\&spage=611
%X The fuzzy information retrieval model was proposed some years ago to solve several limitations of the Boolean model without a need of a complete redesign of the information
   retrieval system. However, the complexity of the fuzzy query language makes it difficult to formulate user queries. Among other proposed approaches to solve this problem,
   we find the Inductive Query by Example (IQBE) framework, where queries are automatically derived from sets of documents provided by the user. In this work we test the
   applicability of a multiobjective evolutionary IQBE technique for fuzzy queries in a machine learning environment. To do so, the Cranfield documentary collection is divided
   into two different document sets, labeled training and test, and the algorithm is run on the former to obtain several queries that are then validated on the latter.
%8 June  30 -  July  2
%@ 3-540-40383-3

%A Oscar Cordon
%A Enrique Herrera-Viedma
%A Maria Luque
%A Felix Moya
%A Carmen Zarco
%T A Realistic Information Retrieval Environment to Validate a Multiobjective GA-P Algorithm for Learning Fuzzy Queries
%B Proceedings if the 8th Online World Conference on Soft Computing in Industrial Applications (WSC8)
%S Advances in Soft Computing
%V 32
%D 2003
%P 299--309
%I Springer
%K genetic algorithms, genetic programming
%X IQBE has been shown as a promising technique to assist the users in the query formulation process. In this framework, queries are automatically derived from sets of
   documents provided by them. However, the different proposals found in the specialized literature are usually validated in non realistic information retrieval environments.
   In this work, we design several experimental setups to create real-like retrieval environments and validate the applicability of a previously proposed multiobjective
   evolutionary IQBE technique for fuzzy queries on them.
%O published by Springer 2005 as Soft Computing: Methodologies and Applications

%A Oscar Cordon
%A Felix {de Moya}
%A Carmen Zarco
%T Fuzzy logic and multiobjective evolutionary algorithms as soft computing tools for persistent query learning in text retrieval environments
%B Proceedings of the 2004 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004)
%V 1
%D 2004
%P 571--576
%I IEEE Press
%C Budapest, Hungary
%K genetic algorithms, genetic programming, Boolean functions, evolutionary computation, fuzzy logic, knowledge engineering, query processing extended Boolean query structure,
   fuzzy logic, information retrieval systems, multiobjective evolutionary algorithms, persistent query learning, soft computing tools, text retrieval environment
%X Persistent queries are a specific kind of queries used in information retrieval systems to represent a user's long-term standing information need. These queries can present
   many different structures, being the "bag of words" that most commonly used. They can be sometimes formulated by the user, although this task is usually difficult for him
   and the persistent query is then automatically derived from a set of sample documents he provides. In this work we aim at getting persistent queries with a more
   representative structure for text retrieval issues. To do so, we make use of soft computing tools: fuzzy logic is considered for representation and inference purposes by
   dealing with the extended Boolean query structure, and multiobjective evolutionary algorithms are applied to build the persistent fuzzy query. Experimental results show how
   both an expressive fuzzy logic-based query structure and a proper learning process to derive it are needed in order to get a good retrieval efficacy, when comparing our
   process to single-objective evolutionary methods to derive both classic Boolean and extended Boolean queries.
%8 25-29  July
%Z also known as \cite1375799

%A O. Cordon
%A F. Moya
%A C. Zarco
%T Automatic Learning of Multiple Extended Boolean Queries by Multiobjective GA-P Algorithms
%B Fuzzy Logic and the Internet
%S STUDIES IN FUZZINESS AND SOFT COMPUTING
%E V. Loia and M. Nikravesh and L. A. Zadeh
%V 137
%D 2004
%P 47--70
%I PHYSICA-VERLAG
%C Germany
%K genetic algorithms, genetic programming
%U http://direct.bl.uk/research/18/0E/RN143659018.html
%Z English

%A Oscar Cordon
%A Enrique Herrera-Viedma
%A Maria Luque
%A Felix Moya
%A Carmen Zarco
%T A Realistic Information Retrieval Environment to Validate a Multiobjective GA-P Algorithm for Learning Fuzzy Queries
%B Soft Computing: Methodologies and Applications
%S Advances in Soft Computing
%E F. Hoffmann and M. Koppen and F. Klawonn and R. Roy
%V 32
%D 2005
%P 299--309
%I Springer-Verlag
%K genetic algorithms, genetic programming
%X IQBE has been shown as a promising technique to assist the users in the query formulation process. In this framework, queries are automatically derived from sets of
   documents provided by them. However, the different proposals found in the specialized literature are usually validated in non realistic information retrieval environments.
   In this work, we design several experimental setups to create real-like retrieval environments and validate the applicability of a previously proposed multiobjective
   evolutionary IQBE technique for fuzzy queries on them.

%A O. Cordon
%A E. Herrera-Viedma
%A M. Luque
%T Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm
%J Information Processing and Management
%V 42
%N 3
%D 2006
%P 615--632
%I 
%K genetic algorithms, genetic programming, Boolean information retrieval systems, Inductive query by example, Multiobjective evolutionary algorithms, Query learning
%X The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this
   paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information
   Science, 23(6), 423-431 \citeMartinPSmith:1997:JIS] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a
   weighted combination of the basic performance criteria, precision and recall. A desirable aspect of IRSs, especially of those based on the IQBE paradigm, is to be able to
   get more than one query for the same information needs, with high precision arid recall values or with different trade-offs between both. IQBE process is proposed combining
   a previous basic algorithm to automatically derive Boolean queries for Boolean IRSs [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries
   for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] and an advanced evolutionary multiobjective approach [Coello, C. A., Van
   Veldhuizen, D. A., & Lamant, G. B. (2002). Evolutionary algorithms for solving multiobjective problems. Kluwer Academic Publishers], which obtains several queries with a
   different precision recall trade-off in a single run. The performance of the new proposal will be tested on the Cranfield and CACM collections and compared to the
   well-known Smith and Smith's algorithm, showing how it improves the learning of queries and thus it could better assist the user in the query formulation process.
%8 May

%A David Corney
%A Ian Parmee
%T N-Dimensional Surface Mapping Using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1230
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-424.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) MSc Thesis --
   text from http://www.cs.ucl.ac.uk/staff/D.Corney/MSc_thesis_abstract.html N-Dimensional Surface Mapping Using Genetic Programming This work introduces an extension to
   Genetic Programming (GP) known as "GP-UDF" which uses multiple User-Defined Functions (UDFs) to solve surface-mapping problems. These UDFs are high-level primitives, such
   as hills and polynomials, which compress the information required to map a surface. UDFs can be used to add real-world knowledge to a genetic search, and also to analyse
   and classify high-dimensional surfaces. GP-UDF also produces more readable solutions than standard GP. The results show that, for the problems considered, GP-UDF does not
   produce more accurate models than standard GP. However, the results also suggest that GP-UDF could be used as a "landscape classifier", a tool for analysing
   high-dimensional surfaces to identify characteristic features. An important consideration in systems identification is the transparency (i.e. readability), of a model.
   GP-UDF is compared with neural networks (both MLP and RBF networks), and is shown to be far more readable, with the cost of being less accurate.
%@ 1-55860-611-4

%A F. Corno
%A M. {Sonza Reorda}
%A G. Squillero
%T The Selfish Gene Algorithm: a New Evolutionary Optimization Strategy
%B SAC: ACM Symposium on Applied Computing
%D 1998
%P 349--355
%I 
%K Approximate Methods, Evolutionary Algorithms, Selfish Gene
%U http://www.cad.polito.it/pap/db/sac98.pdf
%X This paper proposes a new general approach for optimization algorithms in the Evolutionary Computation field. The approach is inspired by the Selfish Gene theory, an
   interpretation of the Darwinian theory given by the biologist Richard Dawkins, in which the basic element of evolution is the gene, rather than the individual. The paper
   defines the Selfish Gene Algorithm, that implements such a view of the evolution mechanism. We tested the approach by implementing a Selfish Gene Algorithm on a case study,
   and we found better results than those provided by a Genetic Algorithm on the same problem and with the same fitness function.

%A F. Corno
%A M. {Sonza Reorda}
%A G. Squillero
%T VEGA: A Verification Tool Based on Genetic Algorithms
%B ICCD: International Conference on Circuit Design
%D 1998
%P 321--326
%I 
%U http://www.cad.polito.it/pap/db/iccd98a.pdf
%X While modern state-of-the-art optimization techniques can handle designs with up to hundreds of flip-flops, equivalence verification is still a challenging task in many
   industrial design flows. This paper presents a new verification methodology that, while sacrificing exactness, is able to handle larger circuits and give designers the
   opportunity to trade off CPU time with confidence on the result. The proposed methodology is able to fruitfully support an exact verification tool, dramatically increasing
   the confidence on the validity of an optimization process. A prototypical tool has been developed and preliminary experimental results that support this claim are shown in
   the paper.

%A Fulvio Corno
%A Matteo {Sonza Reorda}
%A Giovanni Squillero
%T Automatic Validation of Protocol Interfaces Described in VHDL
%B Real-World Applications of Evolutionary Computing
%S LNCS
%E Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter
   and Terence C. Fogarty
%V 1803
%D 2000
%P 205--213
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, ASIC, Approximate Methods, Evolutionary Algorithms, Gate-Level, Low Power, Selfish Gene, Simulation-Based Approaches
%U http://www.cad.polito.it/FullDB/exact/evotel2000a.html
%X Modern VLSI design methodologies and manufacturing technologies are making circuits increasingly fast. The quest for higher circuit performance and integration density
   stems from fields such as the telecommunication one where high speed and capability of dealing with large data sets is mandatory. The design of high-speed circuits is a
   challenging task, and can be carried out only if designers can exploit suitable CAD tools. Among the several aspects of high-speed circuit design, controlling power
   consumption is today a major issue for ensuring that circuits can operate at full speed without damages. In particular, tools for fast and accurate estimation of power
   consumption of high-speed circuits are required. In this paper we focus on the problem of predicting the maximum power consumption of sequential circuits. We formulate the
   problem as a constrained optimization problem, and solve it resorting to an evolutionary algorithm. Moreover, we empirically assess the effectiveness of our problem
   formulation with respect to the classical unconstrained formulation. Finally, we report experimental results assessing the effectiveness of the prototypical tool we
   implemented.
%8 17  April
%Z EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings
   http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61
%@ 3-540-67353-9

%A F. Corno
%A M. {Sonza Reorda}
%A G. Squillero
%A M. Violante
%T On the test of microprocessor IP cores
%B Proceedings of Design, Automation and Test in Europe Conference and Exhibition 2001
%D 2001
%P 209--213
%I IEEE Press
%C Munich, Germany
%K genetic algorithms, genetic programming
%U http://www.date-conference.com/conference/instructions/gl_paper04c_2.pdf
%X Testing is a crucial issue in SOC development and production process. A popular solution for SOCs that include microprocessor cores is based on making them execute a test
   program. Thus, implementing a very attractive BIST solution. This paper describes a method for the generation of effective programs for the self-test of a processor. The
   method can be partially automated and combines ideas from traditional functional approaches and from the ATPG field. We assess the feasibility and effectiveness of the
   method by applying it to a 8051 core
%8 13-16  March
%Z Posted online: 2002-08-07 00:20:42.0 \citesquillero:2005:GPEM p249 says 'The approach relied on a library of fragments of code carefully and skillfully written by hand,
   called macros. The optimal sequence of macros was heuristically determined, and then a genetic algorithm optimized their parameters. The approach is quite effective, but
   hardly scalable. An Intel i8051, a very simple microprocessor, required 213 macros; and the macro list was carefully compiled by an experienced engineer in two working
   days.'

%A F. Corno
%A G. Cumani
%A M. {Sonza Reorda}
%A G. Squillero
%T Efficient Machine-Code Test-Program Induction
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 1486--1491
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Honolulu, Hawaii, USA
%K genetic algorithms, genetic programming, DAG, ATPG, Approximate Methods, Evolutionary Algorithms, Micro-Processors, Simulation-Based Approaches
%U http://citeseer.ist.psu.edu/502344.html
%X Technology advances allow integrating on a single chip entire system, including memories and peripherals. The test of these devices is becoming a major issue for
   manufacturing industries. This paper presents a methodology for inducing test-programs similar to genetic programming. However, it includes the ability to explicitly
   specify registers and resorts to directed acyclic graphs instead of trees. Moreover, it exploits a database containing the assembly-level semantic associated to each graph
   node. This approach is extremely efficient and versatile: candidate solutions are translated into source-code programs allowing millions of evaluations per second. The
   proposed approach is extremely versatile: the macro library allows easily changing target processor and environment. The approach was verified on three processors with
   different instruction sets, different formalisms and different conventions. A complete set of experiments on a test function are also reported for the SPARC processor.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002) Also known as \citeCorno:2002:EMTI \citesquillero:2005:GPEM says corno:2002:emctpi uses straightforward mu+lambda evolution.
%@ 0-7803-7278-6

%A Fulvio Corno
%A Gianluca Cumani
%A Matteo {Sonza Reorda}
%A Giovanni Squillero
%T Evolutionary test program induction for microprocessor design verification
%B Proceedings of the 11th Asian Test Symposium (ATS '02)
%D 2002
%P 368--373
%I IEEE Press
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/574157.html
%X Design verification is a crucial step in the design of any electronic device. Particularly when microprocessor cores are considered, devising appropriate test cases may be
   a difficult task. This paper presents a methodology able to automatically induce a test program for maximising a given verification metric. The methodology is based on an
   evolutionary paradigm and exploits a syntactical description of microprocessor assembly language and an RT-level functional model. Experimental results show the
   effectiveness of the approach.
%8 18-20  November
%Z Posted online: 2003-02-28 18:15:31.0 Cited by \citesquillero:2005:GPEM

%A F. Corno
%A G. Cumani
%A M. {Sonza Reorda}
%A G. Squillero
%T Automatic Test Program Generation for Pipelined Processors
%B Proceedings of the 2003 ACM Symposium on Applied Computing (SAC)
%D 2003
%I ACM
%C Melbourne, FL, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/573140.html
%X The continuous advances in micro-electronics design are creating a significant challenge to design validation in general, but tackling pipelined microprocessors is
   remarkably more demanding. This paper presents a methodology to automatically induce a test program for a microprocessor maximising a given verification metric. The
   approach exploits a new evolutionary algorithm, close to Genetic Programming, able to cultivate effective assembly language programs. The proposed methodology was used to
   verify the DLX/pII, an open-source processor with a 5-stage pipeline. Code-coverage was adopted in the paper, since it can be considered the required starting point for any
   simulation-based functional verification processes. Experimental results clearly show the effectiveness of the approach.
%O The Pennsylvania State University CiteSeer Archives
%8 9-12  March

%A F. Corno
%A F. Cumani
%A G. Squillero
%T Exploiting Auto-adaptive $\mu$-GP for Highly Effective Test Programs Generation
%B Evolvable Systems: From Biology to Hardware, Fifth International Conference, ICES 2003
%S LNCS
%E Andy M. Tyrrell and Pauline C. Haddow and Jim Torresen
%V 2606
%D 2003
%P 262--273
%I Springer-Verlag
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X Integrated-circuit producers are shoved by competitive pressure; new devices require increasingly complex verifications to be performed at increasing pace. This paper
   presents a methodology to automatically induce a test program for a microprocessor that maximizes a given verification metric. The methodology is based on an auto-adaptive
   evolutionary algorithm and exploits a syntactical description of microprocessor assembly language and an RT-level functional model. Experimental results clearly show the
   effectiveness of the approach. Comparisons reveal how auto-adaptive mechanisms dramatically enhance both performances and quality of the results.
%8 17-20  March
%Z ICES-2003
%@ 3-540-00730-X

%A F. Corno
%A G. Squillero
%T An Enhanced Framework for Microprocessor Test-Program Generation
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 307--316
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=307
%X Test programs are fragment of code, but, unlike ordinary application programs, they are not intended to solve a problem, nor to calculate a function. Instead, they are
   supposed to give information about the machine that actually executes them. Today, the need for effective test programs is increasing, and, due to the inexorable increase
   in the number of transistor that can be integrated onto a single silicon die, devising effective test programs is getting more problematical. This paper presents GP, an
   efficient and versatile approach to test-program generation based on an evolutionary algorithm. The proposed methodology is highly versatile and improves previous
   approaches, allowing the test-program generator generating complex assembly programs that include subroutines calls.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Fulvio Corno
%A Ernesto Sanchez
%A Giovanni Squillero
%T On The Evolution of Corewar Warriors
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 133--138
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Evolutionary Computation and Games
%U http://www.cad.polito.it/pap/db/cec2004b.pdf
%X This paper analyzes corewar, a peculiar computer game where different programs fight in the memory of a virtual computer. An evolutionary assembly-program generator, is
   used to evolve efficient programs, and the game is exploited to evaluate new evolutionary techniques. The paper introduces a new migration model that exploits the
   polarization effect and a new hierarchical coarse-grained approach applicable whenever the final goal can be seen as a combination of semi-independent sub goals.
   Additionally, two general enhancements are proposed. Analyzed techniques are orthogonal and broadly applicable to different real-life contexts. Experimental results show
   that all these techniques are able to outperform a previous approach.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A F. Corno
%A E. Sanchez
%A M. S. Reorda
%A G. Squillero
%T Automatic test generation for verifying microprocessors
%J IEEE Potentials
%V 24
%N 1
%D 2005
%P 34--37
%I 
%K genetic algorithms, genetic programming
%X A pipelined processor with a high-level behavioural HDL description is presented in this paper. It generates a set of effective test programs by using a simulator, which is
   able to evaluate with respect to an RTL coverage metric. The proposed optimiser is based on a technique called microGP, an evolutionary system able to automatically device
   and optimizes the program written in an assembly language. Quantitative coverage measurement presented will guide the test-program generation. The approach is fully
   automatic and broadly applicable. The minimal test set with the programmable coverage is attained.
%8 February -  March

%A Steven M. Corns
%A Daniel A. Ashlock
%A Douglas S. McCorkle
%A Kenneth Mark Bryden
%T Improving Design Diversity Using Graph Based Evolutionary Algorithms
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 1037--1043
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X Graph based evolutionary algorithms (GBEAs) have been shown to have superior performance to evolutionary algorithms on a variety of evolutionary computation test problems
   as well as on some engineering applications. One of the motivations for creating GBEAs was to produce a diversity of solutions with little additional computational cost.
   This paper tests that feature of GBEAs on three problems: a real-valued multi-modal function of varying dimension, the plus-one-recall-store (PORS) problem, and an applied
   engineering design problem. For all of the graphs studied the number of different solutions increased as the connectivity of the graph underlying the algorithm decreased.
   This indicates that the choice of graph can be used to control the diversity of solutions produced. The availability of multiple solutions is an asset in a product
   realization system, making it possible for an engineer to explore design alternatives.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D. IEEE Xplore gives pages = "333--339"
%@ 0-7803-9487-9

%A Eduardo Oliveira Costa
%A Silvia Regina Vergilio
%A Aurora Trinidad Ramirez Pozo
%A Gustavo A. {de Souza}
%T Modeling Software Reliability Growth with Genetic Programming
%B 16th International Symposium on Software Reliability Engineering (ISSRE 2005)
%D 2005
%P 171--180
%I IEEE Computer Society
%C Chicago, IL, USA
%K genetic algorithms, genetic programming
%X Reliability Models are very useful to estimate the probability of the software fail along the time. Several different models have been proposed to estimate the reliability
   growth, however, none of them has proven to perform well considering different project characteristics. In this work, we explore Genetic Programming (GP) as an alternative
   approach to derive these models. GP is a powerful machine learning technique based on the idea of genetic algorithms and has been acknowledged as a very suitable technique
   for regression problems. The main motivation to choose GP for this task is its capability of learning from historical data, discovering an equation with different variables
   and operators. experiment were conducted to confirm this hypotheses and the results were compared with traditional and Neural Network models.
%8 8-11  November
%@ 0-7695-2482-6

%A Eduardo Oliveira Costa
%A Aurora Pozo
%A Silvia Regina Vergilio
%T Using Boosting Techniques to Improve Software Reliability Models Based on Genetic Programming
%B 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)
%D 2006
%P 643--650
%I IEEE Computer Society
%C Washington, D.C, USA
%K genetic algorithms, genetic programming
%X Software reliability models are used to estimate the probability of a software fails along the time. They are fundamental to plan test activities and to ensure the quality
   of the software being developed. Two kind of models are generally used: time or test coverage based models. In our previous work, we successfully explored Genetic
   Programming (GP) to derive reliability models. However, nowadays Boosting techniques (BT) have been successfully applied with other Machine Learning techniques, including
   GP. BT merge several hypotheses of the training set to get better results. With the goal of improving the GP software reliability models, this work explores the combination
   GP and BT. The results show advantages in the use of the proposed approach.
%8 November  13-15

%A Eduardo Oliveira Costa
%A Aurora Pozo
%T A New Approach to Genetic Programming based on Evolution Strategies
%B IEEE International Conference on Systems, Man and Cybernetics, ICSMC '06
%V 6
%D 2006
%P 4832--4837
%I IEEE
%C Taipei, Taiwan
%K genetic algorithms, genetic programming
%X This paper proposes a new approach to induction of programs by Genetic Progranuning (GP) using the ideas of Evolutionary Strategies (ES). The goal of this work is to
   develop a variety of Genetic Programming algorithm by doing some modifications on the classical GP algorithm and adding some concepts of Evolutionary Strategies. The new
   approach was evaluated using two instances of the Symbolic Regression problem - the Binomial-3 problem (a tunably difficult problem), proposed in [5] and the Time Series
   problem (an application of symbolic regression) - and a problem of a different domain, the Santa Fe Artificial Ant problem. The results discovered were compared with the
   classical GP algorithm. The Symbolic Regression problems obtained excellent results and an improvement was detected, but this does not happened with the Artificial Ant
   problem.
%8 8-11  October
%Z Computer Science Department, Federal University of Parana (UFPR), PO Box 19081, 81531-970, Curitiba, Brazil,
%@ 1-4244-0100-3

%A E. O. Costa
%A A. Pozo
%T A (mu + lambda) - GP Algorithm and its use for Regression Problems
%B 8th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '06
%D 2006
%P 10--17
%I IEEE
%C Arlington, VA, USA
%K genetic algorithms, genetic programming
%X The genetic programming (GP) is a powerful technique for symbolic regression. However, because it is a new area, many improvements can be obtained changing the basic
   behaviour of the method. In this way, this work develop a different genetic programming algorithm doing some modifications on the classical GP algorithm and adding some
   concepts of evolution strategies. The new approach was evaluated using two instances of symbolic regression problem - the binomial-3 problem (a tunably difficult problem),
   proposed in (J.M. Daida et al., 2001) and the problem of modelling software reliability growth (an application of symbolic regression). The discovered results were compared
   with the classical GP algorithm. The symbolic regression problems obtained excellent results and an improvement was detected using the proposed approach
%8 November
%Z Dept. of Comput. Sci., Fed. Univ. of Parana, Curitiba
%@ 0-7695-2728-0

%A Eduardo Oliveira Costa
%A Gustavo Alexandre {de Souza}
%A Aurora Trinidad Ramirez Pozo
%A Silvia Regina Vergilio
%T Exploring Genetic Programming and Boosting Techniques to Model Software Reliability
%J IEEE Transactions on Reliability
%V 56
%N 3
%D 2007
%P 422--434
%I 
%K genetic algorithms, genetic programming, Fault prediction, machine learning techniques, software reliability models
%X Software reliability models are used to estimate the probability that a software fails at a given time. They are fundamental to plan test activities, and to ensure the
   quality of the software being developed. Each project has a different reliability growth behaviour, and although several different models have been proposed to estimate the
   reliability growth, none has proven to perform well considering different project characteristics. Because of this, some authors have introduced the use of Machine Learning
   techniques, such as neural networks, to obtain software reliability models. Neural network-based models, however, are not easily interpreted, and other techniques could be
   explored. In this paper, we explore an approach based on Genetic Programming, and also propose the use of Boosting techniques to improve performance. We conduct experiments
   with reliability models based on time, and on test coverage. The obtained results show some advantages of the introduced approach. The models adapt better to the
   reliability curve, and can be used in projects with different characteristics.
%8 September

%A Eduardo Oliveira Costa
%A Aurora Trinidad Ramirez Pozo
%A Silvia Regina Vergilio
%T A Genetic Programming Approach for Software Reliability Modeling
%J IEEE Transactions on Reliability
%D 2010
%I 
%K genetic algorithms, genetic programming, Fault prediction, machine learning techniques, software reliability models, SBSE
%X Genetic Programming (GP) models adapt better to the reliability curve when compared with other traditional, and non-parametric models. In a previous work, we conducted
   experiments with models based on time, and on coverage. We introduced an approach, named Genetic Programming and Boosting (GPB), that uses boosting techniques to improve
   the performance of GP. This approach presented better results than classical GP, but required ten times the number of executions. Therefore, we introduce in this paper a
   new GP based approach, named $(mu+lambda)$ GP. To evaluate this new approach, we repeated the same experiments conducted before. The results obtained show that the
   $(mu+lambda)$ GP approach presents the same cost of classical GP, and that there is no significant difference in the performance when compared with the GPB approach. Hence,
   it is an excellent, less expensive technique to model software reliability.
%Z Also known as \cite5409534

%A Lino Costa
%A Pedro Oliveira
%T GAs in Global Optimization of Mixed Integer Non-Linear Problems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1773
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-740.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Paolo Costa
%T A Methodology for the Analysis of Complex Systems based on Qualitative Reasoning, Stochastic Complexity and Genetic Programming
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 35--41
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Dan Costelloe
%A Conor Ryan
%T Genetic Programming for Subjective Fitness Function Identification
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 259--268
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=259
%X We address modelling fitness functions for Interactive Evolutionary Systems. Such systems are necessarily slow because they need human interaction for the fundamental task
   of fitness allocation. The research presented here demonstrates that Genetic Programming can be used to learn subjective fitness functions from human subjects, using
   historical data from an Interactive Evolutionary system for producing pleasing drum patterns. The results indicate that GP is capable of performing symbolic regression even
   when the number of training cases is substantially less than the number of inputs.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Dan Costelloe
%A Conor Ryan
%T Towards models of user preferences in interactive musical evolution
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 2254--2254
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, grammatical evolution, Real-World Applications: Poster, human factors, interactive evolution
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2254.pdf
%X We describe the bottom-up construction of a system which aims to build models of human musical preferences with strong predictive power. We use Grammatical Evolution to
   construct models from toy datasets which mimic real world user-generated data. These models will ultimately substitute for the subjective fitness functions that human users
   employ during Interactive Evolution of melodies.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Dan Costelloe
%A Conor Ryan
%T On Improving Generalisation in Genetic Programming
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 61--72
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%X This paper is concerned with the generalisation performance of GP. We examine the generalisation of GP on some well-studied test problems and also critically examine the
   performance of some well known GP improvements from a generalisation perspective. From this, the need for GP practitioners to provide more accurate reports on the
   generalisation performance of their systems on problems studied is highlighted. Based on the results achieved, it is shown that improvements in training performance thanks
   to GP-enhancements represent only half of the battle.
%8 April  15-17
%Z overfitting, Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Dan Costelloe
%T Evolutionary Optimisation and Prediction in Subjective Problem Domains
%R Ph.D. Thesis
%D 2009
%I 
%I University of Limerick
%C Limerick, Ireland
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Costelloe_thesis.pdf
%X Artificial Evolution is a powerful tool for generating realistic solutions to a large range of computationally difficult problems. It has been applied with great success to
   many optimisation problems in engineering and science, yet its application is not restricted to problems specific to these fields. The power of evolution can also be
   coupled with human supervision to tackle problems whose solutions must be (wholly or partly) subjectively evaluated. This thesis describes the design, implementation and
   use of Evolutionary-based system used for the evolution of such entities whose 'goodness' is commonly only subjectively defined. Additionally, this research investigates
   and tests formal models of subjective notions for a specific problem: the Interactive Evolution of music. It is demonstrated by this research how various evolutionary
   techniques can be used to generate and evolve pleasing musical sequences. It is also shown how similar techniques are used to build models of the subjective notions used by
   human users, when evaluating the goodness of musical pieces. The research presented here also makes it possible to understand what environmental conditions lead to the
   construction of artificial models that have good predictive power. Finally, an investigation of the generalisation performance of a specific Evolutionary technique, Genetic
   Programming, is presented in the context of more recently developed improvement techniques. It is demonstrated that any improvement must take generalisation performance
   into account in order to be considered a worthy addition to the field. It is also shown how a combination of recent improvement techniques make significant performance
   improvements on both artificial and real-world symbolic regression problems.
%8 November
%Z Supervisor: Dr. Conor Ryan

%A Alban Cotillon
%A Philip Valencia
%A Raja Jurdak
%T Android Genetic Programming Framework
%B Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012
%S LNCS
%E Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta
%V 7244
%D 2012
%P 13--24
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, AGP, Embedded, Smartphone
%U http://jurdak.com/eurogp12.pdf
%X Personalisation in smart phones requires adaptability to dynamic context based on application usage and sensor inputs. Current personalisation approaches do not provide
   sufficient adaptability to dynamic and unexpected context. This paper introduces the Android Genetic Programming Framework (AGP) as a personalisation method for smart
   phones. AGP considers the specific design challenges of smart phones, such as resource limitation and constrained programming environments. We demonstrate AGP's usefulness
   through empirical experiments on two applications: a news reader and energy efficient localisation. AGP successfully adapts application behaviour to user context.
%8 11-13  April
%Z Android open source, Java code. RSS reader. Online fitness monitoring. Online GP. Part of \citeMoraglio:2012:GP EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012,
   EvoMusArt2012 and EvoApplications2012

%A Carlos Cotta
%A E. Alba
%A J. M. Troya
%T Evolutionary Design of Fuzzy Logic Controllers
%B Proceedings of the 1996 IEEE International Symposium on Intelligent Control
%D 1996
%P 127--132
%I IEEE Control Systems Society
%C Dearborn MI, USA
%K genetic algorithms, genetic programming
%U http://www.lcc.uma.es/~ccottap/papers/isic96flc.pdf
%X An evolutionary approach to fuzzy logic controller design is presented in this paper. We propose the use of a class of genetic algorithms to produce suboptimal fuzzy
   rule-bases (internally represented as constrained syntactic trees). This model has been applied to the cart centering problem. The obtained results show that a good
   parameterisation of the algorithm and an appropriate evaluation function lead to near-optimal solutions.
%8 15-18 Septmeber

%A Carlos Cotta
%A Enrique Alba
%A Jose M. Troya
%T Improving the Scalability of Dynastically Optimal Forma Recombination by Tuning the Granularity of the Representation
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 783
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-800.PS
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Carlos Cotta
%A Pablo Moscato
%T Inferring Phylogenetic Trees Using Evolutionary Algorithms
%B Parallel Problem Solving from Nature - PPSN VII
%S Lecture Notes in Computer Science, LNCS
%E Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel
%N 2439
%D 2002
%P 720--729
%I Springer-Verlag
%C Granada, Spain
%K genetic algorithms, genetic programming, Biology and chemistry, Comparisons of representations
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=720
%X We consider the problem of estimating the evolutionary history of a collection of organisms in terms of a phylogenetic tree. This is a hard combinatorial optimization
   problem for which different EA approaches are proposed and evaluated. Using two problem instances of different sizes, it is shown that an EA that directly encodes trees and
   uses ad-hoc operators performs better than several decoder-based EAs, but does not scale well with the problem size. A greedy-decoder EA provides the overall best results,
   achieving near 100%-success at a lower computational cost than the remaining approaches.
%O Available from http://link.springer.de/link/service/series/0558/papers/2439/243900720.pdf
%8 7-11  September
%@ 3-540-44139-5

%A Carlos Cotta
%A Juan-Julian Merelo
%T Where is evolutionary computation going? A temporal analysis of the EC community
%J Genetic Programming and Evolvable Machines
%V 8
%N 3
%D 2007
%P 239--253
%I 
%K genetic algorithms, genetic programming, evolvable hardware, Complex networks, Evolutionary computation, Social network analysis
%X Studying an evolving complex system and drawing some conclusions from it is an integral part of nature-inspired computing; being a part of that complex system, some insight
   can also be gained from our knowledge of it. In this paper we study the evolution of the evolutionary computation co-authorship network using social network analysis tools,
   with the aim of extracting some conclusions on its mechanisms. In order to do this, we first examine the evolution of macroscopic properties of the EC co-authorship graph,
   and then we look at its community structure and its corresponding change along time. The EC network is shown to be in a strongly expansive phase, exhibiting distinctive
   growth patterns, both at the macroscopic and the mesoscopic level.
%8 September

%A Jorge Couchet
%A Daniel Manrique
%A Juan Rios
%A Alfonso Rodriguez-Paton
%T Crossover and mutation operators for grammar-guided genetic programming
%J Soft Computing
%V 11
%N 10
%D 2007
%P 943--955
%I 
%K genetic algorithms, genetic programming, Grammar-guided genetic programming, Crossover, Mutation, Breast cancer prognosis
%X This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the
   evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and,
   therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals
   that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system
   assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison
   of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of
   breast cancer prognosis.
%8 August
%Z p945 'ambiguous' context free grammars. p950 PCT2 SSGA 75percent crossover 5percent mutation. p952 315 breast lesions X-ray images characteristics by human: size (apparent
   diameter mm), morphology (5 values), margins (5 values), density (4 values). Biopsy as ground truth, Comparison with two human experts. Evolved rule: if margins=spiculated
   and morphology=irregular then prognosis=malignant. p953 benefit of ambiguous grammar (not given).

%A Dale E. Courte
%T Hybrid Evolutionary Code Generation Optimizing Both Functional Form and Parameter Values
%B ANNIE 2007, Intelligent Engineering Systems through Artificial Neural Networks
%E Cihan H. Dagli
%V 17
%D 2007
%I 
%C St. Louis, MO, USA
%K genetic algorithms, genetic programming, grammatical evolution
%X Evolutionary computation (EC) is an effective tool in the optimisation of complex systems. It is desirable to model such a system with appropriate computer commands and
   parameter settings. Automated determination of both commands and settings, based on observed system behaviour, is a desirable goal. Of the many forms of evolutionary
   computation, one recently developed discipline is that of grammatical evolution (GE). This approach can evolve executable functions in any computer language that can be
   represented in BNF form. The ability to synthesise arbitrary functions from a formal grammar is an attractive alternative to the expression tree generation of the more
   common genetic programming (GP) approach. However, the GE approach may not be ideal for the optimisation of any real-valued parameters of the functions generated. This work
   combines the use of grammatical evolution for function synthesis with the use of evolutionary programming (EP) to optimise the parameters (constants) required by the
   synthesised functions. These two evolutionary processes combine to explore a rich and complex search space of functional forms and floating point values. A prototype system
   is implemented and applied to the problem of function approximation.
%O Part III: Evolutionary Computation

%A George S. Cowan
%A Robert G. Reynolds
%T Acquisition of Software Engineering Knowledge SWEEP: An Automatic Programming System Based on Genetic Programming and Cultural Algorithms
%S Software Engineering and Knowledge Engineering
%V 14
%D 2003
%I World Scientific
%C Singapore
%K genetic algorithms, genetic programming
%U http://www.worldscibooks.com/compsci/3338.html
%X This is the first book that attempts to provide a framework in which to embed an automatic programming system based on evolutionary learning (genetic programming) into a
   traditional software engineering environment. As such, it looks at how traditional software engineering knowledge can be integrated with an evolutionary programming process
   in a symbiotic way. Contents: * SWEEP: A System for the Software Engineering of Evolving Programs * The Genetic Programming Element Agents * The Metrics Apprentice: Using
   Cultural Algorithms to Formulate Quality Metrics for Software Systems * An Example Problem for Automatic Programming: Solving the Noisy Sine Problem with Discipulus * Data
   Collection and Analysis * Analysis: The Relationship of Software Metrics to Bloat * Defining a New Software Metric to Estimate Generalisation Using the Metrics Apprentice
%8 August
%Z http://www.worldscibooks.com/compsci/3338.html Wayne State University, USA
%@ 981-02-2920-8

%A Iain Craig
%T Genetic Programming and Data Structures
%J Robotica
%V 17
%N 4
%D 1999
%P 462
%I 
%K genetic algorithms, genetic programming
%O Review
%Z Review of \citelangdon:book

%A Nichael Lynn Cramer
%T A representation for the Adaptive Generation of Simple Sequential Programs
%B Proceedings of an International Conference on Genetic Algorithms and the Applications
%E John J. Grefenstette
%D 1985
%P 183--187
%I 
%C Carnegie-Mellon University, Pittsburgh, PA, USA
%K genetic algorithms, genetic programming, memory
%U http://www.sover.net/~nichael/nlc-publications/icga85/index.html
%X An adaptive system for generating short sequential computer functions is described. The created functions are written in the simple "number-string" language JB, and in TB,
   a modified version of JB with a tree-like structure. These languages have the feature that they can be used to represent well-formed, useful computer programs while still
   being amenable to suitably defined genetic operators. The system is used to produce two-input, single-output multiplication functions that are concise and well-defined.
   Future work, dealing with extensions to more complicated functions and generalizations of the techniques, is also discussed.
%8 24-26  July
%Z The earliest description of the tree-like representation and operators for use in the application of Genetic Algorithms to computer programs - N.L.Cramer Evolves a
   multiplier, "72% more often than control sample" "PL- not fully Turing Equivalent", addition of :SET and :BLOCK lead to JB language (nb a list of statements language). JB
   has problems with crossover -> TB which is as JB but instead of calls to other statements, these other statements are expanded in the first yielding a tree shaped syntax.
   Crossover operator changed to deal with sub trees! Both languages contain small numbers of global integers. TB Mutation restricted to frindges of tree, ie leaves or first
   level functions. Inversion: crossover within same program! Goldberg(1989, p 303) says "Cramer does not present any results from the use of JB in any genetic trials; however
   he abandoned these first efforts because of some limited computational experiments". Fitness based, to some extent, upon internals of program. Limits on prog size via
   fitness. Forced timeout Goldberg(1987) says timed out progs fitness was calculated. =>Smith,S.F. IJCAI-83 Publisher not known, sponsored by USA Navy.

%A Ellery Fussell Crane
%A Nicholas Freitag McPhee
%T The effects of size and depth limits on tree based genetic programming
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 223--240
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Size limits, Depth limits, Population distributions, Tree Shape, bloat
%X Bloat is a common and well studied problem in genetic programming. Size and depth limits are often used to combat bloat, but to date there has been little detailed
   exploration of the effects and biases of such limits. In this paper we present empirical analysis of the effects of size and depth limits on binary tree genetic programs.
   We find that size limits control population average size in much the same way as depth limits do. Our data suggests, however that size limits provide finer and more
   reliable control than depth limits, which has less of an impact upon tree shapes.
%O 15
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Constantin Cranganu
%A Elena Bautu
%T Using Gene Expression Programming to estimate sonic log distributions based on the natural gamma ray and deep resistivity logs: A case study from the Anadarko Basin,
   Oklahoma
%J Journal of Petroleum Science and Engineering
%V 70
%N 3-4
%D 2010
%P 243--255
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming, soft computing, sonic log, Anadarko Basin, overpressured zones
%U http://www.sciencedirect.com/science/article/B6VDW-4XTNG6D-7/2/f3e31340cb8a863475bff4f643de28a9
%X In the oil and gas industry, characterisation of pore-fluid pressures and rock lithology, along with estimation of porosity, permeability, fluid saturation and other
   physical properties is of crucial importance for successful exploration and exploitation. Along with other well logging methods, the compressional acoustic (sonic) log (DT)
   is often used as a predictor because it responds to changes in porosity or compaction and, in turn, DT data are used to estimate formation porosity, to map abnormal
   pore-fluid pressure, or to perform petrophysical studies. However, despite its intrinsic value, the sonic log is not routinely recorded during well logging. Here we propose
   the use of a soft computing method -- Gene Expression Programming (GEP) -- to synthesise missing DT logs when only common logs (such as natural gamma ray -- GR, or deep
   resistivity -- REID) are present. The Gene Expression Programming approach can be divided into three steps: (1) supervised training of the model; (2) confirmation and
   validation of the model by blind-testing the results in wells containing both the predictor (GR, REID) and the target (DT) values used in the supervised training; and (3)
   applying the predicted model to wells containing the predictor data and obtaining the synthetic (simulated) DT log. GEP methodology offers significant advantages over
   traditional deterministic methods. It does not require a precise mathematical model equation describing the dependency between the predictor values and the target values.
   Unlike linear regression techniques, GEP does not over predict mean values and thereby preserves original data variability. GEP also deals greatly with uncertainty
   associated with the data, the immense size of the data and the diversity of the data type. A case study from the Anadarko Basin, Oklahoma, involving estimating the presence
   of over pressured zones, is presented. The results are promising and encouraging.

%A Kyle S. Cranmer
%T Multivariate Analysis from a Statistical Point of View
%B Phystat2003
%E Louis Lyons and Richard Mount and Rebecca Reitmeyer
%D 2003
%P 211--214
%I 
%C SLAC, Stanford, USA
%K genetic algorithms, genetic programming, VC dimension
%U http://www.citebase.org/abstract?id=oai:arXiv.org:physics/0310110
%X Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, many of the common approaches were borrowed from other fields. We clarify
   what the goal of a multivariate algorithm should be for the search for a new particle and compare different approaches. We also translate the Neyman-Pearson theory into the
   language of statistical learning theory.
%8 September  8-11
%Z http://www.slac.stanford.edu/econf/C030908/ Talk from PhyStat2003, Stanford, Ca, USA, September 2003, LaTeX, 1 eps figures. PSN WEJT002 Section 6 page 214 talks about GP

%A Kyle Cranmer
%A R. Sean Bowman
%T PhysicsGP: A Genetic Programming Approach to Event Selection
%D 2004
%I 
%K genetic algorithms, genetic programming, Triggering, Classification, VC Dimension, Neural Networks, Support Vector Machines
%U http://arXiv.org/abs/physics/0402030
%X We present a novel multivariate classification technique based on Genetic Programming. The technique is distinct from Genetic Algorithms and offers several advantages
   compared to Neural Networks and Support Vector Machines. The technique optimizes a set of human-readable classifiers with respect to some user-defined performance measure.
   We calculate the Vapnik-Chervonenkis dimension of this class of learning machines and consider a practical example: the search for the Standard Model Higgs Boson at the
   LHC. The resulting classifier is very fast to evaluate, human-readable, and easily portable. The software may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.html
%O Comment: 16 pages 9 figures, 1 table. Submitted to Comput. Phys. Commun
%8 February ~05
%Z Published as \citecranmer:2005:CPC. cites \citeluke:2000:2ftcaGP. Population is converted to C and compiled for fitness evaluation. (Details of GP including fitness
   definition (Gaussian/Poisson significance?) are vague). selection pressure based on inverse cumulative fitness distribution. Recentered fitness? Ring topology CORBA
   parallel programming. Island model. Higgs Boson, large hadron collier CERN.

%A Kyle Cranmer
%A R. Sean Bowman
%T PhysicsGP: A Genetic Programming approach to event selection
%J Computer Physics Communications
%V 167
%N 3
%D 2005
%P 165--176
%I 
%K genetic algorithms, genetic programming, Triggering, Classification, VC dimension, Genetic algorithms, Neural networks, Support vector machines
%U http://arxiv.org/abs/physics/0402030
%X We present a novel multivariate classification technique based on Genetic Programming. The technique is distinct from Genetic Algorithms and offers several advantages
   compared to Neural Networks and Support Vector Machines. The technique optimises a set of human-readable classifiers with respect to some user-defined performance measure.
   We calculate the Vapnik-Chervonenkis dimension of this class of learning machines and consider a practical example: the search for the Standard Model Higgs Boson at the
   LHC. The resulting classifier is very fast to evaluate, human-readable, and easily portable. The software may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.html.
%8 1  May
%Z replaces oai:arXiv.org:physics/0402030 http://www.elsevier.com/wps/find/journaldescription.cws_home/505710/description#description p171 "It is meaningless to calculate the
   VCD (Vapnik-Chervonenkis dimension) for GP in general..." "by placing a bound on either size... or the degree of the polynomial, we can calculate a sensible VCD." GP
   compared with ANN (backprop + momentum) and SVM with RBF kernel (BSVM-2.0). Training data subsampled. p174 "GP approach does not seem particularly sensitive to the size
   penalty of mutation rates".

%A Kyle S. Cranmer
%T Searching for New Physics: Contributions to LEP and the LHC
%R Ph.D. Thesis
%D 2005
%I 
%I University of Wisconsin-Madison
%K genetic algorithms, genetic programming, PhysicsGP
%U http://www.theoryandpractice.org/kyle/Files/cranmer_thesis.pdf
%X This dissertation is divided into two parts and consists of a series of contributions to searches for new physics with LEP and the LHC. In the first part, an exhaustive
   comparison of ALEPH's LEP2 data and Standard Model predictions is made for several hundred final states. The observations are in agreement with predictions with the
   exception of the e
%Z 'I would like to use one page to point out that if this dissertation was not double-spaced, then it would be fifty pages shorter. This is practice is silly and antiquated
   in the era of LATEX typesetting.' 'In total, three multivariate analyses were performed: a Neural Network analysis using back-propagation with momentum, a Support Vector
   Regression analysis using Radial Basis Functions, and a Genetic Programming analysis using the software described in Appendix E.' Copyright by Kyle S. Cranmer 2005 Some
   Rights Reserved This work is licensed under the Creative Commons Attribution-ShareAlike License. To view a copy of this license, visit
   http://creativecommons.org/licenses/by-sa/2.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

%A P. F. Crapper
%A P. A. Whigham
%T Modelling Rainfall-runoff Relationships
%B 24th Hydrology and Water Resources Symposium
%D 1997
%I 
%C Auckland, New Zealand
%K genetic algorithms, genetic programming
%U http://books.google.co.uk/books/about/24th_Hydrology_Water_resources_Symposium.html?id=bLC5PwAACAAJ&redir_esc=y

%A Kelly D. Crawford
%A Michael D. McCormack
%A Donald J. MacAllister
%T Modified Gradient Techniques for Normalized Solution Vectors
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1498--1503
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-720.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Raphael Crawford-Marks
%A Lee Spector
%T Size Control Via Size Fair Genetic Operators In The PushGP Genetic Programming System
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 733--739
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%T Hampshire College Student Uses J.K. Rowling's Quidditch as Basis for Artificial Intelligence Experiment
%J AScribe Newswire
%D 2004
%I 
%K genetic algorithms, genetic programming
%U http://www.ascribe.org/cgi-bin/spew4th.pl?ascribeid=20040504.114704&time=12%2033%20PDT&year=2004&public=1
%X Although enrolled in Hampshire College, not Hogwarts Academy, Raphael Crawford-Marks has spent the past year fine-tuning his Quidditch skills. Crawford-Marks - set to
   graduate on May 22 - has created a computerized version of the rapid-fire game played by young witches and warlocks in J.K. Rowling's series of Harry Potter novels. But
   Crawford-Marks is doing far more than playing a video game: he's running an artificial intelligence experiment that involves computerized generation of teams that either
   proceed in competition or fall by the wayside according to their ability to adapt to the Quidditch environment.
%O online
%8 4  May
%Z references \citespector:2001:vqacpapsa

%A Raphael Crawford-Marks
%T Virtual Witches and Warlocks: Computational Evolution of Teamwork and Strategy in a Dynamic, Heterogeneous and Noisy 3D Environment
%R Division III (senior) Thesis
%D 2004
%I 
%I School of Cognitive Science, Hampshire College
%K genetic algorithms, genetic programming, Coevolution, breve, Push
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.949
%X Games make excellent challenge problems for Artificial Intelligence. Two-player turn-based games (Backgammon, Checkers, Chess) are easy to program, and AI players can be
   benchmarked against humans of varying skill levels. Recently, more complicated real-time team games have received attention from researchers in the Distributed Artificial
   Intelligence (DAI) and Multi-Agent Systems (MAS) fields because of the dynamic environments and necessity for coordination. The RoboCup Soccer Simulator is the most popular
   and well-known of these environments. However, the soccer simulator is restricted to only two dimensions, and does not realistically model physics. This Division III thesis
   describes a simulator of the imaginary game Quidditch, and the automatic programming of quidditch-playing teams by Genetic Programming. These evolved teams of heterogeneous
   agents have offensive and defensive behaviours, and show the beginnings of real teamwork. Now, I want a nice fair game, all of you, she said, once they were all gathered
   around her. Harry noticed that she seemed to be speaking particularly to the Slytherin Captain, Marcus Flint, a sixth year. Harry thought Flint looked as if he had some
   troll blood in him. Out of the corner of his eye he saw the fluttering banner high above, flashing Potter for President over the crowd. His heart skipped. He felt braver.
   Mount your brooms, please.
%8 18  May
%Z http://www.spiderland.org/breve.

%A Raphael Crawford-Marks
%A Lee Spector
%A Jon Klein
%T Virtual Witches and Warlocks: A Quidditch Simulator and Quidditch-Playing Teams Coevolved via Genetic Programming
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP046.pdf
%X Games make excellent challenge problems for Artificial Intelligence. Two-player turn-based games (Backgammon, Checkers, Chess) are easy to program, and AI players can be
   benchmarked against humans of varying skill levels. Recently, more complicated real-time team games have received attention because of their dynamic environments and the
   necessity for coordination. The RoboCup Soccer Simulator is the most popular and well-known of these environments. However, the soccer simulator is restricted to only two
   dimensions, and does not realistically model physics. In 2001, Spector et al. proposed creating a simulator of the imaginary game Quidditch from the Harry Potter Books by
   J.K. Rowling. This article describes such a simulator and the coevolved quidditch-playing teams created for it using Genetic Programming.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Steven L. Creighton
%T Structural Shape Optimization using a Genetic Algorithm
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 108--116
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Ronald L. Crepeau
%T Genetic Evolution of Machine Language Software
%B Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications
%E Justinian P. Rosca
%D 1995
%P 121--134
%I 
%C Tahoe City, California, USA
%K genetic algorithms, genetic programming, memory
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GEMS_Article.pdf
%X Genetic Programming (GP) has a proven capability to routinely evolve software that provides a solution function for the specified problem. Prior work in this area has been
   based upon the use of relatively small sets of pre-defined operators and terminals germane to the problem domain. This paper reports on GP experiments involving a large set
   of general purpose operators and terminals. Specifically, a microprocessor architecture with 660 instructions and 255 bytes of memory provides the operators and terminals
   for a GP environment. Using this environment, GP is applied to the beginning programmer problem of generating a desired string output, e.g., "Hello World". Results are
   presented on: the feasibility of using this large operator set and architectural representation; and, the computations required to breed string outputting programs vs. the
   size of the string and the GP parameters employed.
%8 9  July
%Z Z80 Machine code evolved to write "Hello World" HWP 660 instructions and 255 byte RAM (modular arithmetic used to address indexed memory) GEMS genetic evolution of machine
   language software Breeding system similar to crowding and Tackett's Softbrood selection (max litter size of 12). GA like crossover acts on code and contents of memory. Pool
   of 1500 member 0.20 mutation rate. "indicates that the problem difficulty, over the range of the test and in terms of required spawns, while increasing rapidly, does not
   appear to be combinatorial or exponential" (suggests O(n**3) ). Discussion of statistics of number of useful terminals in random and later populations. Memory initialised
   to random values. "Cultural memory" cf \citespector:1996:ctiGP. Steady state GA. 2 types of Mutation (20 percent). While JP jump and subroutines are discussed the problem
   does not need iteration to solve it. part of \citerosca:1995:ml

%A Matej Crepinsek
%A Marjan Mernik
%A Barrett R. Bryant
%A Faizan Javed
%A Alan Sprague
%T Inferring Context-Free Grammars for Domain-Specific Languages
%J Electronic Notes in Theoretical Computer Science
%V 141
%N 4
%D 2005
%P 99--116
%I 
%K genetic algorithms, genetic programming, Grammar induction, Grammar inference, Learning from positive and negative examples, Exhaustive search
%X In the area of programming languages, context-free grammars (CFGs) are of special importance since almost all programming languages employ CFG's in their design. Recent
   approaches to CFG induction are not able to infer context-free grammars for general-purpose programming languages. In this paper it is shown that syntax of a small
   domain-specific language can be inferred from positive and negative programs provided by domain experts. In our work we are using the genetic programming approach in
   grammatical inference. Grammar-specific heuristic operators and nonrandom construction of the initial population are proposed to achieve this task. Suitability of the
   approach is shown by examples where underlying context-free grammars are successfully inferred.
%O Proceedings of the Fifth Workshop on Language Descriptions, Tools, and Applications (LDTA 2005)
%8 12  Decemeber

%A Guillaume Cretin
%A Evelyne Lutton
%A Jacques Levy-Vehel
%A Philippe Glevarec
%A Cedric Roll
%T Mixed IFS: Resolution of the Inverse Problem Using Genetic Programming
%B Artificial Evolution
%S LNCS
%E Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald and Marc Schoenauer and Dominique Snyers
%V 1063
%D 1996
%P 247--258
%I Springer Verlag
%K genetic algorithms, genetic programming
%X We address here the resolution of the so-called inverse problem for IFS. This problem has already been widely considered, and some studies have been performed for affine
   IFS, using deterministic or stochastic methods (simulated annealing or Genetic Algorithm) [9, 12, 6]. When dealing with non affine IFS, the usual techniques do not perform
   well, except if some a priori hypotheses on the structure of IFS (number and type functions) are made. A Genetic Programming method is investigated to solve the general
   inverse problem, which permits to perform at the same time a numeric and a symbolic optimisation. The use of mixed IFS, as we call them, may enlarge the scope of some
   applications, as for example image compression, because they allow to code a wider range of shapes.
%Z Selected papers from two conferences: Evolution Artificielle 94 and Evolution Artificielle 95 http://www.cmap.polytechnique.fr/www.eark/ea95.html see also
   \citelutton:1995:IFScs and \citelutton:1995:IFScs

%A H. Brown {Cribbs III}
%T Aircraft Maneuvering via Genetics-Based Adaptive Agent
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1249--1256
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-035.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Mark Crosbie
%A Gene Spafford
%T Defending a Computer System using Autonomous Agents
%R Technical Report 95-022
%D 1994
%I 
%I Department of Computer Science, Perdue University
%C West Lafayette, IN, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/265557.html
%X This report presents a prototype architecture of a defense mechanism for computer systems. The intrusion detection problem is introduced and some of the key aspects of any
   solution are explained. Standard intrusion detection systems are built as a single monolithic module. A finer-grained approach is proposed, where small, independent agents
   monitor the system. These agents are taught how to recognise intrusive behaviour. The learning mechanism in the agents is built using Genetic Programming. This is
   explained, and some sample agents are described. The flexibility, scalability and resilience of the agent approach are discussed. Future issues are also outlined.
%O The Pennsylvania State University CiteSeer Archives
%8 11  March
%Z citeseer 1999 oct 21

%A Mark Crosbie
%A Eugene H. Spafford
%T Applying Genetic Programming to Intrusion Detection
%B Working Notes for the AAAI Symposium on Genetic Programming
%E E. V. Siegel and J. R. Koza
%D 1995
%P 1--8
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025, USA
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/197480.html
%X This paper presents a potential solution to the intrusion detection problem in computer security. It uses a combination of work in the fields of Artificial Life and
   computer security. It shows how an intrusion detection system can be implemented using autonomous agents, and how these agents can be built using Genetic Programming. It
   also shows how Automatically Defined Functions (ADFs) can be used to evolve genetic programs that contain multiple data types and yet retain type-safety. Future work
   arising from this is also discussed.
%8 10--12  November
%Z AAAI-95f GP. Part of \citesiegel:1995:aaai-fgp \em Telephone: 415-328-3123 \em Fax: 415-321-4457 \em email info@aaai.org \em URL: http://www.aaai.org/

%A Mark Crosbie
%A Eugene H. Spafford
%T Evolving Event Driven Programs
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 273--278
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X This paper examines how Genetic Programming has shortcomings in an event-driven environment. The need for event-driven programming is motivated by some examples. We then
   describe the difficulty in handling these examples using the traditional genetic programming approach. A potential solution that uses colored Petri nets is outlined. We
   present an experimental setup to test our theory.
%8 28--31  July
%Z GP-96

%A B. Csukas
%A R. Lakner
%A K. Varga
%A S. Balogh
%T Combining generated structural models with genetic programming in evolutionary synthesis
%J Computers \& Chemical Engineering
%V 20
%N Supplement 1
%D 1996
%P S61--S66
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6TFT-48JC24K-F/2/d8223cad7192932d658ef2274794f502
%X A new methodology has been proposed that combines structural modelling with genetic programming, and establishes an integrated toolkit for chemical process engineering. The
   principle is that similarly to the engineering way of thinking the modelling is based on the a priori known structures, while the final evaluation is made in the knowledge
   of the best detailed simulation experiences. The basic features of the method are the following: - -The conservational processes are mapped directly onto a descriptive
   computer program that can be executed by the help of a general purpose simulator automatically.- -The applied structural modelling technique, separating the invariant and
   the problem specific actual knowledge, supports the integrated problem solving.- -The genetic model of the typical time varied process engineering networks is synthesised
   automatically.- -There is an evaluation feedback from the synthesised and simulated variants to the genetic elements.
%O European Symposium on Computer Aided Process Engineering-6

%A Bela Csukas
%A Sandor Balogh
%T Combining genetic programming with generic simulation models in evolutionary synthesis
%J Computers in Industry
%V 36
%N 3
%D 1998
%P 181--197
%I 
%K genetic algorithms, genetic programming, Generic simulation, Genetic evolution, Process design, Structural modeling, Multicriteria evaluation
%U http://www.sciencedirect.com/science/article/B6V2D-3VW737S-3/1/87e285c0690af97d9d081c4f2582fdcd
%X In the proposed combined model of the engineering synthesis, the simulation and the parametric design are organized by the genetic building elements, while the genetic
   possibilities are evaluated by the experiences, obtained from the detailed dynamic simulation. Using this methodology, a new, integrated toolkit can de developed for the
   creative problem solving in (chemical) process engineering. The combination of the structural modeling with the genetic programming suggests a possible theoretical
   framework and proposes a practical methodology for the solution of the various synthesis (design, planning, scheduling, ...) problems.
%8 1  June

%A Wei Cui
%A Anthony Brabazon
%A Michael O'Neill
%T Efficient trade execution using a genetic algorithm in an order book based artificial stock market
%B GECCO-2009 Late-Breaking Papers
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 2023--2028
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to the issue of
   efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. This paper
   introduces the concept of trade execution and outlines the limited prior work applying evolutionary computing methods for this task. Furthermore, we build an Agent-based
   Artificial Stock Market and apply a Genetic Algorithm to evolve an efficient trade execution strategy. Finally we suggest a number of opportunities for future research.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Wei Cui
%A Anthony Brabazon
%A Michael O'Neill
%T Evolving Dynamic Trade Execution Strategies Using Grammatical Evolution
%B EvoFIN
%S LNCS
%E Cecilia Di Chio and Anthony Brabazon and Gianni A. Di Caro and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and
   Michael O'Neill and Ernesto Tarantino and Neil Urquhart
%V 6025
%D 2010
%P 192--201
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming, grammatical evolution
%X Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to potential use of
   these methods for efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of
   interest. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. In this paper we use a GE algorithm to
   discover dynamic, efficient, trade execution strategies which adapt to changing market conditions. The strategies are tested in an artificial limit order market. GE was
   found to be able to evolve quality trade execution strategies which are highly competitive with two benchmark trade execution strategies.
%8 7-9  April
%Z EvoFIN'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Wei Cui
%A Anthony Brabazon
%A Michael O'Neill
%T Evolving Efficient Limit Order Strategy using Grammatical Evolution
%B 2010 IEEE World Congress on Computational Intelligence
%D 2010
%P 2408--2413
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Barcelona, Spain
%K genetic algorithms, genetic programming, grammatical evolution
%X Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. A practical problem in trade execution
   is how to trade a large order as efficiently as possible. A trade execution strategy is designed for this task to minimise total trade cost. Grammatical Evolution (GE) is
   an evolutionary automatic programming methodology which can be used to evolve rule sets. It has been proved successfully to be able to evolve quality trade execution
   strategies in our previous work. In this paper, the previous work is extended by adopting two different limit order lifetimes and three benchmark limit order strategies. GE
   is used to evolve efficient limit order strategies which can determine the aggressiveness levels of limit orders. We found that GE evolved limit order strategies were
   highly competitive against three benchmark strategies and the limit order strategies with long-term lifetime performed better than those with short-term lifetime.
%8 18-23  July

%A Jamie Cullen
%T Evolutionary Meta Compilation: Evolving Programs Using Real World Engineering Tools
%B Proceedings of the 8th International Conference Evolvable Systems: From Biology to Hardware, ICES 2008
%S Lecture Notes in Computer Science
%E Gregory Hornby and Luk\'as Sekanina and Pauline C. Haddow
%V 5216
%D 2008
%P 414--419
%I Springer
%C Prague, Czech Republic
%K genetic algorithms, genetic programming, grammatical evolution
%X A general purpose system and technique is presented for the separation of target program compilation and fitness evaluation from the primary evolutionary computation
   system. Preliminary results are presented for two broadly different domains: (1) Software generated in the C programming language, (2) Hardware designs in Verilog, suitable
   for synthesis. The presented approach frees the developer from implementing and debugging a complex interpreter, and potentially enables the rapid integration of previously
   unsupported languages, as well as complex methods of fitness evaluation, by leveraging the availability of external tools. It also enables engineers (especially those in
   industry) to use preferred/approved tools for which source code may not be readily available, or which may be cost or time prohibitive to reimplement. Efficiency gains are
   also expected, particularly for complex domains where the fitness evaluation is computationally intensive.
%8 September  21-24
%Z Artificial Intelligence Laboratory, University of New South Wales, Sydney, NSW. Santa Fe ant. Taxi problem (loops). gcc. tiny c (tcc), full adder circuit

%A Jamie Cullen
%T Evolving Digital Circuits in an Industry Standard Hardware Description Language
%B Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)
%S Lecture Notes in Computer Science
%E Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb
   and Kay Chen Tan and J\"urgen Branke and Yuhui Shi
%V 5361
%D 2008
%P 514--523
%I Springer
%C Melbourne, Australia
%K genetic algorithms, genetic programming, Grammatical Evolution
%X Evolutionary Meta Compilation (EMC) is a recent technique that enables unmodified external applications to seamlessly perform target program compilation and fitness
   evaluation for an Evolutionary Computation system. Grammatical Evolution (GE) is a method for evolving computer programs in an arbitrary programming language using a
   grammar specified in Backus-Naur Form. This paper combines these techniques to demonstrate the evolution of both sequential and combinational digital circuits in an
   Industry Standard Hardware Description Language (Verilog) using an external hardware synthesis engine and simulator. Overall results show the successful evolution of core
   digital circuit components. An extension to GE is also presented to attempt to increase the probability of maintaining an evolved program's semantic integrity after
   crossover operations are performed. Early results show performance improvements in applying this technique to the majority of the presented test cases. It is suggested that
   this feature may also be considered for use in the evolution of software programs in C and other languages.
%8 Decemeber  7-10

%A Jamie Cullen
%T Evolving common LISP programs in a linear-genotype evolutionary computation system
%B GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
%E Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding
%D 2009
%P 75--80
%I ACM New York, NY, USA
%I SigEvo
%C Shanghai, China
%K genetic algorithms, genetic programming
%X Evolutionary Meta Programming (EMP) is an approach to Evolutionary Computation, which allows freedom of programming language choice in the evolved programs, as well as the
   ready use of external tools and testbenches, with which to perform fitness evaluation. The current implementation of EMP uses a linear genotype in a manner similar to
   Grammatical Evolution (GE). In contrast, traditional Genetic Programming (GP) typically uses a subset of the LISP programming language to represent target programs in a
   tree-based structure. The ability of EMP to leverage external tools and arbitrary languages enables the rapid prototyping of possibly novel approaches to Evolutionary
   Computation. One such experiment is presented herein: The evolution of Common LISP language constructs using a linear genotype and associated grammar, and evaluation using
   a real external LISP interpreter. An exploratory study is performed with three classic problems: Symbolic Regression, Ant Trail, and Towers of Hanoi. Solutions to these
   problems were evolved in both Common LISP and ANSI C versions, and runtime and performance results collected. Present results are relatively unintuitive, when compared to
   conventional programming wisdom, with some problems apparently favoring a paradigm not traditionally suited to them in a non-evolutionary programming setting.
%8 June  12-14
%Z Also known as \citeDBLP:conf/gecco/Cullen09 part of \citeDBLP:conf/gec/2009

%A Jamie Cullen
%T Evolutionary meta programming
%B GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
%E Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding
%D 2009
%P 81--88
%I ACM New York, NY, USA
%I SigEvo
%C Shanghai, China
%K genetic algorithms, genetic programming
%X The Evolutionary Meta Programming (EMP) approach towards the evolution of computer programs is presented. An EMP system is divided into two interacting parts: The Host
   Environment, and the Target Environment. Programs are evolved in an arbitrary target language by the Host Environment and are injected into the Target Environment, where
   they are evaluated for fitness in their `natural surroundings'. Early results from three significantly different domains are discussed: (1) Compiling C programs using a
   well-known compiler (GNU C compiler) (2) Circuit synthesis of digital hardware in an industry standard Hardware Description Language (Verilog), and (3) Functional
   Programming in an external Common LISP interpreter. The presented approach has now been used to evolve solutions to some well-known problems in the field of Evolutionary
   Computation, as well as enabling the initial examination of some novel problem domains that are typically not amenable to exploration by common techniques. Possible
   strengths of this approach, when compared to techniques such as Genetic Programming, include more rapid and natural problem specification and testbench development for some
   types of problems, reduced software development time, and the potential to more readily examine problems that require complex methods of fitness evaluation.
%8 June  12-14
%Z Also known as \citeDBLP:conf/gecco/Cullen09a part of \citeDBLP:conf/gec/2009

%A Ronan Cummins
%A Colm O'Riordan
%T Using Genetic Programming to Evolve Weighting Schemes for the Vector Space Model of Information Retrieval
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP038.pdf
%X Term weighting in many Information Retrieval models is of crucial importance in the research and development of accurate retrieval systems. This paper explores a method to
   automatically determine suitable term weighting schemes for the vector space model. Genetic Programming is used to automatically evolve weighting schemes that return a high
   average precision. These weighting functions are tested on well-known test collections and compared to the tf-idf based weighting scheme using standard Information
   Retrieval performance metrics.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Ronan Cummins
%A Colm O'Riordan
%T Determining General Term Weighting Schemes for the Vector Space Model of Information Retrieval Using Genetic Programming
%B 15th Artificial Intelligence and Cognitive Science Conference (AICS 2004)
%E Lorraine McGinty
%D 2004
%I 
%C Galway-Mayo Institute of Technology, Castlebar Campus, Ireland
%K genetic algorithms, genetic programming
%8 8-10  September
%Z http://www.gmit.ie/aics_2004/

%A Ronan Cummins
%A Colm O'Riordan
%T Evolving, Analysing and Improving Global Term-Weighting Schemes in Information Retrieval
%R Technical Report NUIG-IT-071204
%D 2004
%I 
%I National University of Ireland, Galway
%C Ireland
%K genetic algorithms, genetic programming, information retrieval, term-weighting
%U http://www.it.nuigalway.ie/Publications/TR/abstracts/NUIG-IT-071204.pdf
%X The ability of a term to distinguish documents, and ultimately topics, is crucial to the performance of many Information Retrieval models. We present and analyse global
   weighting schemes for the vector space model developed by means of evolutionary computation. The global schemes presented are shown to increase average precision over the
   IDF measure on TREC data. The global schemes are also shown to be consistent with Luhns theory of resolving power as certain middle frequency terms are assigned the highest
   weight. The use of the collection frequency measure of a term is seen as crucial to the performance of these schemes. We also show that the analysis of these evolved
   schemes is an important step to understanding and improving their performance.
%Z 25 January 2005

%A Ronan Cummins
%A Colm O'Riordan
%T Evolving Term-Selection Schemes for Pseudo-Relevance Feedback in Information Retrieval
%R Technical Report NUIG-IT-201205
%D 2005
%I 
%I National University of Ireland, Galway
%C Ireland
%K genetic algorithms, genetic programming
%U http://www.it.nuigalway.ie/publications/TR/abstracts/NUIG-IT-201205.ps
%Z Problem displaying page 1

%A Ronan Cummins
%A Colm O'Riordan
%T An evaluation of evolved term-weighting schemes in information retrieval
%B CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management
%E Otthein Herzog and Hans-Jorg Schek and Norbert Fuhr and Abdur Chowdhury and Wilfried Teiken
%D 2005
%P 305--306
%I ACM press New York, NY, USA
%I ACM
%C Bremen, Germany
%K genetic algorithms, genetic programming, information retrieval, term-weighting, Poster Session
%U http://portal.acm.org/citation.cfm?doid=1099639
%X presents an evaluation of evolved term-weighting schemes on short, medium and long TREC queries. A previously evolved global (collection-wide) term-weighting scheme is
   evaluated on unseen TREC data and is shown to increase mean average precision over idf. A local (within-document) evolved term-weighting scheme is presented which is
   dependent on the best performing global scheme. The full evolved scheme (i.e. the combined local and global scheme) is compared to both the BM25 scheme and the Pivoted
   Normalisation scheme. Our results show that the local evolved solution does not perform well on some collections due to its document normalisation properties and we
   conclude that Okapi-tf can be tuned to interact effectively with the evolved global weighting scheme presented and increase mean average precision over the standard BM25
   scheme.
%8 31  October  - 5  November
%Z Proceedings of the 14th ACM international conference on Information and knowledge management
%@ 1-59593-140-6

%A Ronan Cummins
%A Colm O'Riordan
%T Evolving Co-occurrence Based Query Expansion Schemes in Information Retrieval Using Genetic Programming
%B The 16th Irish conference on Artificial Intelligence and Cognitive Science (AICS05)
%E Norman Creaney
%D 2005
%P 137--146
%I University of Ulster Cromore Road, Coleraine, BT52 1SA, UK
%C School of Computing and Information Engineering, University of Ulster
%K genetic algorithms, genetic programming, information retrieval, query expansion
%U http://www.infc.ulst.ac.uk/~norman/aics05/AICS05_Proceedings_V3.pdf
%X Global query expansion techniques have long been proposed as a solution to overcome the problem of term mismatch between a query and its relevant documents. This paper
   describes a method which automatically tackles the problems of how to find the best terms for the expansion of a particular query and secondly, how to weight these terms
   for use with the original query. Genetic Programming is used to evolve schemes for term selection using global (collection-wide) co-occurrence measures. The schemes evolved
   are also used to weight the term in the expanded query as they are a measure of the term's importance in relation to the query. As a result, the genetic program has to
   learn a suitable scheme for identifying the best correlates for the query concept and also a scheme that correctly weights these in relation to each other. These schemes
   are tested on standard test collections and show a significant increase in performance on the training data but only modest improvement on the collections that are not
   included in training.
%8 7-9  September
%Z http://www.infc.ulst.ac.uk/~norman/aics05/
%@ 1-85923-197-7

%A Ronan Cummins
%A Colm O'Riordan
%T Evolving General Term-Weighting Schemes for Information Retrieval: Tests on Larger Collections
%J Artificial Intelligence Review
%V 24
%N 3-4
%D 2005
%P 277--299
%I 
%K genetic algorithms, genetic programming, term-weighting schemes, Information Retrieval
%X Term-weighting schemes are vital to the performance of Information Retrieval models that use term frequency characteristics to determine the relevance of a document. The
   vector space model is one such model in which the weights assigned to the document terms are of crucial importance to the accuracy of the retrieval system. We describe a
   genetic programming framework used to automatically determine term-weighting schemes that achieve a high average precision. These schemes are tested on standard test
   collections and are shown to perform as well as, and often better than, the modern BM25 weighting scheme. We present an analysis of the schemes evolved to explain the
   increase in performance. Furthermore, we show that the global (collection wide) part of the evolved weighting schemes also increases average precision over idf on larger
   TREC data. These global weighting schemes are shown to adhere to Luhn's resolving power as middle frequency terms are assigned the highest weight. However, the complete
   weighting schemes evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting
   schemes it is necessary to evolve these on large collections.
%8 November
%Z www.kluweronline.com/issn/0269-2821

%A Ronan Cummins
%A Colm O'Riordan
%T Evolving local and global weighting schemes in information retrieval
%J Information Retrieval
%V 9
%N 3
%D 2006
%P 311--330
%I 
%K genetic algorithms, genetic programming, Information Retrieval, Term-Weighting Schemes
%X This paper describes a method, using Genetic Programming, to automatically determine term weighting schemes for the vector space model. Based on a set of queries and their
   human determined relevant documents, weighting schemes are evolved which achieve a high average precision. In Information Retrieval (IR) systems, useful information for
   term weighting schemes is available from the query, individual documents and the collection as a whole. We evolve term weighting schemes in both local (within-document) and
   global (collection-wide) domains which interact with each other correctly to achieve a high average precision. These weighting schemes are tested on well-known test
   collections and are compared to the traditional tf-idf weighting scheme and to the BM25 weighting scheme using standard IR performance metrics. Furthermore, we show that
   the global weighting schemes evolved on small collections also increase average precision on larger TREC data. These global weighting schemes are shown to adhere to Luhn's
   resolving power as both high and low frequency terms are assigned low weights. However, the local weightings evolved on small collections do not perform as well on large
   collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections.
%8 June

%A Ronan Cummins
%A Colm O'Riordan
%T Term-Weighting in Information Retrieval using Genetic Programming: A Three Stage Process
%B The 17th European Conference on Artificial Intelligence, ECAI-2006
%E Gerhard Brewka and Silvia Coradeschi and Anna Perini and Paolo Traverso
%D 2006
%P 793--794
%I IOS Press
%C Riva del Garda, Italy
%K genetic algorithms, genetic programming, poster, information retrieval, term-weighting
%U http://ww2.it.nuigalway.ie/cirg/localpubs/CumminsECAI2006.pdf
%8 August  28th -  September  1st
%Z ECAI-2001 http://ecai2006.itc.it/cda/aree/index.php?section=76&area=13
%@ 1-58603-642-4

%A Ronan Cummins
%A Colm O'Riordan
%T A Framework for the study of Evolved Term-Weighting Schemes in Information Retrieval
%B TIR-06 Text based Information Retrieval, Workshop. ECAI 2006
%E Benno Stein and Odej Kao
%D 2006
%I 
%C Riva del Garda, Italy
%K genetic algorithms, genetic programming, information retrieval, phenotype distance
%U http://www-ai.upb.de/aisearch/tir-06/proceedings/cummins06-framework-for-the-study-evolved-term-weighting-schemes-IR.pdf
%X Evolutionary algorithms and, in particular, Genetic Programming (GP) are increasingly being applied to the problem of evolving term-weighting schemes in Information
   Retrieval (IR). One fundamental problem with the solutions generated by these stochastic processes is that they are often difficult to analyse. A number of questions
   regarding these evolved term-weighting schemes remain unanswered. One interesting question is; do different runs of the GP process bring us to similar points in the
   solution space? This paper deals with determining a number of measures of the distance between the ranked lists (phenotype) returned by different term-weighting schemes.
   Using these distance measures, we develop trees that show the phenotypic distance between these termweighting schemes. This framework gives us a representation of where
   these evolved solutions lie in the solution space. Finally, we evolve several global term-weighting schemes and show that this framework is indeed useful for determining
   the relative closeness of these schemes and for determining the expected performance on general test data.
%8 29  August
%Z TIR-06 http://www.aisearch.de/tir-06/

%A Ronan Cummins
%A Colm O'Riordan
%T An analysis of the Solution Space for Genetically Programmed Term-Weighting Schemes in Information Retrieval
%B 17th Irish Artificial Intelligence and Cognitive Science Conference (AICS 2006)
%E D. A. Bell
%D 2006
%I 
%I Artificial Intelligence Association of Ireland
%C Queen's University, Belfast
%K genetic algorithms, genetic programming
%8 11th-13th  September
%Z http://www.cs.qub.ac.uk/aics06/aics.html

%A Ronan Cummins
%A Colm O'Riordan
%T Using genetic programming for information retrieval: local and global query expansion
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 2255--2255
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Real-World Applications: Poster, information retrieval, query-expansion
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2255.pdf
%X This poster presents results for two approaches using Genetic Programming (GP) to overcome the problem of term mismatch in Information Retrieval (IR). We use automatic
   query expansion techniques which add terms to a user's initial query in the hope that these words better describe the information need and ultimately return more relevant
   documents to the user.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Ronan Cummins
%A Colm O'Riordan
%T An Axiomatic Comparison of Learned Term-weighting Schemes in Information Retrieval
%B 18th Irish Conference on Artificial Intelligence and Cognitive Science
%E Sarah Jane Delany and Michael Madden
%D 2007
%I 
%C Dublin Institute of Technology
%K genetic algorithms, genetic programming
%8 29-31  August
%Z http://www.comp.dit.ie/aics07/program.html

%A Ronan Cummins
%A Colm O'Riordan
%T Evolved term-weighting schemes in Information Retrieval: an analysis of the solution space
%J Artificial Intelligence Review
%V 26
%N 1-2
%D 2006
%P 35--47
%I 
%K genetic algorithms, genetic programming, Information Retrieval, Term-weighting schemes
%X Evolutionary computation techniques are increasingly being applied to problems within Information Retrieval (IR). Genetic programming (GP) has previously been used with
   some success to evolve term-weighting schemes in IR. However, one fundamental problem with the solutions generated by this stochastic, non-deterministic process, is that
   they are often difficult to analyse. In this paper, we introduce two different distance measures between the phenotypes (ranked lists) of the solutions (term-weighting
   schemes) returned by a GP process. Using these distance measures, we develop trees which show how different solutions are clustered in the solution space. We show, using
   this framework, that our evolved solutions lie in a different part of the solution space than two of the best benchmark term-weighting schemes available.
%8 October
%Z Published online: 12 September 2007

%A Ronan Cummins
%A Colm O'Riordan
%T An axiomatic comparison of learned term-weighting schemes in information retrieval: clarifications and extensions
%J Artificial Intelligence Review
%V 28
%N 1
%D 2007
%P 51--68
%I 
%K genetic algorithms, genetic programming, Information retrieval, Axiomatic constraints
%X Machine learning approaches to information retrieval are becoming increasingly widespread. In this paper, we present term-weighting functions reported in the literature
   that were developed by four separate approaches using genetic programming. Recently, a number of axioms (constraints), from which all good term-weighting schemes should be
   deduced, have been developed and shown to be theoretically and empirically sound. We introduce a new axiom and empirically validate it by modifying the standard BM25
   scheme. Furthermore, we analyse the BM25 scheme and the four learned schemes presented to determine if the schemes are consistent with the axioms. We find that one learned
   term-weighting approach is consistent with more axioms than any of the other schemes. An empirical evaluation of the schemes on various test collections and query lengths
   shows that the scheme that is consistent with more of the axioms outperforms the other schemes.
%8 June
%Z Published online: 13 September 2008

%A Ronan Cummins
%A Colm O'Riordan
%T An Axiomatic Study of Learned Term-Weighting Schemes
%B SIGIR 2007 workshop: Learning to Rank for Information Retrieval
%E Thorsten Joachims and Hang Li and Tie-Yan Liu and ChengXiang Zhai
%D 2007
%I 
%I Microsoft
%K genetic algorithms, genetic programming
%U http://ww2.it.nuigalway.ie/cirg/localpubs/axioms.pdf
%X At present, there exists many term-weighting schemes each based on different underlying models of retrieval. Learn- ing approaches are increasingly being applied to the
   term- weighting problem, further increasing the number of useful term-weighting approaches available. Many of these term- weighting schemes have certain features and
   properties in common. As such, it is beneficial to formally model these common features and properties. In this paper, we introduce a term-weighting scheme that has been
   developed incrementally using an evolutionary learn- ing approach. We analyse one such term-weighting function produced from the evolutionary approach by decomposing it
   into inductive query and document growth functions. Con- sequently, we show that it is consistent with a number of axioms previously postulated for term-weighting schemes.
   Interestingly, we show that a further constraint can be de- rived from the resultant scheme. Finally, we empirically validate our analysis, and the newly developed
   constraint, by showing that the newly developed nonparametric term-weighting scheme can outperform BM25 and the pivoted document length normalisation scheme over many
   different query types and collections. We conclude that the scheme produced from the learning approach adds further evidence to the validity of the axioms.
%8 27  July
%Z https://research.microsoft.com/en-us/um/beijing/events/LR4IR-2007/

%A Ronan Cummins
%T The Evolution and Analysis of Term-Weighting Schemes in Information Retrieval
%R Ph.D. Thesis
%D 2008
%I 
%I National University of Ireland, Galway
%K genetic algorithms, genetic programming
%U http://www3.it.nuigalway.ie/cirg/rcummins_thesis.pdf
%X Information Retrieval is concerned with the return of relevant documents from a document collection given a user query. Term-weighting schemes assign weights to keywords
   (terms) based on how useful they are likely to be in identifying the topic of a document and are one of the most crucial aspects in relation to the performance of
   Information Retrieval systems. Much research has focused on developing both term-weighting schemes and theories to support them. Genetic Programming is a
   biologically-inspired search algorithm useful for searching large complex search spaces. It uses a Darwinian-inspired survival of the fittest approach to search for
   solutions of a suitable fitness. This thesis outlines experiments that use Genetic Programming to search for term-weighting schemes. A study of term-weighting schemes in
   the literature is undertaken and consequently, the function space is separated into three areas that represent three fundamental concepts in term weighting. Experiments
   using Genetic Programming to search these three function spaces show that term-weighting schemes that outperform state of the art term-weighting benchmarks can be found.
   These experiments also show that the new term-weighting schemes have general properties as they achieve high performance on unseen test data. An analysis of the solution
   space of the term-weighting schemes shows that the evolved solutions exist in a different part of the space than the current benchmarks. These experiments show that the
   Genetic Programming approach consistently evolves solutions that return similar ranked lists in each of the three function spaces. Furthermore, the best performing
   term-weighting schemes are formally analysed and are shown to satisfy a number of axioms in Information Retrieval. A detailed analysis of the existing axioms is presented
   together with some amendments and additions to the existing axioms. This analysis aids in theoretically validating the term-weighting schemes evolved in the framework.
   Finally, a secondary application of Genetic Programming to Information Retrieval is presented to show the potential for Genetic Programming in addressing other issues in
   Information Retrieval. This experiment shows that Genetic Programming can be used to combine further evidence in the retrieval process to enhance performance. This approach
   evolves schemes for use with two automatic query expansion techniques to increase retrieval effectiveness.
%8 May
%Z Supervisor: Colm O'Riordan

%A Ronan Cummins
%A Colm O'Riordan
%T Learning in a pairwise term-term proximity framework for information retrieval
%B SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
%E James Allan and Javed Aslam
%D 2009
%P 251--258
%I ACM New York, NY, USA
%C Boston, MA, USA
%K genetic algorithms, genetic programming, information retrieval, learning to rank, proximity
%X Traditional ad hoc retrieval models do not take into account the closeness or proximity of terms. Document scores in these models are primarily based on the occurrences or
   non-occurrences of query-terms considered independently of each other. Intuitively, documents in which query-terms occur closer together should be ranked higher than
   documents in which the query-terms appear far apart. This paper outlines several term-term proximity measures and develops an intuitive framework in which they can be used
   to fully model the proximity of all query-terms for a particular topic. As useful proximity functions may be constructed from many proximity measures, we use a learning
   approach to combine proximity measures to develop a useful proximity function in the framework. An evaluation of the best proximity functions show that there is a
   significant improvement over the baseline ad hoc retrieval model and over other more recent methods that employ the use of single proximity measures.
%Z Also known as \cite1571986

%A Jean A Cunge
%T Of data and models
%J Journal of Hydroinformatics
%V 5
%N 2
%D 2003
%P 75--98
%I 
%K genetic algorithms, genetic programming
%U http://www.iwaponline.com/jh/005/0075/0050075.pdf
%X Relationship between the data, such as direct observations of nature and recorded measurements, and the models is very complicated in the 'water domain'. It is not at all
   as clear and explicit as it is often presented by teachers to students, by consultants to clients, or by authors to readers of publications. A number of aspects of this
   relationship are discussed using examples to illustrate the author's views. Limitations of data-driven tools (correlations, Artificial Neuronal Networks, Genetic
   Algorithms, etc.) and data-mining, when applied without physical knowledge of the relevant phenomena, are discussed, as are those of deterministic models. The currently
   used 'good practice' paradigm in modelling (the model is to be set up, calibrated, validated and run) is rejected when deterministic models are concerned. They should not
   be calibrated. A new paradigm, a new 'code of good practice', is proposed instead. Strategic and tactical aspects of various available approaches to modelling of physical
   phenomena and data exploitation have practical engineering and financial consequences, most often immediate and sometimes very important: hence the significance of the
   subject that concerns the everyday occupations of modellers, their clients and end-users.
%8 April
%Z GP amongst others

%A Tucker Cunningham
%T Using the Genetic Algorithm to Evolve a Winning Strategy for Othello
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 31--37
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2003/Cunningham.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Leandro F. Cupertino
%A Cleomar P. Silva
%A Douglas M. Dias
%A Marco Aurelio C. Pacheco
%A Cristiana Bentes
%T Evolving CUDA PTX programs by quantum inspired linear genetic programming
%B GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)
%E Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis
%D 2011
%P 399--406
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, EDA, Artificial Intelligence, automatic programming, program synthesis, Performance, GPU, CUDA, PTX, quantum-inspired algorithms
%X The tremendous computing power of Graphics Processing Units (GPUs) can be used to accelerate the evolution process in Genetic Programming (GP). The automatic generation of
   code using the GPU usually follows two different approaches: compiling each evolved or interpreting multiple programs. Both approaches, however, have performance drawbacks.
   In this work, we propose a novel approach where the GPU pseudo-assembly language, PTX (Parallel Thread Execution), is evolved. Evolving PTX programs is faster, since the
   compilation of a PTX program takes orders of magnitude less time than a CUDA program compilation on the CPU, and no interpreter is necessary. Another important aspect of
   our approach is that the evolution of PTX programs follows the Quantum Inspired Linear Genetic Programming (QILGP). Our approach, called QILGP3U (QILGP + GPGPU), enables
   the evolution on a single machine in a reasonable time, enhances the quality of the model with the use of PTX, and for big databases can be much faster than the CPU
   implementation.
%8 12-16  July
%Z No absolute measure of speed given. Mexican Hat. Almost all time spent compiling PTX. Header-body(evolved)-foot. nVidia Tesla C1060 GPU. Also known as \cite2002026
   Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Dara Curran
%A Eugene Freuder
%A Thomas Jansen
%T Incremental evolution of local search heuristics
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 981--982
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, incremental evolution, genetic programming, local search heuristics, graph colouring, hyperheuristics, Poster
%X In evolutionary computation, incremental evolution refers to the process of employing an evolutionary environment that becomes increasingly complex over time. We present an
   implementation of this approach to develop randomised local search heuristics for constraint satisfaction problems, combining research on incremental evolution with local
   search heuristics evolution. A population of local search heuristics is evolved using a genetic programming framework on a simple problem for a short period and is then
   allowed to evolve on a more complex problem. Experiments compare the performance of this population with that of a randomly initialised population evolving directly on the
   more complex problem. The results obtained show that incremental evolution can represent a significant improvement in terms of optimisation speed, solution quality and
   solution structure.
%8 7-11  July
%Z Culberon's random graph generator. Also known as \cite1830660 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and
   the fifteenth annual genetic programming conference (GP-2010)

%A Robert Curry
%A Malcolm I. Heywood
%T Towards Efficient Training on Large Datasets for Genetic Programming
%B 17th Conference of the Canadian Society for Computational Studies of Intelligence
%S LNAI
%E Ahmed Y. Tawfik and Scott D. Goodwin
%V 3060
%D 2004
%P 161--174
%I Springer-Verlag
%C London, Ontario, Canada
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3060&spage=161
%X Genetic programming (GP) has the potential to provide unique solutions to a wide range of supervised learning problems. The technique, however, does suffer from a widely
   acknowledged computational overhead. As a consequence applications of GP are often confined to datasets consisting of hundreds of training exemplars as opposed to tens of
   thousands of exemplars, thus limiting the widespread applicability of the approach. In this work we propose and thoroughly investigate a data sub-sampling algorithm
   hierarchical dynamic subset selection that filters the initial training dataset in parallel with the learning process. The motivation being to focus the GP training on the
   most difficult or least recently visited exemplars. To do so, we build on the dynamic sub-set selection algorithm of Gathercole \citega94aGathercole and extend it into a
   hierarchy of subset selections, thus matching the concept of a memory hierarchy supported in modern computers. Such an approach provides for the training of GP solutions to
   data sets with hundreds of thousands of exemplars in tens of minutes whilst matching the classification accuracies of more classical approaches.
%8 17-19  May
%@ 3-540-22004-6

%A Robert Curry
%A Peter Lichodzijewski
%A Malcolm I. Heywood
%T Scaling Genetic Programming to Large Datasets Using Hierarchical Dynamic Subset Selection
%J IEEE Transactions on Systems, Man, and Cybernetics: Part B - Cybernetics
%V 37
%N 4
%D 2007
%P 1065--1073
%I 
%K genetic algorithms, genetic programming, active learning, classification, unbalanced data, hierarchical DSS, RSS, linear genetic programming, casGP
%U http://www.cs.dal.ca/~mheywood/X-files/GradPubs.html#curry
%X The computational overhead of Genetic Programming (GP) may be directly addressed without recourse to hardware solutions using active learning algorithms based on the Random
   or Dynamic Subset Selection heuristics (RSS or DSS). This work begins by presenting a family of hierarchical DSS algorithms: RSS-DSS, cascaded RSS-DSS, and the Balanced
   Block DSS algorithm; where the latter has not been previously introduced. Extensive benchmarking over four unbalanced real-world binary classification problems with 30,000
   to 500,000 training exemplars demonstrates that both the cascade and Balanced Block algorithms are able to reduce the likelihood of degenerates, whilst providing a
   significant improvement in classification accuracy relative to the original RSS-DSS algorithm. Moreover, comparison with GP trained without an active learning algorithm
   indicates that classification performance is not compromised, while training is completed in minutes as opposed to half a day.
%8 August
%Z max prog length=8, comparsion with lilGP, binary classification, unbalanced training sets, selecting balanced training subsets, page based crossover

%A R. Curry
%A M. I. Heywood
%T One-Class Learning with Multi-Objective Genetic Programming
%B Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics
%D 2007
%P 1938--1945
%I IEEE Press
%C Montreal
%K genetic algorithms, genetic programming, evolutionary multi-criteria optimisation, one-class learning
%U http://users.cs.dal.ca/~mheywood/X-files/Publications/rcurry_SMC07.pdf
%X One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multi-objective genetic programming
   (GP) allows the one-class learning problem to be decomposed by multiple GP classifiers, each attempting to identify only a subset of the target data to classify. In order
   for GP to identify appropriate subsets of the one-class data, artificial outclass data is generated in and around the provided inclass data. A local Gaussian wrapper is
   employed where this reinforces a novelty detection as opposed to a discrimination approach to classification. Furthermore, a hierarchical subset selection strategy is used
   to deal with the necessarily large number of generated outclass exemplars. The proposed approach is demonstrated on three one-class classification datasets and was found to
   be competitive with a one-class SVM classifier and a binary SVM classifier.
%8 7-10  October
%Z http://www.smc2007.org/program.html rcurry_SMC07.pdf is twenty pages
%@ 1-4244-0991-8

%A Robert Curry
%A Malcolm Heywood
%T One-Class Genetic Programming
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 1--12
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Franci Cus
%A Joze Balic
%A Uros Zuperl
%T Genetic algorithm based optimisation of end milling parameters
%J Machine Engineering
%V 3
%N 1/2
%D 2003
%P 116--126
%I 
%K genetic algorithms
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/cus_2003_ME.pdf
%X The paper proposes a new optimization technique based on genetic algorithms for the determination of the cutting parameters in machining operations. In metal cutting
   processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology
   for continual improvement of cutting conditions with GA (Genetic Algorithms). It performs the following: the modification of recommended cutting conditions obtained from a
   machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed
   GA. Operators usually select the machining parameters according to handbooks or their experience, and the selected machining parameters are usually conservative to avoid
   machining failure. Compared to traditional optimisation methods, a GA is robust, global and may be applied generally without recourse to domain-specific heuristics.
   Experimental results show that the proposed genetic algorithm- based procedure for solving the optimisation problem is both effective and efficient, and can be integrated
   into an intelligent manufacturing system for solving complex machining optimisation problems.
%Z Also appears as: "Manufacturing flexibility design and development", Jerzy Jedrzejewski (editor), NOT, Karpacz, Poland, Wroclaw: Editorial institution of the Wroclaw board
   of federation of scientific societies. For Machine Engineering journal see also \citekusiak:2001:ME

%A Franci Cus
%A Joze Balic
%T Optimization of cutting process by GA approach
%J Robotics and Computer-Integrated Manufacturing
%V 19
%N 1-2
%D 2003
%P 113--121
%I 
%K genetic algorithms, genetic programming, Cutting parameters, Manufacturing, simulation
%X The paper proposes a new optimization technique based on genetic algorithms (GA) for the determination of the cutting parameters in machining operations. In metal cutting
   processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology
   for continual improvement of cutting conditions with GA. It performs the following: the modification of recommended cutting conditions obtained from a machining data,
   learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. Experimental
   results show that the proposed genetic algorithm-based procedure for solving the optimisation problem is both effective and efficient, and can be integrated into an
   intelligent manufacturing system for solving complex machining optimisation problems.
%8 February -  April
%Z http://www.elsevier.com/wps/find/journaldescription.cws_home/704/description#description

%A Franc Cus
%A Matjaz Milfelner
%A Joze Balic
%T Optimization of cutting forces in ball-end milling by GA
%J Machine Engineering
%V 4
%N 1/2
%D 2004
%P 281--288
%I 
%K genetic algorithms, genetic programming
%X This paper presents the system for optimization of ball-end milling process. The system combines different methods and technologies like evolutionary methods, manufacturing
   technology, measuring and control technology and intelligent process technology with the adequate hardware and software support. The system for optimization of ball-end
   milling process combines the process monitoring system of ball-end milling process and the optimization model. The monitoring system is designed for monitoring and
   collecting variables of the milling process by means of sensors and transformation of those data into numerical values which are a starting point for the optimization of
   the ball-end milling process. The optimization model is used for the optimisation of milling parameters with genetic algorithms. The optimization is based on the analytic
   and genetic cutting force model and tool wear model. The developed methods can be used for the cutting force estimation and optimization of cutting parameters. The
   integration of the proposed system will lead to the reduction in production costs and production time, flexibility in machining parameter selection, and improvement of
   product quality. The system for optimization of ball-end milling process of steels can be extended to machining different materials and to other cutting techniques such as
   conventional turning, drilling, grinding and high speed turning.
%Z Also appears as: "Machine tools and factories of the knowledge", Jerzy Jedrzejewsk (editor). For Machine Engineering journal see also \citekusiak:2001:ME

%A F. Cus
%A M. Milfelner
%A J. Balic
%T An intelligent system for monitoring and optimization of ball-end milling process
%J Journal of Materials Processing Technology
%V 175
%N 1-3
%D 2006
%P 90--97
%I 
%K Genetic algorithm, Ball-end milling, Cutting forces, Monitoring, Optimization
%U http://www.sciencedirect.com/science/article/B6TGJ-4GJKTR6-4/2/5b1e17c8ac5f2a7435ab419b4db98260
%X The paper presents an intelligent system for on-line monitoring and optimization of the cutting process on the model of the ball-end milling. An intelligent system for
   monitoring and optimization in ball-end milling is developed both in hardware and software. It is based on a PC, which is connected to the CNC main processor module through
   a serial-port so that control and communication can be realised. The monitoring system is based on LabVIEW software, the data acquisition system and the measuring devices
   (sensors) for the cutting force measuring. The system collects the variables of the cutting process by means of sensors. The measured values are delivered to the computer
   program through the data acquisition system for data processing and analysis. The optimization technique is based on genetic algorithms for the determination of the cutting
   conditions in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a
   final product. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is effective and efficient, and can be
   integrated into a real-time intelligent manufacturing system for solving complex machining optimization problems.
%O Achievements in Mechanical and Materials Engineering

%A Sylvain Cussat-Blanc
%A Herve Luga
%A Yves Duthen
%T From single cell to simple creature morphology and metabolism
%B Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems
%E S. Bullock and J. Noble and R. Watson and M. A. Bedau
%D 2008
%P 134--141
%I MIT Press Cambridge, MA, USA
%C Winchester, Hants
%K genetic algorithms, genetic programming
%U http://www.alifexi.org/papers/ALIFExi_pp134-141.pdf
%8 5-8  August

%A Sylvain Cussat-Blanc
%A Herve Luga
%A Yves Duthen
%T Cell2Organ: Self-Repairing Artificial Creatures Thanks to a Healthy Metabolism
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P -
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X For living organisms, the robustness property is capital. For almost all of them, robustness rhymes with self repairing. Indeed, organisms are subject to various injuries
   brought by the environment. To maintain their integrity, organisms are able to regenerate dead parts of themselves. This mechanism, commonly named self-repairing, is
   interesting to reproduce. Many works exist about self-repairing in robotics and electronics but fewer are in our domain of interest, artificial embryogenesis. In this
   paper, we show the self-repairing abilities of our model, Cell2Organ, designed to generate artificial creatures for artificial worlds. This model has previously been
   presented in \citealifexi_cussatblanc_134.
%8 18-21  May
%Z Gene Regulartory Network GRN, promoter, enhance, inhibitor. Java. Grid5000.fr powered parallel GA \citeCussat-Blanc:2008:gecco has three chromosomes. ProActive. CEC 2009 -
   A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Dragan Cvetkovic
%A Ian C. Parmee
%T Use of Preferences for GA-based Multi-objective Optimisation
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1504--1509
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-764.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Luis E. {Da Costa}
%A Jacques-Andre Landry
%T Relaxed genetic programming
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 937--938
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming: Poster, bloat, generalisation error, measurement
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p937.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Luis E. {Da Costa}
%A Jacques-Andre Landry
%A Yan Levasseur
%T Treating Noisy Data Sets with Relaxed Genetic Programming
%B Artificial Evolution
%S Lecture Notes in Computer Science
%E Nicolas Monmarch\'e and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton
%V 4926
%D 2007
%P 1--12
%I Springer
%C Tours, France
%K genetic algorithms, genetic programming
%X In earlier papers we presented a technique (RelaxGP) for improving the performance of the solutions generated by Genetic Programming (GP) applied to regression and
   approximation of symbolic functions. RelaxGP changes the definition of a perfect solution: in standard symbolic regression, a perfect solution provides exact values for
   each point in the training set. RelaxGP allows a perfect solution to belong to a certain interval around the desired values. We applied RelaxGP to regression problems where
   the input data is noisy. This is indeed the case in several real-world problems, where the noise comes, for example, from the imperfection of sensors. We compare the
   performance of solutions generated by GP and by RelaxGP in the regression of 5 noisy sets. We show that RelaxGP with relaxation values of 10percent to 100percent of the
   Gaussian noise found in the data can outperform standard GP, both in terms of generalization error reached and in resources required to reach a given test error.
%8 31-29  October
%Z EA'07

%A Mansi Daga
%A M. C. Deo
%T Alternative data-driven methods to estimate wind from waves by inverse modeling
%J Natural Hazards
%V 49
%N 2
%D 2009
%P 293--310
%I 
%K genetic algorithms, genetic programming, Locally weighted learning, Model trees, Inverse modeling, Wind estimation, LWOR, MT, GP
%X An attempt is made to derive wind speed from wave measurements by carrying out an inverse modeling. This requirement arises out of difficulties occasionally encountered in
   collecting wave and wind data simultaneously. The wind speed at every 3-h interval is worked out from corresponding simultaneous measurements of significant wave height and
   average wave periods with the help of alternative data-driven methods such as program-based genetic programming, model trees, and locally weighted projection regression.
   Five different wave buoy locations in Arabian Sea, representing nearshore and offshore as well as shallow and deep water conditions, are considered. The duration of
   observations ranged from 15 months to 29 months for different sites. The testing performance of calibrated models has been evaluated with the help of eight alternative
   error statistics, and the best model for all locations is determined by averaging out the error measures into a single evaluation index. All the three methods
   satisfactorily estimated the wind speed from known wave parameters through inverse modeling. The genetic programming is found to be the most suitable tool in majority of
   the cases.
%8 May
%Z Discipulus. Goa, Minicoy Island, Marmagoa. Storm modelling Department of Civil Engineering, Indian Institute of Technology, Bombay, Mumbai, 400076, India

%A J. M. Daida
%A S. J. Ross
%A B. C. Hannan
%T Biological Symbiosis as a Metaphor for Computational Hybridization
%B Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)
%E Larry J. Eshelman
%D 1995
%P 248--255
%I Morgan Kaufmann San Francisco, CA, USA
%C Pittsburgh, PA, USA
%K Genetic Algorithms
%U ftp://ftp.eecs.umich.edu/people/daida/papers/icga95.pdf
%8 15-19  July
%@ 1-55860-370-0

%A J. M. Daida
%A J. D. Hommes
%A S. J. Ross
%A J. F. Vesecky
%T Extracting curvilinear features from SAR images of arctic ice: Algorithm discovery using the genetic programming paradigm
%B Proceedings of IEEE International Geoscience and Remote Sensing
%E T. Stein
%D 1995
%P 673--675
%I IEEE Press Washington
%C Florence, Italy
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/406479.html
%Z This paper focuses on how a method for automated programming (i.e., genetic programming) applies in the computeraided discovery of algorithms that enhance and extract
   features from remotely sensed images. Highlighted as a case study is the use of this method in the problem of extracting pressure ridge features from ERS-1 SAR imagery; a
   problem for which there has been no known satisfactory solution. The research on algorithm discovery uses the genetic programming paradigm to assist geoscientists in
   extracting textural features from satellite synthetic aperture radar imagery (i.e., ERS-1). Manual methods are extremely time consuming and limited to a few frames (in this
   case, a 1k by 1k low-res data product, or a 8k by 8k hi-res data product). Desirable are semi-automated, automated, or computer-assisted algorithm developmental tools for
   data analysis. (gp-list 13 Apr 95) Firenze, Italy

%A J. M. Daida
%A A. Freeman
%A R. Onstott
%T Evaluation of hybrid symbiotic systems on segmenting SAR imagery
%B Proceedings of IEEE International Geoscience and Remote Sensing
%E T. Stein
%D 1995
%P 1415--1417
%I IEEE Press Washington
%C Florence, Italy
%K genetic algorithms
%U ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_symbiosis.pdf
%Z Invited Paper Firenze, Italy

%A J. M. Daida
%A D. E. Lund
%A C. Wolf
%A G. A. Meadows
%A K. Schroeder
%A J. F. Vesecky
%A D. R. Lyzenga
%A R. Bertram
%T Measuring topography of small-scale waves
%B Proceedings of IEEE International Geoscience and Remote Sensing
%E T. Stein
%D 1995
%P 1881--1883
%I IEEE Press Washington
%C Florence, Italy
%K genetic algorithms
%U ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_GA.pdf
%Z Firenze, Italy

%A Jason M. Daida
%A Jonathan D. Hommes
%A Tommaso F. Bersano-Begey
%A Steven J. Ross
%A John F. Vesecky
%T Algorithm Discovery Using the Genetic Programming Paradigm: Extracting Low-Contrast Curvilinear Features from SAR Images of Arctic Ice
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 417--442
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming, GAIA
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/434
%X We discuss the application of genetic programming (GP) to image analysis problems in geoscience and remote sensing and describes how a GP can be adapted for processing
   large data sets (in our case, 1024 x 1024 pixel images plus texture channels). The featured problem is one that has not been adequately solved for this type of imagery. We
   describe the placement of GP in the overall scheme of algorithm discovery in geoscience image analysis and describe how GP complements a scientist's hypothesis-test
   derivation of such algorithms. The featured solution consists of a standard non-ADF GP that incorporates a dynamic fitness function.
%O 21
%Z see also http://www.sprl.umich.edu/acers/gaia/aigpGaia.html
%@ 0-262-01158-1

%A J. M. Daida
%A C. S. Grasso
%A S. A. Stanhope
%A S. J. Ross
%T Symbionticism and Complex Adaptive Systems I: Implications of Having Symbiosis Occur in Nature
%B Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming
%E Lawrence J. Fogel and Peter J. Angeline and Thomas Baeck
%D 1996
%P 177--186
%I MIT Press Cambridge, MA, USA
%C San Diego
%U ftp://ftp.eecs.umich.edu/people/daida/papers/EP96_symbiosis.pdf
%8 February  29-  March  3
%Z EP-96, Invited Paper http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383
%@ 0-262-06190-2

%A Jason M. Daida
%A Tommaso F. Bersano-Begey
%A Steven J. Ross
%A John F. Vesecky
%T Computer-Assisted Design of Image Classification Algorithms: Dynamic and Static Fitness Evaluations in a Scaffolded Genetic Programming Environment
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 279--284
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A J. M. Daida
%A R. G. Onstott
%A T. F. Bersano-Begey
%A S. J. Ross
%A J. F. Vesecky
%T Ice Roughness Classification and ERS SAR Imagery of Arctic Sea Ice: Evaluation of Feature-Extraction Algorithms by Genetic Programming
%B Proceedings of the 1996 International Geoscience and Remote Sensing Symposium
%D 1996
%P 1520--1522
%I IEEE Press Washington
%K genetic algorithms, genetic programming
%U ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GP_Valid.pdf

%A J. M. Daida
%A T. F. Bersano-Begey
%A S. J. Ross
%A J. F. Vesecky
%T Evolving Feature-Extraction Algorithms: Adapting Genetic Programming for Image Analysis in Geoscience and Remote Sensing
%B Proceedings of the 1996 International Geoscience and Remote Sensing Symposium
%D 1996
%P 2077--2079
%I IEEE Press Washington
%K genetic algorithms, genetic programming
%U ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GP.pdf

%A J. M. Daida
%A R. R. Bertram
%A D. R. Lyzenga
%A C. Wolf
%A D. T. Walker
%A S. A. Stanhope
%A G. A. Meadows
%A J. F. Vesecky
%A D. E. Lund
%T Measuring Small-Scale Water Surface Waves: Nonlinear Interpolation and Integration Techniques for Slope Image Data
%B Proceedings of the 1996 International Geoscience and Remote Sensing Symposium
%D 1996
%P 2219--2221
%I IEEE Press Washington
%K genetic algorithms, genetic programming
%U ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GA/igarss96_GAfig.pdf
%Z note: these pages are reverse ordered

%A Jason Daida
%A Steven Ross
%A Jeffrey McClain
%A Derrick Ampy
%A Michael Holczer
%T Challenges with Verification, Repeatability, and Meaningful Comparisons in Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 64--69
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U ftp://ftp.eecs.umich.edu/people/daida/papers/GP97challenges.pdf
%8 13-16  July
%Z GP-97

%A Jason M. Daida
%A Robert R. Bertram
%A Catherine S. Grasso
%A Stephen A. Stanhope
%T Tagging as a Means for Self-Adaptive Hybridization
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 42--50
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Jason M. Daida
%A Robert R. Bertram
%A John A. {Polito~2}
%A Stephen A. Stanhope
%T Analysis of Single-Node (Building) Blocks in Genetic Programming
%B Advances in Genetic Programming 3
%E Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline
%D 1999
%P 217--241
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1123
%X What is a building block in genetic programming? by examining the smallest subtree possible--a single leaf node. The analysis of these subtrees indicates a considerably
   more complex portrait of what exactly is meant by a building block in GP than what has traditionally been considered.
%O 10
%8 June
%Z AiGP3
%@ 0-262-19423-6

%A Jason M. Daida
%T Reconnoiter by Candle: Identifying Assumptions in Genetic Programming
%B Foundations of Genetic Programming
%E Thomas Haynes and William B. Langdon and Una-May O'Reilly and Riccardo Poli and Justinian Rosca
%D 1999
%P 53--54
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/daida.ps.gz
%8 13  July
%Z GECCO'99 WKSHOP, part of \citehaynes:1999:fogp GECCO-99WKS Part of wu:1999:GECCOWKS

%A Jason M. Daida
%A Derrick S. Ampy
%A Michael Ratanasavetavadhana
%A Hsiaolei Li
%A Omar A. Chaudhri
%T Challenges with Verification, Repeatability, and Meaningful Comparison in Genetic Programming: Gibson's Magic
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1851--1858
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, methodology, pedagogy and philosophy
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/MP-604.ps
%X This paper examines some of the reporting and research practices concerning empirical work in genetic programming. We describe several common loopholes and offer three case
   studies---two in data modeling and one in robotics---that illustrate each. We show that by exploiting these loopholes, one can achieve performance gains of up two orders of
   magnitude without any substantiative changes to GP. We subsequently offer several recommendations.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jason M. Daida
%A John A. Polito
%A Steven A. Stanhope
%A Robert R. Bertram
%A Jonathan C. Khoo
%A Shahbaz A. Chaudhary
%T What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 982--989
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-444.ps
%X This paper addresses the issue of what makes a problem GP-hard by considering the binomial-3 problem. In the process, we discuss the efficacy of the metaphor of an adaptive
   fitness landscape to explain what is GP-hard. We show that for at least this problem, the metaphor is misleading.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jason M. Daida
%A Seth P. Yalcin
%A Paul M. Litvak
%A Gabriel A. Eickhoff
%A John A. Polito
%T Of Metaphors and Darwinism: Deconstructing Genetic Programming's Chimera
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 1
%D 1999
%P 453--462
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, biomodeling
%U http://citeseer.ist.psu.edu/242099.html
%X his paper discusses several metaphors from Darwinism that have influenced the development of genetic programming (GP) theory. It specifically examines the historical
   lineage of these metaphors in evolutionary computation and their corresponding concepts in evolutionary biology and Darwinism. It identifies problems that can arise from
   using these metaphors in the development of GP theory.
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143
%@ 0-7803-5537-7 (Microfiche)

%A Jason M. Daida
%A Robert R. Bertram
%A Stephen A. Stanhope
%A Jonathan C. Khoo
%A Shahbaz A. Chaudhary
%A Omer A. Chaudhri
%A John A. {Polito II}
%T What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 2
%N 2
%D 2001
%P 165--191
%I 
%K genetic algorithms, genetic programming, problem difficulty, test problems, fitness landscapes, GP theory
%X This paper addresses the issue of what makes a problem genetic programming (GP)-hard by considering the binomial-3 problem. In the process, we discuss the efficacy of the
   metaphor of an adaptive fitness landscape to explain what is GP-hard. We indicate that, at least for this problem, the metaphor is misleading.
%8 June
%Z patched lilgp. Mersenne Twister. Size and Shape of solutions to 3 binomial - tunably difficult by changing random constants used. Edvard Munch Scream. Inconsistency of ERC
   value within parse tree context. Destructive crossover. P180 "the fitness function did not need to be rugged for GP to encounter difficulty." GP as error correcting.
   Mathematica. p186 "increased population meant more individuals gathered around the" suboptimal "0.8 attractor". Article ID: 335714

%A Jason M. Daida
%T Limits to Expression in Genetic Programming: Lattice-Aggregate Modeling
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 273--278
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%U http://sitemaker.umich.edu/daida/files/CEC7272.pdf
%X This paper describes a general theoretical model of size and shape evolution in genetic programming. The proposed model incorporates a mechanism that is analogous to
   ballistic accretion in physics. The model indicates a four-region partition of GP search space. It further suggests that two of these regions are not searchable by GP.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Jason M. Daida
%T What Makes a Problem GP-Hard? A Look at How Structure Affects Content
%B Genetic Programming Theory and Practice
%E Rick L. Riolo and Bill Worzel
%D 2003
%P 99--118
%I Kluwer
%K genetic algorithms, genetic programming, GP theory, tree structures, problem difficulty, GP-hard, test problems
%X Theoretical work at the University of Michigan that concerns the question "What makes a problem difficult for genetic programming to solve?" Specifically describes linkages
   between content, tree structures, and problem difficulty in genetic programming. The significance of structure in influencing problem difficulty.
%O 7
%Z great pictures Part of \citeRioloWorzel:2003

%A Jason M. Daida
%A Adam M. Hilss
%T Identifying Structural Mechanisms in Standard Genetic Programming
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1639--1651
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming
%U http://sitemaker.umich.edu/daida/files/LNCS2724lattice.pdf
%X hypothesis about an undiscovered class of mechanisms that exist in standard GP. Rather than being intentionally designed, these mechanisms would be an unintended
   consequence of using trees as information structures. A model is described that predicts outcomes in GP that would arise solely from such mechanisms. Comparisons with
   empirical results from GP lend support to the existence of these mechanisms.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003). Also
   known as \citedaida:2003:gecco
%@ 3-540-40603-4

%A Jason M. Daida
%A Adam M. Hilss
%A David J. Ward
%A Stephen L. Long
%T Visualizing Tree Structures in Genetic Programming
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1652--1664
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming
%U http://sitemaker.umich.edu/daida/files/LNCS2724viz.pdf
%X methods to visualise the structure of trees that occur in genetic programming. These allow for the inspection of structure of entire trees of arbitrary size. The methods
   also scale to allow for the inspection of structure for an entire population. Examples are given from a typical problem. The examples indicate further studies that might be
   enabled by visualising structure at these scales.
%8 12-16  July
%Z GECCO-2003 A joint meeting of the twelvth international conference on genetic algorithms (ICGA-99) and the eigth annual genetic programming conference (GP-2003) Also known
   as daida2:2003:gecco
%@ 3-540-40603-4

%A Jason M. Daida
%A Hsiaolei Li
%A Ricky Tang
%A Adam M. Hilss
%T What Makes a Problem GP-Hard? Validating a Hypothesis of Structural Causes
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1665--1677
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming
%X empirical test of a hypothesis, which describes the effects of structural mechanisms in genetic programming. In doing so, the paper offers a test problem anticipated by
   this hypothesis. The problem is tunably difficult, but has this property because tuning is accomplished through changes in structure. Content is not involved in tuning. The
   results support a prediction of the hypothesis - that GP search space is significantly constrained as an outcome of structural mechanisms.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Jason Daida
%T Considering the Roles of Structure in Problem Solving by a Computer
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 67--86
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, GP theory, tree structures, problem difficulty, GP-hard, test problems, Lid, Highlander, Binomial-3
%X This chapter presents a tiered view of the roles of structure in genetic programming. This view can be used to frame theory on how some problems are more difficult than
   others for genetic programming to solve. This chapter subsequently summarises my group's current theoretical work at the University of Michigan and extends the implications
   of that work to real-world problem solving.
%O 5
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A Jason M. Daida
%A Michael E. Samples
%A Bryan T. Hart
%A Jeffry Halim
%A Aditya Kumar
%T Demonstrating Constraints to Diversity with a Tunably Difficulty Problem for Genetic Programming
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 1217--1224
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Theoretical Foundations of Evolutionary Computation
%U http://sitemaker.umich.edu/daida/files/CEC04highlander.pdf
%X This paper introduces a tunably difficult problem for genetic programming (GP) that probes for an upper bound to the amount of heterogeneity that can be represented by a
   single individual. Although GP's variable-length representation would suggest that there is no upper bound, our results indicate otherwise. The results provide insight into
   the dynamics that occur during the course of a GP run.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Jason M. Daida
%A David J. Ward
%A Adam M. Hilss
%A Stephen L. Long
%A Mark R. Hodges
%A Jason T. Kriesel
%T Visualizing the Loss of Diversity in Genetic Programming
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 1225--1232
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Theoretical Foundations of Evolutionary Computation
%U http://sitemaker.umich.edu/daida/files/CEC04viz.pdf
%X This paper introduces visualization techniques that allow for a multivariate approach in understanding the dynamics that underlie genetic programming (GP). Emphasis is
   given toward understanding the relationship between problem difficulty and the loss of diversity. The visualizations raise questions about diversity and problem solving
   efficacy, as well as the role of the initial population in determining solution outcomes.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Jason M. Daida
%A Adam M. Hilss
%A David J. Ward
%A Stephen L. Long
%T Visualizing Tree Structures in Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 6
%N 1
%D 2005
%P 79--110
%I 
%K genetic algorithms, genetic programming
%X presents methods to visualize the structure of trees that occur in genetic programming. These methods allow for the inspection of structure of entire trees even though
   several thousands of nodes may be involved. The methods also scale to allow for the inspection of structure for entire populations and for complete trials even though
   millions of nodes may be involved. Examples are given that demonstrate how this new way of ldquoseeingrdquo can afford a potentially rich way of understanding dynamics that
   underpin genetic programming. The examples indicate further studies that might be enabled by visualizing structure at these scales.
%8 March
%Z Mathematica source code at http://library.wolfram.com/infocenter/MathSource/5163 See also http://www.cs.ucl.ac.uk/staff/W.Langdon/gp2lattice/gp2lattice.html

%A Jason Daida
%T Challenges in Open-Ended Problem Solving with Genetic Programming
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 259--274
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, open-ended problem solving, McMaster Problem Solving
%X how GP might be integrated as a tool into the human context of discovery. To accomplish this, a comparison is made between GP and a well-regarded strategy in open-ended
   problem solving. The comparison not only indicates which tasks and skills are likely to be complemented by GP, but also the kinds of problems that may or may not be suited
   for it. Furthermore, the comparison indicates directions in research that may need to be taken for GP to be further leveraged as a tool that assists discovery.
%O 17
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Jason M. Daida
%T Towards identifying populations that increase the likelihood of success in genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1627--1634
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, binomial-3, building blocks, experimentation, genetic programming problem difficulty, initial populations, performance, population
   dynamics, selection methods, theory
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1627.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Jason M. Daida
%A Michael E. Samples
%A Matthew J. Byom
%T Probing for limits to building block mixing with a tunably-difficult problem for genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1713--1720
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, building blocks, experimentation, highlander problem, initial populations, performance, tunably-difficult problems, theory
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1713.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Jason M. Daida
%A Ricky Tang
%A Michael E. Samples
%A Matthew J. Byom
%T Phase Transitions in Genetic Programming Search
%B Genetic Programming Theory and Practice IV
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%V 5
%D 2006
%P -
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%X Phase transitions and critical phenomena occur not only in thermodynamic systems but also in nonphysical systems that occur in computation. Of particular interest is the
   possibility that phase transitions occur in GP search. If this were so, it would allow for a statistical mechanics approach that would allow for quantitative comparisons of
   GP with a broad variety of rigorously described systems. This chapter summarises our research group's work in this area and describes a case study that illustrates what is
   involved in establishing the existence of phase transitions in GP search.
%O 1
%8 11-13  May
%Z part of \citeRiolo:2006:GPTP Published Jan 2007 after the workshop
%@ 0-387-33375-4

%A Jason M. Daida
%T Characterizing the dynamics of symmetry breaking in genetic programming
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 799--806
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, analysis methods, computational geometry, data structures, design patterns, graphics techniques, languages, measurement, patterns,
   program synthesis, symmetry breaking, synthesis, theory, tree
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p799.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Robert A. Dain
%T Genetic Programming For Mobile Robot Wall-Following Algorithms
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 70
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/dain_1997_GPmrwfa.pdf
%8 13-16  July
%Z GP-97

%A Robert A. Dain
%T Developing Mobile Robot Wall-Folowing Algorithms Using Genetic Programming
%J Applied Intelligence
%V 8
%N 5
%D 1998
%P 33--41
%I 
%K genetic algorithms, genetic programming, computational genetics, machine learning, adaptive systems
%8 January
%Z Special Issues on Evolutionary Learning, Xin Yao and Don Potter, Guest Editors

%A Robert A. Dain
%T Develoopment of Mobile Robot Wall-Following Algorithms using Genetic Programming
%B Industrial Applications of Genetic Algorithms
%S Computational Intelligence
%E Charles L. Karr and L. Michael Freeman
%D 1999
%P 269--283
%I CRC Press
%C Boca Raton, FL, USA
%K genetic algorithms, genetic programming
%@ 0-8493-9801-0

%A Richard Dallaway
%T Genetic programming and cognitive models
%R Technical Report CSRP 300
%D 1993
%I 
%I School of Cognitive \& Computing Sciences, University of Sussex,
%C Brighton, UK
%K genetic algorithms, genetic programming
%U http://www.dallaway.com/acad/evolution/evocog.html
%X Genetic programming (GP) is a general purpose method for evolving symbolic computer programs (e.g. Lisp code). Concepts from genetic algorithms are used to evolve a
   population of initially random programs so that they are able to solve the problem at hand. This paper describes genetic programming and discuss the usefulness of the
   method for building cognitive models. Although it appears that an arbitrary fit to the training examples will be evolved, it is shown that GP can be constrained to produce
   small, general programs.
%O In: Brook \& Arvanitis, eds., 1993 The Sixth White House Papers: Graduate Research in the Cognitive \& Computing Sciences at Sussex
%Z symbolic regression of 2.719x^2 + 3.14161x from 20 random points, parsimony pressure used http://cogslib.cogs.susx.ac.uk/csr_abs.php?type=csrp&num=300&id=7657

%A Robertas Damasevicius
%T Structural analysis of regulatory DNA sequences using grammar inference and Support Vector Machine
%J Neurocomputing
%V 73
%N 4-6
%D 2010
%P 633--638
%I 
%K genetic algorithms, genetic programming, DNA sequence analysis, Grammar inference, L-grammar, Support Vector Machine, SVM
%U http://www.sciencedirect.com/science/article/B6V10-4XRYT4P-1/2/2e5b008bc8df4d5a39553b40fe6728c3
%X Regulatory DNA sequences such as promoters or splicing sites control gene expression and are important for successful gene prediction. Such sequences can be recognized by
   certain patterns or motifs that are conserved within a species. These patterns have many exceptions which makes the structural analysis of regulatory sequences a complex
   problem. Grammar rules can be used for describing the structure of regulatory sequences; however, the manual derivation of such rules is not trivial. In this paper,
   stochastic L-grammar rules are derived automatically from positive examples and counterexamples of regulatory sequences using genetic programming techniques. The fitness of
   grammar rules is evaluated using a Support Vector Machine (SVM) classifier. SVM is trained on known sequences to obtain a discriminating function which serves for
   evaluating a candidate grammar ruleset by determining the percentage of generated sequences that are classified correctly. The combination of SVM and grammar rule inference
   can mitigate the lack of structural insight in machine learning approaches such as SVM.
%O Bayesian Networks / Design and Application of Neural Networks and Intelligent Learning Systems (KES 2008 / Bio-inspired Computing: Theories and Applications (BIC-TA 2007)
%Z TATA box

%A Peter Danielson
%T From Artificial Morality to NERD: Models, Experiments, \& Robust Reflective Equilibrium
%B Artificial Life X. Workshop Proceedings
%D 2006
%P 45--48
%I 
%C Bloomington, IN, USA
%U http://www.alifex.org/program/wkshp_proceed.pdf
%X Artificial ethics deploys the tools of computational science and social science to improve the improve ethics, conceived as pro-social engineering. This paper focuses on
   three key techniques used in the three stages of the research program of the Norms Evolving in Response to Dilemmas (NERD) research group: 1. Artificial Morality.
   Technique: Moral functionalism -- principles expressed as parameterised strategies and tested against a simplified game theoretic goal. 2. Evolving Artificial Moral
   Ecologies. Technique: Genetic programming, agent-based modelling and evolutionary game theory (replicator dynamics). 3. NERD (Norms Evolving in Response to Dilemmas):
   Computer mediated ethics for real people, problems, and clients. Technique: An experimental platform to test and improve ethical mechanisms.
%8 3-7  June

%A Christian Darabos
%A Mario Giacobini
%A Ting Hu
%A Jason H. Moore
%T L\'evy-flight GP: towards a new mutation paradigm
%B 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012
%S LNCS
%E Mario Giacobini and Leonardo Vanneschi and William S. Bush
%V 7246
%D 2012
%P 38--49
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming
%X Levy flights are a class of random walks inspired directly by observing animal foraging habits, in which the stride length is drawn from a power-law distribution. This
   implies that the vast majority of the strides will be short. However, on rare occasions, the stride are gigantic. We use this technique to self-adapt the mutation rate used
   in Linear Genetic Programming. We apply this original approach to three different classes of problems: Boolean regression, quadratic polynomial regression, and surface
   reconstruction. We find that in all cases, our method outperforms the generic, commonly used constant mutation rate of 1 over the size of the genotype. We compare different
   common values of the power-law exponent to the regular spectrum of constant values used habitually. We conclude that our novel method is a viable alternative to constant
   mutation rate, especially because it tends to reduce the number of parameters of genetic programing.
%8 11-13  April
%Z Part of \citeGiacobini:2012:EvoBio EvoBio'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoMusArt2012 and EvoApplications2012

%A Paul Darwen
%A Xin Yao
%T Automatic Modularization by Speciation
%B Third IEEE International Conference on Evolutionary Computation
%D 1996
%I IEEE press
%K genetic algorithms
%U http://www.demo.cs.brandeis.edu/papers/icec96darwen.ps.gz
%X Real-world problems are often too difficult to be solved by a single monolithic system. There are many examples of natural and artificial systems which show that a modular
   approach can reduce the total complexity of the system whilesolving a difficult problem satisfactorily. The success of modular artificial neural networks in speech and
   image processing is a typical example. However, designing a modular system is a difficult task. It relies heavily on human experts and prior knowledge about the problem.
   There is no systematic and automatic way to form a modular system for a problem. This paper proposes a novel evolutionary learning approach to designing a modular system
   automatically, without human intervention. Our starting point is speciation, using a technique based on fitness sharing. While speciation in genetic algorithms is not new,
   no effort has been made towards using a speciated population as a complete modular system. We harness the specialized expertise in the species of an entire population,
   rather than a single individual, by introducing a gating algorithm. We demonstrate our approach to automatic modularization by improving co-evolutionary game learning.
   Following earlier researchers, we learn to play iterated prisoner's dilemma. We review some problems of earlier co-evolutionary learning, and explain their poor
   generalization ability and sudden mass extinctions. The generalization ability of our approach is significantly better than past efforts. Using the specialised expertise of
   the entire speciated population though a gating algorithm, instead of the best individual, is the main contributor to this improvement.

%A Abhishek Das
%T Analyses of Crash Occurrence and Injury Severities on Multi Lane Highways using Machine Learning Algorithms
%R Ph.D. Thesis
%D 2009
%I 
%I Department of Civil, Environmental, and Construction Engineering (CECE) of the University of Central Florida
%C Orlando, USA
%K genetic algorithms, genetic programming
%U http://www.cecs.ucf.edu/graddefense/pdf/10
%X Reduction of crash occurrence on the various roadway locations (mid-block segments; signalized intersections; un-signalized intersections) and the mitigation of injury
   severity in the event of a crash are the major concerns of transportation safety engineers. Multi lane arterial roadways (excluding freeways and expressways) account for
   forty-three percent of fatal crashes in the state of Florida. Significant contributing causes fall under the broad categories of aggressive driver behavior; unforgiving
   weather and environmental conditions; and roadway geometric and traffic factors. The objective of this research was the implementation of innovative, state-of-the-art
   analytical methods to identify the contributory factors for crashes and injury severity. Advances in computational methods render the use of modern statistical and machine
   learning algorithms. Even though most of the contributing factors are known a-priori, advanced methods unearth changing trends. Heuristic evolutionary processes such as
   linear genetic programming; sophisticated data mining methods like conditional inference tree; and mathematical treatments in the form of sensitivity analyses outline the
   major contributions in this research. Application of traditional statistical methods like simultaneous ordered probit models, identification and resolution of crash data
   problems are also key aspects of this study. In order to eliminate the use of unrealistic uniform intersection influence radius of 250 ft, heuristic rules were developed
   for assigning crashes to roadway segments, junctions with traffic lights intersection and access points using parameters, such as 'site location' and 'traffic control'. Use
   of Conditional Inference Forest instead of Classification and Regression Tree to identify variables of significance for injury severity analysis removed the bias towards
   the selection of continuous variable or variables with large number of categories. Concepts of evolutionary biology like crossover and mutation were implemented to develop
   models for classification and regression analyses based on the highest hit rate and minimum error rate, respectively. Annual daily traffic; friction coefficient of
   pavements; on-street parking; curbed medians; surface and shoulder widths; alcohol / drug usage are some of the significant factors that played a role in both the crash
   occurrence and injury severities. Relative sensitivity analyses were used to identify the effect of continuous variables on the variation of crash counts. This study
   improved the understanding of the significant factors that could play an important role in designing better safety countermeasures on multi lane highways, and hence enhance
   their safety by reducing the frequency of crashes and severity of injuries.
%8 13  October

%A Abhishek Das
%A Mohamed Abdel-Aty
%T A genetic programming approach to explore the crash severity on multi-lane roads
%J Accident Analysis \& Prevention
%V 42
%N 2
%D 2010
%P 548--557
%I 
%K genetic algorithms, genetic programming, Crash severity, Multi-lane roads, Genetic algorithm, Discipulus
%U http://www.sciencedirect.com/science/article/B6V5S-4XFXSWB-3/2/d3dd6df818f461070f758ebe4fb9f1f3
%X The study aims at understanding the relationship of geometric and environmental factors with injury related crashes as well as with severe crashes through the development
   of classification models. The Linear Genetic Programming (LGP) method is used to achieve these objectives. LGP is based on the traditional genetic algorithm, except that it
   evolves computer programs. The methodology is different from traditional non-parametric methods like classification and regression trees which develop only one model, with
   fixed criteria, for any given dataset. The LGP on the other hand not only evolves numerous models through the concept of biological evolution, and using the evolutionary
   operators of crossover and mutation, but also allows the investigator to choose the best models, developed over various runs, based on classification rates. Discipulus(TM)
   software was used to evolve the models. The results included vision obstruction which was found to be a leading factor for severe crashes. Percentage of trucks, even if
   small, is more likely to make the crashes injury prone. The [`]lawn and curb' median are found to be safe for angle/turning movement crashes. Dry surface conditions as well
   as good pavement conditions decrease the severity of crashes and so also wider shoulder and sidewalk widths. Interaction terms among variables like on-street parking with
   higher posted speed limit have been found to make injuries more probable.

%A Abhishek Das
%A Mohamed A. Abdel-Aty
%T A combined frequency-severity approach for the analysis of rear-end crashes on urban arterials
%J Safety Science
%D 2011
%I 
%K genetic algorithms, genetic programming, Arterial safety, Injury severity, Crash frequency, Sensitivity analysis
%U http://www.sciencedirect.com/science/article/B6VF9-52T1BCG-2/2/dbc605442a050a3d5a59a825025f0f40
%X Analysis of both the crash count and the severity of injury are required to provide the complete picture of the safety situation of any given roadway. The randomness of
   crashes, the one-way dependency of injury on crash occurrence and the difference in response types have typically led researchers into developing independent statistical
   models for crash count and severity classification. The Genetic Programming (GP) methodology adopts the concepts of evolutionary biology such as crossover and mutation in
   effectively giving a common heuristic approach to model the development for the two different modelling objectives. The chosen GP models have the highest hit rate for
   rear-end crash classification problem and the least error for function fitting (regression) problems. Higher Average Daily Traffic (ADT) is more likely to result in more
   crashes. Absence of on-street parking may result in diminished severity of injuries resulting from crashes as they may provide soft crash barrier in contrast to fixed road
   side objects. Graphical presentation of the frequency of crashes with varying input variables shed new light on the results and its interpretation. Higher friction
   coefficient of roadways result in reduced frequency of crashes during the morning peak hours, with the trend being reversed during the afternoon peak hours. Crash counts
   have been observed to be at a maximum at a surface width of 30 ft. Sensitivity analysis results reflect that ADT is responsible for the largest variation in crash counts on
   urban arterials.
%O In Press, Corrected Proof

%A Angan Das
%A Ranga Vemuri
%T A graph grammar based approach to automated multi-objective analog circuit design
%B Design, Automation Test in Europe Conference Exhibition, DATE '09.
%D 2009
%P 700--705
%I 
%K genetic algorithms, genetic programming, VCO, automated multiobjective analog circuit design, automated topology synthesis, bookish circuits, derivation tree,
   design-suitable building blocks, encoding, graph grammar-based approach, opamp, analogue circuits, graph grammars, network topology, operational amplifiers, tree codes,
   voltage-controlled oscillators
%U http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5090755
%X This paper introduces a graph grammar based approach to automated topology synthesis of analog circuits. A grammar is developed to generate circuits through production
   rules, that are encoded in the form of a derivation tree. The synthesis has been sped up by using dynamically obtained design-suitable building blocks. Our technique has
   certain advantages when compared to other tree-based approaches like GP based structure generation. Experiments conducted on an opamp and a vco design show that unlike
   previous works, we are capable of generating both manual-like designs (bookish circuits) as well as novel designs (unfamiliar circuits) for multi-objective analog circuit
   design benchmarks.
%8 20-24  April
%Z Also known as \cite5090755

%A Sumit Das
%A Terry Franguidakis
%A Michael Papka
%A Thomas A. DeFanti
%A Daniel J. Sandin
%T A genetic programming application in virtual reality
%B Proceedings of the first IEEE Conference on Evolutionary Computation
%V 1
%D 1994
%P 480--484
%I IEEE Press
%I IEEE
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/8701.html
%X Genetic programming techniques have been applied to a variety of different problems. In this paper, the authors discuss the use of these techniques in a virtual
   environment. The use of genetic programming allows the authors a quick method of searching shape and sound spaces. The basic design of the system, problems encountered, and
   future plans are all discussed.
%O Part of 1994 IEEE World Congress on Computational Intelligence, Orlando, Florida
%8 27-29  June
%Z Displays 4 simple geometric 3dee items in virtual reality CAVE. User breeds from those he likes.

%A Dipankar Dasgupta
%A Yuehua Cao
%A Congjun Yang
%T An Immunogenetic Approach to Spectra Recognition
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 149--155
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A D. Dasgupta
%T Computational Intelligence in Cyber Security
%B Proceedings of the 2006 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety
%D 2006
%P 2--3?
%I IEEE
%C Alexandria, VA, USA
%K genetic algorithms, genetic programming
%X This keynote speech will be devoted to the application of the state-of-the-art CI (computational intelligence)-based technologies - fuzzy systems, evolutionary computation,
   genetic programming, neural networks and artificial immune systems, and highlight how CI-based technologies play critical roles in various computer and information security
   problems
%8 October
%Z Center for Inf. Assurance & Intelligent Security Syst. Res. Lab., Memphis Univ., TN
%@ 1-4244-0744-3

%A Adelino R. Ferreira {da Silva}
%T Evolutionary Wavelet Bases in Signal Spaces
%B Real-World Applications of Evolutionary Computing
%S LNCS
%E Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter
   and Terence C. Fogarty
%V 1803
%D 2000
%P 44--53
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=45
%8 17  April
%Z Evolution of wavlet trees. EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings
   http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61
%@ 3-540-67353-9

%A Adelino R. Ferreira {da Silva}
%T Genetic Algorithms for Component Analysis
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 243--250
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GA050.ps
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Alexandre P. {Alves da Silva}
%A Pedro Jose Abrao
%T Applications of Evolutionary Computation in Electric Power Systems
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 1057--1062
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming, evolutionary computation, optimisation, power system analysis computing, power system control, power system identification, search
   problems, IEEE Transactions, control, evolution strategies, evolutionary algorithms, evolutionary computation, evolutionary programming, model identification, optimization,
   particle swarm optimization, power systems, simulated annealing, tabu search
%X This survey covers the broad area of evolutionary computation applications to optimization, model identification, and control in power systems [1]. Due to space limitation,
   all reviewed papers have been selected since 1996, from the IEEE Transactions only. A total of 85 articles are listed in this survey. It shows the development of the area
   and identifies the current trends. The following techniques are considered under the scope of evolutionary computation: evolutionary algorithms (e.g., genetic algorithms,
   evolution strategies, evolutionary programming, and genetic programming), simulated annealing, tabu search, and particle swarm optimization.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Eyal Dassau
%A Benyamin Grosman
%A Daniel R. Lewin
%T Modeling and temperature control of rapid thermal processing
%J Computers and Chemical Engineering
%V 30
%N 4
%D 2006
%P 686--697
%I 
%K genetic algorithms, genetic programming, Rapid thermal processing (RTP), Non-linear model predictive control (NMPC), GA, GP
%U http://tx.technion.ac.il/~dlewin/publications/rtp_paper_v9.pdf
%X In the past few years, rapid thermal processing (RTP) has gained acceptance as mainstream technology for semiconductor manufacturing. This single wafer approach allows for
   faster wafer processing and better control of process parameters on the wafer. However, as feature sizes become smaller, and wafer uniformity demands become more stringent,
   there is an increased demand from rapid thermal (RT) equipment manufacturers to improve control, uniformity and repeatability of processes on wafers. In RT processes, the
   main control problem is that of temperature regulation, which is complicated due to the high non-linearity of the heating process, process parameters that often change
   significantly during and between the processing of each wafer, and difficulties in measuring temperature and edge effects. This paper summarises work carried out in
   cooperation with Steag CVD Systems, in which algorithms for steady state and dynamic temperature uniformity were developed. The steady-state algorithm involves the reverse
   engineering of the required power distribution, given a history of past distributions and the resulting temperature profile. The algorithm for dynamic temperature
   uniformity involves the development of a first-principles model of the RTP chamber and wafer, its calibration using experimental data, and the use of the model to develop a
   controller.
%8 15  February

%A Mehdi Dastani
%A Elena Marchiori
%A Robert Voorn
%T Finding Perceived Pattern Structures using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 3--10
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, visual perception, gestalt, simplicity principle, structural information theory (SIT), perceptual regularity
%U http://people.cs.uu.nl/mehdi/publication/Gecco01.ps
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Kerstin Dautenhahn
%T Book Review: Swarm Intelligence
%J Genetic Programming and Evolvable Machines
%V 3
%N 1
%D 2002
%P 93--97
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%X Review of Kennedy+Eberhart's "Swarm Intelligence" http://www.mkp.com/books_catalog/catalog.asp?ISBN=1-55860-595-9 James Kennedy and Russell C. Eberhart, with Yuhui Shi,
   2001, MKP ISBN 1-55860-595-9
%8 March
%Z Article ID: 395992

%A G. F. Davenport
%A M. D. Ryan
%A V. J. Rayward-Smith
%T Rule Induction Using a Reverse Polish Representation
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 990--995
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-433.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Robert Davidge
%T Looping as a Means of Survival: Playing Russian Roulette in a Harsh Environment
%B ECAL-93 Self organisation and life: from simple rules to global complexity
%D 1993
%P 259--273
%I 
%I Centre for Non-Linear Phenomena and Complex Systems
%C CP 231, Universite Libre de Bruxelles, Bld. du Triomphe, 1050 Brussels, Belgium, Fax 32-2-659.5767 Phone 32-2-650.5776 Email sgross@ulb.ac.be
%K genetic algorithms
%X Cline 4bit processor runs across 2dee memory array. Controlled by 16 chromosome of micro-instruction sequences of fixed length.
%8 24--26  May
%Z There seems to be some doubt as to wether ECAL-93 was published. This copy from attendee.

%A J. W. Davidson
%A D. A. Savic
%A G. A. Walters
%T Symbolic and numerical regression: a hybrid technique for polynomial approximators
%B Proceedings of Recent Advances in Soft Computing'99
%E Robert John and Ralph Birkenhead
%D 1999
%P 111--116
%I Physica Verlag
%C De Montfort University, Leicester, UK
%K genetic algorithms, genetic programming, least squares, polynomial expressions, symbolic algebra, symbolic regression
%8 1-2  July

%A J. W. Davidson
%A D. A. Savic
%A G. A. Walters
%T Method for the identification of explicit polynomial formulae for the friction in turbulent pipe flow
%J Journal of Hydroinformatics
%V 1
%N 2
%D 1999
%P 115--126
%I 
%K genetic algorithms, genetic programming, least squares, polynomial expressions, symbolic algebra, symbolic regression
%U http://www.iwaponline.com/jh/001/0115/0010115.pdf
%X The paper describes a new regression method for creating polynomial models. The method combines numerical and symbolic regression. Genetic programming finds the form of
   polynomial expressions, and least squares optimisation finds the values for the constants in the expressions. The incorporation of least squares optimization within
   symbolic regression is made possible by a rule-based component that algebraically transforms expressions to equivalent forms that are suitable for least squares
   optimisation. The paper describes new operators of crossover and mutation that improve performance, and a new method for creating starting solutions that avoids the problem
   of under-determined functions. An example application demonstrates the trade-off between model complexity and accuracy of a set of approximator functions created for the
   Colebrook-White formula.
%Z Improving on Ephemeral random constants

%A J. W. Davidson
%A D. A. Savic
%A G. A. Walters
%T Approximators for the Colebrook-White Formula Obtained through a Hybrid Regression Method
%B Proceedings of XIII International Conference on Computational Methods in Water Resources
%D 2000
%I 
%C Calgary, Canada
%K genetic algorithms, genetic programming
%8 25-29  June

%A J. W. Davidson
%A D. A. Savic
%A G. A. Walters
%T Rainfall Runoff Modeling Using a New Polynomial Regression Method
%B Proceedings of the 4th International Conference on Hydroinformatics
%D 2000
%I 
%I Iowa Institute of Hydraulic Research
%C Iowa City, Iowa, USA
%K genetic algorithms, genetic programming
%O CD-ROM only
%8 23-27  July
%Z Kirkton, Scotland. Mallows Cp to avoid overfitting. GP limited to just polynomials (actually produced by post processing) constants fitted by least-squares. Comparison with
   previously published GP and ANN. Overfitting (consistency) v. model instability. Population size 40. (l,m) = (100,40) ?
%@ none

%A J. W. Davidson
%A D. A. Savic
%A G. A. Walters
%T Symbolic and numerical regression: experiments and applications
%B Developments in Soft Computing
%E Robert John and Ralph Birkenhead
%D 2001
%P 175--182
%I Physica Verlag
%C De Montfort University, Leicester, UK
%K genetic algorithms, genetic programming, least-squares, rule-based programming, stepwise regression, symbolic regression
%X This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example
   problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the Colebrook-White equation and rainfall-runoff
   modelling
%8 29-30  June  2000.
%@ 3-7908-1361-3

%A J. W. Davidson
%A D. A. Savic
%A G. A. Walters
%T Symbolic and numerical regression: Experiments and applications
%J Information Sciences
%V 150
%N 1-2
%D 2003
%P 95--117
%I 
%K genetic algorithms, genetic programming, Least squares, Rule-based programming, Stepwise regression, Symbolic regression
%U http://www.sciencedirect.com/science/article/B6V0C-474DD2V-1/2/3368220198ea15f93a793594af73d8d1
%X This paper describes a new method for creating polynomial regression models. The new method is compared with stepwise regression and symbolic regression using three example
   problems. The first example is a polynomial equation. The two examples that follow are real-world problems, approximating the Colebrook-White equation and rainfall-runoff
   modelling. The three example problems illustrate the advantages of the new method.

%A Omid David-Tabibi
%A Moshe Koppel
%A Nathan S. Netanyahu
%T Expert-driven genetic algorithms for simulating evaluation functions
%J Genetic Programming and Evolvable Machines
%V 12
%N 1
%D 2011
%P 5--22
%I 
%K genetic algorithms, Computer chess, Fitness evaluation, Games, Parameter tuning
%X In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an
   appropriate expert (or mentor), we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion.
   This performance gain is achieved by evolving a program that mimics the behavior of a superior expert. The resulting evaluation function of the evolved program consists of
   a much smaller number of parameters than the expert's. The extended experimental results provided in this paper include a report on our successful participation in the 2008
   World Computer Chess Championship. In principle, our expert-driven approach could be used in a wide range of problems for which appropriate experts are available.
%8 March
%Z Not GP. A preliminary version of this paper appeared in Proceedings of the 2008 Genetic and Evolutionary Computation Conference \citeDavid-Tabibi:2008:gecco and received
   the Best Paper Award in the conference's Real-World Applications track.

%A James Davis
%T Single Populations v. Co-Evolution
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 20--27
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z Steady state GP model. Tank control strategies co-evolved competitively against each other. This volume contains 22 papers written and submitted by students describing
   their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994
   http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Richard A. Davis
%A Adrian J. Charlton
%A Sarah Oehlschlager
%A Julie C. Wilson
%T Novel feature selection method for genetic programming using metabolomic 1H NMR data
%J Chemometrics and Intelligent Laboratory Systems
%V 81
%N 1
%D 2006
%P 50--59
%I 
%K genetic algorithms, genetic programming, Metabolomics, Multivariate data analysis, Feature selection, NMR
%X A novel technique for multivariate data analysis using a two-stage genetic programming (GP) routine for feature selection is described. The method is compared with
   conventional genetic programming for the classification of genetically modified barley. Metabolic fingerprinting by 1H NMR spectroscopy was used to analyse the differences
   between transgenic and null-segregant plants. We show that the method has a number of major advantages over standard genetic programming techniques. By selecting a minimal
   set of characteristic features in the data, the method provides models that are easier to interpret. Moreover the new method achieves better classification results and
   convergence is reached significantly faster.
%8 March

%A Jennifer P. Day
%A Douglas B. Kell
%A Gareth W. Griffith
%T Differentiation of Phytophthora infestans Sporangia from Other Airborne Biological Particles by Flow Cytometry
%J Applied and Environmental Microbiology
%V 68
%N 1
%D 2002
%P 37--45
%I 
%K genetic algorithms, genetic programming
%U http://intl-aem.asm.org/cgi/reprint/68/1/37.pdf
%X The ability of two different flow cytometers, the Microcyte (Optoflow) and the PAS-III (Partec), to differentiate sporangia of the late-blight pathogen Phytophthora
   infestans from other potential airborne particles was compared. With the PAS-III, light scatter and intrinsic fluorescence parameters could be used to differentiate
   sporangia from conidia of Alternaria or Botrytis spp., rust urediniospores, and pollen of grasses and plantain. Differentiation between P. infestans sporangia and powdery
   mildew conidia was not possible by these two methods but, when combined with analytical rules evolved by genetic programming methods, could be achieved after staining with
   the fluorescent brightener Calcofluor white M2R. The potential application of these techniques to the prediction of late-blight epiphytotics in the field is discussed.
%8 January
%Z GMax-Bio

%A Peter Day
%A Asoke K. Nandi
%T Robust Text-Independent Speaker Verification Using Genetic Programming
%J IEEE Transactions on Audio, Speech and Language Processing
%V 15
%N 1
%D 2007
%P 285--295
%I 
%K genetic algorithms, genetic programming, feature extraction, speaker recognition, telephone networks additive noise, convolutive noise, feature selection, remote security
   verification, robust text-independent speaker verification, telephone network
%X Robust automatic speaker verification has become increasingly desirable in recent years with the growing trend toward remote security verification procedures for telephone
   banking, bio-metric security measures and similar applications. While many approaches have been applied to this problem, genetic programming offers inherent feature
   selection and solutions that can be meaningfully analysed, making it well suited to this task. This paper introduces a genetic programming system to evolve programs capable
   of speaker verification and evaluates its performance with the publicly available TIMIT corpora. We also show the effect of a simulated telephone network on classification
   results which highlights the principal advantage, namely robustness to both additive and convolutive noise
%8 January
%Z see also IEEE Transactions on Speech and Audio Processing

%A Peter Day
%A Asoke K. Nandi
%T Sunspot prediction using genetic programming augmented by Binary String Fitness Characterisation and Comparative Partner Selection
%B IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
%D 2008
%P 175--180
%I 
%K genetic algorithms, genetic programming, binary string fitness characterisation, comparative partner selection, pair-wise mating strategy, population-wide weaknesses,
   sunspot prediction, prediction theory, string matching, sunspots
%X The paper addresses the sunspot prediction problem using a novel strategy for evaluating individual's relative strengths and weaknesses, by representing these in the form
   of a binary string fitness characterisation (BSFC), in addition to an overall fitness value for each individual. Using a combination of the BSFC and a pair-wise mating
   strategy, comparative partner selection (CPS), appears to promote effective solutions by reducing population-wide weaknesses. This strategy offers better solution to the
   sunspot prediction problem.
%8 October
%Z Also known as \cite4685475

%A Peter Day
%A Asoke K. Nandi
%T Binary String Fitness Characterization and Comparative Partner Selection in Genetic Programming
%J IEEE Transactions on Evolutionary Computation
%V 12
%N 6
%D 2008
%P 724--735
%I 
%K genetic algorithms, genetic programming, binary string fitness characterization, comparative partner selection, evolutionary methods, genetic programming benchmarking
   problems, adaptive crossover and mutation, mate selection, CPS
%X The premise behind all evolutionary methods is survival of the fittest and consequently, individuals require a quantitative fitness measure. This paper proposes a novel
   strategy for evaluating individual's relative strengths and weaknesses, as well as representing these in the form of a binary string fitness characterization (BSFC); in
   addition, as customary, an overall fitness value is assigned to each individual. Using the BSFC, we demonstrate both novel population evaluation measures and a pairwise
   mating strategy, comparative partner selection (CPS), with the aim of evolving a population that promotes effective solutions by reducing population-wide weaknesses. This
   strategy is tested with six standard genetic programming benchmarking problems.
%8 Decemeber
%Z Also known as \cite4472181 3 bit parity, 5-even parity, 11 mux, quartic, Rastrigin, Sunspot, parsimony pressure, bloat,

%A Peter Day
%A Asoke Nandi
%T Genetic Programming for Robust Text Independent Speaker Verification
%B Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering
%E Raymond Chiong
%D 2010
%P 259--280
%I IGI Global
%K genetic algorithms, genetic programming
%U http://www.igi-global.com/Bookstore/Chapter.aspx?TitleId=36319
%X Robust Automatic Speaker Verification has become increasingly desirable in recent years with the growing trend toward remote security verification procedures for telephone
   banking, bio-metric security measures and similar applications. While many approaches have been applied to this problem, Genetic Programming offers inherent feature
   selection and solutions that can be meaningfully analyzed, making it well suited for this task. This chapter introduces a Genetic Programming system to evolve programs
   capable of speaker verification and evaluates its performance with the publicly available TIMIT corpora. Also presented are the effects of a simulated telephone network on
   classification results which highlight the principal advantage, namely robustness to both additive and convolutive noise.
%Z http://www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=794&DetailsType=Description

%A M. Dayik
%A M. C. Kayacan
%A H. Calis
%A E. Cakmak
%T Control of warp tension during weaving procedure using evaluation programming
%J The Journal of the Textile Institute
%V 97
%N 4
%D 2006
%P 313--324
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming, Weaving, warp tension, let-off control, warp break
%X In this study, gene expression programming (GEP), one of the Evolution Programming methods, is used for the control of the let-off system in a weaving loom. For this
   control, the function of warp tension occurring in a complete rotation of the main shaft of weaving loom is determined by the method of GEP. The control of let-off system
   is implemented using this function. Particularly, during the shed opening and beat-up processes to make warp tension constant, warp beam is rotated clockwise and
   counterclockwise. The values of warp tension obtained by GEP are compared with the values of conventional controlled methods. As a conclusion, the obtained warp tension
   values are 11.2percent less than values of classical approach. At the same time it is also provided that break rate of warp tension is decreased by 20percent. It has shown
   that GEP is an effective tool for the decreasing of warp break rate.
%Z 1. Department of Textile Engineering, Suleyman Demirel University, Isparta, Turkey 2. Department of Textile Engineering, Suleyman Demirel University, Isparta, Turkey 3.
   Department of Electronics and Computer Education, Suleyman Demirel University, Isparta, Turkey 4. Department of Textile Engineering, Suleyman Demirel University, Isparta,
   Turkey

%A Anthony G. Deakin
%A Derek F. Yates
%T Genetic Programming Tools Available on the Web: A First Encounter
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 420
%I MIT Press Cambridge, MA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.liv.ac.uk/~anthonyd/gp9632.ps
%8 28--31  July
%Z GP-96 10 page version at http://www.csc.liv.ac.uk/~anthony/gp961.ps (broken 2006)

%A Anthony G. Deakin
%A Derek F. Yates
%T Economical Solutions with Genetic Programming: the Non-Hamstrung Squadcar Problem, FvM and EHP
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 71--76
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Deakin_1997_esGP.pdf
%8 13-16  July
%Z GP-97

%A Anthony G. Deakin
%A Derek F. Yates
%T Phase Transition Networks: A Modelling technique supporting the Evolution of Autonomous Agents' Tactical and Operational Activities
%B Evolutionary Computing
%S Lecture Notes in Computer Science
%E David Corne and Jonathan L. Shapiro
%V 1305
%D 1997
%P 263--273
%I Springer-Verlag
%I AISB
%C Manchester, UK
%K genetic algorithms, genetic programming, agents, MPHaSys
%8 11-13  April
%Z Proceedings of the Workshop on Artificial Intelligence and Simulation of Behaviour (AISB) International Workshop on Evolutionary Computing. Workshop in Manchester, UK,
   April 7-8, 1997 Phase Transfer Networks PTN, egs traffic lights, blood glucose regulation,
%@ 3-540-63476-2

%A Anthony G. Deakin
%A Derek F. Yates
%T Evolving and Optimizing Autonomous Agents' Strategies with Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 42--47
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Gustavo Maia {de Almeida}
%A Valceres Vieira Rocha {e Silva}
%A Erivelton Geraldo Nepomuceno
%A Ryuichi Yokoyama
%T Application of Genetic Programming for Fine Tuning PID Controller Parameters Designed Through Ziegler-Nichols Technique
%B Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part III
%S Lecture Notes in Computer Science
%E Lipo Wang and Ke Chen and Yew-Soon Ong
%V 3612
%D 2005
%P 313--322
%I Springer
%C Changsha, China
%K genetic algorithms, genetic programming
%X PID optimal parameters selection have been extensively studied, in order to improve some strict performance requirements for complex systems. Ziegler-Nichols methods give
   estimated values for these parameters based on the system's transient response. Therefore, a fine tuning of these parameters is required to improve the system's behaviour.
   In this work, genetic programming is used to optimise the three parameters Kp , Ti and Td , after been tuned by Ziegler-Nichols method, to control a high-order process, a
   large time delay plant and a highly non-minimum phase process. The results were compared to some other tuning methods, and showed to be promising.
%8 August  27-29
%@ 3-540-28320-X

%A Humberto Mossri {de Almeida}
%A Marcos Andre Goncalves
%A Marco Cristo
%A Pavel Calado
%T A combined component approach for finding collection-adapted ranking functions based on genetic programming
%B Proceedings of the 30th Annual International ACM Conference on Research and Development in Information Retrieval, SIGIR 2007
%E Wessel Kraaij and Arjen P. de Vries and Charles L. A. Clarke and Norbert Fuhr and Noriko Kando
%D 2007
%P 399--406
%I ACM
%C Amsterdam, The Netherlands
%K genetic algorithms, genetic programming, Information Retrieval, Ranking Functions, Term-weighting, Machine Learning
%X In this paper, we propose a new method to discover collection-adapted ranking functions based on Genetic Programming (GP). Our Combined Component Approach (CCA)is based on
   the combination of several term-weighting components (i.e.,term frequency, collection frequency, normalization) extracted from well-known ranking functions. In contrast to
   related work, the GP terminals in our CCA are not based on simple statistical information of a document collection, but on meaningful, effective, and proven components.
   Experimental results show that our approach was able to out perform standard TF-IDF, BM25 and another GP-based approach in two different collections. CCA obtained
   improvements in mean average precision up to 40.87percent for the TREC-8 collection, and 24.85percent for the WBR99 collection (a large Brazilian Web collection), over the
   baseline functions. The CCA evolution process also was able to reduce the over training, commonly found in machine learning methods, especially genetic programming, and to
   converge faster than the other GP-based approach used for comparison.
%8 July  23-27

%A Marconi {de Arruda Pereira}
%A Clodoveu Augusto {Davis Junior}
%A Joao Antonio {de Vasconcelos}
%T A Niched Genetic Programming Algorithm for Classification Rules Discovery in Geographic Databases
%B Simulated Evolution and Learning - 8th International Conference, SEAL 2010, Kanpur, India, December 1-4, 2010. Proceedings
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Arnab Bhattacharya and Nirupam Chakraborti and Partha Chakroborty and Swagatam Das and Joydeep Dutta and Santosh K. Gupta and Ashu Jain and Varun Aggarwal
   and J\"urgen Branke and Sushil J. Louis and Kay Chen Tan
%V 6457
%D 2010
%P 260--269
%I Springer
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.1007/978-3-642-17298-4

%A R. Deaton
%A M. Garzon
%A R. C. Murphy
%A J. A. Rose
%A D. R. Franceschetti
%A S. E. {Stevens, Jr.}
%T Genetic Search of Reliable Encodings for DNA-Based Computation
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 9--15
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.csce.uark.edu/~rdeaton/dna/papers/gp-96.pdf
%X In DNA-based computation, the problem instances are encoded in DNA oligonucleotides that must hybridise correctly to produce a solution. Depending on reaction conditions,
   oligonucleotides can bind with imperfect matching of complementary base pairs. These mismatched hybridisations are a potential source of errors. For reliable DNA-based
   computation, the encodings should be a minimum distance apart. This distance could be estimated from empirical curves of DNA melting, but they remain difficult to produce.
   In fact, the probability of a good encoding in a randomly chosen sample goes to zero fairly quickly with the number of errors for arbitrary encoding lengths. We use genetic
   programming methods to nd good encodings and analyse their performance in actual laboratory experiments.
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-201031-7

%A R. Deaton
%A M. Garzon
%A R. C. Murphy
%A D. R. Franceschetti
%A J. A. Rose
%A S. E. {Stevens, Jr.}
%T Information Transfer through Hybridization Reactions in DNA based Computing
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 463--471
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K DNA Computing
%8 13-16  July
%Z GP-97

%A Russell Deaton
%T Reaction Temperature Constraints in DNA Computing
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1803--1804
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K dna and molecular computing
%U http://csce.uark.edu/~rdeaton/dna/papers/dn-101.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Kalyanmoy Deb
%A Surendra Gulati
%A Sekhar Chakrabarti
%T Optimal Truss-Structure Design using Real-Coded Genetic Algorithms
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 479--486
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Kalyanmoy Deb
%T Construction of Test Problems for Multi-Objective Optimization
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 164--171
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Kalyanmoy Deb
%A Hans-Georg Beyer
%T Self-Adaptation in Real-Parameter Genetic Algorithms with Simulated Binary Crossover
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 172--179
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://ls11-www.informatik.uni-dortmund.de/people/deb/papers/gecco1.ps.gz
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%T Genetic and Evolutionary Computation -- GECCO-2004, Part I
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3102
%D 2004
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/content/978-3-540-22344-3
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22344-4

%T Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/content/978-3-540-22343-6
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Moises G. {de Carvalho}
%A Marcos Andre Goncalves
%A Alberto H. F. Laender
%A Altigran S. {da Silva}
%T Learning to deduplicate
%B Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '06
%D 2006
%P 41--50
%I IEEE
%C Chapel Hill, NC, USA
%K genetic algorithms, genetic programming, Deduplication, Digital Libraries
%U http://delivery.acm.org/10.1145/1150000/1141760/p41-decarvalho.pdf?key1=1141760&key2=6906456911&coll=GUIDE&dl=GUIDE&CFID=45325455&CFTOKEN=75817203
%X Identifying record replicas in digital libraries and other types of digital repositories is fundamental to improve the quality of their content and services as well as to
   yield eventual sharing efforts. Several deduplication strategies are available, but most of them rely on manually chosen settings to combine evidence used to identify
   records as being replicas. In this paper, we present the results of experiments we have carried out with a novel machine learning approach we have proposed for the de
   duplication problem. This approach, based on genetic programming (GP), is able to automatically generate similarity functions to identify record replicas in a given
   repository. The generated similarity functions properly combine and weight the best evidence available among the record fields in order to tell when two distinct records
   represent the same real-world entity. The results of the experiments show that our approach outperforms the baseline method by Fellegi and Sunter by more than 12percent
   when identifying replicas in a data set containing researcher's personal data, and by more than 7percent, in a data set with article citation data
%8 June
%Z Comput. Sci. Dept., Fed. Univ. of Minas Gerais, Belo Horizonte
%@ 1-59593-354-9

%A Moises G. {de Carvalho}
%A Alberto H. F. Laender
%A Marcos Andre Goncalves
%A Thiago C. Porto
%T The Impact of Parameters Setup on a Genetic Programming Approach to Record Deduplication
%B XXIII Simp\'osio Brasileiro de Banco de Dados
%E Sandra de Amo
%D 2008
%P 91--105
%I SBC
%C Campinas, S\~ao Paulo, Brasil
%K genetic algorithms, genetic programming
%U http://www.lbd.dcc.ufmg.br:8080/colecoes/sbbd/2008/007.pdf
%X Several systems that rely on the integrity of the data in order to offer high quality services, such as digital libraries and e-commerce brokers, may be affected by the
   existence of duplicates, quasi-replicas, or near-duplicates entries in their repositories. Because of that, there has been a huge effort from private and government
   organizations in developing effective methods for removing replicas from large data repositories. This is due to the fact that cleaned, replica-free repositories not only
   allow the retrieval of higher-quality information but also lead to a more concise data representation and to potential savings in computational time and resources to
   process this data. In this work, we extend the results of a GP-based approach we proposed to record deduplication by performing a comprehensive set of experiments regarding
   its parameterization setup. Our experiments show that some parameter choices can improve the results to up 30percent Thus, the obtained results can be used as guidelines to
   suggest the most effective way to set up the parameters of our GP-based approach to record deduplication.
%8 13-15  October
%Z SDG SBBD 2008.

%A Moises G. {de Carvalho}
%A Alberto H. F. Laender
%A Marcos Andre Goncalves
%A Altigran S. {da Silva}
%T A Genetic Programming Approach to Record Deduplication
%J IEEE Transactions on Knowledge and Data Engineering
%V 24
%N 3
%D 2012
%P 399--412
%I 
%K genetic algorithms, genetic programming, computational time, data repositories, database administration, database integration, digital libraries, e-commerce brokers, fixed
   replica identification boundary, information retrieval, record deduplication, replica removal, replica-free repositories, genetic algorithms, information retrieval,
   replicated databases
%X Several systems that rely on consistent data to offer high quality services, such as digital libraries and e-commerce brokers, may be affected by the existence of
   duplicates, quasi-replicas, or near-duplicate entries in their repositories. Because of that, there have been significant investments from private and government
   organisations in developing methods for removing replicas from its data repositories. This is due to the fact that clean and replica-free repositories not only allow the
   retrieval of higher-quality information but also lead to more concise data and to potential savings in computational time and resources to process this data. In this
   article, we propose a genetic programming approach to record deduplication that combines several different pieces of evidence extracted from the data content to find a
   deduplication function that is able to identify whether two entries in a repository are replicas or not. As shown by our experiments, our approach outperforms an existing
   state-of-the-art method found in the literature. Moreover, the suggested functions are computationally less demanding since they use fewer evidence. In addition, our
   genetic programming approach is capable of automatically adapting these functions to a given fixed replica identification boundary, freeing the user from the burden of
   having to choose and tune this parameter.
%8 March
%Z Also known as \cite5645623

%A Robert {De Caux}
%T Using Genetic Programming to Evolve Strategies for the Iterated Prisoner's Dilemma
%R M.S. Thesis
%D 2001
%I 
%I University College, London
%K genetic algorithms, genetic programming, java, gpsys, ipd, Coevolution, Pareto scoring, strongly typed
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/decaux.masters.pdf
%X The technique of Genetic Programming (GP) uses Darwinian principles of natural selection to evolve simple programs with the aim of finding better or fitter solutions to a
   problem. Based on the technique of Genetic Algorithms (GA), a population of potential solutions stored in tree form are evaluated against a fitness function. The fittest
   ones are then modified by a genetic operation, and used to form the next generation. This process is repeated until certain criteria have been met. This could be an
   ultimate solution, or a certain number of generations having been evolved. Genetic Programming is a fast developing field with potential uses in medicine, finance and
   artificial intelligence. This project attempts to use the technique to evolve strategies for the game of Prisoner's Dilemma. Although a simple game, the range of possible
   strategies when the game is iterated is vast, but what makes it particularly interesting is the absence of an ultimate strategy and the possibility of mutual benefit by
   cooperation. A system was created to allow strategies to be evolved by either playing against fixed opponents or against each other (coevolution). The strategies are stored
   as trees, with GP used to form the next generation. The main advantage of GP over GA is that the trees do not need to be of a fixed size, so strategies can be developed
   which use the entire game history as opposed to just the last few moves. This implementation has advantages over previous investigations, as information about which go is
   being played can be used, thus allowing cleverer strategies. Work has also been conducted into a hunting phase, where strategies roam a two dimensional grid to find a
   suitable opponent. By studying the history of potential opponents and using GA, evidence emerged of an increase in cooperative behaviour as strategies sought out suitable
   opponents, demonstrating parallels with biological models of population dynamics. The system has been developed to allow a user to alter important parameters, select the
   evolution method and seed the population with pre-defined strategies by means of a graphical user interface.
%8 September
%Z Awarded a distinction. Supervised by Robin Hirsch. Zip archive contains msword document

%A Rob P. DeConde
%T Evolving Programs for Distributed Multi-Agent Configuration in Two Dimensions
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 38--44
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/DeConde.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A James Decraene
%A George G. Mitchell
%A Barry McMullin
%A Ciaran Kelly
%T The Holland Broadcast Language and the Modeling of Biochemical Networks
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 361--370
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X The Broadcast Language is a programming formalism devised by Holland in 1975, which aims at improving the efficiency of Genetic Algorithms (GAs) during long-term evolution.
   The key mechanism of the Broadcast Language is to allow GAs to employ an adaptable problem representation. Fixed problem encoding is commonly used by GAs but may limit
   their performance in particular cases. This paper describes an implementation of the Broadcast Language and its application to modelling biochemical networks. Holland
   presented the Broadcast Language in his book 'Adaptation in Natural and Artificial Systems' where only a description of the language was provided, without any
   implementation. Our primary motivation for this work was the fact that there is currently no published implementation of the Broadcast Language available. Secondly, no
   additional examination of the Broadcast Language and its applications can be found in the literature. Holland proposed that the Broadcast Language would be suitable for the
   modeling of biochemical models. However, he did not support this belief with any experimental work. In this paper, we propose an implementation of the Broadcast Language
   which is then applied to the modelling of a signal transduction network. We conclude the paper by proposing that with some refinements it will be possible to use the
   Broadcast Language to evolve biochemical networks in silico.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A M. Conte
%A G. Tautteur
%A I. {De Falco}
%A A. Della Cioppa
%A E. Tarantino
%T Genetic Programming Estimates of Kolmogorov Complexity
%B Genetic Algorithms: Proceedings of the Seventh International Conference
%E Thomas Back
%D 1997
%P 743--750
%I Morgan Kaufmann San Francisco, CA, USA
%C Michigan State University, East Lansing, MI, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/355332.html
%X In this paper the problem of the Kolmogorov complexity related to binary strings is faced. We propose a Genetic Programming approach which consists in evolving a population
   of Lisp programs looking for the optimal program that generates a given string. This evolutionary approach has permited to overcome the intractable space and time
   difficulties occurring in methods which perform an approximation of the Kolmogorov complexity function. The experimental results are quite significant and also show
   interesting computational strategies so proving the effectiveness of the implemented technique.
%8 19-23  July
%Z ICGA-97
%@ 1-55860-487-1

%A I. {De Falco}
%A A. Iazzetta
%A E. Tarantino
%A A. Della Cioppa
%A A. Iacuelli
%T Towards a Simulation of Natural Mutation
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 156--163
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A I. {De Falco}
%A A. Iazzetta
%A E. Tarantino
%A A. Della Cioppa
%A G. Trautteur
%T A Kolmogorov Complexity-based Genetic Programming tool for string compression
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 427--434
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP124.ps
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A I. {De Falco}
%A A. {Della Cioppa}
%A E. Tarantino
%T Discovering interesting classification rules with genetic programming
%J Applied Soft Computing
%V 1
%N 4
%D 2001
%P 257--269
%I 
%K genetic algorithms, genetic programming, Data mining, Classification
%U http://www.elsevier.com/gej-ng/10/10/65/45/43/28/article.pdf
%X Data mining deals with the problem of discovering novel and interesting knowledge from large amount of data. This problem is often performed heuristically when the
   extraction of patterns is difficult using standard query mechanisms or classical statistical methods. In this paper a genetic programming framework, capable of performing
   an automatic discovery of classification rules easily comprehensible by humans, is presented. A comparison with the results achieved by other techniques on a classical
   benchmark set is carried out. Furthermore, some of the obtained rules are shown and the most discriminating variables are evidenced.
%8 May
%Z comparsison in \citeyu:2004:ECDM

%A Ivanoe {De Falco}
%A Antonio Della Cioppa
%A Ernesto Tarantino
%T Unsupervised Spectral Pattern Recognition for Multispectral Images by means of a Genetic Programming approach
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 231--236
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%X An innovative approach to spectral pattern recognition for multispectral images based on Genetic Programming is introduced. The problem is faced in terms of unsupervised
   pixel classification. The system is tested on a multispectral image with 31 spectral bands and 256 by 256 pixels. A good quality clustered output image is obtained.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A I. {De Falco}
%A A. {Della Cioppa}
%A F. Fontanella
%A E. Tarantino
%T An Innovative Approach to Genetic Programming-based Clustering
%B 9th Online World Conference on Soft Computing in Industrial Applications
%E Ajith Abraham and Mario K\"oppen
%D 2004
%P Paper No. 073
%I 
%I World Federation on Soft Computing (WFSC)
%C On the World Wide Web
%K genetic algorithms, genetic programming, clustering
%X Most of the classical clustering algorithms are strongly dependent on, and sensitive to, parameters such as number of expected clusters and resolution level. To overcome
   this drawback, in this paper a Genetic Programming framework, capable of performing an automatic data clustering is presented. Moreover, a novel way of representing
   clusters which provides intelligible information on patterns is introduced together with an innovative clustering process. The effectiveness of the implemented partitioning
   system is estimated on a medical domain by means of evaluation indices
%8 20  September  - 8  October
%Z WSC9 Clusters represented using GP evolved functions. Grammar. Fitness is linear combination of cluster homogeneity and separation. Non standard crossover. Multiple
   classes. UCI dermatological benchmark.

%A Ivan {De Falco}
%A Ernesto Tarantino
%A Antonio {Della Cioppa}
%A F. Gagliardi
%T A novel grammar-based genetic programming approach to clustering
%B Proceedings of the 2005 ACM Symposium on Applied Computing (SAC)
%E Hisham Haddad and Lorie M. Liebrock and Andrea Omicini and Roger L. Wainwright
%D 2005
%P 928--932
%I ACM
%I ACM
%C Santa Fe, New Mexico, USA
%K genetic algorithms, genetic programming, Information Storage and Retrieval, Information search and retrieval, clustering, retrieval methods, Artificial Intelligence,
   Problem Solving, Control Methods, and Search heuristic methods, Algorithms, Experimentation, data clustering, EM, Expectation-Maximisation
%X Most of the classical methods for clustering analysis require the user setting of number of clusters. To surmount this problem, in this paper a grammar-based Genetic
   Programming approach to automatic data clustering is presented. An innovative clustering process is conceived strictly linked to a novel cluster representation which
   provides intelligible information on patterns. The efficacy of the implemented partitioning system is estimated on a medical domain by exploiting expressly defined
   evaluation indices. Furthermore, a comparison with other clustering tools is performed.
%8 March  13-17
%@ 1-58113-964-0

%A Ivan {De Falco}
%A Ernesto Tarantino
%A Antonio {Della Cioppa}
%A A. Passaro
%T Inductive inference of chaotic series by Genetic Programming: a Solomonoff-based approach
%B Proceedings of the 2005 ACM Symposium on Applied Computing (SAC)
%E Hisham Haddad and Lorie M. Liebrock and Andrea Omicini and Roger L. Wainwright
%D 2005
%P 957--958
%I ACM
%I ACM
%C Santa Fe, New Mexico, USA
%K genetic algorithms, genetic programming, Automatic Programming, Algorithms, Experimentation, Inductive inference, Chaotic series
%X A Genetic Programming approach to inductive inference of chaotic series, with reference to Solomonoff complexity, is presented. It consists in evolving a population of
   mathematical expressions looking for the 'optimal' one that generates a given chaotic data series. Validation is performed on the Logistic, the Henon and the Mackey-Glass
   series. The method is shown effective in obtaining the analytical expression of the first two series, and in achieving very good results on the third one.
%8 March  13-17
%@ 1-58113-964-0

%A Ivan {De Falco}
%A Antonio {Della Cioppa}
%A A. Passaro
%A Ernesto Tarantino
%T Genetic Programming for Inductive Inference of Chaotic Series
%B Fuzzy Logic and Applications, 6th International Workshop, WILF 2005, Revised Selected Papers
%S Lecture Notes in Computer Science
%E Isabelle Bloch and Alfredo Petrosino and Andrea Tettamanzi
%V 3849
%D 2005
%P 156--163
%I Springer
%C Crema, Italy
%K genetic algorithms, genetic programming, Solomonoff complexity, chaotic series
%X In the context of inductive inference Solomonoff complexity plays a key role in correctly predicting the behavior of a given phenomenon. Unfortunately, Solomonoff
   complexity is not algorithmically computable. This paper deals with a Genetic Programming approach to inductive inference of chaotic series, with reference to Solomonoff
   complexity, that consists in evolving a population of mathematical expressions looking for the 'optimal' one that generates a given series of chaotic data. Validation is
   performed on the Logistic, the Henon and the Mackey-Glass series. The results show that the method is effective in obtaining the analytical expression of the first two
   series, and in achieving a very good approximation and forecasting of the Mackey-Glass series.
%8 September  15-17
%@ 3-540-32529-8

%A Ivanoe {De Falco}
%A Antonio {Della Cioppa}
%A Domenico Maisto
%A Ernesto Tarantino
%T A Genetic Programming Approach to Solomonoff's Probabilistic Induction
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 24--35
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050024.pdf
%X In the context of Solomonoff's Inductive Inference theory, Induction operator plays a key role in modelling and correctly predicting the behaviour of a given phenomenon.
   Unfortunately, this operator is not algorithmically computable. The present paper deals with a Genetic Programming approach to Inductive Inference, with reference to
   Solomonoff's algorithmic probability theory, that consists in evolving a population of mathematical expressions looking for the `optimal' one that generates a collection of
   data and has a maximal a priori probability. Validation is performed on Coulomb's Law, on the Henon series and on the Arosa Ozone time series. The results show that the
   method is effective in obtaining the analytical expression of the first two problems, and in achieving a very good approximation and forecasting of the third.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Ivanoe {De Falco}
%A Antonio {Della Cioppa}
%A Domenico Maisto
%A Umberto Scafuri
%A Ernesto Tarantino
%T Parsimony doesn't mean Simplicity: Genetic Programming for Inductive Inference on Noisy Data
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 351--360
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X A Genetic Programming algorithm based on Solomonoff probabilistic induction is designed and used to face an Inductive Inference task, i.e., symbolic regression. To this
   aim, some test functions are dressed with increasing levels of noise and the algorithm is employed to denoise the resulting function and recover the starting functions.
   Then, the algorithm is compared against a classical parsimony-based GP. The results shows the superiority of the Solomonoff-based approach.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Michael {Defoin Platel}
%A Manuel Clergue
%A Philippe Collard
%T Maximum Homologous Crossover for Linear Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 194--203
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=194
%X We introduce a new recombination operator, the Maximum Homologous Crossover for Linear Genetic Programming. In contrast to standard crossover, it attempts to preserve
   similar structures from parents, by aligning them according to their homology, thanks to an algorithm used in Bio-Informatics. To highlight disruptive effects of crossover
   operators, we introduce the Royal Road landscapes and the Homology Driven Fitness problem, for Linear Genetic Programming. Two variants of the new crossover operator are
   described and tested on this landscapes. Results show a reduction in the bloat phenomenon and in the frequency of deleterious crossovers.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Michael {Defoin Platel}
%A Sebastien Verel
%A Manuel Clergue
%A Philippe Collard
%T From Royal Road to Epistatic Road for Variable Length Evolution Algorithm
%B Evolution Artificielle, 6th International Conference
%S Lecture Notes in Computer Science
%E Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer
%V 2936
%D 2003
%P 3--14
%I Springer
%C Marseilles, France
%K genetic algorithms, genetic programming, Artificial Evolution, String Edit Distance, Levenshtein distance
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2936&spage=3
%X Although there are some real world applications where the use of variable length representation (VLR) in Evolutionary Algorithm is natural and suitable, an academic
   framework is lacking for such representations. In this work we propose a family of tunable fitness landscapes based on VLR of genotypes. The fitness landscapes we propose
   possess a tunable degree of both neutrality and epistasis; they are inspired, on the one hand by the Royal Road fitness landscapes, and the other hand by the NK fitness
   landscapes. So these landscapes offer a scale of continuity from Royal Road functions, with neutrality and no epistasis, to landscapes with a large amount of epistasis and
   no redundancy. To gain insight into these fitness landscapes, we first use standard tools such as adaptive walks and correlation length. Second, we evaluate the
   performances of evolutionary algorithms on these landscapes for various values of the neutral and the epistatic parameters; the results allow us to correlate the
   performances with the expected degrees of neutrality and epistasis.
%O Revised Selected Papers
%8 27-30  October
%Z EA'03
%@ 3-540-21523-9

%A Michael {Defoin Platel}
%A Manuel Clergue
%A Philippe Collard
%T Homology gives size control in genetic programming
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 281--288
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%U http://www.i3s.unice.fr/~defoin/publications/cec_03.pdf
%X The Maximum Homologous Crossover attempts to preserve similar structures from parents by aligning them according to their homology. In this paper, it is successfully tested
   on the classical Even-N Parity Problem where it demonstrates interesting abilities in bloat reduction. Then, we show that this operator gives an accurate control of the
   size of programs during the evolution and thus, allows the development of new strategies for the search space exploration.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Michael Defoin-Platel
%A Malik Chami
%A Manuel Clergue
%A Philippe Collard
%T Teams of Genetic Predictors for Inverse Problem Solving
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 341--350
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=341
%X Genetic Programming (GP) has been shown to be a good method of predicting functions that solve inverse problems. In this context, a solution given by GP generally consists
   of a sole predictor. In contrast, Stack-based GP systems manipulate structures containing several predictors, which can be considered as teams of predictors. Work in
   Machine Learning reports that combining predictors gives good results in terms of both quality and robustness. In this paper, we use Stack-based GP to study different
   cooperations between predictors. First, preliminary tests and parameter tuning are performed on two GP benchmarks. Then, the system is applied to a real-world inverse
   problem. A comparative study with standard methods has shown limits and advantages of teams prediction, leading to encourage the use of combinations taking into account the
   response quality of each team member.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Michael Defoin-Platel
%A Manuel Clergue
%A Philippe Collard
%T Size Control with Maximum Homologous Crossover
%B 7th International Conference on Artificial Evolution EA 2005
%S Lecture Notes in Computer Science
%E El-Ghazali Talbi and Pierre Liardet and Pierre Collet and Evelyne Lutton and Marc Schoenauer
%V 3871
%D 2005
%P 13--24
%I Springer
%C Lille, France
%K genetic algorithms, genetic programming
%O Revised Selected Papers
%8 October  26-28
%Z published 2006
%@ 3-540-33589-7

%A M. D. Platel
%A M. Clergue
%T Monitoring Genetic Variations in Variable Length Evolutionary Algorithms
%B Sixth International Conference on Hybrid Intelligent Systems, HIS '06
%D 2006
%P 4--4?
%I IEEE
%C Rio de Janeiro, Brazil
%K genetic algorithms, genetic programming, bloat
%X Initially, Artificial Evolution focuses on Evolutionary Algorithms handling solutions coded in fixed length structures. In this context, the role of crossover is clearly
   the mixing of information between solutions. The development of Evolutionary Algorithms operating on structures with variable length, of which genetic programming is one of
   the most representative instances, opens new questions on the effects of crossover. Beside mixing, two new effects are identified : the diffusion of information inside
   solutions and the variation of the solutions sizes. In this paper, we propose a experimental framework to study these three effects and apply it on three different
   crossovers for genetic programming : the Standard Crossover, the One-Point Crossover and the Maximum Homologous Crossover. Exceedingly different behaviours are reported
   leading us to consider the necessary future decoupling of the mixing, the diffusion and the size variation.
%8 Decemeber
%Z Laboratoire d'Oceanographie de Villefranche (LOV), France;
%@ 0-7695-2662-4

%A Michael {Defoin Platel}
%A S\'ebastien Verel
%A Manuel Clergue
%A Malik Chami
%T Density estimation with Genetic Programming for Inverse Problem solving
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 45--54
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X This paper addresses the resolution, by Genetic Programming (GP) methods, of ambiguous inverse problems, where for a single input, many outputs can be expected. We propose
   two approaches to tackle this kind of many-to-one inversion problems, each of them based on the estimation, by a team of predictors, of a probability density of the
   expected outputs. In the first one, Stochastic Realisation GP, the predictors outputs are considered as the realisations of an unknown random variable which distribution
   should approach the expected one. The second one, Mixture Density GP, directly models the expected distribution by the mean of a Gaussian mixture model, for which genetic
   programming has to find the parameters. Encouraging results are obtained on four test problems of different difficulty, exhibiting the interests of such methods.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Junio {de Freitas}
%A Gisele L. Pappa
%A Altigran S. {da Silva}
%A Marcos A. Goncalves
%A Edleno Moura
%A Adriano Veloso
%A Alberto H. F. Laender
%A Moises G. {de Carvalho}
%T Active Learning Genetic programming for record deduplication
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%U http://www.dcc.ufmg.br/~adrianov/papers/CEC10/cec10.pdf
%X The great majority of genetic programming (GP) algorithms that deal with the classification problem follow a supervised approach, i.e., they consider that all fitness cases
   available to evaluate their models are labelled. However, in certain application domains, a lot of human effort is required to label training data, and methods following a
   semi-supervised approach might be more appropriate. This is because they significantly reduce the time required for data labelling while maintaining acceptable accuracy
   rates. This paper presents the Active Learning GP (AGP), a semi-supervised GP, and instantiates it for the data deduplication problem. AGP uses an active learning approach
   in which a committee of multi-attribute functions votes for classifying record pairs as duplicates or not. When the committee majority voting is not enough to predict the
   class of the data pairs, a user is called to solve the conflict. The method was applied to three datasets and compared to two other deduplication methods. Results show that
   AGP guarantees the quality of the deduplication while reducing the number of labeled examples needed.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586104

%A Hugo {de Garis}
%T Artificial Embryology
%B Artificial Life III
%D 1992
%I 
%C Santa Fe
%K genetic algorithms, cellular automata
%U http://citeseer.ist.psu.edu/512552.html
%X This paper introduces some ideas and early results concerning the Genetic Programming of artificial shapes. Genetic Programming (GP) [de GARIS 1990, 1991, 1992] is defined
   to be "the art of using Genetic Algorithms to build/evolve complex systems". Complex systems are defined to be systems which are too complex in their structures or dynamics
   to be predictable or analyzable. Embryos and brains are obvious examples. This paper shows how GP techniques can be applied to (reproductive) cellular automata [WOLFRAM
   1986] to build a colony of cells having a desired global shape. This paper shows that this type of work can be extended to building shapes sequentially, e.g. "limbs" can be
   "grown" out of "bodies", so that a 2D "artificial embryo" is grown. It is hoped that such techniques will contribute towards the creation of a new branch of ALife, called
   "Artificial Embryology", which is defined to be "the art of generating instructions to enable abstract cells to reproduce and differentiate in abstract media, such that a
   final agglomeration of cells has certain properties (such as a desired shape, or desired behaviors etc)". It may be possible that these ideas will be taken over into a form
   of "embryological electronics", which uses GP techniques to "grow" electronic circuits in an electronic substrate, using special devices called "Darwin Machines".
%O The Pennsylvania State University CiteSeer Archives
%8 June
%Z at the end of the paper, the author suggests his use of "genetic programming" is different from Koza's. Growing CAs into non-convex shapes: snow man, L, turtle.
   Embryological self-assembly of nanomachines. Diffentiable chromosome. "Shaping is simply splitting up an evolutionary process into intermediate phases, with intermediate
   targets" (section 4). References contain list of de Garis papers using "genetic programming" in their title.

%A Hugo {de Garis}
%A Hitoshi Iba
%A Tatsumi Furuya
%T Differentiable Chromosomes: The Genetic Programming of switchable Shape-Genes
%B Parallel Problem Solving from Nature 2
%E R Manner and B Manderick
%D 1992
%P 489--498
%I Elsevier Science
%C Brussels, Belgium
%K genetic algorithms, genetic programming
%U http://www.iss.whu.edu.cn/degaris/papers/PPSN92.pdf
%8 28-30  September
%Z Wants to build machines with billions of components, proposes these grow themselves in an embryonic fashion. Does some experiments with two stage, hence differentiable,
   chromosomes which control the states of a cellular automata. Stages are switched on by psuedo chemical gradient. Can grow convex shapes but pretty poor at using GA to
   evolve concave shapes. PPSN2

%A Hugo {de Garis}
%T Evolving a Replicator The Genetic Programming of Self Reproduction in Cellular Automata
%B ECAL-93 Self organisation and life: from simple rules to global complexity
%D 1993
%P 274--284
%I 
%I Centre for Non-Linear Phenomena and Complex Systems
%C CP 231, Universite Libre de Bruxelles, Bld. du Triomphe, 1050 Brussels, Belgium, Fax 32-2-659.5767 Phone 32-2-650.5776 Email sgross@ulb.ac.be
%K genetic algorithms, genetic programming, nonotechnology, nanots, artificial life, Qantum-electronic computers, Darwin machines
%U http://citeseer.ist.psu.edu/521663.html
%X Presents results from the evolution of cellular automata replicators using GP (ie using GAs to build/evolve systems. 1: How difficult is the evolution of CA replicators
   (intersity to Artificial Life), 2: Evolving CAs may provide tools for quantum-electronic computers (eg quantum dot arrays)
%8 24--26  May
%Z There seems to be some doubt as to wether ECAL-93 was published. This copy from attendee. GA chromosome is fixed (1024 * 4 CA state values) encoding the CA state transition
   rules. "Evolving CA replicators is much harder than initially thought" Now working on CA networks cf Von Neuman, Codd, Burks.

%A Hugo {de Garis}
%T CAM-BRAIN The Genetic Programming of an Artificial Brain Which Grows/Evolves at Electronic Speeds in a Cellular Automata Machine
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 337--339b
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, cellular automata, neural networks
%X The paper reports on a project which aims to build (i.e. grow/evolve) an artificial brain by the year 2001. This artificial brain should initially contain thousands of
   interconnected artificial neural network modules, and be capable of controlling approximately 1000 behaviours in a robot kitten. The name given to this research project is
   CAM-Brain, because the neural networks (based on cellular automata) will be grown inside special hardware called cellular automata machines (CAMs). Using a family of CAMs,
   each with its own processor to measure the performance quality or fitness of the evolved neural circuits, will allow the neural modules and their interconnections to be
   grown/evolved at electronic speeds. State of the art in CAM design is about 10 to the power 9 or 10 cells. Since a neural module of about 15 connected neurons can fit
   inside a cube of 100 cells on a side (1 million cells), a CAM which is specially adapted for CAM-Brain could contain thousands of interconnected modules, i.e. an artificial
   brain
%8 27-29  June
%Z It appears growth of cellular automata are controlled by linear fixed length chromosome, ie does not use Koza style tree. The CA grow in channels which convey signals that
   are isolated from each other except at junctions (synapses). Artificial brain by 2000AD.

%A Hugo {de Garis}
%T Alife-V 1996 Conference Report
%D 1996
%I 
%K genetic algorithms, genetic programming, artificial life
%U http://www.hip.atr.co.jp/~degaris/AlifeV.txt broken
%X Personal account of the 5th World Artificial Life Conference, 16-18 May 1996, Nara, Japan
%8 July

%A Hugo {de Garis}
%A Andrzej Buller
%A Michael Korkin
%A Felix Gers
%A Norberto Eija Nawa
%A Michael Hough
%T ATR's Artificial Brain (``CAM-Brain'') Project: A Sample of What Individual ``CoDi-1Bit'' Model Evolved Neural Net Modules Can Do with Digital and Analog I/O
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1233
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, poster papers
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) 11 Nov 2005 Ten
   page version at citeseer.ist.psu.edu/22456.html See also CEC 1999
%@ 1-55860-611-4

%A Hugo {de Garis}
%A Jonathan Dinerstein
%A Ravichandra Sriram
%T A Reversible Evolvable Network Architecture and Methodology to Overcome the Heat Generation Problem in Molecular Scale Brain Building
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 83--90
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%U http://www.iss.whu.edu.cn/degaris/papers/RENN.pdf
%X Today's irreversible computing style, in which bits of information are routinely wiped out (e.g. a NAND gate has 2 input bits, and only 1 output bit), cannot continue. If
   Moore's Law remains valid until 2020, as many commentators think, then the heat generated in molecular scale circuits that Moore's Law will provide, would be so intense
   that they will explode [Hall 1992]. To avoid such heat generation problems, it has been known since the early 1970s [Bennet 1973] that the secret to ``heatless
   computation'' is to compute reversibly, i.e. not to destroy bits, by sending in the input bit-string through a computer built from reversible logic gates (e.g. Fredkin
   gates [Fredkin et al 1982], to record the output answer and then send the output bit-string backwards through the computer to obtain the original input bit-string. This
   reversible style of computing takes twice as long, but does not destroy bits, hence does not generate heat. (Landauer's principle states that the heat generated from
   irreversible computing is derived from the destruction of bits of information [Landauer 1961]). The first author intends to build artificial brains over the remaining 20
   years of his active research career, by evolving (neural) network modules directly in electronics (at electronic speeds) in their 100,000s and assembling them into
   artificial brains. In the next 10-20 years, electronic circuitry will reach molecular scales; hence a conceptual problem needs to be faced. How to make evolvable (neural)
   networks that are reversible? This paper proposes a reversible evolvable Boolean network architecture and methodology which, it is hoped, will stimulate the evolvable
   hardware and evolvable neural network research communities to devote more effort towards solving this problem, which can only accentuate as Moore's Law continues to bite.
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp

%A Hugo {de Garis}
%T Evolvable Hardware 2005
%J Evolutionary Computation
%V 13
%N 4
%D 2005
%P 545--550
%I 
%K genetic algorithms, genetic programming, EHW
%8 Winter
%Z Conference report http://ic.arc.nasa.gov/projects/eh2005/ IEEE Press ISBN 0-7695-2399-4, June 2005. Mentions papers by Dmitri Berenson, John Koza \citekoza:2005:EH, Simon
   Harding \citeharding:2005:EH, and Tim Gordon \citegordon:2005:EH.

%A W. Bas {de Haas}
%A and Martin Rohrmeier
%A Remco C. Veltkamp
%A Frans Wiering
%T Modeling Harmonic Similarity Using a Generative Grammar of Tonal Harmony
%B 10th International Society for Music Information Retrieval Conference
%E Keiji Hirata and George Tzanetakis
%D 2009
%P 549--554
%I 
%C Kobe, Japan
%U http://ismir2009.ismir.net/proceedings/OS7-2.pdf
%X In this paper we investigate a new approach to the similarity of tonal harmony. We create a fully functional remodeling of an earlier version of Rohrmeier's grammar of
   harmony. With this grammar an automatic harmonic analysis of a sequence of symbolic chord labels is obtained in the form of a parse tree. The harmonic similarity is
   determined by finding and examining the largest labeled common embeddable subtree (LLCES) of two parse trees. For the calculation of the LLCES a new O(min(n,m)nm) time
   algorithm is presented, where n and m are the sizes of the trees. For the analysis of the LLCES we propose six distance measures that exploit several structural
   characteristics of the Combined LLCES. We demonstrate in a retrieval experiment that at least one of these new methods significantly outperforms a baseline string matching
   approach and thereby show that using additional musical knowledge from music cognitive and music theoretic models actually helps improving retrieval performance.
%8 26-30  October

%A L. Deias
%A G. Mazzarella
%A N. Sirena
%T Bandwidth optimization of EBG surfaces using genetic programming
%B Loughborough Antennas Propagation Conference, LAPC 2009
%D 2009
%P 593--596
%I 
%C Loughborough, UK
%K genetic algorithms, genetic programming, bandwidth optimization, evolutionary strategy, full-wave MoM, planar periodic EBG surface, unit cell geometry, bandwidth
   allocation, method of moments, periodic structures, photonic band gap, surface electromagnetic waves
%X In this paper genetic programming is applied to the synthesis of planar periodic EBG. We constrained our design to the unit cell geometry and used a full-wave MoM to
   evaluate all individuals. The evolutionary strategy is then employed in order to find a geometry with a larger bandwidth.
%8 16-17  November
%Z Also known as \cite5352381

%A L. Deias
%A G. Mazzarella
%A N. Sirena
%T EBG substrate synthesis for 2.45 GHz applications using Genetic Programming
%B Antennas and Propagation Society International Symposium (APSURSI), 2010 IEEE
%D 2010
%I 
%K genetic algorithms, genetic programming, EBG structure electromagnetic behaviour, EBG substrate synthesis, FSS, antenna structures, artificial magnetic conductor ground
   planes, communication system, dielectric substrate, electric crystals, electromagnetic band gap materials, electromagnetic property, electromagnetic wave propagation,
   frequency 2.45 GHz, frequency 5 GHz, frequency selective surfaces, metamaterial substrate, microwave region, millimeter-wave region, periodic metal patches, reflection
   coefficient, wireless networking bands, UHF antennas, electromagnetic wave propagation, frequency selective surfaces, microwave antennas, photonic band gap, substrates
%X In the last decade the study of frequency selective surfaces (FSS), i.e. periodic metal patches printed on a dielectric substrate, has regained interest both in the
   microwave and millimeter-wave region, with the introduction of electromagnetic band gap (EBG) materials. This entirely new class of structures, encompassing FSS as one of
   its subclasses (planar EBG), were named in analogy to the band gaps present in electric crystals and present some very interesting new electromagnetic properties. By
   choosing the proper geometry of the periodic surface we can shape the electromagnetic behaviour of EBGs structures in order to prevent the propagation of electromagnetic
   waves in a given frequency band. In particular, EBG surfaces can be made to act as artificial magnetic conductors (AMC) ground planes, showing a reflection coefficient with
   magnitude 1 and phase 0. The ultimate goal is then to design and incorporate such metamaterial-substrates in antenna structures in order to improve antenna performance.
   Currently there is a growing interest in antennas integrated with an EBG surface for communication system applications, covering the 2.45 GHz and the 5 GHz wireless
   networking bands. The main drawback of this strategy is the reduced bandwidth of the complete antenna, since the frequency range over which these EBG surfaces behave as an
   AMC is usually narrowband and fixed by their geometrical configuration. For this reason we focused our research both on the optimisation of EBGs and the synthesis of new
   promising geometries using genetic programming (GP).
%8 11-17  July
%Z ECJ Dept. of Electr. & Electron. Eng., Univ. of Cagliari, Cagliari, Italy. Also known as \cite5562232

%A Edwin D. {de Jong}
%A Luc Steels
%T Generation and Selection of Sensory Channels
%B Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99
%S LNCS
%E Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty
%V 1596
%D 1999
%P 90--100
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://arti.vub.ac.be/~edwin/publications/channels.ps.gz
%X Sensory channels determine the way an agent views the world. We investigate the question of how sensory channels may be autonomously constructed using generation and
   selection. The context is the discrimination of geometric shapes. In a first experiment, elements of a solution were attributed fitness based on the part of the problem
   they solved. In two subsequent experiments, cooperation between elements was respectively required and encouraged by means of a fitness function which only rewards complete
   solutions. Differences between the approaches are discussed, and generation and selection is concluded to provide a successful mechanism for the autonomous construction of
   sensory channels.
%8 28-29  May
%Z EvoIASP99'99
%@ 3-540-65837-8

%A Edwin D. {de Jong}
%A Richard A. Watson
%A Jordan B. Pollack
%T Reducing Bloat and Promoting Diversity using Multi-Objective Methods
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 11--18
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, code growth, bloat, introns, diversity maintenance, evolutionary multi-objective optimization, Pareto, optimality
%U http://citeseer.ist.psu.edu/440305.html
%X Two important problems in genetic programming (GP) are its tendency to find unnecessarily large trees (bloat), and the general evolutionary algorithms problem that
   diversity in the population can be lost prematurely. The prevention of these problems is frequently an implicit goal of basic GP. We explore the potential of techniques
   from multi-objective optimization to aid GP by adding explicit objectives to avoid bloat and promote diversity. The even 3, 4, and 5-parity problems were solved efficiently
   compared to basic GP results from the literature. Even though only non-dominated individuals were selected and populations thus remained extremely small, appropriate
   diversity was maintained. The size of individuals visited during search consistently remained small, and solutions of what we believe to be the minimum size were found for
   the 3, 4, and 5-parity problems.
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Edwin D. {de Jong}
%A Jordan B. Pollack
%T Multi-Objective Methods for Tree Size Control
%J Genetic Programming and Evolvable Machines
%V 4
%N 3
%D 2003
%P 211--233
%I 
%K genetic algorithms, genetic programming, variable size representations, bloat, code growth, multi-objective optimization, Pareto optimality, interpretability
%U http://www.cs.uu.nl/~dejong/index.html#bloatgpem
%X Variable length methods for evolutionary computation can lead to a progressive and mainly unnecessary growth of individuals, known as bloat. First, we propose to measure
   performance in genetic programming as a function of the number of nodes, rather than trees, that have been evaluated. Evolutionary Multi-Objective Optimisation (EMOO)
   constitutes a principled way to optimise both size and fitness and may provide parameterless size control. Reportedly, its use can also lead to minimisation of size at the
   expense of fitness. We replicate this problem, and an empirical analysis suggests that multi-objective size control particularly requires diversity maintenance. Experiments
   support this explanation. The multi-objective approach is compared to genetic programming without size control on the 11-multiplexer, 6-parity, and a symbolic regression
   problem. On all three test problems, the method greatly reduces bloat and significantly improves fitness as a function of computational expense. Using the FOCUS algorithm,
   multi-objective size control is combined with active pursuit of diversity, and hypothesised minimum-size solutions to 3-, 4- and 5-parity are found. The solutions thus
   found are furthermore easily interpretable. When combined with diversity maintenance, EMOO can provide an adequate and parameterless approach to size control in variable
   length evolution.
%8 September
%Z Article ID: 5141122 Tue, 23 Mar 2004 01:22:36 +0100 See erratum in issue 5:1 Initial drop in size. 5-Parity given XOR!

%A Kenneth {De Jong}
%T On Using Genetic Algorithms to Search Program Spaces
%B Genetic Algorithms and their Applications: Proceedings of the second international conference on Genetic Algorithms
%E John J. Grefenstette
%D 1987
%P 210--216
%I Lawrence Erlbaum Associates Hillsdale, NJ, USA
%I AAAI
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%8 28-31  July
%Z Argues against using LISP (but reference to LISP in ICGA-87) as too order dependant and fragile. Suggests instead production languages as in Holland and others classifiers.
   Warns new representations and crossover operators must obey schema theorem, so crossover is not disruptive and building blocks can be formed
%@ 0-8058-0158-8

%A Marina {de la Cruz Echeandia}
%A Alfonso {Ortega de la Puente}
%A Manuel Alfonseca
%T Attribute Grammar Evolution
%B Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial
   Computation, IWINAC 2005, Part II
%S Lecture Notes in Computer Science
%E Jos\'e Mira and Jos\'e R. \'Alvarez
%V 3562
%D 2005
%P 182--191
%I Springer
%C Las Palmas, Canary Islands, Spain
%K genetic algorithms, genetic programming
%X This paper describes Attribute Grammar Evolution (AGE), a new Automatic Evolutionary Programming algorithm that extends standard Grammar Evolution (GE) by replacing
   context-free grammars by attribute grammars. GE only takes into account syntactic restrictions to generate valid individuals. AGE adds semantics to ensure that both
   semantically and syntactically valid individuals are generated. Attribute grammars make it possible to semantically describe the solution. The paper shows empirically that
   AGE is as good as GE for a classical problem, and proves that including semantics in the grammar can improve GE performance. An important conclusion is that adding too much
   semantics can make the search difficult.
%8 June  15-18
%Z cited by \citeOrtega:2007:ieeeTEC
%@ 3-540-26319-5

%A Marina {de la Cruz Echeandia}
%A Alba Martin Lazaro
%A Alfonso Ortega {de la Puente}
%T The role of Keeping Semantic Blocks Invariant - Effects in Linear Genetic Programming Performance
%B Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)
%E Agostinho Rosa
%D 2010
%P Paper Nr: 78
%I 
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X This paper is focused on two different approaches (previously proposed by the authors) that perform better than Genetic Programming in typical symbolic regression problems:
   straight-line program genetic programming (SLP-GP) and evolution with attribute grammars (AGE). Both approaches have different characteristics. One of the most important is
   that SLP-GP keeps semantic blocks invariant (the crossover operator always exchanges complete subexpressions). In this paper we compare both methods and study the possible
   effect on their performance of keeping these blocks invariant.
%8 24-26  October
%Z http://www.icec.ijcci.org/ICEC2010/home.asp http://www.ecta.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm

%A Carlos A. {Del Carpio M.}
%A Mohamed Ismael
%A Eichiro Ichiishi
%A Michihisa Koyama
%A Momoji Kubo
%A Akira Miyamoto
%T An Evolving Automaton for RNA Secondary Structure Prediction
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 4533--4540
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X Conventional methods for RNA 2D structure prediction search for minimal free energy structures. RNA's, however, RNA's do not always adopt global minimum structures. Rather,
   their structure is the result of the folding pathway followed by the structure in nature, which adopts sub-optimal folds occurring along the pathway. Our algorithm consists
   of an automaton that generates RNA structures by searching for optimal folding pathways. The automaton is endowed of operations to travel throughout the hyperspace of
   conformers embedded in a base pairing matrix. Using genetic programming it evolves optimising its ability to find optimal pathways and finally 2D structures. Comparing the
   evolving automaton with conventional methods shows its potential.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Andrea {Del Duce}
%A Polina Bayvel
%T Quantum Logic Circuits and Optical Signal Generation for a Three-Qubit, Optically Controlled, Solid-State Quantum Computer
%J IEEE Journal of Selected Topics in Quantum Electronics
%V 15
%N 6
%D 2009
%P 1694--1703
%I 
%K genetic algorithms, genetic programming, Deutsch-Jozsa algorithm, controlled-phase gates, entangling gates, optical control, optical signal generation, picosecond optical
   pulse sequences, quantum logic circuits, random fluctuations, solid-state quantum computer, logic circuits, optical control, optical pulse generation, optical signal
   detection, quantum computing, quantum entanglement
%X We analyze the preparation of an experimental demonstration for a three-qubit, optically controlled, solid-state quantum computational system. First, using a genetic
   programming approach, we design quantum logic circuits, specifically tailored for our computational model, which implement a three-qubit refined Deutsch-Jozsa algorithm.
   Aiming at achieving the shortest possible computational time, we compare two design strategies based on exploiting two different sets of entangling gates. The first set
   comprises fast approximations of controlled-phase gates, while in the second case, we exploit arbitrary entangling gates with gate computational times shorter than those of
   the first set. Then, considering some recently proposed material implementations of this quantum computational system, we discuss the generation of the near-midinfrared,
   multi wavelength and picosecond optical pulse sequences necessary for controlling the presented quantum logic circuits. Finally, we analyze potential sources of errors and
   assess the impact of random fluctuations of the parameters controlling the entangling gates on the overall quantum computational system performance.
%8 November -  Decemeber
%Z Also known as \cite5290118 See also \citeoai:arXiv.org:0910.1673 http://arxiv.org/abs/0910.1673

%A Andrea {Del Duce}
%T Quantum Logic circuits for solid-state quantum information processing
%R Ph.D. Thesis
%D 2009
%I 
%I University College London
%C UK
%K genetic algorithms, genetic programming
%U http://eprints.ucl.ac.uk/20166/
%X This thesis describes research on the design of quantum logic circuits suitable for the experimental demonstration of a three-qubit quantum computation prototype. The
   design is based on a proposal for optically controlled, solid-state quantum logic gates. In this proposal, typically referred to as SFG model, the qubits are stored in the
   electron spin of donors in a solid-state substrate while the interactions between them are mediated through the optical excitation of control particles placed in their
   proximity. After a brief introduction to the area of quantum information processing, the basics of quantum information theory required for the understanding of the thesis
   work are introduced. Then, the literature on existing quantum computation proposals and experimental implementations of quantum computational systems is analysed to
   identify the main challenges of experimental quantum computation and typical system parameters of quantum computation prototypes. The details of the SFG model are
   subsequently described and the entangling characteristics of SFG two-qubit quantum gates are analysed by means of a geometrical approach, in order to understand what
   entangling gates would be available when designing circuits based on this proposal. Two numerical tools have been developed in the course of the research. These are a
   quantum logic simulator and an automated quantum circuit design algorithm based on a genetic programming approach. Both of these are used to design quantum logic circuits
   compatible with the SFG model for a three-qubit Deutsch-Jozsa algorithm. One of the design aims is to realise the shortest possible circuits in order to reduce the
   possibility of errors accumulating during computation, and different design procedures which have been tested are presented. The tolerance to perturbations of one of the
   designed circuits is then analysed by evaluating its performance under increasing fluctuations on some of the parameters relevant in the dynamics of SFG gates. Because
   interactions in SFG two-qubit quantum gates are mediated by the optical excitation of the control particles, the solutions for the generation of the optical control signal
   required for the proposed quantum circuits are discussed. Finally, the conclusions of this work are presented and areas for further research are identified.
%8 October

%A Myriam Delgado
%A Fernando Von Zuben
%A Fernando Gomide
%T Modular and Hierarchial Evolutionary Design of Fuzzy Systems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 180--187
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-850.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Myriam Regattieri Delgado
%A Fernando {Von Zuben}
%A Fernando Gomide
%T Multi-Objective Decision Making: Towards Improvement of Accuracy, Interpretability and Design Autonomy in Hierarchical Genetic Fuzzy Systems
%B Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE-02
%D 2002
%P 1222--1227
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE
%C Hilton Hawaiian Village Hotel, Honolulu, Hawaii
%K genetic algorithms, genetic programming
%X This paper presents fuzzy modeling as a multi-objective decision making problem considering accuracy, interpretability and autonomy as goals. The proposed approach assumes
   that these goals can be handled via corresponding single-objective e-constrained decision making problems whose solution is produced by a hierarchical evolutionary process.
   The fitting, generalization, and interpretation characteristics of the resulting fuzzy models are discussed using a classification problem.
%8 12-17  May
%Z IJCNN 2002 Held in connection with the World Congress on Computational Intelligence (WCCI 2002) The length of the chromosome, fixed by the constraint e2, determines the
   maximum number of fuzzy rules but smaller rule-bases are always aimed at first.
%@ 0-7803-7280-8

%A Myriam Regattieri De Biase da Silva Delgado
%T Projeto Automatico de Sistemas Nebulosos: Uma Abordagem Co-Evolutiva
%R Ph.D. Thesis
%D 2002
%I 
%I FACULDADE DE ENGENHARIA ELETRICA E DE COMPUTACAO, UNIVERSIDADE ESTADUAL DE CAMPINAS
%K genetic algorithms, fuzzy systems
%U http://www.dca.fee.unicamp.br/~vonzuben/research/myriam_dout.html
%X This thesis proposes a co-evolutionary-based approach to solve the problem of automatic fuzzy system design. The co-evolution supports hierarchical and collaborative
   relations among individuals representing different parameters of fuzzy models. The proposed approach takes species which encode partial solutions to fuzzy modeling
   problems, organized into four hierarchical levels. Each hierarchical level encodes membership functions, individual rules, rule-bases and fuzzy systems, respectively. A
   special fitness evaluation scheme is proposed to measure the performance of each individual of different species. Constraints and local objectives must be observed at all
   hierarchical levels to guarantee the occurrence of individuals characterized by the simplicity of fuzzy rules, rule compactness, rule base consistency and visibility in the
   universe partition. The approach allows the evolution of Mamdani or Takagi-Sugeno fuzzy models. In addition to performance improvement in terms of accuracy and
   interpretability, the co-evolutionary approach increases autonomy by minimizing user intervention, since it allows automatic tuning of a number of critical parameters, like
   type and total of fuzzy rules, relevant variables (for each rule and for the whole application), shape and location of membership functions, antecedent aggregation
   operator, and, for Mamdani models, aggregation operator, rule semantic, and the defuzzification method. The performance of the approach is evaluated via function
   approximation and pattern classification problems.
%8 26  February
%Z Prof. Dr. Fernando Jose Von Zuben (Orientador) Prof. Dr. Fernando Gomide (Co-orientador) Tese apresentada a Pos-graduacao da Faculdade de Engenharia Eletrica e de
   Computacao da Universidade Estadual de Campinas como requisito parcial a obtencao do grau de Doutor em Engenharia Eletrica na area de Engenharia de Computacao. Campinas, 26
   de Fevereiro de 2002 In Portuguese

%A Elisa Boari {de Lima}
%A Gisele L. Pappa
%A Jussara Marques {de Almeida}
%A Marcos A. Goncalves
%A Wagner Meira
%T Tuning Genetic Programming parameters with factorial designs
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Parameter setting of Evolutionary Algorithms is a time consuming task with two main approaches: parameter tuning and parameter control. In this work we describe a new
   methodology for tuning parameters of Genetic Programming algorithms using factorial designs, one-factor designs and multiple linear regression. Our experiments show that
   factorial designs can be used to determine which parameters have the largest effect on the algorithm's performance. This way, parameter setting efforts can focus on them,
   largely reducing the parameter search space. Two classical GP problems were studied, with six parameters for the first problem and seven for the second. The results show
   the maximum tree depth as the parameter with the largest effect on both problems. A one-factor design was performed to fine-tune tree depth on the first problem and a
   multiple linear regression to fine-tune tree depth and number of generations on the second.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586084

%A Robert Kirk DeLisle
%A Steven L. Dixon
%T Induction of Decision Trees via Evolutionary Programming
%J Journal of Chemical Information and Modeling
%V 44
%N 3
%D 2004
%P 862--870
%I 
%K genetic algorithms, genetic programming, EP, EPTree
%X Decision trees have been used extensively in cheminformatics for modelling various biochemical endpoints including receptor-ligand binding, ADME properties, environmental
   impact, and toxicity. The traditional approach to inducing decision trees based upon a given training set of data involves recursive partitioning which selects partitioning
   variables and their values in a greedy manner to optimise a given measure of purity. This methodology has numerous benefits including classifier interpretability and the
   capability of modeling nonlinear relationships. The greedy nature of induction, however, may fail to elucidate underlying relationships between the data and endpoints.
   Using evolutionary programming, decision trees are induced which are significantly more accurate than trees induced by recursive partitioning. Furthermore, when assessed on
   previously unseen data in a 10-fold cross-validated manner, evolutionary programming induced trees exhibit a significantly higher accuracy on previously unseen data. This
   methodology is compared to single-tree and multiple-tree recursive partitioning in two domains (aerobic biodegradability and hepatotoxicity) and shown to produce less
   complex classifiers with average increases in predictive accuracy of 5-10\% over the traditional method.
%Z http://pubs.acs.org/journals/jcisd8/index.html American Chemical Society, ACS Publications Division S0095-2338(03)04188-X Department of Molecular Modeling, Pharmacopeia,
   P.O. Box 5350, Princeton, New Jersey 08543-5350, and Schrodinger, 120 West 45th Street, 32nd Floor, New York, New York 10036
   http://ai-depot.com/Tutorial/DecisionTrees-EP.html

%A Emilio {del Rosal}
%A Marina {de la Cruz}
%A Alfonso {Ortega de la Puente}
%T Towards the Automatic Programming of NEPs
%B Proceedings of the 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, Part I
%S Lecture Notes in Computer Science
%E Jose Manuel Ferrandez and Jose Ramon Alvarez Sanchez and Felix de la Paz and F. Javier Toledo
%V 6686
%D 2011
%P 303--312
%I Springer
%C La Palma, Canary Islands, Spain
%K genetic algorithms, genetic programming, grammatical evolution
%X This paper shows the platform with which we implement a general methodology to automatically design NEPs to solve specific problems. We use CGE/AGE (a new genetic
   programming algorithm) and jNEP (a Java NEP simulator), two applications we have previously developed. This work is just a proof of viability. We are interested on linking
   all the modules and generating the initial population. Building this platform is relevant, because our methodology includes several non trivial steps, such as designing a
   grammar, and implementing and using a simulator. For this first proof we have chosen a well known problem that other authors have solved by means of NEPs.
%8 May  30-  June  3
%Z Java. jNEP http://jnep.e-delrosal.net p304 Just to generate an initial valid population Attribute grammar and Christianen grammars AGE/CGE but actually uses context free
   grammar. Computing cells. 4 Cells to rotate string. Both filter inputs and outputs to Cell. Cites \citeDBLP:conf/iwinac/CruzPA05

%A Kurt DeMaagd
%A Johannes Bauer
%T A Genetic Programming Approach to Network Management Regulation
%B 43rd Hawaii International Conference on System Sciences (HICSS 2010)
%D 2010
%I 
%K genetic algorithms, genetic programming, United States, business incentives, discriminatory prices, economic growth, network management regulation, commerce,
   telecommunication industry, telecommunication network management, telecommunication services
%X Although next-generation information network infrastructure is prerequisite for continued economic growth, the United States is falling behind in this area relative to many
   other countries. Businesses and regulators have grown concerned that the U.S. lacks the correct regulatory and business incentives to upgrade its network. Due to the
   complex and dynamic nature of this problem, traditional analytic tools have proven inadequate. This paper discusses a Genetic Programming (GP) approach to the problem.
   Although only a first step towards addressing the problem, the GP discovered several interesting results stemming from the complex interactions. For example,
   telecommunications companies would actually be hurt by the option to charge discriminatory prices but application providers would benefit.
%8 5-8  January
%Z Also known as \cite5428681

%A Adar Dembo
%T Evolving Musical Scores using the Genetic Algorithm
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 65--72
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2002/Dembo.pdf
%8 June
%Z part of \citekoza:2002:gagp

%A Lilian M. {de Menezes}
%A Nikolay Y. Nikolaev
%T Forecasting with genetically programmed polynomial neural networks
%J International Journal of Forecasting
%V 22
%N 2
%D 2006
%P 249--265
%I 
%K genetic algorithms, genetic programming, Nonlinear models, Tree-structured polynomial neural network models, Statistical learning algorithms
%X Recent literature on nonlinear models has shown genetic programming to be a potential tool for forecasters. A special type of genetically programmed model, namely
   polynomial neural networks, is addressed. Their outputs are polynomials and, as such, they are open boxes that are amenable to comprehension, analysis, and interpretation.
   This paper presents a polynomial neural network forecasting system, PGP, which has three innovative features: polynomial block reformulation, local ridge regression for
   weight estimation, and regularised weight subset selection for pruning that uses a least absolute shrinkage and selection operator. The relative performance of this system
   to other established forecasting procedures is the focus of this research and is illustrated by three empirical studies. Overall, the results are very promising and
   indicate areas for further research.
%8 April -  June

%A Ian Dempsey
%A Michael O'Neill
%A Anthony Brabazon
%T Live Trading with Grammatical Evolution
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WGEW001.pdf
%8 26-30  June
%Z GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A Ian Dempsey
%A Michael O'Neill
%A Anthony Brabazon
%T Meta-grammar constant creation with grammatical evolution by grammatical evolution
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1665--1671
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, constant creation, digit concatenation, ephemeral random constants, grammatical evolution, metagrammars, theory
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1665.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Ian Dempsey
%T Constant Generation for the Financial Domain using Grammatical Evolution
%B Genetic and Evolutionary Computation Conference (GECCO2005) workshop program
%E Franz Rothlauf and Misty Blowers and J\"urgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J\"orn Grahl and Gregory Hornby and Edwin D. de Jong and
   Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor\`a and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and
   Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton
   and Alden H. Wright
%D 2005
%P 350--353
%I ACM Press
%C Washington, D.C., USA
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0350.pdf
%X This study reports the work to date on the analysis of different methodologies for constant creation with the aim of applying the most advantageous method to the dynamic
   real world problem of a live trading system. The methodologies explored here are Digit Concatenation and Grammatical Ephemeral Random Constants with clear advantages
   identified for a digit concatenation approach in combination with the ability to form new constants through their recombination within expressions.
%8 25-29  June
%Z Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006

%A Ian Dempsey
%A Michael O'Neill
%A Anthony Brabazon
%T Adaptive Trading with Grammatical Evolution
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%P 9137--9142
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, grammatical evolution
%X This study reports on the performance of an on-line evolutionary automatic programming methodology for uncovering technical trading rules for the S&P 500 and Nikkei 225
   indices. The system adopts a variable sized investment strategy based on the strength of the signals produced by the trading rules. Two approaches are explored, one using a
   single population of rules which is adapted over the lifetime of the data and another whereby a new population is created for each step across the time series. The results
   show profitable performance for the trading periods explored with clear advantages for an adaptive population of rules.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-9487-9

%A Ian Dempsey
%A Michael O'Neill
%A Anthony Brabazon
%T Constant Creation in Grammatical Evolution
%J International Journal of Innovative Computing and Applications
%V 1
%N 1
%D 2007
%P 23--38
%I 
%K genetic algorithms, genetic programming, grammatical evolution, constant creation, digit concatenation, ephemeral random constants, grammar based genetic programming,
   persistent random constants
%U http://www.inderscience.com/search/index.php?action=record&rec_id=13399&prevQuery=&ps=10&m=or
%X We present an investigation into constant creation in Grammatical Evolution (GE), a form of grammar-based Genetic Programming (GP). The methods for constant creation
   evaluated include digit Concatenation (Cat) and a grammatical version of ephemeral random constants called persistent random constants. Experiments conducted on a diverse
   range of benchmark problems uncover clear advantages for a digit Cat approach.

%A Ian Dempsey
%A Anthony Brabazon
%A Michael O'Neill
%T A Grammatical Genetic Programming Representation for Radial Basis Function Networks
%B Engineering Evolutionary Intelligent Systems
%S Studies in Computational Intelligence
%E Ajith Abraham and Crina Grosan and Witold Pedrycz
%V 82
%D 2007
%I Springer
%K genetic algorithms, genetic programming, grammatical evolution
%Z http://www.springer.com/east/home/engineering?SGWID=5-175-22-173762620-0

%A Ian Dempsey
%T Grammatical Evolution in Dynamic Environments
%R Ph.D. Thesis
%D 2007
%I 
%I University College Dublin
%C Ireland
%K genetic algorithms, genetic programming, grammatical evolution, dynamic environments
%X Many real-world problems are anchored in dynamic environments, where some element of the problem domain changes with time. The application of Evolutionary Computation (EC)
   to dynamic environments creates challenges different to those encountered in static environments. Foremost among these, are issues of premature convergence, and the
   evolution of overfit solutions. This study aims to identify mechanisms that address these problems. A recent powerful addition to the stable of EC methodologies is
   Grammatical Evolution (GE). GE uses BNF grammars for the evolution of variable length programs. Thus far, there has been little study of the utility of GE in dynamic
   environments. A comprehensive analysis of prior work in EC and GE in the context of dynamic environments is presented. From this, it is seen that GE offers substantial
   potential due to the flexibility provided by the BNF grammar and the many-to-one genotype-to-phenotype mapping. Subsequently novel methods of constant creation are
   introduced that incorporate greater levels of latent evolvability through the use of BNF grammars. These methods are demonstrated to be more accurate and adaptable than the
   standard methods adopted. Through placing GE in the context of a dynamic real-world problem, the trading of financial indices, phenotypic diversity is demonstrated to be a
   function of the fitness landscape. That is, phenotypic entropy fluctuates with the universe of potentially fit solutions. Evidence is also presented of the evolution of
   robust solutions that provide superior out-of-sample performance over a statically trained population. The findings in this study highlight the importance of the
   genotype-to-phenotype mapping for evolution in dynamic environments and uncover some of the potential benefits of the incorporation of BNF grammars in GE.

%A Ian Dempsey
%A Michael O'Neill
%A Anthony Brabazon
%T Foundations in Grammatical Evolution for Dynamic Environments
%S Studies in Computational Intelligence
%V 194
%D 2009
%I Springer
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.springer.com/engineering/book/978-3-642-00313-4
%X Table of contents Introduction.- Grammatical Evolution.- Survey of EC in Dynamic Environments.- GE in Dynamic Environments.- Constant Creation and Adaptation in Grammatical
   Evolution.- Constant Creation with meta-Grammars.- Controlled Static Trading with GE.- Adaptive Dynamic Trading with GE.- Conclusions & The Future.
%8 April

%A M. A. H. Dempster
%A C. M. Jones
%T The Profitability of Intra-Day FX Trading Using Technical Indicators
%R Working Paper 35/00
%D 2000
%I 
%I Judge Institute of Management Studies, University of Cambridge
%C Trumpington Street, Cambridge, CB2 1AG
%K genetic algorithms, genetic programming
%U http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/1999/profitability.pdf
%X Technical analysis indicators are widely used by traders to predict future price levels and hence enhance trading profitability. Traders often use high frequency price (ie.
   intra-day) data when calculating such indicators, which are then used as the basis for trade entry rules. Similar rules, along with standard exit rules aimed at reducing
   downside risk, are then used to exit these trades. In this paper we test a wide range of well known technical indicators on a set of US Dollar/British Pound Spot FX tick
   data from 1989-1997 aggregated ti various intra-day frequencies. We find that few of the rules, whether based on well known and tested moving average crossover or on some
   of the more esoteric and untested indicators, are consistently profitable when traded under realistic slippage conditions. Furthermore, we vary the lippage regime to
   represent differences in the efficiency of trade execution eg. between a bank trader and a small 'hedge' fund but still find the rules to be loss-making. When the rules are
   reversed, losses are still found indicating the losses not to be economically significant - a result that supports the efficient market hypothesis.

%A M. A. H. Dempster
%A C. M. Jones
%T A real-time adaptive trading system using genetic programming
%J Quantitative Finance
%V 1
%D 2000
%P 397--413
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/dempster01realtime.html
%X Technical analysis indicators are widely used by traders in financial and commodity markets to predict future price levels and enhance trading profitability. We have
   previously shown a number of popular indicator-based trading rules to be loss-making when applied individually in a systematic manner. However, technical traders typically
   use combinations of a broad range of technical indicators. Moreover, successful traders tend to adapt to market conditions by dropping trading rules as soon as they become
   loss-making or when more profitable rules are found. In this paper we try to emulate such traders by developing a trading system consisting of rules based on combinations
   of different indicators at different frequencies and lags. An initial portfolio of such rules is selected by a genetic algorithm applied to a number of indicators
   calculated on a set of US Dollar/British Pound spot foreign exchange tick data from 1994 to 1997 aggregated to various intraday frequencies. The genetic algorithm is
   subsequently used at regular intervals on out-of-sample data to provide new rules and a feedback system is used to rebalance the rule portfolio, thus creating two levels of
   adaptivity. Despite the individual indicators being generally loss-making over the data period, the best rule found by the developed system is found to be modestly, but
   significantly, profitable in the presence of realistic transaction costs.
%Z INSTITUTE OF PHYSICS PUBLISHING quant.iop.org

%A M. A. H. Dempster
%A Tom W. Payne
%A Yazann Romahi
%A G. W. P. Thompson
%T Computational learning techniques for intraday FX trading using popular technical indicators
%J IEEE Transactions on Neural Networks
%V 12
%N 4
%D 2001
%P 744--754
%I 
%K genetic algorithms, genetic programming, Markov processes, foreign exchange trading, genetic algorithms, learning (artificial intelligence), Markov decision, computational
   learning, foreign exchange trading, heuristic, reinforcement learning, technical trading, transaction costs
%U http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/ieeetrading.pdf
%X We consider strategies which use a collection of popular technical indicators as input and seek a profitable trading rule defined in terms of them. We consider two popular
   computational learning approaches, reinforcement learning and genetic programming, and compare them to a pair of simpler methods: the exact solution of an appropriate
   Markov decision problem, and a simple heuristic. We find that although all methods are able to generate significant in-sample and out-of-sample profits when transaction
   costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction
   costs. We also find that there is a substantial danger of overfitting if in-sample learning is not constrained
%8 July
%Z CODEN: ITNNEP. INSPEC Accession Number:6997298 Location: technical report WP30/2000

%A M. A. H. Dempster
%A Y. S. Romahi
%T Intraday FX Trading: An Evolutionary Reinforcement Learning Approach
%B Proceedings of Third International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2002
%S Lecture Notes in Computer Science
%E Hujun Yin and Nigel M. Allinson and Richard T. Freeman and John A. Keane and Simon J. Hubbard
%V 2412
%D 2002
%P 347--358
%I Springer
%C Manchester
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/2412/24120347.htm
%X We have previously described trading systems based on unsupervised learning approaches such as reinforcement learning and genetic algorithms which take as input a
   collection of commonly used technical indicators and generate profitable trading decisions from them. This article demonstrates the advantages of applying evolutionary
   algorithms to the reinforcement learning problem using a hybrid credit assignment approach. In earlier work, the temporal difference reinforcement learning approach
   suffered from problems with overfitting the in-sample data. This motivated the present approach. Technical analysis has been shown previously to have predictive value
   regarding future movements of foreign exchange prices and this article presents methods for automated high-frequency FX trading based on evolutionary reinforcement learning
   about signals from a variety of technical indicators. These methods are applied to GBPUSD, USDCHF and USDJPY exchange rates at various frequencies. Statistically
   significant profits are made consistently at transaction costs of up to 4bp for the hybrid system while the standard RL is only able to trade profitably up to about 1bp
   slippage per trade.
%8 12-14  August
%Z Location: technical report WP03/2002
%@ 3-540-44025-9

%A M. A. H. Dempster
%A V. Leemans
%T An automated FX trading system using adaptive reinforcement learning
%J Expert Systems with Applications
%V 30
%N 3
%D 2006
%P 543--552
%I 
%K genetic algorithms, genetic programming
%U http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2004/WP18.pdf
%X This paper introduces adaptive reinforcement learning (ARL) as the basis for a fully automated trading system application. The system is designed to trade foreign exchange
   (FX) markets and relies on a layered structure consisting of a machine learning algorithm, a risk management overlay and a dynamic utility optimisation layer. An existing
   machine-learning method called recurrent reinforcement learning (RRL) was chosen as the underlying algorithm for ARL. One of the strengths of our approach is that the
   dynamic optimization layer makes a fixed choice of model tuning parameters unnecessary. It also allows for a risk-return trade-off to be made by the user within the system.
   The trading system is able to make consistent gains out-of-sample while avoiding large draw-downs.
%O Special Issue on Financial Engineering
%8 April
%Z Centre for Financial Research, Judge Business School, University of Cambridge & Cambridge Systems Associates Limited, Cambridge, UK Also technical report WP18/2004

%A J. L. Deneubourg
%A S. Aron
%A S. Goss
%A J. M. Pasteels
%A G. Duerinck
%T Random behaviour, amplification processes and number of participants: How they contribute to the foraging properties of ants
%J Physica D: Nonlinear Phenomena
%V 22
%N 1-3
%D 1986
%P 176--186
%I 
%U http://www.sciencedirect.com/science/article/B6TVK-4CVPV04-F/2/80230b3fab67ba01fc8a22aa94873a7e
%O Proceedings of the Fifth Annual International Conference
%Z Not on GP

%A Monica Malen Denham
%T Predicci\'on de la Evoluci\'on de los Incendios Forestales Guiada Din\'amicamente por los Datos
%R Ph.D. Thesis
%D 2009
%I 
%I Universitat Autonoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius,Universitat Aut\`onoma de Barcelona
%C Spain
%K genetic algorithms, forest fire prediction
%U http://www.tesisenxarxa.net
%X These Methods reduce total execution time, on account of the acceleration of searching convergence. Using real fire progress knowledge, genetic algorithm is fast guided to
   promising individual search space zones [10] [11] [13]. In order to obtain real fire progress characteristics, additional methods were developed. These methods had showed
   good performance when they where used for di_erent kind of maps: linear maps, elliptical maps, real cases, synthetic cases, di_erent sizes of cells, etc. Parallel
   application proposed was tested in order to evaluate its scalability. Master and worker process times had decreased when number of computing elements had increased. On
   demand dealing of work, communication reduction (because of chunks communication), etc. achieve a good performance application [12]. For most of the performed tests similar
   behavior could be seen: Computational Method convergences more quickly to good individual zones. Then, fewer iterations can be executed and steering methods finds good
   results. Thus, execution time of two stages prediction method can be reduced as well.

%A E. {den Heijer}
%A A. E. Eiben
%T Comparing Aesthetic Measures for Evolutionary Art
%B EvoMUSART
%S LNCS
%E Cecilia Di Chio and Anthony Brabazon and Gianni A. Di Caro and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and
   Michael O'Neill and Ernesto Tarantino and Neil Urquhart
%V 6025
%D 2010
%P 311--320
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X In this paper we investigate and compare four aesthetic measures within the context of evolutionary art. We evolve visual art with an unsupervised evolutionary art system
   using genetic programming and an aesthetic measure as the fitness function. We perform multiple experiments with different aesthetic measures and examine their influence on
   the evolved images. To this end we store the 5 fittest individuals of each run and hand-pick the best 9 images after finishing the whole series. This way we create a
   portfolio of evolved art for each aesthetic measure for visual inspection. Additionally, we perform a cross-evaluation by calculating the aesthetic value of images evolved
   by measure i according to measure j. This way we investigate the flexibility of each aesthetic measure (i.e., whether the aesthetic measure appreciates different types of
   images). The results show that aesthetic measures have a rather clear style and that these styles can be very different. Furthermore we find that some aesthetic measures
   show very little flexibility and appreciate only a limited set of images.
%8 7-9  April
%Z EvoMUSART'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Eelco {den Heijer}
%A A. E. Eiben
%T Using aesthetic measures to evolve art
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X In this paper we investigate and compare three aesthetic measures within the context of evolutionary art. We evolve visual art with an unsupervised evolutionary art system
   using genetic programming and an aesthetic measure as the fitness function. We perform multiple experiments with different aesthetic measures and examine their influence on
   the evolved images. Additionally, we perform a cross-evaluation by calculating the aesthetic value of images evolved by measure i according to measure j. This way we
   investigate the flexibility of each aesthetic measure (i.e., whether the aesthetic measure appreciates different types of images). Last, we perform an image analysis using
   a fixed set of image statistics functions. The results show that aesthetic measures have a rather clear 'style' and that these styles can be very different. Furthermore we
   find that some aesthetic measures show little flexibility and appreciate only a limited set of images. The images in this paper might only be in colour in the electronic
   version.
%8 18-23  July
%Z Computational Aesthetics. Benford distribution, Shannon entropy+Kolmogorov complexity. Arabitat (Art Habitat) http://www.few.vu.nl/~eelco/ indexed colour table, 'an image
   that can be compressed using PNG to 3percent or less of its original size is discarded'. Table II - very little agreement between different fitness measures. WCCI 2010.
   Also known as \cite5586245

%A Eelco {den Heijer}
%A Agoston Endre Eiben
%T Evolving art with scalable vector graphics
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 427--434
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution, Digital entertainment technologies and arts
%X In this paper we introduce the use of Scalable Vector Graphics (SVG) as a representation for evolutionary art. We describe the technical aspects of using SVG in
   evolutionary art, and explain the genetic operators mutation and crossover. Furthermore, we compare the use of SVG with existing representations in evolutionary art. We
   performed a number of experiments in an unsupervised evolutionary art system using two aesthetic measures as fitness functions, and compared the outcome of the different
   experiments with each other and with previous work with symbolic expressions as the representation. All images and SVG code examples in this paper are available at
   http://www.few.vu.nl/~eelco
%8 12-16  July
%Z crossover XML grammar path gradient defs mutation fixup (genetic repair. Aim more Art than computer art. Two automatic fitness measures tried: Brian Ross, William Ralph,
   and Hai Zong. Evolutionary image synthesis using a model of aesthetics. In IEEE Congress on Evolutionary Computation (CEC) 2006, pages 1087-1094, \citeRoss:EIS:cec2006.
   Kresimir Matkovic, Laszlo Neumann, Attila Neumann, Thomas Psik, and Werner Purgathofer. Global contrast factor-a new approach to image contrast. In Laszlo Neumann, Mateu
   Sbert, Bruce Gooch, and Werner Purgathofer, editors, Computational Aesthetics, pages 159-168. Eurographics Association, 2005. Also known as \cite2001635 GECCO-2011 A joint
   meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)

%A Stanislaw Deniziak
%A Adam Gorski
%T Hardware/Software Co-synthesis of Distributed Embedded Systems Using Genetic Programming
%B Proceedings of the 8th International Conference Evolvable Systems: From Biology to Hardware, ICES 2008
%S Lecture Notes in Computer Science
%E Gregory Hornby and Luk\'as Sekanina and Pauline C. Haddow
%V 5216
%D 2008
%P 83--93
%I Springer
%C Prague, Czech Republic
%K genetic algorithms, genetic programming
%X This work presents a novel approach to hardware-software co-synthesis of distributed embedded systems, based on the developmental genetic programming. Unlike the other
   genetic approaches where chromosomes represent solutions, in our method chromosomes represent system construction procedures. Thus, not the system architecture but the
   co-synthesis process is evolved. Finally a tree describing a construction of a final solution is obtained. The optimisation process will be illustrated with examples.
   According to our best knowledge it is the first DGP approach that deals with the hardware-software co-synthesis.
%8 September  21-24
%Z Cracow University of Technology, Dept. of Computer Engineering, Warszawska 24, 31-155 Cracow, Poland

%A M. C. Deo
%T Reply to: Discussion on "Genetic programming for retrieving missing information in wave records along the west coast of India" [Applied Ocean Research 2007; 29(3): 99-111];
   A.H. Gandomi, A.H. Alavi, S.S. Sadat Hosseini
%J Applied Ocean Research
%V 30
%N 4
%D 2008
%P 340
%I 
%U http://www.sciencedirect.com/science/article/B6V1V-4VY6FSK-1/2/70a6592b22ba65b93887b8122e985f75
%Z Reply to \citeGandomi2008338. Original article \citeKalra200799

%A Omkar Deo
%A V. Jothiprakash
%A M. C. Deo
%T Genetic Programming to Predict Spillway Scour
%J International Journal of Tomography \& Statistics
%V 8
%N W08
%D 2008
%P 32--45
%I 
%K genetic algorithms, genetic programming, neural networks, scour predictions spillway scour, skijump bucket
%U http://www.ceser.res.in/ceserp/index.php/ijts/article/view/532
%X Investigators in the past had noticed that application of a soft computing tool like artificial neural networks (ANN) in place of traditional statistics based data mining
   techniques produce more attractive results in hydrologic as well as hydraulic predictions. Mostly these works pertained to applications of ANN. Recently another tool of
   soft computing namely genetic programming (GP) has caught attention of researchers in civil engineering computing. This paper examines the usefulness of the GP based
   approach to predict the depth and geometry of the scour hole produced downstream of a common type of spillway, namely, the ski-jump bucket. Hydraulic model measurements
   were used to develop the GP models. The GP based estimations were found to be equally, and possibly more, accurate than the ANN based ones,especially when the underlying
   cause-effect relationship became more uncertain to model.
%8 Winter
%Z Discipulus. Datta Meghe College of Engineering, Airoli, Navi Mumbai, 400708, India

%A Sushamna Deodhar
%A Alison A. Motsinger-Reif
%T Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions
%B 8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2010)
%S Lecture Notes in Computer Science
%E Clara Pizzuti and Marylyn D. Ritchie and Mario Giacobini
%V 6023
%D 2010
%P 98--109
%I Springer
%C Istanbul, Turkey
%K genetic algorithms, genetic programming, grammatical evolution
%8 April  7-9

%A Daniel Derrig
%A James D. Johannes
%T Hierarchical Exemplar Based Credit Allocation for Genetic Classifier Systems
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 622--628
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, classifiers
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Daniel Derrig
%A James Johannes
%T Deleting End-of-Sequence Classifiers
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Larry M. Deschaine
%A Fred A. Zafran
%A Janardan J. Patel
%A David Amick
%A Robert Pettit
%A Frank D. Francone
%A Peter Nordin
%A Edward Dilkes
%A Laurene V. Fausett
%T Solving the Unsolved Using Machine Learning, Data Mining and Knowledge Discovery to Model a Complex Production Process
%B Advanced Technology Simulation Conference
%E M. Ades
%D 2000
%I 
%I Society for Computer Simulations
%C Wasington, DC, USA
%K genetic algorithms, genetic programming, discipulus
%U http://citeseer.ist.psu.edu/deschaine00solving.html
%8 22-26  April
%Z http://www.scs.org/confernc/astc00/final-program/BIS-details.htm Soil Stabilization via Evolutionary Computation - Linear Genetic Programming, Simulated Annealing, and ANN.
   Predict hydraulic condictivity, strangth, leach, of stabilised waste given grain size and grout composition. 'The speed savings alone made GP the technology of Choice.'
   Author orignally misspelt Larry M. Deschain:-(

%A Larry M. Deschaine
%A Janardan J. Patel
%A Ronald D. Guthrie
%A Joseph T. Grimski
%A M. J. Ades
%T Using Linear Genetic Programming to Develop a C/C++ Simulation Model of a Waste Incinerator
%B Advanced Technology Simulation Conference
%E M. Ades
%D 2001
%I 
%I Society for Computer Simulations
%C Seattle
%K genetic algorithms, genetic programming, discipulus, DSS, 10 demes
%U http://citeseer.ist.psu.edu/396498.html
%X Abstract We explore whether Linear Genetic Programming (LGP) can evolve a C/C++ computer simulation model that accurately models the performance of a waste incinerator.
   Human expert written simulation models are used worldwide in a variety of industrial and business applications. They are expensive to develop, may or may not be valid for
   the specific process that is being modeled, and may be erroneous. LGP is a machine learning technique that uses information about a process's inputs and outputs to
   simultaneously write the simulation model, calibrate and optimize the model's constants, and validate the solution. The result is a calibrated, validated, error-free C/C++
   computer model specific to the desired process. To evaluate whether this is feasible for complex industrial processes, the method on data obtained from the operation of a
   hazardous waste incinerator. This process is difficult to model. Previously, in a well-conducted study, the popular machine learning technique, analytic neural networks,
   was unable to derive useful solutions to this problem. The present study uses various mutation rates (95%, 50%, and 10%), 10 random initial seeds per mutation rate, and a
   large number of generations (1,280 to 4,461). The LGP system provided accurate solutions to this problem with a validation data measure of fitness, R2, equal to 0.961. This
   work demonstrates the value of LGP for process simulation. The study confirms previously published results and found that the distribution of outputs from multiple genetic
   programming (GP) runs tends to include an extended tail of outstanding solutions. Such a tail was not found in previous studies of neural networks. This result emphasizes
   the need for employing a strategy of multiple runs using various initial seeds and mutation rates to find good solutions to complex problems using LGP. This result also
   demonstrates the value of a fast LGP algorithm implemented at the machine code level for both static scientific data mining and real-time process control. The work consumed
   600 hours of CPU time; it is estimated that other GP algorithms would have required between 4 and 136 years of CPU time to achieve similar results.
%8 22-26  April
%Z ASTC 2001 http://www.scs.org/confernc/astc01/prelim-program/astc01prelim.html Science Applications International Corporation Model of C02 concentration from 1 weeks live
   running hourly logs. Interactive Evaluation (Unclear what this means). Print out of PDF poor Author orignally misspelt Larry M. Deschain:-(

%A Larry M. Deschaine
%T Tackling Real-World Environmental Challenges with Linear Genetic Programming
%J PCAI
%V 15
%N 5
%D 2000
%P 35--37
%I 
%K genetic algorithms, genetic programming
%8 September /  October
%Z advocates a unique approach to the challenges of engineering and scientific data mining, control, and process optimization by using fast linear genetic programming
   technique. Author orignally misspelt Larry M. Deschain:-(

%A L. M. Deschaine
%A Jennifer McCormack
%A D. Pyle
%A F. Francone
%T Genetic Algorithms and Intelligent Agents Team Up: Techniques for Data Assembly, Preprocessing, Modeling, and Decision Optimization
%J PCAI magazine
%V 15
%N 3
%D 2001
%P 38--44
%I 
%K genetic algorithms, genetic programming
%X Discussing a set of techniques for optimal real-time decision making from distributed, heterogeneous information found in financial, industrial, and scientific data
%8 May /  June
%Z http://www.pcai.com/web/indexes/index_vol_15.html

%A Larry M. Deschaine
%A Frank D. Francone
%T Design Optimization Integrating the Outer Approximation Method with Process Simulators and Linear Genetic Programming
%B Proceedings of the 6th Joint Conference on Information Science
%E H. John Caulfield and Shu-Heng Chen and Heng-Da Cheng and Richard J. Duro and Vasant Honavar and Etienne E. Kerre and Mi Lu and Manuel Grana Romay and Timothy K. Shih and
   Dan Ventura and Paul P. Wang and Yuanyuan Yang
%D 2002
%P 618--621
%I JCIS / Association for Intelligent Machinery, Inc.
%C Research Triangle Park, North Carolina, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/FEA_2002_Design_Optimization.pdf
%X Fast process optimisation is a challenge. Processes are often complex and the intricate simulators written to solve them can take hours or days per simulation to run.
   Optimization techniques that require many calls to a simulator can take days or months to solve. While advances in optimisation algorithms, such as the outer approximation
   method have reduced the solution time by a factor of ten or more when compared to other methods, long solutions times still can occur. This work explores the development of
   simulating a simulator to enable optimal solution development in an accelerated time frame. The technique used to develop the simulated simulator is linear genetic
   programming (LGP). LGP approximated a complex industrial process simulator that took hours to execute per run with a high fitness program - applied (testing) data set R2
   fitness of 0.989. The LGP solution executes in less than a second. This success opens up the possibility of optimising functions faster using these LGP derived high fitness
   simulator approximations. Since the LGP simulated process simulator now executes in less than a second, as opposed to hours, using an intensive multiple call optimisation
   technique such as genetic algorithms and evolutionary strategies is now also feasible.
%8 March  8-13
%Z FEA2002 In conjunction with Sixth Joint Conference on Information Sciences My printer refuses to deal with this as PDF
%@ 0-9707890-1-7

%A Larry M. Deschaine
%A Richard A. Hoover
%A Joseph N. Skibinski
%A Janardan J. Patel
%A Frank Francone
%A Peter Nordin
%A M. J. Ades
%T Using Machine Learning to Compliment and Extend the Accuracy of UXO Discrimination Beyond the Best Reported Results of the Jefferson Proving Ground Technology Demonstration
%B 2002 Advanced Technology Simulation Conference
%D 2002
%I 
%I The Society for Modeling and Simulation International
%C San Diego, CA, USA
%K genetic algorithms, genetic programming, Unexploded ordnance, anomaly detection, geophysics, UXO
%U http://www.scs.org/docInfo.cfm?get=1488
%X The accurate discrimination of unexploded ordnance from geophysical signals is very difficult. Research has demonstrated that using a machine learning technique known as
   linear genetic programming in concert with human expertise can extend the accuracy of unexploded ordnance discrimination past currently published results. This paper
   describes how linear genetic programming offers the promise of creating real-time unexploded ordnance discrimination.
%8 14-18  April
%Z http://www.scs.org/confernc/astc/astc02/ASTC02finalprogram.pdf

%A Larry Deschaine
%A Janos D. Pinter
%A Sudip Regmi
%T Developing High Fidelity Approximations to Expensive Simulation Models for Expedited Optimization
%B INFORMS Annual Meeting Conference
%D 2003
%I 
%C Atlanta, Georgia, USA
%K genetic algorithms, genetic programming
%U http://informs.emeetingsonline.com/emeetings/formbuilder/clustersessiondtl.asp?csnno=1278
%X Integrated simulation and optimisation typically requires a sequence of 'expensive' function calls. While extremely valuable in concept, when the computation cost of
   simulations functions is high (hours / days) and or the optimization paradigm is inefficient (thousands of function calls), real-time or timely 'optimal' solutions are
   elusive. We discuss the use of machine learning to develop a high fidelity model of a process simulator that executes quickly (milliseconds). This function is then
   optimised using the LGO solver, thus enabling optimisation in real-time.
%O Presented at
%8 October  19-22

%A Larry Deschaine
%T Using Information fusion, machine learning, and global optimisation to increase the accuracy of finding and understanding items interest in the subsurface
%J GeoDrilling International
%N 122
%D 2006
%P 30--32
%I 
%C London
%K genetic algorithms, genetic programming, Groundwater plumes, Source zones, Landmines and unexploded ordnance UXO
%U http://www.mining-journal.com/gdi_magazine/pdf/GDI0605scr.pdf
%8 May
%Z SUMMARY Exploration in the subsurface is expensive and complex. By appropriately using analysis tools that synergistically exploit the information content of data and other
   information, better decisions can be made.

%A Larry M. Deschaine
%A Frank D. Francone
%A Janos D. Pinter
%A Melissa McKay
%A Jeff Warren
%A Seth Blanchard
%T Finding and Identifying Objects Based on Noisy Data: A Global Optimization Approach - Part 1: Theoretical Approach and Applicability with Deployment Examples; and Part 2
   UXO Finding and Discrimination. Results from Field Production: Translation of R\&D work into Field Production Tools UXOMF
%B EURO XXI
%E Tuula Kinnunen
%D 2006
%I 
%I Icelandic Operations Research Society and The Association of European OR Societies
%C Reykjavik, Iceland
%K genetic algorithms, genetic programming
%U https://www.euro-online.org/euro21/display.php?page=treate_abstract&frompage=edit_session_cluster&sessionid=661&paperid=3361
%X Automated object recognition of images or signals is important, to identify items of interest, or anomalies (such as tumours in tissues). In such analyses it is often
   necessary to deal with noise in the values observed. Such noise complicates automated search procedures, and can affect the solution. In our example, the location,
   orientation and dimensions of an elliptical object are determined based on noisy data from electromagnetic surveys. We then use a global optimisation approach to find the
   best function fit. Our results demonstrate the success of this general approach.
%8 2-6  July
%Z http://www.euro2006.org/

%A Larry M. Deschaine
%T Tina Yu, David Davis, Cem Baydar, Rajkumar Roy (eds): Evolutionary Computation in Practice: Studies in Computational Intelligence, Springer, 2008, 322 pp, ISBN
   978-3-540-75770-2
%J Genetic Programming and Evolvable Machines
%V 9
%N 4
%D 2008
%P 371--372
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%O Book Review
%8 Decemeber
%Z review of \citeTinaYu:2008:book

%A Nishant Deshpande
%T Comparison of a Job-Shop Scheduler using Genetic Algorithms with a SLACK Based Scheduler
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 73--82
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2002/Deshpande.pdf
%8 June
%Z part of \citekoza:2002:gagp

%A Lisa M. Desjarlais
%A Mohammad-R. Akbarzadeh-T.
%A Craig W. Wright
%T Control System Optimization Using Genetic Algorithms within the SoftLab Toolkit
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1774
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-781.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Janaina S. {de Sousa}
%A Lalinka {de C. T. Gomes}
%A George B. Bezerra
%A Leandro N. {de Castro}
%A Fernando J. {Von Zuben}
%T An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data
%J Genetic Programming and Evolvable Machines
%V 5
%N 2
%D 2004
%P 157--179
%I 
%K genetic algorithms, genetic programming, gene expression, microarray, artificial immune systems, clustering, evolutionary algorithms
%X Microarray technologies are employed to simultaneously measure expression levels of thousands of genes. Data obtained from such experiments allow inference of individual
   gene functions, help to identify genes from specific tissues, to analyse the behaviour of gene expression levels under various environmental conditions and under different
   cell cycle stages, and to identify inappropriately transcribed genes and several genetic diseases, among many other applications. As thousands of genes may be involved in a
   microarray experiment, computational tools for organising and providing possible visualisations of the genes and their relationships are crucial to the understanding and
   analysis of the data. This work proposes an algorithm based on artificial immune systems for organizing gene expression data in order to simultaneously reveal multiple
   features in large amounts of data. A distinctive property of the proposed algorithm is the ability to provide a diversified set of high-quality rearrangements of the genes,
   opening up the possibility of identifying various co-regulated genes from representative graphical configurations of the expression levels. This is a very useful approach
   for biologists, because several coregulated genes may exist under different conditions.
%8 June
%Z Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster Department of Computer Engineering and
   Industrial Automation, State University of Campinas (Unicamp), CP. 6101, Campinas, SP, 13083-970, Brazil

%A Luzia Vidal {de Souza}
%A Aurora T. R. Pozo
%A Anselmo C. Neto
%A Joel M. C. {da Rosa}
%T Genetic Programming and Boosting Technique to Improve Time Series Forecasting
%B Evolutionary Computation
%E Wellington Pinheiro dos Santos
%D 2009
%I InTech
%K genetic algorithms, genetic programming
%U http://www.intechopen.com/articles/show/title/genetic-programming-and-boosting-technique-to-improve-time-series-forecasting
%O 6
%8 October
%Z http://www.intechopen.com/books/show/title/evolutionary-computation

%A Luzia Vidal {de Souza}
%A Aurora Pozo
%A Joel Mauricio Correa {da Rosa}
%A Anselmo Chaves Neto
%T Applying correlation to enhance boosting technique using genetic programming as base learner
%J Applied Intelligence
%V 33
%N 3
%D 2010
%P 291--301
%I Springer Netherlands
%K genetic algorithms, genetic programming
%X This paper explores the Genetic Programming and Boosting technique to obtain an ensemble of regressors and proposes a new formula for the updating of weights, as well as
   for the final hypothesis. Differently from studies found in the literature, in this paper we investigate the use of the correlation metric as an additional factor for the
   error metric. This new approach, called Boosting using Correlation Coefficients (BCC) has been empirically obtained after trying to improve the results of the other
   methods. To validate this method, we conducted two groups of experiments. In the first group, we explore the BCC for time series forecasting, in academic series and in a
   widespread Monte Carlo simulation covering the entire ARMA spectrum. The Genetic Programming (GP) is used as a base learner and the mean squared error (MSE) has been used
   to compare the accuracy of the proposed method against the results obtained by GP, GP using traditional boosting and the traditional statistical methodology (ARMA). The
   second group of experiments aims at evaluating the proposed method on multivariate regression problems by choosing Cart (Classification and Regression Tree) as the base
   learner.

%A Antonello Dessi
%A Antonella Giani
%A Antonina Starita
%T An Analysis of Automatic Subroutine Discovery in Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 996--1001
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-432.ps
%X This paper analyses Rosca's ARL as a general framework for automatic subroutine discovery. We review and compare a number of heuristics for code selection, and
   experimentally test their effectiveness in the ARL framework. We also propose and analyse a new heuristic, the Saliency, and two extensions to ARL: diffusion of the new
   subroutines through mutation and the MaxFit technique to adaptively change the length of an epoch. In spite of the effectiveness of the proposed extensions, the main result
   is that any attempt to improve the selection criterion seems not able to produce better results than a simple near-random heuristic.
%8 13-17  July
%Z 6-mux, symbolic regression, sort (loop, swap, memory array) GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth
   annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A C. {De Stefano}
%A A. Della Cioppa
%A A. Marcelli
%T Character preclassification based on genetic programming
%J Pattern Recognition Letters
%V 23
%N 12
%D 2002
%P 1439--1448
%I 
%K genetic algorithms, genetic programming, Character recognition, Preclassification
%U http://www.sciencedirect.com/science/article/B6V15-45J91MV-4/2/3e5c2ac0c51428d0f7ea9fc0142f6790
%X This paper presents a learning system that uses genetic programming as a tool for automatically inferring the set of classification rules to be used during a
   pre-classification stage by a hierarchical handwritten character recognition system. Starting from a structural description of the character shape, the aim of the learning
   system is that of producing a set of classification rules able to capture the similarities among those shapes, independently of whether they represent characters belonging
   to the same class or to different ones. In particular, the paper illustrates the structure of the classification rules, the grammar used to generate them and the genetic
   operators devised to manipulate the set of rules, as well as the fitness function used to drive the inference process. The experimental results obtained by using a set of
   10,000 digits extracted from the NIST database show that the proposed pre classification is efficient and accurate, because it provides at most 6 classes for more than 87%
   of the samples, and the error rate almost equals the intrinsic confusion found in the data set.

%A Judith E. Devaney
%T Converting pvmmake to mpimake under LAM, and MPI and Parallel Genetic Programming
%B MPI Developers Conference
%E Andrew Lumsdaine
%D 1995
%I 
%C University of Notre Dame
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/devaney95experience.html
%X This looks at the issues which arose in porting the pvmmake utility from pvm to mpi. Pvmmake is a pvm application which allows a user to send files, execute commands, and
   receive results from a single machine on any machine in the virtual machine. It's actions are controlled by the contents of an agenda file. It's most common use is to
   enable management of the development of a parallel program in a heterogeneous environment. A utility with the same features, mpimake, was coded up to run under LAM. Genetic
   programming is an algorithm which evolves a program to solve your input problem. The implementation under MPI requires the transfer of data structures such as lists and
   trees. The match between the requirements of this algorithm and the datatype feature in mpi will be discussed.
%8 22-23  June
%Z Data from http://www.cse.nd.edu/mpidc95/proceedings/abstracts/html/devaney/ 4 Nov 1997

%A Judith Devaney
%A John Hagedorn
%A Olivier Nicolas
%A Gagan Garg
%A Aurelien Samson
%A Martial Michel
%T A Genetic Programming Ecosystem
%B Proceedings 15th International Parallel and Distributed Processing Symposium, Abstracts and CDROM
%D 2001
%P 1323--1330
%I IEEE Computer Society
%C Los Alamitos, CA, USA
%K genetic algorithms, genetic programming
%U http://math.nist.gov/mcsd/savg/papers/bio.pp.gz
%X Algorithms are needed in every aspect of parallel computing. Genetic Programming is an evolutionary technique for automating the design of algorithms through iterative
   steps of mutation and crossover operations on an initial population of randomly generated computer programs. This paper describes a novel parallel genetic programming (GP)
   system inspired by the symbiogenesis model of evolution, wherein new organisms are generated through the absorption of different life-forms in addition to the usual
   mutation and crossover operations. Different organisms are expressed in this GP system through multiple program representations. Two program representations considered in
   this paper are the procedural representation (PR) and the tree representation (TR). Populations of these representations evolve separately. Individuals in each population
   migrate to the other and participate in evolution via representation change algorithms. Parallelism is achieved through use of the AutoMap/AutoLink MPI library. The
   differences in the locality properties of the representations serve as a source of new ideas for creating the final algorithm.
%O Abstracts and CD-ROM
%O IPDPS2001:WS
%8 23-27  April
%@ 0-7695-0990-8

%A Judith E. Devaney
%A John G. Hagedorn
%T The Role of Genetic Programming in Describing the Microscopic Structure of Hydrating Plaster
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 91--98
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp pruning anti-bloat, fitness based on correlation. Maple algebraic simplification. sensitivity, true-positives. C5.
   "clear and concise decision algorithm that accurately predicts" p96

%A Judith Ellen Devaney
%A John G. Hagedorn
%T Discovery in Hydrating Plaster Using Machine Learning Methods
%B 5th International Conference on Discovery Science, DS 2002
%S Lecture Notes in Computer Science
%E Steffen Lange and Ken Satoh and Carl H. Smith
%V 2534
%D 2002
%P 47--58
%I Springer
%C L\"ubeck, Germany
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/2534/25340047.htm
%X We apply multiple machine learning methods to obtain concise rules that are highly predictive of scientifically meaningful classes in hydrating plaster over multiple time
   periods. We use three dimensional data obtained through X-ray microtomography at greater than one micron resolution per voxel at five times in the hydration process:
   powder, after 4 hours, 7 hours, 15.5 hours, and after 6 days of hydration. Using statistics based on locality, we create vectors containing eight attributes for subsets of
   size 1000 of the data and use the autoclass unsupervised classification system to label the attribute vectors into three separate classes. Following this, we use the C5
   decision tree software to separate the three classes into two parts: class 0 and 1, and class 0 and 2. We use our locally developed procedural genetic programming system,
   GPP, to create simple rules for these. The resulting collection of simple rules are tested on a separate 1000 subset of the plaster datasets that had been labeled with
   their autoclass predictions. The rules were found to have both high sensitivity and high positive predictive value. The classes accurately identify important structural
   components in the hydrating plaster. Moreover, the rules identify the center of the local distribution as a critical factor in separating the classes.
%8 November  24-26

%A Alexandre Devert
%A Nicolas Bredeche
%A Marc Schoenauer
%T Blindbuilder : A new encoding to evolve Lego-like structures
%B Proceedings of the 9th European Conference on Genetic Programming
%R ARTCOLLOQUE
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 61--72
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming, context free grammar
%U http://link.springer.de/link/service/series/0558/papers/3905/39050061.pdf
%X This paper introduces a new representation for assemblies of small Lego-like elements: structures are indirectly encoded as construction plans. This representation shows
   some interesting properties such as hierarchy, modularity and easy constructibility checking by definition. Together with this representation, efficient GP operators are
   introduced that allow efficient and fast evolution, as witnessed by the results on two construction problems that demonstrate that the proposed approach is able to achieve
   both compactness and reusability of evolved components.
%O Alexandre Devert <devert@lri.fr>
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Alexandre Devert
%A Nicolas Bredeche
%A Marc Schoenauer
%T Evolution design of buildable objects with blind builder: an empirical study
%B Proceedings of the Third Asian-Pacific workshop on Genetic Programming
%E The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen
%D 2006
%P 98--109
%I 
%C Military Technical Academy, Hanoi, VietNam
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/devert-bredeche-schoenauer-ASPGP2006.pdf
%X In a previous paper, we presented BlindBuilder, a new representation formalism for Evolutionary Design based on construction plans. As for other indirect encoding
   approaches in the literature, BlindBuilder makes it possible to easily represent possible solutions but makes it difficult to perform structural optimisation. While
   satisfying results are provided, it becomes more and more difficult to build larger structures during the course of evolution. This is due to the high disruptive rate of
   variation operators as construction plans grow. In this paper, we provide an analysis of such a problem and propose new construction operators to avoid this. Then, we
   perform extensive experiments so as to identify the key parameters and discuss the advantages, limitations and possible perspectives of the indirect encoding approach.
%Z http://www.aspgp.org

%A Simon {de Visscher}
%A Michel Herquet
%T Automatic anomaly detection in high energy collider data
%D 2011
%I 
%K genetic algorithms, genetic programming, high energy physics, phenomenology, experiment, data analysis
%U http://arxiv.org/abs/1104.2404
%X We address the problem of automatic anomaly detection in high energy collider data. Our approach is based on the random generation of analytic expressions for kinematical
   variables, which can then be evolved following a genetic programming procedure to enhance their discriminating power. We apply this approach to three concrete scenarios to
   demonstrate its possible usefulness, both as a detailed check of reference Monte-Carlo simulations and as a model independent tool for the detection of New Physics
   signatures.
%8 April ~13
%Z Comment: 5 pages, 2 figures

%A Bart {De Vylder}
%T Learning of Manipulation Behaviour by Demonstration using Genetic Programming
%B GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2003
%P 268--271
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C Chigaco
%K genetic algorithms, genetic programming
%8 11  July
%Z Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming
   Conference (GP-2003) part of barry:2003:GECCO:workshop

%A Larry D. Dewell
%A P. K. Menon
%T Low-Thrust Orbit Transfer Optimization Using Genetic Search
%B AIAA Guidance, Navigation and Control Conference
%D 1999
%I American Institute of Aeronautics and Astronautics
%C Portland, OR, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/513854.html
%X Most techniques for solving dynamic optimisation problems involve a series of gradient computations and one-dimensional searches at some point in the optimization process.
   A large class of problems, however, does not possess the necessary smoothness properties that such algorithms require for good convergence. Even when smoothness conditions
   are met, poor initial guesses at the solution often result in convergence to local minima or even a lack of convergence altogether. For such cases, genetic search
   techniques can be used to obtain a solution. In this paper, trajectory optimisation using genetic search methods is illustrated by solving a complex, nonlinear problem
   involving low-thrust orbit transfer.

%A Patrik D'haeseleer
%A Jason Bluming
%T Effects of Locality in Individual and Population Evolution
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 177--198
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%O 8

%A Patrik D'haeseleer
%T Context preserving crossover in genetic programming
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 256--261
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, S-expression tree, context-preserving crossover, crossover operators, matching coordinates, node coordinate scheme,
   subtrees,optimisation, path planning, programming, trees (mathematics)
%U http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00350006
%X This paper introduces two new crossover operators for Genetic Programming (GP). Contrary to the regular GP crossover, the operators presented attempt to preserve the
   context in which subtrees appeared in the parent trees. A simple coordinate scheme for nodes in an S-expression tree is proposed, and crossovers are only allowed between
   nodes with exactly or partially matching coordinates.
%8 27-29  June
%Z Two new crossover operators for GP (Strong Context preserving (SCPC) and Weak context preserving(WCPC)). These attempt to preserve the context of swapped subtrees. SCPC
   best used 50percent with Koza crossover. 100percent WCPC not performing as well. Obstacle avoiding (simulated) robot, 11-multiplexor, food foraging.

%A Prisdha Dharma
%T Automatic Model Construction for Time Series Analysis via Genetic Algorithm
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 28--35
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Philip Dhingra
%T Evolution of Simple Intelligence Distribution in Artificial Organisms
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 83--92
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1093
%X This paper uses Genetic Programming to evolve groups of ants to push a box from the center of a room to a wall. Group sizes and ant capabilities are varied to observe the
   speed, effectiveness, and nature of the intelligence that evolves for each ant. As expected, larger groups compensate for lesser intelligent ants by having more of them to
   solve the task. The ant-boxpushing problem then becomes a coverage problem whereby solutions are found by adequately covering the space in which the task is to be
   completed.
%8 June
%Z part of \citekoza:2002:gagp GP to evolve group of ants to push a box from the centre of a room to a wall. Trade off between "intelligence" of individual ants and number of
   ants in the group. LISP

%A Wenhui Di
%A Bo Sun
%A Lixin Xu
%T Dynamic Simulations of Nonlinear Multi-Domain Systems Based on Genetic Programming and Bond Graphs
%J Tsinghua Science \& Technology
%V 14
%N 5
%D 2009
%P 612--616
%I 
%K genetic algorithms, genetic programming, bond graph (BG), evolutionary computation, system simulation
%U http://www.sciencedirect.com/science/article/B7RKT-4XBR35X-B/2/f79f7984ea487a2629d93cc7ae6e2651
%X A dynamic simulation method for non-linear systems based on genetic programming (GP) and bond graphs (BG) was developed to improve the design of nonlinear multi-domain
   energy conversion systems. The genetic operators enable the embryo bond graph to evolve towards the target graph according to the fitness function. Better simulation
   requires analysis of the optimization of the eigenvalue and the filter circuit evolution. The open topological design and space search ability of this method not only gives
   a more optimized convergence for the operation, but also reduces the generation time for the new circuit graph for the design of nonlinear multi-domain systems.

%A Riccardo Poli
%A Cecilia {Di Chio}
%A William B. Langdon
%T Exploring extended particle swarms: a genetic programming approach
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 1
%D 2005
%P 169--176
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Swarm Intelligence, particle swarm optimisation, PSO, performance
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p169.pdf
%X Particle Swarm Optimisation (PSO) uses a population of particles fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces
   that encourage each particle to fly back both towards the best point sampled by it and towards the swarm's best point, while its momentum tries to keep it moving in its
   current direction. Previous research \citepoli:2005:eurogp started exploring the possibility of evolving the force generating equations which control the particles through
   the use of genetic programming (GP). We independently verify the findings of \citepoli:2005:eurogp and then extend it by considering additional meaningful ingredients for
   the PSO force-generating equations, such as global measures of dispersion and position of the swarm. We show that, on a range of problems, GP can automatically generate new
   PSO algorithms that outperform standard human-generated as well as some previously evolved ones.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052, XPS, ACM gecco-2005 key 1068036
%@ 1-59593-010-8

%A Cecilia {Di Chio}
%A Riccardo Poli
%A William B. Langdon
%T Evolution of Force-Generating Equations for PSO using GP
%B AI*IA Workshop on Evolutionary Computation, Evoluzionistico GSICE05
%E Sara Manzoni and Matteo Palmonari and Fabio Sartori
%D 2005
%I 
%C University of Milan Bicocca, Italy
%K genetic algorithms, genetic programming, XPS
%U http://www.cs.essex.ac.uk/staff/poli/papers/gsice2005.pdf
%X We extend our previous research on evolving the physical forces which control particle swarms by considering additional ingredients, such as the velocity of the
   neighbourhood best and time, and different neighbourhood topologies, namely the global and local ones. We test the evolved extended PSOs (XPSOs) on various classes of
   benchmark problems. We show that evolutionary computation, and in particular genetic programming (GP), can automatically generate new PSO algorithms that outperform
   standard PSOs designed by people as well as some previously evolved ones.
%8 20  September
%Z http://www.ce.unipr.it/people/cagnoni/gsice2005/gsice-eng.pdf Workshop proceedings on CD-ROM only. Workshop held in-conjunction with the IX Congress of the Italian
   Association for Artificial Intelligence. In English. Winner of Best Paper Award
%@ 88-900910-0-2

%A Cecilia {Di Chio}
%T Extended Particle Swarm to Simulate Biology-Like Systems
%B European Graduate Student Workshop on Evolutionary Computation
%E Mario Giacobini and Jano van Hemert
%D 2006
%P 31--43
%I 
%C Budapest, Hungary
%K genetic algorithms, genetic programming, PSO, XPS
%U http://www.vanhemert.co.uk/publications/EvoPhD2006.pdf
%X Is it possible to simulate socio-biological behaviours using particle swarm systems? And if so, what should it be the best approach to use? These are the questions which I
   would like to answer with my research. Particle swarm systems have been originally developed to model social behaviours. My research will therefore follow the initial
   socio-biological metaphor underlying particle systems. The idea is to use a genetic programming approach to automatically evolve the particle swarm equations to model
   animal social behaviours. This research is intended to be a first example of application of genetic programming and particle swarm to simulate animal behaviours.
%8 10  April
%Z http://evonet.lri.fr/eurogp2006/?page=evophd

%A Cecilia {Di Chio}
%A Paolo {Di Chio}
%T Group-Foraging with Particle Swarms and Genetic Programming
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 331--340
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X This paper has been inspired by two quite different works in the field of Particle Swarm theory. Its main aims are to obtain particle swarm equations via genetic
   programming which perform better than hand-designed ones on the group-foraging problem, and to provide insight into behavioural ecology. With this work, we want to start a
   new research direction: the use of genetic programming together with particle swarm algorithms in the simulation of problems in behavioural ecology.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Andrew Dickinson
%T Evolution of Damage-Immune Programs using Genetic Programming
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 21--30
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 Decemeber
%Z This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-187263-3

%A Andrew Dickson
%T Evolution of Optimum Genetic Algorithms for Specific Spaces
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 41--48
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A David Digby
%A William Seffens
%T Evolutionary Algorithm Analysis of the Biological Genetic Codes
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1440
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-013.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Stephen Dignum
%A Riccardo Poli
%T Multi-agent Foreign Exchange Market Modelling via GP
%R Technical Report CSM-400
%D 2004
%I 
%I Department of Computer Science, University of Essex
%C Colchester, UK
%K genetic algorithms, genetic programming
%U http://cswww.essex.ac.uk/technical-reports/2004/csm400.pdf
%X we combine Genetic Programming (GP) and intelligent agents to build a realistic foreign exchange currency market simulator. GP is used to express and evolve trading
   strategies. We analyse the decisions made in the design of the simulator with respect to authenticity of the representation and the efficiency of the system. A number of
   experimental results are also reported.

%A Stephen Dignum
%A Riccardo Poli
%T Multi-agent Foreign Exchange Market Modelling Via GP
%B Genetic and Evolutionary Computation -- GECCO-2004, Part I
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3102
%D 2004
%P 255--256
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming, Poster
%U http://link.springer.de/link/service/series/0558/bibs/3102/31020255.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22344-4

%A Stephen Dignum
%A Riccardo Poli
%T Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloat
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1588--1595
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, bloat, crossover Bias, initialisation, program Size distribution
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1588.pdf
%X Recent research [1] has found that standard sub-tree crossover with uniform selection of crossover points, in the absence of fitness pressure, pushes a population of GP
   trees towards a Lagrange distribution of tree sizes. However, the result applied to the case of single arity function plus leaf node combinations, e.g., unary, binary,
   ternary, etc trees only. In this paper we extend those findings and show that the same distribution is also applicable to the more general case where the function set
   includes functions of mixed arities. We also provide empirical evidence that strongly corroborates this generalisation. Both predicted and observed results show a distinct
   bias towards the sampling of shorter programs irrespective of the mix of function arities used. Practical applications and implications of this knowledge are investigated
   with regard to search efficiency and program bloat. Work is also presented regarding the applicability of the theory to the traditional 0.90-function 0.10-terminal
   crossover node selection policy.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Stephen Dignum
%A Riccardo Poli
%T Operator Equalisation and Bloat Free GP
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 110--121
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Also known as \citeconf/eurogp/DignumP08 Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Stephen Dignum
%A Riccardo Poli
%T Crossover, Sampling, Bloat and the Harmful Effects of Size Limits
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 158--169
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Also known as \citeconf/eurogp/DignumP08a Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Stephen Dignum
%T An Analysis of Genetic Programming Operator Bias regarding the Sampling of Program Size with Potential Applications
%B EvoPhD 2008
%E Jano van Hemert and Mario Giacobini and Cecilia Di Chio
%D 2008
%I 
%C Naples
%K genetic algorithms, genetic programming
%8 27  March
%Z EvoPHD'2008 held in conjunction with EuroGP-2008, EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Stephen Dignum
%A Riccardo Poli
%T Sub-Tree Swapping Crossover, Allele Diffusion and GP Convergence
%B Parallel Problem Solving from Nature - PPSN X
%S LNCS
%E Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume
%V 5199
%D 2008
%P 368--377
%I Springer
%C Dortmund
%K genetic algorithms, genetic programming, Search, Crossover Bias, Allele Diffusion, Convergence
%X We provide strong evidence that sub-tree swapping crossover when applied to tree-based representations will cause alleles (node labels) to diffuse within length classes.
   For a-ary trees we provide further confirmation that all programs are equally likely to be sampled within any length class when sub-tree swapping crossover is applied in
   the absence of selection and mutation. Therefore, we propose that this form of search is unbiased - within length classes - for a-ary trees. Unexpectedly, however, for
   mixed-arity trees this is not found and a more complicated form of search is taking place where certain tree shapes, hence programs, are more likely to be sampled than
   others within each class. We examine the reasons for such shape bias in mixed arity representations and provide the practitioner with a thorough examination of sub-tree
   swapping crossover bias. The results of this, when combined with crossover length bias research, explain Genetic Programming's lack of structural convergence during later
   stages of an experimental run. Several operators are discussed where a broader form of convergence may be detected in a similar way to that found in Genetic Algorithm
   experimentation.
%8 13-17  September
%Z PPSN X
%@ 3-540-87699-5

%A Stephen Dignum
%A Riccardo Poli
%T Sub-Tree Swapping Crossover and Arity Histogram Distributions
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 38--49
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X Recent theoretical work has characterised the search bias of GP sub-tree swapping crossover in terms of program length distributions, providing an exact fixed point for
   trees with internal nodes of identical arity. However, only an approximate model (based on the notion of average arity) for the mixed-arity case has been proposed. This
   leaves a particularly important gap in our knowledge because multi-arity function sets are commonplace in GP and deep lessons could be learnt from the fixed point. In this
   paper, we present an accurate theoretical model of program length distributions when mixed-arity function sets are employed. The new model is based on the notion of an
   arity histogram, a count of the number of primitives of each arity in a program. Empirical support is provided and a discussion of the model is used to place earlier
   findings into a more general context.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A S. van Dijk
%A D. Thierens
%A M. de Berg
%T On The Design of Genetic Algorithms for Geographical Applications
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 188--195
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-809.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A K. V. S. Dileep
%A Venkatachalam Chandrasekaran
%T Learning Data Dependent Composite Kernels for Robust Image Retrieval - A Genetic Programming Approach
%B Proceedings of the 2010 International Conference on Image Processing, Computer Vision, \& Pattern Recognition, IPCV 2010, July 12-15, 2010, Las Vegas, Nevada, USA, 2
   Volumes
%E Hamid R. Arabnia and Leonidas Deligiannidis and Gerald Schaefer and Ashu M. G. Solo
%D 2010
%P 294--299
%I CSREA Press
%K genetic algorithms, kernel methods, composite kernel, learning the kernel, image retrieval
%X Kernel methods are a class of pattern recognition and machine learning algorithms that map data to a high dimensional space and perform various learning tasks like
   clustering or regression in that space. The mapping from the low dimensional space to the high dimension is done implicitly by the use of a kernel function. But the
   question of how to choose the kernel is an interesting and intriguing one. The choice of the kernel and its parameters is usually done using cross-validation. We propose a
   methodology of learning a kernel from data using genetic programming. With the aid of genetic algorithms, we constructed composite kernels and compared their performance
   with an ad-hoc kernel in the domain of image retrieval. The learned composite kernels showed consistent better performance compared to the individual kernel.
%Z Despite abstract this is a GA not a GP

%A Karen M. Dill
%A Marek A. Perkowski
%T Minimization of GRM Forms with a Genetic Algorithm
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 362
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Genetic Algorithms
%8 13-16  July
%Z GP-97

%A Karen M. Dill
%A James H. Herzog
%A Marek A. Perkowski
%T Genetic programming and its applications to the synthesis of digital logic
%B IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM 1997
%V 2
%D 1997
%P 823--826
%I 
%C Victoria, BC, Canada
%K genetic algorithms, genetic programming, EHW, logic circuits, logic CAD, digital logic synthesis, arbitrary logic expressions, logic synthesis, problem applicability,
   optimization criterion, logic gates, population sizes, complete function coverage, experimental test results, randomly designed functions, input variables, logic equations,
   function coverage, training set size, small training sets, function recognition
%X Genetic programming is applied to the synthesis of arbitrary logic expressions. As a new method of logic synthesis, this technique is uniquely advantageous in its
   flexibility for both problem applicability and optimisation criterion. A number of experiments were conducted exploring this method with different types of logic gates and
   population sizes. While complete function coverage is not guaranteed, the best experimental test results over eight randomly designed functions, of four to seven input
   variables, have produced logic equations with a 98.4percent function coverage. In addition, the relation between the training set size for the genetic program and function
   coverage was also empirically explored. These experiments showed that only small training sets were necessary for function recognition.
%O Networking the Pacific Rim, 10 Years PACRIM 1987-1997
%8 20-22  August
%@ 0-7803-3905-3

%A Thomas Dillon
%T Evolution of General Algorithmic Solutions for Simple Sliding Tile Puzzles
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 65--75
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A R. C. Dimitriu
%A H. K. D. H. Bhadeshia
%A C. Fillon
%A C. Poloni
%T Strength of Ferritic Steels: Neural Networks and Genetic Programming
%J Materials and Manufacturing Processes
%V 24
%N 1
%D 2009
%P 10--15
%I 
%K genetic algorithms, genetic programming, ANN, Creep strength, Ferritic steels, Hot strength, Neural networks, Steel
%U http://www.msm.cam.ac.uk/phasetrans/2009/Dimitriu.html
%X An analysis is presented of a complex set of data on the strength of steels as a function of chemical composition, heat treatment, and test temperature. The steels
   represent a special class designed to resist deformation at elevated temperatures (750-950 K) over time periods in excess of 30 years, whilst serving in hostile
   environments. The aim was to compare two methods, a neural network based on a Bayesian formulation, and genetic programming in which the data are formulated in an
   evolutionary procedure. It is found that in the present context, the neural network is able more readily to capture greater complexity in the data whereas a genetic program
   seems to require greater intervention to achieve an accurate representation.
%8 January
%Z Affiliations: Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, England, UK Department of Electrical Engineering and Computer Science,
   University of Trieste, Trieste, Italy

%A Christos Dimopoulos
%A Ali M. S. Zalzala
%T Evolving Scheduling Policies through a Genetic Programming Framework
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1231
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-448.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Christos Dimopoulos
%A Ali M. S. Zalzala
%T A Genetic Programming Heuristic for the One-Machine Total Tardiness Problem
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 3
%D 1999
%P 2207--2214
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, manufacturing optimization
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143
%@ 0-7803-5537-7 (Microfiche)

%A Christos Dimopoulos
%A Neil Mort
%T Genetic programming for cellular manufacturing
%B Proceedings of the 2nd Workshop on European Scientific and Industrial Collaboration (WESIC-99)
%D 1999
%I 
%K genetic algorithms, genetic programming, cellular manufacturing
%X Evolutionary computation methods have been applied successfully to a wide range of manufacturing optimisation problems. However, Genetic Programming applications to
   manufacturing optimisation have rarely been reported. In this paper we present a Genetic Programming methodology for the diagonalisation of binary machine-component
   matrices in cellular manufacturing. The procedure is based on the evolution of a similarity coefficient for each problem considered. The application of the method is
   illustrated with the help of a test problem taken from the literature

%A Christos Dimopoulos
%A A M S Zalzala
%T Recent developments in evolutionary computation for manufacturing optimisation: problems, solutions and comparisons
%J IEEE Transactions on Evolutionary Computation
%V 4
%N 2
%D 2000
%P 93--113
%I 
%K genetic algorithms, genetic programming, evolutionary computation, manufacturing optimization
%X The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most of manufacturing optimisation problems are
   combinatorial and NP hard. This report examines recent developments in the field of evolutionary computation for manufacturing optimisation. Significant papers in various
   areas are highlighted and comparisons of results are given wherever data is available. A wide range of problems is covered, from job shop and flow shop scheduling, to
   process planning and assembly line balancing

%A Christos Dimopoulos
%A Neil Mort
%T Solving cell-formation problems under alternative quality criteria and constraints with a genetic programming-based hierarchical clustering algorithm
%B Proceedings of the Sixth International Conference on Control, Automation, Robotics and Vision
%D 2000
%I 
%K genetic algorithms, genetic programming, cell formation
%X Cellular manufacturing is a modern approach to the implementation of efficient manufacturing systems. The solution of the cell formation problem is an essential step for
   the design of a cellular manufacturing system. In this paper we present a novel Genetic Programming-based methodology for the solution of the cell-formation problem. The
   proposed methodology is tested on a cell formation problem taken from the literature under alternative quality criteria and size constraints

%A Christos Dimopoulos
%A Neil Mort
%T Evolving similarity coefficients for the solution of cellular manufacturing problems
%B Proceedings of the Congress on Evolutionary Computation (CEC 2000)
%D 2000
%P 617--624
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, cell formation, similarity coefficients, engineering applications
%X The cell formation problem is a classic manufacturing optimisation problem associated with the implementation of a cellular manufacturing system. A variety of hierarchical
   clustering procedures have been proposed for the solution of this problem. Essential for the operation of a clustering procedure is the determination of a form of
   similarity between the objects that are going to be grouped. In this paper we employ a Genetic Programming algorithm for the evolution of new similarity coefficients for
   the solution of simple cell formation problems. Evolved coefficients are tested against the well-known Jaccard's similarity coefficient on a large number of problems taken
   from the literature
%8 6-9  July
%Z also called dimopoulos:2000:ESCSCMP CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library
   of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Christos Dimopoulos
%A Neil Mort
%T A genetic programming-based hierarchical clustering procedure for the solution of the cell-formation problem
%B Adaptive Computing in Design and Manufacture (ACDM 2000)
%D 2000
%P 211--222
%I 
%K genetic algorithms, genetic programming, cellular manufacturing
%X Cellular manufacturing is the implementation of group technology in the manufacturing process. A key issue during the design of a cellular manufacturing system is the
   configuration of machine cells and part families within the plant. In this paper we present a hierarchical clustering procedure for the solution of the cell-formation
   problem which is based on the use of Genetic Programming for the evolution of similarity coefficients between pairs of machines in the plant. The performance of the
   methodology is illustrated on a number of test problems taken from the literature

%A C. Dimopoulos
%A A. M. S. Zalzala
%T Investigating the use of genetic programming for a classic one-machine scheduling problem
%J Advances in Engineering Software
%V 32
%N 6
%D 2001
%P 489--498
%I 
%K genetic algorithms, genetic programming, Evolutionary computation, Manufacturing optimisation, Tardiness, Scheduling
%U http://www.sciencedirect.com/science/article/B6V1P-42YFC02-7/1/6be8f2e3206dccb17801b7a7833a6299
%X Genetic programming has rarely been applied to manufacturing optimisation problems. We investigate the potential use of genetic programming for the solution of the
   one-machine total tardiness problem. Genetic programming is used for the evolution of scheduling policies in the form of dispatching rules. These rules are trained to cope
   with different levels of tardiness and tightness of due dates.
%8 June

%A Christos Dimopoulos
%A Neil Mort
%T A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems
%J International Journal of Production Research
%V 39
%N 1
%D 2001
%P 1--19
%I 
%K genetic algorithms, genetic programming
%X The problem of identifying machine cells and corresponding part families in cellular manufacturing has been extensively researched over the last thirty years. However, the
   complexity of the problem and the considerable number of issues involved in its solution create the need for increasingly efficient algorithms. In this paper we investigate
   the use of Genetic Programming for the solution of a simple version of the problem. The methodology is tested on a number of test problems taken from the literature and
   comparative results are presented

%A Christos Dimopoulos
%T A Genetic Programming methodology for the solution of the multi-objective cell-formation problem
%B Proceedings of the 8th Joint Conference in Information Systems (JCIS 2005)
%E Heng-Da Cheng
%D 2005
%P 1487--1494
%I 
%C Salt Lake City, USA
%K genetic algorithms, genetic programming
%8 21-25  July
%Z homepage sting.cycollege.ac.cy/~dimopoulos/main.htm http://www.jcis.org/jcis_program/master_schedule.pdf

%A Christos Dimopoulos
%T A Novel Approach for the Solution of the Multiobjective Cell-Formation Problem
%B Proceedings of the International Conference of Production Research (ICPR 05)
%D 2005
%I 
%K genetic algorithms, genetic programming, cellular manufacturing, production research, multiobjective optimisation
%U http://www.lania.mx/~ccoello/EMOO/dimopoulos05.pdf.gz
%X We present a hybrid heuristic methodology for the solution of the multi-objective cell-formation problem. Traditional optimisation methodologies employ aggregating schemes
   in order to transform the problem into a single-objective case. In this way the designer is not presented with a set of non-dominated solutions but with a single compromise
   solution based on pre-specified weighting priorities. The proposed methodology combines a traditional hierarchical clustering analysis technique with a genetic programming
   algorithm that is based on the principles of evolutionary computation. The hybrid methodology evolves an approximation of the Pareto set of solutions for multi-objective
   cell-formation problems. The benefits brought by the proposed approach in comparison to traditional optimisation methodologies are illustrated using a typical example taken
   from the literature
%Z http://icpr18.unisa.it/ Tuesday, August 2 - 16.00/18.00 - Room M Session 45 Cellular Manufacturing

%A Li-ying Ding
%A Yu-gang Li
%A Fang-yu Han
%T Combinational Application of Genetic Programming and Simulated Annealing in Distillation Process Synthesis
%R Journal of Qingdao Institute of Chemical Technology Supplement
%V 24
%D 2003
%I 
%I Qingdao University of Science and Technology
%C China
%K genetic algorithms, genetic programming, SA, simulated annealing, distillation synthesis,heat integration
%U http://xbzr.qust.edu.cn/WEB2003-zeng/03zk11.htm
%X Genetic Programming is combined with Simulated Annealing and applied in synthesis of multi-component distillation process. On one hand, Genetic Programming is used to
   determine the optimal distillation process structure. On the other hand, Simulated Annealing is used to optimize the continuous variables in the process, that is, the
   reflux. Therefore, by the combination of Genetic Programming and Simulated Annealing, an optimal distillation process is obtained. An example is given to illustrate that
   the method is effective.
%8 September
%Z http://xbzr.qust.edu.cn/new_page_1.htm Research Center for Computer and Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, China College of
   Chemical Engineering, South China University of Technology, Guangzhou 510640, China

%A Li-ying Ding
%A Yu-gang Li
%A Fang-yu Han
%T Design of Complex Distillation Process Based on Genetic Programming
%R Journal of Qingdao Institute of Chemical Technology 5
%V 24
%D 2003
%I 
%I Qingdao University of Science and Technology
%K genetic algorithms, genetic programming, complex distillation process
%U http://xbzr.qust.edu.cn/WEB2003-5/ee5-2.HTM
%X Genetic programming was applied to design multi-components complex distillation process by setting up interrelationship between the individual code and the process
   structure. With the help of equivalent simple distillation process, the objective value, that is the fitness, of complex process was determined. After a series of
   operations, such as reproduction, crossover and mutation between individuals, the process structure with optimum economic objective (the sum of equipment cost and operation
   cost) was obtained. An example was given to illustrate the effectiveness of this method.
%8 October
%Z http://xbzr.qust.edu.cn/new_page_1.htm 1001-4764-(2003)05-0382-05 Computer and Chemical Engineering Research Centre, Qingdao University of Science and Technology, Qingdao
   266042, China College of Chemical Engineering, South China University of Technology, Guangzhou 510640, China

%A Shengchao Ding
%A Zhi Jin
%A Qing Yang
%T Evolving Quantum Oracles with Hybrid Quantum-inspired Evolutionary Algorithm
%D 2008
%I 
%K genetic algorithms, genetic programming
%U http://arxiv.org/PS_cache/quant-ph/pdf/0610/0610105v1.pdf
%X Quantum oracles play key roles in the studies of quantum computation and quantum information. But implementing quantum oracles efficiently with universal quantum gates is a
   hard work. Motivated by genetic programming, this paper proposes a novel approach to evolve quantum oracles with a hybrid quantum-inspired evolutionary algorithm. The
   approach codes quantum circuits with numerical values and combines the cost and correctness of quantum circuits into the fitness function. To speed up the calculation of
   matrix multiplication in the evaluation of individuals, a fast algorithm of matrix multiplication with Kronecker product is also presented. The experiments show the
   validity and the effects of some parameters of the presented approach. And some characteristics of the novel approach are discussed too.
%O arXiv
%O arXiv:quant-ph/0610105 v1
%8 13  October
%Z 1 Institute of Computing Technology, Chinese Academy of Sciences 2 Academy of Mathematics and Systems Science, Chinese Academy of Sciences 3 Graduate University of the
   Chinese Academy of Sciences Beijing 100080, China 4 School of Computer Science and Technology, South-Central University for Nationalities, Wuhan 430074, China

%A Laura Dio\c{s}an
%A Mihai Oltean
%T Evolving crossover operators for function optimization
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 97--108
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050097.pdf
%X A new model for evolving crossover operators for evolutionary function optimisation is proposed in this paper. The model is a hybrid technique that combines a Genetic
   Programming (GP) algorithm and a Genetic Algorithm (GA). Each GP chromosome is a tree encoding a crossover operator used for function optimization. The evolved crossover is
   embedded into a standard Genetic Algorithm which is used for solving a particular problem. Several crossover operators for function optimisation are evolved using the
   considered model. The evolved crossover operators are compared to the human-designed convex crossover. Numerical experiments show that the evolved crossover operators
   perform similarly or sometimes even better than standard approaches for several well-known benchmarking problems.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Laura Diosan
%A Mihai Oltean
%A Alexandrina Rogozan
%A Jean Pierre Pecuchet
%T Genetically designed multiple-kernels for improving the SVM performance
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1873--1873
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Genetics-Based Machine Learning: Poster, kernel, Support Vector Machines, SVM
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1873.pdf
%X Classical kernel-based classifiers only use a single kernel, but the real-world applications have emphasised the need to consider a combination of kernels - also known as a
   multiple kernel - in order to boost the performance. Our purpose is to automatically find the mathematical expression of a multiple kernel by evolutionary means. In order
   to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. Each GP
   chromosome is a tree encoding the mathematical expression of a multiple kernel. Numerical experiments show that the SVM embedding the evolved multiple kernel performs
   better than the standard kernels for the considered classification problems.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071 Evolved SVM Kernel forced to remain valid because only legal combinations (ie plus, times, exp) of legal kernels (linear, plynomial, RBF) is allowed.

%A Laura Diosan
%A Alexandrina Rogozan
%A Jean-Pierre Pecuchet
%T Evolving kernel functions for SVMs by genetic programming
%B Sixth International Conference on Machine Learning and Applications, ICMLA 2007
%D 2007
%P 19--24
%I IEEE
%C Cincinnati, Ohio, USA
%K genetic algorithms, genetic programming, support vector machines, GP chromosome, SVM kernel functions, evolved kernel, kernel expression, mathematical expression, tree
   encoding
%X hybrid model for evolving support vector machine (SVM) kernel functions is developed in this paper. The kernel expression is considered as a parameter of the SVM algorithm
   and the current approach tries to find the best expression for this SVM parameter. The model is a hybrid technique that combines a genetic programming (GP) algorithm and a
   support vector machine (SVM) algorithm. Each GP chromosome is a tree encoding the mathematical expression for the kernel function. The evolved kernel is compared to several
   human-designed kernels and to a previous genetic kernel on several datasets. Numerical experiments show that the SVM embedding our evolved kernel performs statistically
   better than standard kernels, but also than previous genetic kernel for all considered classification problems.
%8 13-15  Decemeber
%Z also known as \cite4457202. http://www.icmla-conference.org/icmla07/

%A Laura Diosan
%A Alexandrina Rogozan
%A Jean-Pierre Pecuchet
%T Optimising Multiple Kernels for SVM by Genetic Programming
%B Proceedings of the 8th European Conference, Evolutionary Computation in Combinatorial Optimization, EvoCOP
%S Lecture Notes in Computer Science
%E Jano I. van Hemert and Carlos Cotta
%V 4972
%D 2008
%P 230--241
%I Springer
%C Naples, Italy
%K genetic algorithms, genetic programming
%8 March  26-28

%A Laura Diosan
%A Mihai Oltean
%T Evolutionary design of Evolutionary Algorithms
%J Genetic Programming and Evolvable Machines
%V 10
%N 3
%D 2009
%P 263--306
%I 
%K genetic algorithms, genetic programming, Evolving evolutionary algorithms, Meta genetic programming, Function optimization
%X Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to
   construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and
   parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based
   on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function
   optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for
   several well-known benchmarking problems.
%8 September

%A Laura Diosan
%A Alexandrina Rogozan
%A Jean Pierre Pecuchet
%T Learning SVM with Complex Multiple Kernels Evolved by Genetic Programming
%J International Journal on Artificial Intelligence Tools
%V 19
%N 5
%D 2010
%P 647--677
%I 
%K genetic algorithms, genetic programming, Multiple kernel learning, hybrid model, SVM
%X Classic kernel-based classifiers use only a single kernel, but the real-world applications have emphasised the need to consider a combination of kernels, also known as a
   multiple kernel (MK), in order to boost the classification accuracy by adapting better to the characteristics of the data. Our purpose is to automatically design a complex
   multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based
   Support Vector Machine (SVM) classifier. In our model, each GP chromosome is a tree that encodes the mathematical expression of a multiple kernel. The evolutionary search
   process of the optimal MK is guided by the fitness function (or efficiency) of each possible MK. The complex multiple kernels which are evolved in this manner (eCMKs) are
   compared to several classic simple kernels (SKs), to a convex linear multiple kernel (cLMK) and to an evolutionary linear multiple kernel (eLMK) on several real-world data
   sets from UCI repository. The numerical experiments show that the SVM involving the evolutionary complex multiple kernels perform better than the classic simple kernels.
   Moreover, on the considered data sets, the new multiple kernels outperform both the cLMK and eLMK linear multiple kernels. These results emphasise the fact that the SVM
   algorithm requires a combination of kernels more complex than a linear one in order to boost its performance.
%Z IJAIT Laboratoire d'Informatique, de Traitement de l'Information et des Systemes, EA 4108, Institut National des Sciences Appliquees, Rouen, France

%A Steve DiPaola
%T Evolving Portrait Painter Programs using Genetic Programming to Explore Computer Creativity
%D 2006?
%I 
%K genetic algorithms, genetic programming, cartesian genetic programming
%U http://www.units.muohio.edu/codeconference/papers/papers/idmapaper1.pdf
%X Creative systems as opposed to standard evolutionary systems favor exploration over optimization, finding innovative or novel solutions over a preconceived notion of a
   specific optimal solution. The best creative evolutionary systems only provide tools, allowing the evolutionary process to discover novelty and innovation on its own. We
   experiment with computer creativity by employing and modifying techniques from evolutionary computation to create a related family of abstract portraits. A new type of
   Genetic Programming (GP) system is used called Cartesian GP, which uses typical GP Darwinian evolutionary techniques (crossover, mutation, and survival), but has several
   features that allow the GP system to favor creative solutions over optimized solutions including accommodating for genetic drift where different genotypes map to the same
   phenotype, visual mapping modules and a knowledge of a painterly color space. This work with its specific goal of evolving portrait painter programs to create a portrait
   'sparked' by the famous portrait of Darwin, speaks to the evolutionary processes as well as creativity, as seen by the early results where the evolving programs use
   recurring, emergent and merged creative strategies to become good abstract portraitists.
%Z iDMAa, Journal of the International Digital Media and Arts Association, volume 3 published by lulu.com????

%A Steve R. DiPaola
%A Liane Gabora
%T Incorporating characteristics of human creativity into an evolutionary art algorithm
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2007)
%E Peter A. N. Bosman
%D 2007
%P 2450--2456
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, creative evolutionary systems, evolutionary art, mechanisms of creativity
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2450.pdf
%X A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one
   generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome.
   As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve
   abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies
   characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative
   structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A Steve DiPaola
%A Liane Gabora
%T Incorporating characteristics of human creativity into an evolutionary art algorithm
%J Genetic Programming and Evolvable Machines
%V 10
%N 2
%D 2009
%P 97--110
%I 
%K genetic algorithms, genetic programming, Creative evolutionary systems, Mechanisms of creativity, Cognitive science, Evolutionary art
%X A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one
   generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome.
   As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve
   abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies
   characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative
   structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.
%8 June

%A Steve DiPaola
%A Nathan Sorenson
%T CGP, Creativity and Art
%B Cartesian Genetic Programming
%S Natural Computing Series
%E Julian F. Miller
%D 2011
%P 293--307
%I Springer
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%U http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7
%X This chapter looks at evolved art and creativity using Cartesian Genetic Programming (CGP). Besides an overview of evolutionary art, we discuss our work in modelling of
   artistic creativity based on the notion of contextual focus, which is the capacity for creative individuals to exhibit both intense concentration on a precise goal, as well
   as broad, associative thought processes, which produce radical departures from convention. We implement our model with Cartesian Genetic Programming, and CGP's genetic
   neutrality proves to be essential in reproducing contextual focus. The model is used to generate creative portraits of Darwin, which serve to illustrate the focused and
   exploratory aspects of the creative process.
%O 10
%Z part of \citeMiller:CGP

%A Gary Diplock
%T The application of evolutionary computing techniques to spatial interaction modelling
%R Ph.D. Thesis
%D 1996
%I 
%I Leeds University, UK
%K genetic algorithms, genetic programming
%U ftp://gam.leeds.ac.uk/pub/gary/thesis/thesis.zip broken
%8 September
%Z The research involved using both GAs and GP to build new forms of spatial models which predict the flows of products and services, population, etc between spatial areas.
   GAs were also used to calibrate existing spatial interaction models. The GP was implemented on the 512-processor T3D facility in Edinburgh (Scotland) using a MPI shell
   "Please note that" ftp://gam.leeds.ac.uk/pub/gary/thesis/thesis.zip (word for windows) is "a draft version which has a few typing errors, etc. but this should not be a
   problem" 5-0ct-1997

%A Peter Dittrich
%A Andreas Burgel
%A Wolfgang Banzhaf
%T Learning to Move a Robot with Random Morphology
%B Proceedings of the First European Workshop on Evolutionary Robotics
%S LNCS
%E Phil Husbands and Jean-Arcady Meyer
%V 1468
%D 1998
%P 165--178
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%U http://www.cs.mun.ca/~banzhaf/papers/evorobot_final.pdf
%8 16-17  April
%Z EvoRobot'98 See also \citedittrich:1998:rmr
%@ 3-540-64957-3

%A Peter Dittrich
%A Andreas Burgel
%A Wolfgang Banzhaf
%T Random Morphology Robot - A Test Platform for Online Evolution
%J Robots and Autonomous Systems
%D 1998
%I 
%K genetic algorithms, genetic programming
%O To appear
%Z See also \citedittrich:1998:lmrrm

%A Peter Dittrich
%A Andre Skusa
%A Wolfgang Kantschik
%A Wolfgang Banzhaf
%T Dynamical Properties of the Fitness Landscape of a GP Controlled Random Morphology Robot
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1002--1008
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, evolvable hardware, evolutionary robotics, on-line evolution, dynamical fitness landscape, reference fitness
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-454.ps
%X The aim of this contribution is: (1) to present an easy to maintain robot hardware platform which allows on-line evolutionary experiments and demonstrations; (2) to
   introduce a simple method to measure dynamical characteristics of the time-dependent fitness landscape by using reference individuals; (3) to demonstrate dynamical
   properties of the fitness landscape based on fitness measurements of reference individuals. The implication of the observations for the design of on-line EAs in
   time-dependent fitness landscapes are discussed.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Peter Dittrich
%A Thomas Kron
%A Christian Kuck
%A Wolfgang Banzhaf
%T Iterated Mutual Observation with Genetic Programming
%J Sozionik Aktuell
%V 2
%D 2001
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/444392.html
%X This paper introduces a simple model of interacting agents that learn to predict each other. For learning to predict the other's intended action we apply genetic
   programming. The strategy of an agent is rational and fixed. It does not change like in classical iterated prisoners dilemma models. Furthermore the number of actions an
   agent can choose from is infinite. Preliminary simulation results are presented. They show that by varying the population size of genetic programming, different learning
   characteristics can easily be achieved, which lead to quite different communication patterns.
%O The Pennsylvania State University CiteSeer Archives
%8 July
%Z http://www.sozionik-aktuell.de/

%A Peter Dittrich
%A Jens Ziegler
%A Wolfgang Banzhaf
%T Artificial Chemistries -- A Review
%J Artificial Life
%V 7
%N 3
%D 2001
%P 225--275
%I 
%K complex systems, evolution, self-organisation, emergence, molecular simulation, origin of life, chemical computing
%X This article reviews the growing body of scientific work in artificial chemistry. First, common motivations and fundamental concepts are introduced. Second, current
   research activities are discussed along three application dimensions: modelling, information processing, and optimization. Finally, common phenomena among the different
   systems are summarized. It is argued here that artificial chemistries are "the right stuff" for the study of prebiotic and biochemical evolution, and the provide a
   productive framework for questions regarding the origin and evolution of organisations in general. Furthermore, artificial chemistries have a broad application range of
   practical problems, and shown in this review.
%8 Summer

%A Peter Dittrich
%A Thomas Kron
%A Wolfgang Banzhaf
%T On the Scalability of Social Order
%J Journal of Artificial Societies and Social Simulation
%V 6
%N 1
%D 2003
%I 
%K genetic algorithms, genetic programming, Artificial Chemistry, Coordination, Double Contingency, Learning, Networks, Self-organization, System Theory
%U http://jasss.soc.surrey.ac.uk/6/1/3.html
%X We investigate an algorithmic model based first of all on Luhmann's description of how social order may originate [N. Luhmann, Soziale Systeme, Frankfurt/Main, Suhrkamp,
   1984, pp. 148-179]. In a basic 'dyadic' setting, two agents build up expectations during their interaction process. First, we include only two factors into the decision
   process of an agent, namely, its expectation about the future and its expectation about the other agent's expectation (called 'expectation-expectation' by Luhmann).
   Simulation experiments of the model reveal that 'social' order appears in the dyadic situation for a wide range of parameter settings, in accordance with Luhmann. If we
   move from the dyadic situation of two agents to a population of many interacting agents, we observe that the order usually disappears. In our simulation experiments,
   scalable order appears only for very specific cases, namely, if agents generate expectation- expectations based on the activity of other agents and if there is a mechanism
   of 'information proliferation', in our case created by observation of others. In a final demonstration we show that our model allows the transition from a more actor
   oriented perspective of social interaction to a systems-level perspective. This is achieved by deriving an 'activity system' from the microscopic interactions of the
   agents. Activity systems allow to describe situations (states) on a macroscopic level independent from the underlying population of agents. They also allow to draw
   conclusions on the scalability of social order.
%8 January
%Z Is this GP?

%A Federico Divina
%A Elena Marchiori
%T Knowledge Based Evolutionary Programming for Inductive Learning in First-Order Logic
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 173
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Federico Divina
%T Hybrid Genetic Relational Search for Inductive Learning
%R Ph.D. Thesis
%D 2004
%I 
%I Department of Computer Science, Vrije Universiteit
%C Amsterdam, the Netherlands
%K genetic algorithms, genetic programming
%U https://dare.ubvu.vu.nl/bitstream/1871/10280/1/divina_thesis.pdf
%X We are interested in learning concepts expressed in a fragment of first-order logic (FOL). This subject is known as Inductive Logic Programming (ILP), where the knowledge
   to be learn is expressed by Horn clauses, which are used in programming languages based on logic programming like Prolog. Learning systems that use a representation based
   on first-order logic have been successfully applied to relevant real life problems, e.g., learning a specific property related to carcinogenicity. Learning first-order
   hypotheses is a hard task, due to the huge search space one has to deal with. The approach used by the majority of ILP systems tries to overcome this problem by using
   specific search strategies, like the top-down and the inverse resolution mechanism (see chapter 2). However, the greedy selection strategies adopted for reducing the
   computational effort, render techniques based on this approach often incapable of escaping from local optima. An alternative approach is offered by genetic algorithms
   (GAs). GAs have proved to be successful in solving comparatively hard optimization problems, as well as problems like ICL. GAs represents a good approach when the problems
   to solve are characterized by a high number of variables, when there is interaction among variables, when there are mixed types of variables, e.g., numerical and nominal,
   and when the search space presents many local optima. Moreover it is easy to hybridize GAs with other techniques that are known to be good for solving some classes of
   problems. Another appealing feature of GAs is represented by their intrinsic parallelism, and their use of exploration operators, which give them the possibility of
   escaping from local optima. However this latter characteristic of GAs is also responsible for their rather poor performance on learning tasks which are easy to tackle by
   algorithms that use specific search strategies. These observations suggest that the two approaches above described, i.e., standard ILP strategies and GAs, are applicable to
   partly complementary classes of learning problems. More important, they indicate that a system incorporating features from both approaches could profit from the different
   benefits of the approaches. This motivates the aim of this thesis, which is to develop a system based on GAs for ILP that incorporates search strategies used in successful
   ILP systems. Our approach is inspired by memetic algorithms (Moscato, 1989), a population based search method for combinatorial optimization problems. In evolutionary
   computation memetic algorithms are GAs in which individuals can be refined during their lifetime. In particular the thesis introduces a hybrid evolutionary system called
   ECL (Evolutionary Concept Learner). ECL uses four intelligent mutation operators and an optimization phase that follows each mutation. Two mutation operators are used for
   generalization of rules, and the other two for specialization of rules. The optimization phase consists of the repeated application of mutation operators until the fitness
   of the individual being optimized increases. A high level representation of rules is adopted, in order to enable the use of these mutation operators. Rules are represented
   as a list of predicates, variables and constants. In this way at each time of the evolutionary process ECL can distinguish between the various part of the rule. A selection
   mechanisms, called EWUS, is used in order to select individuals and to promote diversity in the population. This last aspect is very important in all EAs system of ICL. A
   method for handling numerical values is used, which evolves discretization intervals along with rules, so that each rule can have a discretization intervals that is good
   for itself. ECL proved to be competitive with other state of the art systems for ICL, both in the relational and in the propositional settings. You can obtain a copy by
   clicking on the picture below. Would you prefer a printed copy of the thesis, request it with an email.

%A Federico Divina
%T Assessing the Effectiveness of Incorporating Knowledge in an Evolutionary Concept Learner
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 13--24
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=13
%X Classical methods for Inductive Concept Learning (ICL) rely mostly on using specific search strategies, as hill climbing and inverse resolution. These strategies have a
   great exploitation power, but run the risk of being incapable of escaping from local optima. An alternative approach to ICL is represented by Evolutionary Algorithms (EAs).
   EAs have a great exploration power, thus they have the capability of escaping from local optima, but their exploitation power is rather poor. These observations suggest
   that the two approaches are applicable to partly complementary classes of learning problems. More important, they indicate that a system incorporating features from both
   approaches could benefit from the complementary qualities of the approaches. In this paper we experimentally validate this statement. To this end, we incorporate different
   search strategies in a framework based on EAs for ICL. Results of experiments show that incorporating standard search strategies helps the EAs in achieving better results.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Asbjoern Djupdal
%T Evolving Static Hardware Redundancy for Defect Tolerant FPGAs
%R Ph.D. Thesis
%D 2008
%I 
%I Department of Computer and Information Science, Faculty of Information Technology, Mathematics and Electrical Engineering, Norwegian University of Science and Technology
%C Trondheim
%K genetic algorithms, genetic programming, cartesian genetic programming, EHW
%U http://www.idi.ntnu.no/research/doctor_theses/djupdal.pdf
%X Integrated circuits have been in constant progression since the first prototype in 1958. The semiconductor industry has maintained a constant rate of miniaturisation of
   transistors and wires, resulting in ever increasing speed, size and complexity of circuits. One challenge that has always been present is reduced yield due to production
   defects. A certain amount of chips must be scrapped because production defects have rendered the chips unusable. Recent predictions suggest that the average number of
   production defects per chip will rise drastically in the future as CMOS scaling approaches the physical limits of what is possible to manufacture. If these predictions are
   true, circuits should exhibit some level of tolerance to defects so to keep yield at acceptable levels. The main contribution of the thesis is to the field of defect
   tolerance, with a focus on FPGAs. Apart from the widespread employment of FPGAs, two technical reasons make the FPGA especially suited for inclusion of defect tolerance
   techniques. The regular structure of the FPGA can be exploited for efficient redundancy techniques. In addition, the FPGA can be seen as a bridge between production and the
   application designer. Through defect tolerance techniques incorporated transparently in the FPGA, a fully functioning gate array can be provided to the application designer
   despite defects from production. The approach taken in this thesis is to search for new ways of introducing static hardware redundancy in a circuit through the application
   of artificial evolution. However, the challenge of applying evolutionary techniques provided a secondary contribution. The work provides a contribution to the field of
   artificial evolution and the subfield evolvable hardware (EHW) by addressing ways in which such techniques may be applied to search for non-specifiable structures. The work
   is also bridging the fields of EHW and traditional hardware design and reliability metrics have been investigated for the purpose of comparing evolved and traditionally
   designed circuits. Redundant structures are first evolved for gate level circuits where both voter based solutions and more intricate non-voter based solutions are
   achieved. Transistor level redundancy structures are targeted next to approach the main goal of defect tolerance for FPGAs. A defect tolerant inverter is evolved which
   forms the basis of a general defect tolerance technique, termed the Multiple Short-Open (MSO) technique. The FPGA look-up table (LUT) is one of the essential components of
   the FPGA and a defect tolerant LUT is, therefore, constructed applying the MSO technique. An evolutionary experiment is also conducted where a defect tolerant 1-input LUT
   is evolved directly.
%8 24  April
%Z http://www.idi.ntnu.no/news/index.php?news=112 24th of April Asbjoern Djupdal completed his trial lecture and thesis defence, and he will eventually be awarded the PhD
   degree. The PhD was completed at the Computer Architecture and Design group, with associate professor Pauline Haddow as supervisor. He defended his PhD thesis: Evolving
   Static Hardware Redunancy for Defect Tolerant FPGAs

%A Asbjoern Djupdal
%A Pauline Haddow
%T The route to a defect tolerant LUT through artificial evolution
%J Genetic Programming and Evolvable Machines
%V 12
%N 3
%D 2011
%P 281--303
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%X Evolutionary techniques may be applied to search for specific structures or functions, as specified in the fitness function. This paper addresses the challenge of finding
   an appropriate fitness function when searching for generic rather than specific structures which, when combined with characteristics of defect tolerance on the circuit.
   Production defects for integrated circuits are expected to increase considerably. To avoid a corresponding drop in yield, improved defect tolerance solutions are needed. In
   the case of Field Programmable Gate Arrays (FPGAs), the pre-designed gate array provides a bridge between production and the application designers. Thus, introduction of
   defect tolerant techniques to the FPGA itself could provide a defect free gate array to the application designer, despite production defects. The search for defect
   tolerance presented herein is directed at finding defect tolerant structures for an important building block of FPGAs: Look-Up Tables (LUTs). Two key approaches are
   presented: (1) applying evolved generic building blocks to a traditional LUT design and (2) evolving the LUT design directly. The results highlight the fact that evolved
   generic defect tolerant structures can contribute to highly reliable circuit designs at the expense of area usage. Further, they show that applying such a technique, rather
   than direct evolution, has benefits with respect to evolvability of larger circuits, again at the expense of area usage.
%O Special Issue Title: Evolvable Hardware Challenges
%8 September

%A Duong Q. Do
%A Raymond C. Rowe
%A Peter York
%T Modelling drug dissolution from controlled release products using genetic programming
%J International Journal of Pharmaceutics
%V 351
%N 1-2
%D 2008
%P 194--200
%I 
%K genetic algorithms, genetic programming, Statistical methods, Modeling, Controlled release, Formulation
%U http://www.sciencedirect.com/science/article/B6T7W-4PWF0M5-1/2/1931c3725d1a803010a1d39e29117a1
%X This study has investigated and compared genetic programming (GP) - a method of automatically generating equations that describe the cause-and-effect relationships in a
   system - and statistical methods for modeling two controlled release formulations--a matrix tablet and microspheres. With the improved GP models exhibiting comparable
   predictive power, as well as simpler equations in some cases, the results obtained indicate that GP can be considered as an effective and efficient method for modelling
   controlled release formulations.

%A Polona {Dobnik Dubrovski}
%A Miran Brezocnik
%T Using genetic programming to predict the macroporosity of woven cotton fabrics
%J Textile research journal
%V 72
%N 3
%D 2002
%P 187--194
%I Sage
%K genetic algorithms, genetic programming, woven cotton fabrics, macroporosity, modelling
%U http://cat.inist.fr/?aModele=afficheN&cpsidt=13560450
%X This paper reports the effect of woven fabric construction on macroporosity properties. The area of a macropore's cross section, equivalent, maximum, and minimum pore
   diameters, pore density, and open porosity are observed in this research involving woven fabric construction parameters-yarn linear density, fabric tightness, weave type,
   and denting. Predictive models, determined by genetic programming, are derived to describe the influence of fabric construction. The results show very good agreement
   between the experimental and predicted values. This work provides guidelines for engineering staple-yarn cotton fabrics in a grey state in terms of macroporosity
   properties.
%8 March

%A Darren Doherty
%A Colm O'Riordan
%T Evolving Agent--Based Team Tactics for Combative Computer Games
%B Proceedings of the 17th Irish Artificial Intelligence and Cognitive Science Conference
%E D. A. Bell and P. Milligan and P. P. Sage
%D 2006
%P 52--61
%I 
%I Artificial Intelligence Association of Ireland
%C Queen's University, Belfast,Belfast, Ireland
%K genetic algorithms, genetic programming, team evolution
%8 11th-13th  September
%Z http://www.cs.qub.ac.uk/aics06/aics.html

%A Darren Doherty
%A Colm O'Riordan
%T Evolving Tactical Behaviours for Teams of Agents in Single Player Action Games
%B Proceedings of the 9th International Conference on Computer Games: AI, Animation, Mobile, Educational \& Serious Games
%E Qasim Mehdi and Fred Mtenzi and Bryan Duggan and Hugh McAtamney
%D 2006
%P 121--126
%I 
%I University of Wolverhampton
%C Dublin Institute of Technology,Dublin, Ireland
%K genetic algorithms, genetic programming, team evolution
%8 22nd-24th  November
%Z http://www.comp.dit.ie/cgames/
%@ 0-9549016-2-2

%A Darren Doherty
%A Colm O'Riordan
%T A phenotypic analysis of GP-evolved team behaviours
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1951--1958
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Real-World Applications, AI, artificial intelligence, cooperative agents, phenotypic analysis, tactical team behaviour
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1951.pdf
%X This paper presents an approach to analyse the behaviours of teams of autonomous agents who work together to achieve a common goal. The agents in a team are evolved
   together using a genetic programming (GP) [8] approach where each team of agents is represented as a single GP tree or chromosome. A number of such teams are evolved and
   their behaviours analysed in an attempt to identify combinations of individual agent behaviours that constitute good (or bad) team behaviour. For each team we simulate a
   number of games and periodically capture the agents' behavioural information from the gaming environment during each simulation. This information is stored in a series of
   status records that can be later analysed. We compare and contrast the behaviours of agents in the evolved teams to see if there is a correlation between a team's
   performance (fitness score) and the combined behaviours of the team's agents. This approach could also be applied to other GP-evolved teams in different domains.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Darren Doherty
%A Colm O'Riordan
%T Evolving Team Behaviours in Environments of Varying Difficulty
%B Proceedings of the 18th Irish Artificial Intelligence and Cognitive Science Conference
%E Sarah Jane Delany and Michael Madden
%D 2007
%P 61--70
%I 
%I Artificial Intelligence Association of Ireland
%C Dublin Institute of Technology,Dublin, Ireland
%K genetic algorithms, genetic programming, team evolution
%8 29th-31st  August
%Z http://www.comp.dit.ie/aics07/program.html

%A Darren Doherty
%T Evolving Tactical Teams for Shooter Games using Genetic Programming
%B Proceedings of the 3rd European Graduate Student Workshop on Evolutionary Computation
%E Jano Van Hemert and Mario Giacobini and Cecilia Di Chio
%D 2008
%P 29--42
%I 
%I Evostar
%C University of Naples Federico II,Naples, Italy
%K genetic algorithms, genetic programming, team evolution
%8 27  March
%Z EvoPHD'2008 held in conjunction with EuroGP-2008, EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Darren Doherty
%A Colm O'Riordan
%T Effects of Shared Perception on the Evolution of Squad Behaviors
%J IEEE Transactions on Computational Intelligence and AI in Games
%V 1
%N 1
%D 2009
%P 50--62
%I 
%K genetic algorithms, genetic programming, artificial intelligence, interactive digital entertainment, nonplayable characters, shared perception, squad behavior evolution,
   squad-based shooter computer games, artificial intelligence, computer games
%X As the nonplayable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviors that
   are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective squad behaviors for shooter games. GP has been used
   to evolve teams capable of defeating a single powerful enemy agent in a number of environments without the use of any explicit team communication. This paper is an
   extension of our paper presented at the 2008 Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE'08). Its aim is to explore the effects of
   shared perception on the evolution of effective squad behaviors. Thus, NPCs are given the ability to explicitly communicate their perceived information during evolution.
   The results show that the explicit communication of perceived information between team members enables an improvement in average team effectiveness.
%8 March
%Z Also known as \cite4804730

%A C. Gregory Doherty
%T Fundamental Analysis Using Genetic Programming for Classification Rule Induction
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 45--51
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/Doherty.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A J. J. Dolado
%A L. Fernandez
%T Genetic Programming, Neural Networks and Linear Regression in Software Project Estimation
%B International Conference on Software Process Improvement, Research, Education and Training
%E C. Hawkins and M. Ross and G. Staples and J. B. Thompson
%D 1998
%P 157--171
%I British Computer Society
%C London
%K genetic algorithms, genetic programming, neural networks, linear regression, SBSE
%U http://www.sc.ehu.es/jiwdocoj/docs/inspir98.pdf
%8 10-11  September
%Z INSPIRE 98 http://www2.unl.ac.uk/~11georgiadou/inspire98/
%@ 1-902505-03-4

%A J. Javier Dolado
%T Limits to the Methods in Software Cost Estimation
%B Proceedings of the 1st International Workshop on Soft Computing Applied to Software Engineering
%E Conor Ryan and Jim Buckley
%D 1999
%P 63--68
%I Limerick University Press
%I SCARE
%C University of Limerick, Ireland
%K genetic algorithms, genetic programming, SBSE
%U http://citeseer.ist.psu.edu/271064.html
%X We present some conclusions related to the use of classical regression, neural networks (NN) and genetic programming (GP) for software cost estimation. Although the
   estimates of classical regression can be improved by NN and GP, the results are not impressive. We conclude that either data points limit the usefulness of the methods, or
   that better ways have to be found for applying soft-computing techniques for software cost estimation.
%8 12-14  April
%Z http://scare.csis.ul.ie/scase99/ SCASE'99
%@ 1-874653-52-6

%A J. Javier Dolado
%A Luis Fernandez
%A M. Carmen Otero
%A Leire Urkola
%T Software Effort Estimation: the Elusive Goal in Project Management
%B International Conference on Enterprise Information Systems 1999
%D 1999
%P 412--418
%I 
%K genetic algorithms, genetic programming
%U http://www.sc.ehu.es/jiwdocoj/docs/dofeotur.ps
%X The estimation of the effort to be spent in a software project is a problem still open. Having a good estimation of the variables just at the beginning of a project makes
   the project manager confident about the future course of the actions, since many of the decisions taken during the development depend on, or are influenced by, the initial
   estimations. The root of the problems can be attributed to the different methods of analysis used, and to the way with which they are applied. On one hand we may not use
   the adequate independent variables for prediction and/or we may not build the correct predictive equations. On the other hand we could think that the method of prediction
   has some effect on the predictions, meaning that it is not the same to use classical regression or other methods of analysis. We have applied linear regression, neural
   networks and genetic programming to several datasets. We infer that the problem of accurate software estimation by means of mathematical analysis of simple relationships
   solely isn?t going to be inmediately solved.
%Z http://www.iceis.org/iceis2003/abstracts_1999.htm
%@ 972-98050-0-8

%A Jose Javier Dolado
%T A validation of the component-based method for software size estimation
%J IEEE Transactions on Software Engineering
%V 26
%N 10
%D 2000
%P 1006--1021
%I 
%K genetic algorithms, genetic programming, software reusability, software component-based method, software size estimation, software management, work planning, lines of code,
   fourth-generation language, Mark II function points, software size prediction, neural networks, SBSE
%U http://ieeexplore.ieee.org/iel5/32/19037/00879821.pdf
%X Estimation of software size is a crucial activity among the tasks of software management. Work planning and subsequent estimations of the effort required are made based on
   the estimate of the size of the software product. Software size can be measured in several ways: lines of code (LOC) is a common measure and is usually one of the
   independent variables in equations for estimating several methods for estimating the final LOC count of a software system in the early stages. We report the results of the
   validation of the component-based method (initially proposed by Verner and Tate, 1988) for software sizing. This was done through the analysis of 46 projects involving more
   than 100,000 LOC of a fourth-generation language. We present several conclusions concerning the predictive capabilities of the method. We observed that the component-based
   method behaves reasonably, although not as well as expected for "global" methods such as Mark II function points for software size prediction. The main factor observed that
   affects the performance is the type of component.
%8 October
%Z data files http://www.sc.ehu.es/jiwdocoj/cbm.htm

%A Jose J. Dolado
%T On the Problem of the Software Cost Function
%J Information and Software Technology
%V 43
%N 1
%D 2001
%P 61--72
%I 
%K genetic algorithms, genetic programming, SBSE, software cost function, Cost estimation, Empirical research
%U http://www.sciencedirect.com/science/article/B6V0B-41NK8BD-5/2/6d97db872ced4148a359673dc3b060c6
%X The question of finding a function for software cost estimation is a long-standing issue in the software engineering field. The results of other works have shown different
   patterns for the unknown function, which relates software size to project cost (effort). In this work, the research about this problem has been made by using the technique
   of Genetic Programming (GP) for exploring the possible cost functions. Both standard regression analysis and GP have been applied and compared on several data sets.
   However, regardless of the method, the basic size-effort relationship does not show satisfactory results, from the predictive point of view, across all data sets. One of
   the results of this work is that we have not found significant deviations from the linear model in the software cost functions. This result comes from the marginal cost
   analysis of the equations with best predictive values.
%8 1  January

%A Brad Dolin
%T Co-Evolution of Populations of Chasers and Evaders that use Sonic Intensity and Interaural Time Difference as Localization Cues
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 117--124
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Brad Dolin
%A Forrest H {Bennett III}
%A Eleanor G. Rieffel
%T Methods for evolving robust distributed robot control software: coevolutionary and single population techniques
%B The Third NASA/DoD workshop on Evolvable Hardware
%E Didier Keymeulen and Adrian Stoica and Jason Lohn and Ricardo S. Zebulum
%D 2001
%P 21--29
%I IEEE Computer Society 1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA
%I Jet Propulsion Laboratory, California Institute of Technology
%C Long Beach, California
%K genetic algorithms, genetic programming
%8 12-14  July
%Z EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/ Note misspeling of Brad Dolin as "Dofin, B.".
%@ 0-7695-1180-5

%A Brad Dolin
%A Forrest H. Bennett III
%A Eleanor G. Rieffel
%T Co-evolving an effective fitness sample: experiments in symbolic regression and distributed robot control
%B Proceedings of the 2002 ACM Symposium on Applied Computing (SAC)
%D 2002
%P 553--559
%I ACM
%C Madrid, Spain
%K genetic algorithms, genetic programming, co-evolution, fitness cases, symbolic regression, robot control, distributed control
%X We investigate two techniques for co-evolving and sampling from a population of fitness cases, and compare these with a random sampling technique. We design three symbolic
   regression problems on which to test these techniques, and also measure their relative performance on a modular robot control problem. The methods have varying relative
   performance, but in all of our experiments, at least one of the co-evolutionary methods outperforms the random sampling method by guiding evolution, with substantially
   fewer fitness evaluations, toward solutions that generalize best on an out-of-sample test set.
%8 March  10-14
%@ 1-58113-445-2

%A Brad Dolin
%A J. J. Merelo
%T Resource Review: A Web-Based Tour of Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 3
%N 3
%D 2002
%P 311--313
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.bgu.ac.il/~sipper/courses/papers/GPweb.pdf
%X Summary of some introductions to GP, tutorials and demos, implementations and useful links for GP research
%8 September
%Z Article ID: 5091793

%A Brad Dolin
%A Maribel Garcia Arenas
%A Juan J. Merelo Guervos
%T Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming
%B Parallel Problem Solving from Nature - PPSN VII
%S Lecture Notes in Computer Science, LNCS
%E Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel
%N 2439
%D 2002
%P 142--152
%I Springer-Verlag
%C Granada, Spain
%K genetic algorithms, genetic programming, Evolutionary computing, Selection
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=142
%O Available from http://link.springer.de/link/service/series/0558/papers/2439/243900142.pdf
%8 7-11  September
%@ 3-540-44139-5

%A J.-U. Dolinsky
%A G. J. Colquhoun
%A I. D. Jenkinson
%T A comparison of techniques for modelling robot dynamics
%B Proceedings of the 14th national conference on manufacturing research
%D 1998
%I 
%C University of Derby, UK
%K ANN
%Z copy in \citeDolinsky:thesis

%A J.-U. Dolinsky
%A G. J. Colquhoun
%A I. D. Jenkinson
%T Structural identification and calibration of kinematic robot models by genetic search
%B Proceedings of the 33rd international MATADOR conference
%D 2000
%I 
%C University of Manchester, Institute for Science and Technology (UMIST), UK
%K genetic algorithms, genetic programming
%Z copy in \citeDolinsky:thesis

%A Jens-Uwe Dolinsky
%T The Development Of A Genetic Programming Method For Kinematic Robot Calibration
%R Ph.D. Thesis
%D 2001
%I 
%I Liverpool John Moores University
%C UK
%K genetic algorithms, genetic programming, coevolution, stochastic inference, robotrak
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.70.7361
%X Kinematic robot calibration is the key requirement for the successful application of offline programming to industrial robotics. To compensate for inaccurate robot tool
   positioning, offline generated poses need to be corrected using a calibrated kinematic model, leading the robot to the desired poses. Conventional robot calibration
   techniques are heavily reliant upon numerical optimisation methods for model parameter estimation. However, the non-linearities of the kinematic equations, inappropriate
   model parameterisations with possible parameter discontinuities or redundancies, typically result in badly conditioned parameter identification. Research in kinematic robot
   calibration has therefore mainly focused on finding robot models and appropriate accommodated numerical methods to increase the accuracy of these models. This thesis
   presents an alternative approach to conventional kinematic robot calibration and develops a new inverse static kinematic calibration method based on the recent genetic
   programming paradigm. In this method the process of robot calibration is fully automated by applying symbolic model regression to model synthesis (structure and parameters)
   without involving iterative numerical methods for parameter identification, thus avoiding their drawbacks such as local convergence, numerical instability and parameter
   discontinuities. The approach developed in this work is focused on the evolutionary design and implementation of computer programs that model all error effects in
   particular non-geometric effects such as gear transmission errors, which considerably affect the overall positional accuracy of a robot. Genetic programming is employed to
   account for these effects and to induce joint correction models used to compensate for positional errors. The potential of this portable method is demonstrated in
   calibration experiments carried out on an industrial robot.
%8 March
%Z http://www.ljmu.ac.uk/GERI/80097.htm

%A J. U. Dolinsky
%A I. D. Jenkinson
%A G. J. Colquhoun
%T Application of genetic programming to the calibration of industrial robots
%J Computers in Industry
%V 58
%N 3
%D 2007
%P 255--264
%I Elsevier Science Publishers B. V. Amsterdam, The Netherlands
%K genetic algorithms, genetic programming, Inverse static kinematic calibration, Distal supervised learning, Co-evolution
%X Robot calibration is a widely studied area for which a variety of solutions have been generated. Most of the methods proposed address the calibration problem by
   establishing a model structure followed by indirect, often ill-conditioned numeric parameter identification. This paper introduces a new inverse static kinematic
   calibration technique based on genetic programming, which is used to establish and identify model structure and parameters. The technique has the potential to identify the
   true calibration model avoiding the problems of conventional methods. The fundamentals of this approach are described and experimental results provided.
%8 April
%Z Codeplay Ltd., Edinburgh, UK School of Engineering, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK

%A Roberto Pinheiro Domingos
%T Non-Linear Nuclear Engineering Models as an Application of Genetic Programming
%R M.S. Thesis
%D 1997
%I 
%I Universidade Federal Rio de Janeiro
%K genetic algorithms, genetic programming
%8 March
%Z details from http://www.genetic-programming.org/gpphdtheses.html Email Mon, 07 Jul 2003 20:25:57 -0300 confirms this as Masters thesis NOT PhD.

%A Roberto Pinheiro Domingos
%T Evolutionary Neuro-Fuzzy Models Applied to Nuclear Engineering Process Identification and Control
%R Ph.D. Thesis
%D 2003
%I 
%I COPPE, Universidade Federal Rio de Janeiro
%C Rua Vilela Tavares 253 apto 801 Lins - Rio de Janeiro -RJ-BRASIL
%K genetic algorithms, genetic programming, additive neurofuzzy
%X This work develops two soft computer models based on genetic programming system, these models are then applied to two engineering problems. At the first application
   obtaining an axial xenon oscillation controller of a nuclear reactor is investigated, several obtained controllers are discussed and the best one is compared with a
   neuro-fuzzy model. In the second application a hybrid model involving different soft computer techniques was developed and applied to a system identification benchmark
   problem, the identified model has its characteristics compared with models obtained through different techniques.
%8 June
%Z neurofuzzy genetic programming Email Mon, 07 Jul 2003 20:25:57 -0300 confirms this as PhD thesis.

%A Roberto P. Domingos
%A Gustavo H. F. Caldas
%A Claudio M. N. A. Pereira
%A Roberto Schirru
%T PWR's Xenon oscillation control through a fuzzy expert system automatically designed by means of genetic programming
%J Applied Soft Computing
%V 3
%N 4
%D 2003
%P 317--323
%I 
%K genetic algorithms, genetic programming, Axial xenon oscillations control; Fuzzy logic
%U http://www.sciencedirect.com/science/article/B6W86-49MX1MH-1/2/50727e0c9a470ae05a1e62675e4555d7
%X This work proposes the use of genetic programming (GP) for automatic design of a fuzzy expert system aimed to provide the control of axial xenon oscillations in pressurized
   water reactors (PWRs). The control methodology is based on three axial offsets of xenon (AOx), iodine (AOi) and neutron flux (AOf), effectively used in former work.
   Simulations were made using a two-point xenon oscillation model, which employs the non-linear xenon and iodine balance equations and the one group, one-dimensional neutron
   diffusion equation, with non-linear power reactivity feedback, also proposed in the literature. Results have demonstrated the ability of the GP in finding a good fuzzy
   strategy, which can effectively control the axial xenon oscillations.
%8 Decemeber

%A Roberto P. Domingos
%A Roberto Schirru
%A Aquilino Senra Martinez
%T Soft computing systems applied to PWR's xenon
%J Progress in Nuclear Energy
%V 46
%N 3-4
%D 2005
%P 297--308
%I 
%K genetic algorithms, genetic programming, evolutionary computation, control, xenon oscillation
%X The present work intends to introduce a soft computing technique as an effective and robust tool available to deal with nuclear engineering problems. This goal is reached
   by the presentation of an application: a genetic programming system (GP) able to automatically design a controller for the axial xenon oscillations in a pressurised water
   reactors (PWRs). The axial xenon oscillations control methodology is based on three axial offsets: the xenon axial offset (AOx), the iodine axial offset (AOi) and the
   neutron flux axial offset (AOf), effectively used in former work. Simulations were made using a two-point xenon oscillation model which employs the non-linear xenon and
   iodine balance equations and the one group, one-dimensional neutron diffusion equation, with non-linear power reactivity feedback, also proposed in the literature. Obtained
   results showed the ability of the GP in finding a strategy which can effectively control the axial xenon oscillations.

%A Keith Mac Donald
%T An Evolutionary Approach to CPU Fault Isolation
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 199--208
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A James A. Donarski
%A Stephen A. Jones
%A Mark Harrison
%A Malcolm Driffield
%A Adrian J. Charlton
%T Identification of botanical biomarkers found in Corsican honey
%J Food Chemistry
%V 118
%N 4
%D 2010
%P 987--994
%I 
%K genetic algorithms, genetic programming, NMR spectroscopy, Honey, Kynurenic acid, Chestnut, Geographical origin, Botanical origin
%U http://www.sciencedirect.com/science/article/B6T6R-4TRK0VB-1/2/c32107c8f3b0b36745ea2bd369053d04
%X Honeys from specified botanical sources often command a premium price due to their organoleptic or pharmacoactive properties. To prevent the fraudulent marketing of honey,
   analytical techniques are required to confirm its origin. NMR spectroscopy has been used to identify biomarkers of botanical and geographical origin for European honey.
   One-dimensional 1H NMR spectra were acquired from 374 authentic European honeys collected during 2 years, with the majority of these (220) taken from the island of Corsica.
   Biomarkers of sweet chestnut, Corsican spring Maquis and Arbousier (strawberry-tree) honeys were identified. Kynurenic acid was found to be a biomarker of sweet chestnut
   honey. [alpha]-Isophorone and 2,5-dihydroxyphenylacetic acid were confirmed as markers of strawberry-tree honey. Additional compounds specific to strawberry-tree and
   Corsican spring Maquis honey were partially characterised.
%O Food Authenticity \& Traceability, Edited by Simon Kelly, Claude Guillou and Paul Brereton
%8 15  February

%A Gyu Lee Dong
%A Whi Lee Byong
%A Heung Chang Soon
%T Genetic programming model for long-term forecasting of electric power demand
%J Electric Power Systems Research
%V 40
%N 1
%D 1997
%P 17--22
%I 
%K genetic algorithms, genetic programming, Forecasting, Electric demand
%U http://www.sciencedirect.com/science/article/B6V30-3WDCJBW-3/2/c71881481512566c7b47d81606334180
%X Genetic programming (GP) involves finding both the functional form and the numeric coefficients for the model. So it does not require the assumption of any functional
   relationship between dependent and independent variables. The use of GP for solving long-term forecasting of the electric power demand problem is discussed; several cases
   which have different combinations of terminal sets and functional sets were investigated. The results of annual forecasting of electric power demand are presented for
   various cases using the GP model. The GP model is compared with the regression model.

%A Hong-Bin Dong
%A Jia Chen
%T Improved Genetic Programming Based on Lineage Information
%B International Conference on Management and Service Science, MASS '09
%D 2009
%P 1--5
%I 
%C Wuhan, China
%K genetic algorithms, genetic programming, chromosome, effective search method, lineage information
%X At present, it is a major challenge to adopt an effective search method in genetic programming in order to produce an acceptable model in the search space. How to improve
   the efficiency of GP in a short period of time to produce better solution is very important. Traditional GP use of all the chromosomes for breeding, its search space for
   complex issues is enormous. In this paper, we introduce lineage relationship of chromosome in GP and propose an improved lineage-based genetic programming algorithm, ILBGP:
   use of lineage information of several ancestors, at the same time only retains one chromosome with the same fitness randomly. This method maintains the diversity, which can
   search the space effectively and avoid premature convergence toward local optima.
%8 September
%Z Also known as \cite5304998

%A Julian Dorado
%A Juan R. Rabu$\tilde{n}$al
%A Jer\'onimo Puertas
%A Antonino Santos
%A Daniel Rivero
%T Prediction and Modelling of the Flow of a Typical Urban Basin through Genetic Programming
%B Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN
%S LNCS
%E Stefano Cagnoni and Jens Gottlieb and Emma Hart and Martin Middendorf and G"unther Raidl
%V 2279
%D 2002
%P 190--201
%I Springer-Verlag Berlin
%I EvoNet
%C Kinsale, Ireland
%K genetic algorithms, genetic programming, evolutionary computation, applications, hydrology, rain-fall run-off, sewage, flooding alarm, transference function, hydraulic
   enginnering, kinematic wave, unit hydographs, STGP
%U http://link.springer-ny.com/link/service/series/0558/papers/2279/22790190.pdf
%X Genetic Programming (GP) is an evolutionary method that creates computer programs that represent approximate or exact solutions to a problem. This paper proposes an
   application of GP in hydrology, namely for modelling the effect of rain on the run-off flow in a typical urban basin. The ultimate goal of this research is to design a real
   time alarm system to warn of floods or subsidence in various types of urban basin. Results look promising and appear to offer some improvement over stochastic methods for
   analysing river basin systems such as unitary radiographs.
%8 3-4  April
%Z EvoWorkshops2002, part of cagnoni:2002:ews Vitoria, Spain, 5 minute pluviometer samples = 288 samples per day. Data for rainless days??? Replicated -288...575 three cycles
   "to avoid this discontinuity" p193. Sine and Cosine but no IF? No details of mutation, no fine constant adjustment, no anti-bloat measures? Fitting average day and rainy
   day are separated. Complex arithmetic, mutlti-typed system. "This execution does not return any value, it only stores the system's poles, zeros and constants" p197. Poles
   outside unit circle lead to immediate death of tree. Tested on 20 hours and 45 minutes of variable rainfall. Average error on GP model less than that of "SCS Unit
   Hydrograph", Table 1.
%@ 3-540-43432-1

%A Julian Dorado
%A Juan R. Rabunal
%A Daniel Rivero
%A Antonino Santos
%A Alejandro Pazos
%T Automatic Recurrent ANN Rule Extraction with Genetic Programming
%B Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN'02
%D 2002
%P 1552--1557
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE
%C Hilton Hawaiian Village Hotel, Honolulu, Hawaii
%K genetic algorithms, genetic programming
%X Various rule-extraction techniques using ANNs have been used so far, most of them being applied on multi-layer ANNs, since they are more easily handled. In many cases,
   extraction methods focusing on different types of networks and training have been implemented, however, there are virtually no methods that view the extraction of rules
   from ANNs as systems which are independent from their architecture, training and internal distribution of weights, connections and activation functions. This paper proposes
   a rule-extraction system of ANNs regardless of their architecture (multi-layer or recurrent), using Genetic programming as a rule-exploration technique.
%8 12-17  May
%Z IJCNN 2002 Held in connection with the World Congress on Computational Intelligence (WCCI 2002)
%@ 0-7803-7278-6

%A Julian Dorado
%A Juan R. Rabunal
%A Antonino Santos
%A Alejandro Pazos
%A Daniel Rivero
%T Automatic Recurrent ANN Rule Extraction with Genetic Programming
%B Parallel Problem Solving from Nature - PPSN VII
%S Lecture Notes in Computer Science, LNCS
%E Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel
%N 2439
%D 2002
%P 485--494
%I Springer-Verlag
%C Granada, Spain
%K genetic algorithms, genetic programming, Neural Networks
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=485
%O Available from http://link.springer.de/link/service/series/0558/papers/2439/243900485.pdf
%8 7-11  September
%@ 3-540-44139-5

%A Marco Dorigo
%A Marco Colombetti
%T Robot Shaping: An Experiment in Behavior Engineering
%D 1997
%I MIT Press/Bradford Books

%A Alan Dorin
%T Koza, J. ``Genetic Programming'' (review)
%R Technical Report
%D 1994
%I 
%I School of Computer Science and Software Engineering, Monash University
%C Clayton, Australia 3168
%K genetic algorithms, genetic programming
%U http://www.cs.monash.edu.au/~aland/reviews/koza.rev.html
%Z www only

%A Giovanni Dosi
%A Luigi Marengo
%A Andrea Bassanini
%A Marco Valente
%T Norms as emergent properties of adaptive learning: The case of economic routines
%J Journal of Evolutionary Economics
%V 9
%N 1
%D 1999
%P 5--26
%I 
%K genetic algorithms, genetic programming, computability, oligopoly
%X Interaction among autonomous decision-makers is usually modelled in economics in game-theoretic terms or within the framework of General Equilibrium. Game-theoretic and
   General Equilibrium models deal almost exclusively with the existence of equilibria and do not analyse the processes which might lead to them. Even when existence proofs
   can be given, two questions are still open. The first concerns the possibility of multiple equilibria, which game theory has shown to be the case even in very simple models
   and which makes the outcome of interaction unpredictable. The second relates to the computability and complexity of the decision procedures which agents should adopt and
   questions the possibility of reaching an equilibrium by means of an algorithmically implementable strategy. Some theorems have recently proved that in many economically
   relevant problems equilibria are not computable. A different approach to the problem of strategic interaction is a "constructivist" one. Such a perspective, instead of
   being based upon an axiomatic view of human behaviour grounded on the principle of optimisation, focuses on algorithmically implementable "satisfycing" decision procedures.
   Once the axiomatic approach has been abandoned, decision procedures cannot be deduced from rationality assumptions, but must be the evolving outcome of a process of
   learning and adaptation to the particular environment in which the decision must be made. This paper considers one of the most recently proposed adaptive learning models:
   Genetic Programming and applies it to one the mostly studied and still controversial economic interaction environment, that of oligopolistic markets. Genetic Programming
   evolves decision procedures, represented by elements in the space of functions, balancing the exploitation of knowledge previously obtained with the search of more
   productive procedures. The results obtained are consistent with the evidence from the observation of the behaviour of real economic agents.

%A Jefersson Alex {dos Santos}
%A Cristiano Dalmaschio Ferreira
%A Ricardo {da Silva Torres}
%T A Genetic Programming Approach for Relevance Feedback in Region-Based Image Retrieval Systems
%B XXI Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI '08
%D 2008
%P 155--162
%I 
%K genetic algorithms, genetic programming, genetic programming approach, local aggregation pattern, local image features, query session, region-based image retrieval systems,
   relevance feedback, image retrieval, relevance feedback
%X This paper presents a new relevance feedback method for content-based image retrieval using local image features. This method adopts a genetic programming approach to learn
   user preferences and combine the region similarity values in a query session. Experiments demonstrate that the proposed method yields more effective results than the local
   aggregation pattern (LAP)-based relevance feedback technique.
%8 October
%Z Also known as \cite4654155

%A J. A. {dos Santos}
%A C. D. Ferreira
%A R. {da S. Torres}
%A M. A. Goncalves
%A R. A. C. Lamparelli
%T A Relevance Feedback Method based on Genetic Programming for Classification of Remote Sensing Images
%J Information Sciences
%V 181
%N 12
%D 2011
%P 2671--2684
%I 
%K genetic algorithms, genetic programming, content-based image retrieval, region descriptors, relevance feedback, remote sensing image classification
%U http://www.sciencedirect.com/science/article/B6V0C-4YBMF9K-2/2/7be908a0802e1675ad8e8258bfbc4e01
%X This paper presents an interactive technique for remote sensing image classification. In our proposal, users are able to interact with the classification system, indicating
   regions of interest (and those which are not). This feedback information is employed by a genetic programming approach to learning user preferences and combining image
   region descriptors that encode spectral and texture properties. Experiments demonstrate that the proposed method is effective for image classification tasks and outperforms
   the traditional MaxVer method.
%8 1  July

%A Leandro {dos Santos Coelho}
%A Antonio Augusto Rodrigues Coelho
%T An Experimental and Comparative Study of Fuzzy PID Controller Structures
%B Advances in Soft Computing - Engineering Design and Manufacturing
%E R. Roy and T. Furuhashi and P. K. Chawdhry
%D 1998
%I 
%K Fuzzy logic control, Fuzzy PID Control, Experimental process, Control applications.
%X Structures and design issues of fuzzy PID (proportional-integral-derivative) controllers (FLC-PIDs) are presented and evaluated in this paper. Configuration and basic
   characteristic of several structures of FLC-PID based on models proposed in the literature (PD + I), (PI + D conventional), incremental (PD + I), (PD + PI) are here
   reviewed and implemented. FLC-PIDs are assessed on a horizontal balance process, consisting of two propellers driven by two DC motors. Such process offers control
   complexities and can become unstable by using classical controllers. Experimental results, robustness and performance of FLC-PIDs are illustrated and discussed.
%8 21-30  June
%Z WSC3
%@ 1-85233-062-7

%A Leandro {dos Santos Coelho}
%A Marcelo Wicthoff Pessoa
%T Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach
%J Mechanical Systems and Signal Processing
%V 23
%N 5
%D 2009
%P 1434--1446
%I 
%K genetic algorithms, genetic programming, System identification, Nonlinear models, Evolutionary algorithm
%U http://www.sciencedirect.com/science/article/B6WN1-4VNH3WJ-1/2/f2de8e8814271f4e5d58e4cee49bd291
%X Most processes in industry are characterized by nonlinear and time-varying behavior. The identification of mathematical models typically nonlinear systems is vital in many
   fields of engineering. The developed mathematical models can be used to study the behavior of the underlying system as well as for supervision, fault detection, prediction,
   estimation of unmeasurable variables, optimization and model-based control purposes. A variety of system identification techniques are applied to the modeling of process
   dynamics. Recently, the identification of nonlinear systems by genetic programming (GP) approaches has been successfully applied in many applications. GP is a paradigm of
   evolutionary computation field based on a structure description method that applies the principles of natural evolution to optimization problems and its nature is a
   generalized hierarchy computer program description. GP adopts a tree structure code to describe an identification problem. Unlike the traditional approximation methods
   where the structure of an approximate model is fixed, the structure of the GP tree itself is modified and optimized and, thus, there is a possibility that GP trees could be
   more appropriate or accurate approximate models. This paper focuses the GP method for structure selection in a system identification applications. The proposed GP method
   combines different techniques for tuning of crossover and mutation probabilities with an orthogonal least-squares (OLS) algorithm to estimate the contribution of the
   branches of the tree to the accuracy of the discrete polynomial Nonlinear AutoRegressive with eXogenous inputs (NARX) model. The nonlinear system identification procedure,
   based on a NARX representation and GP, is applied to empirical case study of an experimental ball-and-tube system. The results demonstrate that the GP with OLS is a
   promising technique for NARX modeling.

%A John Doucette
%A Malcolm I. Heywood
%T GP Classification under Imbalanced Data sets: Active Sub-sampling and AUC Approximation
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 266--277
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A John Doucette
%A Peter Lichodzijewski
%A Malcolm I. Heywood
%T Benchmarking coevolutionary teaming under classification problems with large attribute spaces
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1901--1902
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Poster
%X Benchmarking of a team based model of Genetic Programming demonstrates that the naturally embedded style of feature selection is usefully extended by the teaming metaphor
   to provide solutions in terms of exceptionally low attribute counts. To take this concept to its logical conclusion the teaming model must be able to build teams with a
   non-overlapping behavioral trait, from a single population. The Symbiotic Bid-Based (SBB) algorithm is demonstrated to fit this purpose under an evaluation using data sets
   with 650 to 5,000 attributes. The resulting solutions are one to two orders simpler than solutions identified under the alternative embedded paradigms of C4.5 and MaxEnt.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A John Doucette
%A Peter Lichodzijewski
%A Malcolm Heywood
%T Evolving Coevolutionary Classifiers under Large Attribute Spaces
%B Genetic Programming Theory and Practice VII
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Una-May O'Reilly and Trent McConaghy
%D 2009
%P 37--54
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Problem Decomposition, Bid-based Cooperative Behaviors, Symbiotic Coevolution, Subspace Classifier, Large Attribute Spaces
%O 3
%8 14-16  May
%Z part of \citeRiolo:2009:GPTP

%A John Doucette
%A Malcolm Heywood
%T Novelty-based Fitness: An Evaluation under the Santa Fe Trail
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 50--61
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X We present an empirical analysis of the effects of incorporating novelty-based fitness (phenotypic behavioral diversity) into Genetic Programming with respect to training,
   test and generalization performance. Three novelty-based approaches are considered: novelty comparison against a finite archive of behavioral archetypes, novelty comparison
   against all previously seen behaviors, and a simple linear combination of the first method with a standard fitness measure. Performance is evaluated on the Santa Fe Trail,
   a well known GP benchmark selected for its deceptiveness and established generalization test procedures. Results are compared to a standard quality-based fitness function
   (count of food eaten). Ultimately, the quality style objective provided better overall performance, however, solutions identified under novelty based fitness functions
   generally provided much better test performance than their corresponding training performance. This is interpreted as representing a requirement for layered learning/
   symbiosis when assuming novelty based fitness functions in order to more quickly achieve the integration of diverse behaviors into a single cohesive strategy.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A John Doucette
%A Malcolm Heywood
%T Revisiting the Acrobot `height' task: An example of Efficient Evolutionary Policy Search under an Episodic Goal Seeking Task
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 468--475
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Adaptive dynamic programming and reinforcement learning, Coevolution and collective behaviour
%X Evolutionary methods for addressing the temporal sequence learning problem generally fall into policy search as opposed to value function optimisation approaches. Various
   recent results have made the claim that the policy search approach is at best inefficient at solving episodic `goal seeking' tasks i.e., tasks under which the reward is
   limited to describing properties associated with a successful outcome have no qualification for degrees of failure. This work demonstrates that such a conclusion is due to
   a lack of diversity in the training scenarios. We therefore return to the Acrobot `height' task domain originally used to demonstrate complete failure in evolutionary
   policy search. This time a very simple stochastic sampling heuristic for defining a population of training configurations is introduced. Benchmarking two recent
   evolutionary policy search algorithms -- Neural Evolution of Augmented Topologies (NEAT) and Symbiotic Bid-Based (SBB) Genetic Programming -- under this condition
   demonstrates solutions as effective as those returned by advanced value function methods. Moreover this is achieved while remaining within the evaluation limit imposed by
   the original study.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A John A. Doucette
%A Andrew R. McIntyre
%A Peter Lichodzijewski
%A Malcolm I. Heywood
%T Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces
%J Genetic Programming and Evolvable Machines
%V 13
%N 1
%D 2012
%P 71--101
%I 
%K genetic algorithms, genetic programming, Feature subspace selection, Problem decomposition, Symbiosis, Coevolution, Model complexity, Classification
%X Classification under large attribute spaces represents a dual learning problem in which attribute subspaces need to be identified at the same time as the classifier design
   is established. Embedded as opposed to filter or wrapper methodologies address both tasks simultaneously. The motivation for this work stems from the observation that team
   based approaches to Genetic Programming (GP) have the potential to design multiple classifiers per class. each with a potentially unique attribute subspace. without
   recourse to filter or wrapper style preprocessing steps. Specifically, competitive coevolution provides the basis for scaling the algorithm to data sets with large instance
   counts; whereas cooperative coevolution provides a framework for problem decomposition under a bid-based model for establishing program context. Symbiosis is used to
   separate the tasks of team/ensemble composition from the design of specific team members. Team composition is specified in terms of a combinatorial search performed by a
   Genetic Algorithm (GA); whereas the properties of individual team members and therefore subspace identification is established under an independent GP population. Teaming
   implies that the members of the resulting ensemble of classifiers should have explicitly non-overlapping behaviour. Performance evaluation is conducted over data sets taken
   from the UCI repository with 649-102,660 attributes and 2-10 classes. The resulting teams identify attribute spaces 1-4 orders of magnitude smaller than under the original
   data set. Moreover, team members generally consist of less than 10 instructions; thus, small attribute subspaces are not being traded for opaque models.
%O Special Section on Evolutionary Algorithms for Data Mining
%8 March

%A George Dounias
%A Hubertus Axer
%A Beth Bjerregaard
%A Diedrich {Graf von Keyserlingk}
%A Jan Jantzen
%A Athanasios Tsakonas
%T Genetic Programming for the Generation of Crisp and Fuzzy Rule Bases in Classification and Diagnosis of Medical Data
%B First International NAISO Congress on Neuro Fuzzy Technologies
%D 2002
%I 
%I NAISO (Natural and Artificial Intelligence Systems Organization)
%C Havana, Cuba
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/501552.html
%X This paper demonstrates two methodologies for the construction of rule-based systems in medical decision making. The first approach consists of a method combining genetic
   programming and heuristic hierarchical rule-base construction. The second model is composed by a strongly-typed genetic programming system for the generation of fuzzy
   rule-based systems. Two different medical domains are used to evaluate the models. The first field is the diagnosis of subtypes of Aphasia. Two models for crisp rule-bases
   are presented. The first one discriminates between four major types and the second attempts the classification between all common types. A third model consisted of a
   GPgenerated fuzzy rule-based system is tested on the same domain. The second medical domain is the classification of Pap-Smear Test examinations where a crisp rulebased
   system is constructed. Results denote the effectiveness of the proposed systems. Comparisons on the system's comprehensibility and the transparency are included. These
   comparisons include for the Aphasia domain, previous work consisted of two neural network models.
%O The Pennsylvania State University CiteSeer Archives
%8 16-19  January
%Z http://www.icsc.ab.ca/conferences/nf2002/

%A Eduardo {do Valle Simoes}
%T Evolvable hardware, Springer, Genetic and Evolutionary Computation Series, edited by Tetsuya Higuchi, Yong Liu and Xin Yao, 224 pp, ISBN 0-387-24386-0
%J Genetic Programming and Evolvable Machines
%V 8
%N 3
%D 2007
%P 287--288
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%O Book review
%8 September

%A Steve Dower
%A Clinton J. Woodward
%T ESDL: a simple description language for population-based evolutionary computation
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1045--1052
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X A large proportion of publications in the field of evolutionary computation describe algorithm specialisation and experimentation. Algorithms are variously described using
   text, tables, flowcharts, functions or pseudocode. However, ambiguity that can limit the efficiency of communication is common. Evolutionary System Definition Language
   (ESDL) is a conceptual model and language for describing evolutionary systems efficiently and with reduced ambiguity, including systems with multiple populations and
   adaptive parameters. ESDL may also be machine-interpreted, allowing algorithms to be tested without requiring a hand-coded implementation, as may already be done using the
   esec framework. The style is distinct from existing notations used within the field and is easily recognisable. This paper describes the case for ESDL, provides an overview
   of ESDL and examples of its use.
%8 12-16  July
%Z Also known as \cite2001718 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Carlton Downey
%A Mengjie Zhang
%T Multiclass object classification for computer vision using Linear Genetic Programming
%B Proceeding of the 24th International Conference Image and Vision Computing New Zealand, IVCNZ '09
%D 2009
%P 73--78
%I IEEE
%C Wellington
%K genetic algorithms, genetic programming
%X Multiclass classification problems arise naturally in many tasks in computer vision; typical examples include image segmentation and letter recognition. These are among
   some of the most challenging and important tasks in the area and solutions to them are eagerly sought after. Genetic Programming (GP) is a powerful and flexible machine
   learning technique that has been successfully applied to many binary classification tasks. However, the traditional form of GP performs poorly on multi-class classification
   problems. Linear GP (LGP) is an alternative form of GP where programs are represented as sequences of instructions like Java and C++. This paper discusses results which
   demonstrate the superiority of LGP as a technique for multi class classification. It also discusses a new extension to LGP which results in a further improvement in the
   performance on multiclass classification problems.
%8 23-25  November
%Z Also known as \cite5378356

%A Carlton Downey
%A Mengjie Zhang
%A Will N. Browne
%T New crossover operators in linear genetic programming for multiclass object classification
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 885--892
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%X Genetic programming (GP) has been successfully applied to solving multiclass classification problems, but the performance of GP classifiers still lags behind that of
   alternative techniques. This paper investigates an alternative form of GP, Linear GP (LGP), which demonstrates great promise as a classifier as the division of classes is
   inherent in this technique. By combining biological inspiration with detailed knowledge of program structure two new crossover operators that significantly improve
   performance are developed. The first is a new crossover operator that mimics biological crossover between alleles, which helps reduce the disruptive effect on building
   blocks of information. The second is an extension of the first where a heuristic is used to predict offspring fitness guiding search to promising solutions.
%8 7-11  July
%Z Also known as \cite1830644 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Carlton Downey
%A Mengjie Zhang
%T Parallel Linear Genetic Programming
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 178--189
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X Motivated by biological inspiration and the issue of code disruption, we develop a new form of LGP called Parallel LGP (PLGP). PLGP programs consist of n lists of
   instructions. These lists are executed in parallel, after which the resulting vectors are combined to produce program output. PGLP limits the disruptive effects of
   crossover and mutation, which allows PLGP to significantly outperform regular LGP.
%8 27-29  April
%Z Fixed linear combination rule to combine output of small but fixed number of team members. Cf work on multi classifier systems, linear GP and memory with memory. Part of
   \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Carlton Downey
%A Mengjie Zhang
%T Execution Trace Caching for Linear Genetic Programming
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 1191--1198
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming
%X In this paper we propose a new caching algorithm for LGP based on exploiting inter-generation program relationships. For each program we cache a partial summary of program
   execution, and use this summary to expedite the execution of all progeny. We study the theory behind our new caching algorithm and derive equations for optimising algorithm
   performance. Through both theoretical and empirical results we demonstrate that our caching algorithm can decrease LGP execution time by up to 50percent
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Carlton Downey
%A Mengjie Zhang
%T Caching for parallel linear genetic programming
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 201--202
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming: Poster
%X Parallel Linear Genetic Programming (PLGP) is an exciting new approach to Linear Genetic Programming (LGP) which decreases building block disruption and significantly
   improves performance by the introduction of a parallel architecture. We introduce a caching algorithm for PLGP which exploits this parallel architecture to avoid the
   majority of instruction executions. This allows PLGP programs to be executed an order of magnitude faster than LGP programs with an equal number of instructions.
%8 12-16  July
%Z Also known as \cite2001970 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Carlton Downey
%A Mengjie Zhang
%A Jing Liu
%T Parallel linear genetic programming for multi-class classification
%J Genetic Programming and Evolvable Machines
%I 
%K genetic algorithms, genetic programming, Linear genetic programming, Classification, Parallel structure, Caching
%X Motivated by biological inspiration and the issue of instruction disruption, we develop a new form of Linear Genetic Programming (LGP) called Parallel LGP (PLGP) for
   classification problems. PLGP programs consist of multiple lists of instructions. These lists are executed in parallel after which the resulting vectors are combined to
   produce the classification result. PLGP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform regular LGP. Furthermore,
   PLGP programs are naturally suited to caching due to their parallel architecture. Although caching techniques have been used in tree based GP, to our knowledge, there are
   no caching techniques specifically developed for LGP. Thus, a novel caching technique is also developed with the intrinsic properties of PLGP in mind, which can decrease
   fitness evaluation time by almost an order of magnitude for the classification problems.
%O Online first
%Z Jing Liu = http://see.xidian.edu.cn/faculty/liujing/

%A Keith Downing
%T Combining Genetic Programming and Genetic Algorithms for Ecological Simulation
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 48--53
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Keith Downing
%T Using evolutionary computational techniques in environmental modelling
%J Environmental Modelling and Software
%V 13
%N 5-6
%D 1998
%P 519--528
%I 
%K genetic algorithms, genetic programming, Evolutionary computation, Evolutionary ecology
%U http://www.sciencedirect.com/science/article/B6VHC-3VGHBS1-1G/2/20d163b7dea17eb9b21f06211acd3188
%X Evolutionary Computation (EC) is a field of computer science that borrows concepts such as natural selection and the genotype-phenotype distinction from biology in order to
   solve a wide range of complex problems, such as robot controller design, job-shop schedule optimisation, pattern recognition, electronic circuit design and many more. In
   addition, EC techniques in combination with individual-based modelling can be applied in their domain of origin, biology, to investigate the emergence and evolution of
   natural phenomena. This paper describes the use of EC as both (a) an empirical supplement to analytical approaches to mathematically tractable biological problems, and (b)
   a vital tool for analysing highly complex systems of interacting species in heterogeneous environments. Three EC applications, two tractable and one complex, are used to
   illustrate these points. In general, this work introduces environmental modellers to a cutting-edge computer-science technique that can be of considerable utility,
   especially in a modern world in which accelerated rates of large-scale environmental change heighten the need for evolutionary considerations in analyses of relatively
   short time-scale phenomena.

%A Keith L. Downing
%T Adaptive Genetic Programs via Reinforcement Learning
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 19--26
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, Reinforcement Learning, Baldwin Effect, Lamarckianism, Hybrid Adaptive Systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Keith L. Downing
%T Reinforced Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 2
%N 3
%D 2001
%P 259--288
%I 
%K genetic algorithms, genetic programming, reinforcement learning, the Baldwin Effect, Lamarckism
%U http://www.idi.ntnu.no/grupper/ai/eval/reinforcedGP/
%X This paper introduces the Reinforced Genetic Programming (RGP) system, which enhances standard tree-based genetic programming (GP) with reinforcement learning (RL). RGP
   adds a new element to the GP function set: monitored action-selection points that provide hooks to a reinforcement-learning system. Using strong typing, RGP can restrict
   these choice points to leaf nodes, thereby turning GP trees into classify-and-act procedures. Then, environmental reinforcements channeled back through the choice points
   provide the basis for both lifetime learning and general GP fitness assessment. This paves the way for evolutionary acceleration via both Baldwinian and Lamarckian
   mechanisms. In addition, the hybrid hints of potential improvements to RL by exploiting evolution to design proper abstraction spaces, via the problem-state classifications
   of the internal tree nodes. This paper details the basic mechanisms of RGP and demonstrates its application on a series of static and dynamic maze-search problems.
%8 September
%Z Article ID: 357595

%A Keith L. Downing
%T Tantrix: A Minute to Learn, 100 (Genetic Algorithm) Generations to Master
%J Genetic Programming and Evolvable Machines
%V 6
%N 4
%D 2005
%P 381--406
%I 
%K genetic algorithms, indirect-encoded genomes
%X The game of Tantrix provides a challenging, mathematical and graphic domain for evolutionary computation. The simple task of forming long loops of coloured arcs quickly
   becomes a search nightmare for humans and computers alike as the number of game pieces scales linearly. Tantrix-GA solves several types and sizes of Tantrix puzzles but
   still falls well short of (at least a few) human Tantrix experts. By introducing this problem to evolutionary computation researchers, we hope to motivate an evolutionary
   attack on the holy-grail Tantrix puzzles, one of which has yet to be solved by any intelligence, real or artificial.
%8 Decemeber

%A Richard Mark Downing
%T Evolving Binary Decision Diagrams using Implicit Neutrality
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2107--2113
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~rmd/pubs/evolvingbddsCEC2005.pdf
%X A new algorithm is presented for evolving Binary Decision Diagrams (BDD) that employs the neutrality implicit in the BDD representation. It is shown that an effortless
   neutral walk is taken; that is, a neutral walk that requires no fitness evaluations. Experiments show the algorithm to be robust and scalable across a range of n-parity
   problems up to n = 17, and highly efficient on a range of other functions with compact BDD representations. Evolvability and modularity issues are also discussed, and the
   search space is shown to be free of local optima.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. Sun, 18 Jun 2006 10:40:08 BST 20-mux and 17-parity.
%@ 0-7803-9363-5

%A Richard M. Downing
%T Neutrality and gradualism: encouraging exploration and exploitation simultaneously with Binary Decision Diagrams
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%P 615--622
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~rmd/pubs/gradualism.pdf
%X Search algorithms are subject to the trappings of local optima. Attempts to address the problem are often framed in the context of needing to balance, or trade-off,
   exploitation against exploration. Ideally, it is best to maximise both simultaneously, but this is usually seen as infeasible in the presence of multi-modal search spaces.
   This paper investigates the potential for exploration of both neutrality and mutation rate, and argues that the former is the more important. The most interesting result,
   however, is that the necessity for a trade-off between exploitation and exploration can be avoided within the context of our algorithm for evolving Binary Decision
   Diagrams.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-9487-9

%A Richard M. Downing
%T Evolving Binary Decision Diagrams with emergent variable orderings
%B Parallel Problem Solving from Nature - PPSN IX
%S LNCS
%E Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao
%V 4193
%D 2006
%P 798--807
%I Springer-Verlag Berlin
%C Reykjavik, Iceland
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~rmd/pubs/ppsn06.pdf
%X Binary Decision Diagrams (BDDs) have become the data structure of choice for representing discrete functions in some design and verification applications: They are compact
   and efficient to manipulate with strong theoretical underpinnings. However, and despite many appealing characteristics, BDDs are not a representation commonly considered
   for evolutionary computation (EC). The inherent difficulties associated with evolving graphs combined with the variable ordering problem poses a significant challenge which
   is yet to be overcome. This work addresses this challenge and presents a new approach to evolving BDDs that exhibits good variable orderings as an emergent property.
%8 9-13  September
%Z PPSN-IX
%@ 3-540-38990-3

%A Richard M. Downing
%T On population size and neutrality: facilitating the evolution of evolvability
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 181--192
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X The role of population size is investigated within a neutrality induced local optima free search space. Neutrality decouples genotypic variation in evolvability from
   fitness variation. Population diversity and neutrality work in conjunction to facilitate evolvability exploration whilst restraining its loss to drift, ultimately
   facilitating the evolution of evolvability. The characterising dynamics and implications are discussed.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Richard M. Downing
%T Evolvability Via Modularity-Induced Mutational Focussing
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 194--205
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Dimitris C. Dracopoulos
%A Simon Kent
%T Speeding up Genetic Programming: A Parallel BSP Implementation
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 421
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 5 page version available via citeseer.ist.psu.edu/233993.html

%A Dimitris C. Dracopoulos
%T Evolutionary Control of a Satellite
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 77--81
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Dracopoulos_1997_es.pdf
%8 13-16  July
%Z GP-97

%A Dimitris C. Dracopoulos
%T Evolutionary Learning Algorithms for Neural Adaptive Control
%S Perspectives in Neural Computing
%D 1997
%I Springer Verlag
%C P.O. Box 31 13 40, D-10643 Berlin, Germany
%K genetic algorithms, genetic programming
%U http://www.amazon.co.uk/exec/obidos/ASIN/3540761616/qid%3D1106423488/202-4979008-1846244
%X Neural networks and evolutionary algorithms are constantly expanding their field of application to a variety of new domains. One area of particular interest is their
   applicability to control and adaptive control systems: the limitations of the classical control theory combined with the need for greater robustness, adaptivity and
   ``intelligence'' make neurocontrol and evolutionary control algorithms an attractive (and in some cases, the only) alternative. After an introduction to neural networks and
   genetic algorithms, this volume describes in detail how neural networks and evolutionary techniques (specifically genetic algorithms and genetic programming) can be applied
   to the adaptive control of complex dynamic systems (including chaotic ones). A number of examples are presented and useful tips are given for the application of the
   techniques described. The fundamentals of dynamic systems theory and classical adaptive control are also given.
%8 August
%Z Chapter 7 deals with genetic algorithms, including 8 pages on genetic programming. These include solving the problem described in \citeDracopoulos:1997:es
%@ 3-540-76161-6

%A Dimitris C. Dracopoulos
%T Genetic Algorithms and Genetic Programming for Control
%B Evolutionary Algorithms in Engineering Applications
%E Dipankar Dasupta and Zbigniew Michalewicz
%D 1997
%P 329--343
%I Springer-Verlag
%C Berlin
%K genetic algorithms, genetic programming
%U http://www.springer.com/computer/swe/book/978-3-540-62021-1
%Z brief survey of GA and GP in control. Principly concentrates upon using GP to control a tumbling satellite
%@ 3-540-62021-4

%A Dimitris C. Dracopoulos
%T Autolanding of Commercial Aircrafts by Genetic Programming
%B Proceedings of the World Congress on Engineering, WCE 2007
%V I
%D 2007
%P 83--86
%I 
%C London
%K genetic algorithms, genetic programming, autolanding, aircraft, intelligent control, evolutionary control
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.6342
%X The genetic programming approach is applied to the problem of aircraft autolanding, subject to wind disturbances. The derived control law is tested successfully, using a
   linearised model of a commercial aircraft. The evolutionary control of autolanding is done within the desired operational envelope.
%8 July  2-4

%A Dimitris Dracopoulos
%A Riccardo Piccoli
%T Bioreactor Control by Genetic Programming
%B PPSN 2010 11th International Conference on Parallel Problem Solving From Nature
%S Lecture Notes in Computer Science
%E Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph
%V 6239
%D 2010
%P 181--188
%I Springer
%C Krakow, Poland
%K genetic algorithms, genetic programming, bioreactor control, nonlinear control
%X Genetic programming is applied to the problem of bioreactor control. This highly nonlinear problem has previously been suggested as one of the challenging benchmarks to
   explore new ideas for building automatic controllers. It is shown that the derived control law is successful in a number of test cases.
%8 11-15  September

%A Dimitris C. Dracopoulos
%A Dimitrios Effraimidis
%T Genetic Programming for Generalised Helicopter Hovering Control
%B Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012
%S LNCS
%E Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta
%V 7244
%D 2012
%P 25--36
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Helicopter hovering, Nonlinear control, Neuroevolutionary control, Reinforcement learning
%X We show how genetic programming can be applied to helicopter hovering control, a nonlinear high dimensional control problem which previously has been included in the
   literature in the set of benchmarks for the derivation of new intelligent controllers . The evolved controllers are compared with a neuroevolutionary approach which won the
   first position in the 2008 helicopter hovering reinforcement learning competition. GP performs similarly (and in some cases better) with the winner of the competition, even
   in the case where unknown wind is added to the dynamic system and control is based on structures evolved previously, i.e. the evolved controllers have good generalisation
   capability.
%8 11-13  April
%Z Part of \citeMoraglio:2012:GP EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012

%A Rold Drechsler
%A Bernd Becker
%A Nicole Gockel
%T A Genetic Algorithm for the Construction of Small and Highly Testable OKFDD Circuits
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 473--478
%I MIT Press
%C Stanford University, CA, USA
%K Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 GA paper

%A Nicole Drechsler
%A Frank Schmiedle
%A Daniel Grosse
%A Rolf Drechsler
%T Heuristic Learning based on Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 1--10
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Heuristic Learning, VLSI CAD, BDD, Binary Decision Diagrams
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=1
%X In this paper we present an approach to learning heuristics based on Genetic Programming (GP). Instead of directly solving the problem by application of GP, GP is used to
   develop a heuristic that is applied to the problem instance. By this, the typical large runtimes of evolutionary methods have to be invested only once in the learning
   phase. The resulting heuristic is very fast. The technique is applied to a field from the area of VLSI CAD, i.e. minimization of Binary Decision Diagrams (BDDs). We chose
   this topic due to its high practical relevance and since it matches the criteria where our algorithm works best, i.e. large problem instances where standard evolutionary
   techniques cannot be applied due to their large runtimes. Our experiments show that we obtain high quality results that outperform previous methods, while keeping the
   advantage of low runtimes.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Jean-Philippe Drecourt
%T Application Of Neural Networks And Genetic Programming To Rainfall Runoff modeling
%R D2K Technical Report D2K-0699-1
%D 1999
%I 
%I Danish Hydraulic Institute (Hydro-Informatics Technologies HIT)
%K genetic algorithms, genetic programming
%X The main problem in rainfall/runoff modeling is to obtain data about the catchment with sufficient accuracy. Since self-learning tools only need knowledge about rainfall
   and runoff, they can offer a good alternative to classical model. The present study focuses on Lindenborg, a Danish catchment situated in the northern part of Jutland,
   between Hobro and Alborg. It is characterized by high groundwater contribution and thus a very persistent flow regime. The tools used were artificial neural networks (ANN)
   and genetic programming (GP). The purpose was to compare the efficiency of these tools with a classic lumped model (NAM) and a naive prediction (i.e. the runoff does not
   change between one day and the next one). The study with GP was oriented in two directions: the prediction of the runoff, and the prediction of the variation in the runoff.
   In both cases GP was given the rainfall and runoff of the past days, and it was assumed that the rainfall was predicted without any error for the target day. Each strategy
   has its own advantages. Predicting the variation is considered to be closer to the relationships given by physics, whereas predicting the runoff takes in account the large
   auto-correlation of the runoff time series. Since it is difficult to predict the upper boundary of runoff, the ANN worked exclusively with the time variation. The variation
   in runoff is less likely to saturate the network than the runoff itself, especially in this catchment where the dynamics are relatively slow. Therefore, the sensitivity of
   the prediction is increased. Time lag recurrent network (TLRN) were used for this study as they allow to take in account smoothed version of the past time series, both in
   the input and the hidden layers. The comparison of the different models was based on the Pearson coefficient of correlation, which gives a good overview of the performance
   of the prediction.
%8 June
%Z See also \citedrecourt:1999uANNGPrrm

%A J-P. Drecourt
%T Using Artificial Neural Networks and Genetic Programming in rainfall/runoff modeling
%B 3rd DHI Software Conference \& DHI Software Courses
%D 1999
%I 
%I Danish Hydraulic Institute
%C Helsingor, Denmark
%K genetic algorithms, genetic programming
%U http://www.dhi.dk/softcon/abstract/102.doc
%X The main problem in rainfall/runoff modeling is to obtain data about the catchment with sufficient accuracy. Since self-learning tools only need knowledge about rainfall
   and runoff, they can offer a good alternative to classical model. The present study focuses on Lindenborg, a Danish catchment situated in the northern part of Jutland,
   between Hobro and Alborg. It is characterized by high groundwater contribution and thus a very persistent flow regime. The tools used were artificial neural networks (ANN)
   and genetic programming (GP). The purpose was to compare the efficiency of these tools with a classic lumped model (NAM) and a naive prediction (i.e. the runoff does not
   change between one day and the next one). The study with GP was oriented in two directions : the prediction of the runoff, and the prediction of the variation in the
   runoff. In both cases GP was given the rainfall and runoff of the past days, and it was assumed that the rainfall was predicted without any error for the target day. Each
   strategy has its own advantages. Predicting the variation is considered to be closer to the relationships given by physics, whereas predicting the runoff takes in account
   the large auto-correlation of the runoff time series. Since it is difficult to predict the upper boundary of runoff, the ANN worked exclusively with the time variation. The
   variation in runoff is less likely to saturate the network than the runoff itself, especially in this catchment where the dynamics are relatively slow. Therefore, the
   sensitivity of the prediction is increased. Time lag recurrent network (TLRN) were used for this study as they allow to take in account smoothed version of the past time
   series, both in the input and the hidden layers. The comparison of the different models was based on the Pearson coefficient of correlation, which gives a good overview of
   the performance of the prediction. This study was realized in relationship with the Department of Hydrodynamics and Water Resources of DTU as a special course for the
   Master of Science in Environmental Engineering.
%8 7-11  June
%Z http://www.dhi.dk/softcon/index.htm See also \citedrecourt:1999uANNGPrrmTR

%A Rolf Dreschler
%A Nicole Gockel
%A Elke Mackensen
%A Bernd Becker
%T BEA: Specialized Hardware for Implementation of Evolutionary Algorithms
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 491
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Evolvable Hardware
%8 13-16  July
%Z GP-97

%A Athanasios S. Drigas
%A Katerina Argyri
%A John Vrettaros
%T Decade review (1999-2009): progress of application of artificial intelligence tools in student diagnosis
%J International Journal of Social and Humanistic Computing
%V 1
%D 2009
%P 175--191
%I Inderscience Publishers
%K genetic algorithms, genetic programming, student modelling, student diagnosis, fuzzy logic, neural networks, student assessment, student evaluation, adaptive learning,
   artificial intelligence, soft computing, educational research, intelligent tutoring
%U http://www.inderscience.com/link.php?id=31006
%X Over the last decade, artificial intelligence has offered a wide range of tools that have proved to be of vital importance for educational research. Indeed, logic,
   classifiers and machine learning methods, probabilistic techniques for uncertain reasoning as well as search and optimisation algorithms are only several among the various
   approaches that artificial intelligence has offered in dealing with real life problems. This paper attempts to explore the research that has been conducted on the
   application of the most typical and popular soft computing techniques [fuzzy logic, neural networks, Bayesian networks, genetic programming and hybrid approaches such as
   neuro-fuzzy systems and genetic programming neural networks (GPNNs)] in student modelling over the decade 1999-2009. This latest research trend is a part of every
   intelligent tutoring system and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable
   assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used so as to point out their qualities and then we describe the most
   representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.
%Z See also http://dx.doi.org/doi:10.1007/978-3-642-04757-2_59 Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All Second World
   Summit on the Knowledge Society, WSKS 2009, Chania, Crete, Greece, September 16-18, 2009. Proceedings

%A Joseph A. Driscoll
%A Bill Worzel
%A Duncan MacLean
%T Classification of Gene Expression Data with Genetic Programming
%B Genetic Programming Theory and Practice
%E Rick L. Riolo and Bill Worzel
%D 2003
%P 25--42
%I Kluwer
%K genetic algorithms, genetic programming, classification, molecular diagnostics
%X This paper summarises the use of a genetic programming (GP) system to develop classification rules for gene expression data that hold promise for the development of new
   molecular diagnostics. This work focuses on discovering simple, accurate rules that diagnose diseases based on changes of gene expression profiles within a diseased cell.
   GP is shown to be a useful technique for discovering classification rules in a supervised learning mode where the biological genotype is paired with a biological phenotype
   such as a disease state. In the process of developing these rules it is necessary to develop new techniques for establishing fitness and interpreting the results of
   evolutionary runs because of the large number of independent variables and the comparatively small number of samples. These techniques are described and issues of
   overfitting caused by small sample sizes and the behaviour of the GP system when variables are missing from the samples are discussed.
%O 3
%Z Part of \citeRioloWorzel:2003

%A Stefan Droste
%A Dirk Wiesmann
%T Metric Based Evolutionary Algorithms
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 29--43
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=29
%X In this article a set of guidelines for the design of genetic operators and the representation of the phenotype space is proposed. These guidelines should help to
   systematize the design of problem-specific evolutionary algorithms. Hence, they should be particularly beneficial for the design of genetic programming systems. The
   applicability of this concept is shown by the systematic design of a genetic programming system for finding Boolean functions. This system is the first GP-system, that
   reportedly found the 12 parity function.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Stefan Droste
%T Efficient Genetic Programming for Finding Good Generalizing Boolean Functions
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 82--87
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/326196.html
%X This paper shows how genetic programming (GP) can help in finding generalizing Boolean functions when only a small part of the function values are given. The selection
   pressure favours functions having as few subfunctions as possible while only using essential variables, so the resulting functions should have good generalization
   properties. For efficiency no S-expressions are used for representation, but a special case of directed acyclic graphs known as ordered binary decision diagrams (OBDDs),
   making it possible to learn the 20-multiplexer.
%8 13-16  July
%Z GP-97

%A Stefan Droste
%T Genetic Programming with Guaranteed Quality
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 54--59
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/324287.html
%X When using genetic programming (GP) or other techniques that try to approximate unknown functions, the principle of Occam's razor is often applied: find the simplest
   function that explains the given data, as it is assumed to be the best approximation for the unknown function. Using a well-known result from learning theory, it is shown
   in this paper, how Occam's razor can help GP in finding functions, so that the number of functions that differ from the unknown function by more than a certain degree can
   be bounded theoretically. Experiments show how these bounds can be used to get guaranteed quality assurances for practical applications, even though they are much too
   conservative.
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Stefan Droste
%A Dirk Wiesmann
%T On Representation and Genetic Operators in Evolutionary Algorithms
%R Computational Intelligence CI-41/98
%D 1998
%I 
%I Collaborative Research Center 531, University of Dortmund
%C Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/323494.html
%X The application of evolutionary algorithms (EAs) requires as a basic design decision the choice of a suitable representation of the variable space and appropriate genetic
   operators. In practice mainly problemspecific representations with specific genetic operators and miscellaneous extensions can be observed. In this connection it attracts
   attention that hardly any formal requirements on the genetic operators are stated. In this article we first formalize the representation problem and then propose a package
   of requirements to guide the design of genetic operators. By the definition of distance measures on the geno- and phenotype space it is possible to integrate
   problem-specific knowledge into the genetic operators. As an example we show how this package of requirements can be used to design a genetic programming (GP) system for
   finding Boolean functions.
%O The Pennsylvania State University CiteSeer Archives
%8 July

%A Stefan Droste
%A Thomas Jansen
%A Ingo Wegener
%T Perhaps Not a Free Lunch But At Least a Free Appetizer
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 833--839
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%U http://arc.cs.odu.edu:8080/dp9/getrecord/oai_dc/3050294235/oai:eldorado:0x00000307
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Stefan Droste
%A Dominic Heutelbeck
%A Ingo Wegener
%T Distributed Hybrid Genetic programming for learning Boolean Functions
%N CI-90/00
%D 2000
%I 
%I Department of Computer Science/XI, University of Dortment
%C 44221 Dortmund, Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/411824.html
%X When genetic programming (GP) is used to find programs with Boolean inputs and outputs, ordered binary decision diagrams (OBDDs) are often used successfully. In all known
   OBDD-based GP-systems the variable ordering, a crucial factor for the size of OBDDs, is preset to an optimal ordering of the known test function. Certainly this cannot be
   done in practical applications, where the function to learn and hence its optimal variable ordering are unknown. Here, the first GP-system is presented that evolves the
   variable ordering of the OBDDs and the OBDDs itself by using a distributed hybrid approach. For the experiments presented the unavoidable size increase compared to the
   optimal variable ordering is quite small. Hence, this approach is a big step towards learning well-generalizing Boolean functions.
%8 August

%A Stefan Droste
%A Dominic Heutelbeck
%A Ingo Wegener
%T Distributed Hybrid Genetic Programming for Learning Boolean Functions
%B Parallel Problem Solving from Nature - PPSN VI 6th International Conference
%S LNCS
%E Marc Schoenauer and Kalyanmoy Deb and G\"unter Rudolph and Xin Yao and Evelyne Lutton and Juan Julian Merelo and Hans-Paul Schwefel
%V 1917
%D 2000
%P 181--190
%I Springer Verlag
%C Paris, France
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/322232.html
%X When genetic programming (GP) is used to find programs with Boolean inputs and outputs, ordered binary decision diagrams (OBDDs) are often used successfully. In all known
   OBDD-based GP-systems the variable ordering, a crucial factor for the size of OBDDs, is preset to an optimal ordering of the known test function. Certainly this cannot be
   done in practical applications, where the function to learn and hence its optimal variable ordering are unknown. Here, the first GP-system is presented that evolves the
   variable ordering of the OBDDs and the OBDDs itself by using a distributed hybrid approach. For the experiments presented the unavoidable size increase compared to the
   optimal variable ordering is quite small. Hence, this approach is a big step towards learning well-generalizing Boolean functions
%8 16-20  September

%A Stefan Droste
%A Thomas Jansen
%A G{\"u}nter Rudolph
%A Hans-Paul Schwefel
%A Karsten Tinnefeld
%A Ingo Wegener
%T Theory of Evolutionary Algorithms and Genetic Programming
%B Advances in Computational Intelligence: Theory and Practice
%S Natural Computing Series
%E Hans-Paul Schwefel and Ingo Wegener and Klaus Weinert
%D 2003
%P 107--144
%I Springer
%K genetic algorithms, genetic programming, NFL, Evolutionary Algorithms, Multiobjective Evolutionary Algorithms, Crossover, Takeover Times
%O 5
%Z Dynamization and Adaptation. Black-box Optimisation. Metric-Based EA (MBEA) and an Application in GP
%@ 3-540-43269-8

%A I. Drstvensek
%A I. Pahole
%A J. Balic
%T A model of data flow in lower CIM levels
%J Journal of Materials Processing Technology
%V 157-158
%D 2004
%P 123--130
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6TGJ-4DTM097-5/2/79f4a5e8d987732d6aaad71154b9cf18
%X After years of work in fields of computer-integrated manufacturing (CIM), flexible manufacturing systems (FMS), and evolutionary optimisation techniques, several models of
   production automation were developed in our laboratories. The last model pools the discoveries that proved their effectiveness in the past models. It is based on the idea
   of five levels CIM hierarchy where the technological database (TDB) represents a backbone of the system. Further on the idea of work operation determination by an analyse
   of the production system is taken out of a model for FMS control system, and finally the approach to the optimisation of production is supported by the results of
   evolutionary based techniques such as genetic algorithms and genetic programming.
%8 20  Decemeber

%A Igor Drstvensek
%A Tomaz Brajlih
%A Miha Kovacic
%A Joze Balic
%T Assurance of Accuracy at Polymerisation of Photopolymers
%B 9th International Research/Expert Conference Trends in the Development Machinery and Associated Technology
%E Sabahudin Ekinovic
%D 2005
%P 677--680
%I 
%I UNIVERSITY OF ZENICA (Bosnia and Herzegovina) FACULTY OF MECHANICAL ENGINEERING ZENICA UNIVERSITAT POLITECNICA DE CATALUNYA (Spain) E.T.S.E.I.B. DEPARTAMENT D'ENGINYERIA
   MECANICA BAHCESEHIR UNIVERSITESI ISTANBUL (Turkey) MUHENDISLIK FAKULTESI
%C Antalya, Turkey
%K genetic algorithms, genetic programming
%8 26-30  September
%Z TMT05-107 http://www.mf.unze.ba/tmt2005/submitted3.html
%@ 9958-617-28-5

%A Jan Drugowitsch
%A Alwyn M. Barry
%T Mixing independent classifiers
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1596--1603
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, information fusion, learning classifier system (LCS), XCS
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1596.pdf
%X In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it
   from the perspective of Learning Classifier Systems where a set of classifiers provide the local models. Firstly, we formalise the mixing problem and provide both
   analytical and heuristic approaches to solving it. The analytical approaches are shown to not scale well with the number of local models, but are nevertheless compared to
   heuristic models in a set of function approximation tasks. These experiments show that we can design heuristics that exceed the performance of the current state-of-the-art
   Learning Classifier System XCS, and are competitive when compared to analytical solutions. Additionally, we provide an upper bound on the prediction errors for the
   heuristic mixing approaches.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A A. Drunpob
%A N. B. Chang
%A M. Beaman
%T Stream Flowrate Prediction Using Genetic Programming Model in a Semi-Arid Coastal Watershed
%B World Water and Environmental Resources Congress 2005
%E Raymond Walton
%D 2005
%I 
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming
%X Effective water resources management is a critically important priority across the globe. The availability of adequate fresh water is a fundamental requirement for the
   sustainability of human and terrestrial landscapes, and the importance of understanding and improving predictive capacity regarding all aspects of the global and regional
   water cycle is certain to continue to increase. One fundamental component of the water cycle is stream discharge. Stream flowrate prediction is not only related to regular
   water supply for human, animal, and plant populations, but also relevant for the management of natural hazards, such as drought and flood, that occur abruptly resulting in
   economic loss. Efforts to improve existing methods and develop new methods of stream flow prediction would support the optimal management of water resources at all scales
   in space and time. Recent advances in genetic programming technologies have shown potential to improve the prediction accuracy of stream flow rate in some river systems by
   better capturing the non-linearity of the features embedded in a system. This study elicits microclimatological factors in association with the basin-wide geological
   environment, exhibits the derivation of a representative genetic programming model, summarises the non-linear behaviour between the rainfall/run-off patterns, and conducts
   stream flow rate prediction in a river system given the influence of dynamic basin features such as soil moisture, soil texture, vegetative cover, air temperature, and
   precipitation rate. Three weather stations are deployed as a supplementary data-gathering network in addition to over 10 existing gage stations in the semi-arid Nueces
   River Basin, South Texas. An integrated database of physical basin features is developed and used to support a semi-structure genetic programming modelling approach to
   perform stream flowrate predictions. The genetic programming model is eventually proved useful in forecasting stream flowrate in the study area where water resources scarce
   issues are deemed critical.
%8 May  15-19
%Z c2005 ASCE

%A Xin Du
%A Lixin Ding
%A Chen Wang Xie
%A Xing Xu
%A Shenwen Wang
%A Li Chen2
%T Convergence analysis of gene expression programming based on maintaining elitist
%B GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
%E Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding
%D 2009
%P 823--826
%I ACM New York, NY, USA
%I SigEvo
%C Shanghai, China
%K genetic algorithms, genetic programming, Poster, Gene Expression Programming
%X This paper analyzes the convergence of Gene Expression Programming based on maintaining elitist(ME-GEP).It is proved that ME-GEP algorithm will converge to the global
   optimal solution. The convergence speed of ME-GEP algorithm is estimated by the properties of transition matrices. The result hinges on four factors: population size,
   minimal transposition, mutation and selection probabilities.
%8 June  12-14
%Z Also known as \citeDBLP:conf/gecco/DuDXXWC09 part of \citeDBLP:conf/gec/2009

%A Lei Duan
%A Changjie Tang
%A Tianqing Zhang
%A Dagang Wei
%A Huan Zhang
%T Distance Guided Classification with Gene Expression Programming
%B Advanced Data Mining and Applications, Proceedings of the Second International Conference, ADMA
%S Lecture Notes in Computer Science
%E Xue Li and Osmar R. Za\"iane and Zhanhuai Li
%V 4093
%D 2006
%P 239--246
%I Springer
%C Xi'an, China
%K genetic algorithms, genetic programming, Gene Expression Programming
%X Gene Expression Programming (GEP) aims at discovering essential rules hidden in observed data and expressing them mathematically. GEP has been proved to be a powerful tool
   for constructing efficient classifiers. Traditional GEP-classifiers ignore the distribution of samples, and hence decrease the efficiency and accuracy. The contributions of
   this paper include: (1) proposing two strategies of generating classification threshold dynamically, (2) designing a new approach called Distance Guided Evolution Algorithm
   (DGEA) to improve the efficiency of GEP, and (3) demonstrating the effectiveness of generating classification threshold dynamically and DGEA by extensive experiments. The
   results show that the new methods decrease the number of evolutional generations by 83percent to 90percent, and increase the accuracy by 20percent compared with the
   traditional approach.
%8 August  14-16
%@ 3-540-37025-0

%A Lei Duan
%A Changjie Tang
%A Liang Tang
%A Jie Zuo
%A Tianqing Zhang
%T An Effective Microarray Data Classifier Based on Gene Expression Programming
%B Fifth International Conference on Natural Computation, 2009. ICNC '09
%E Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun
%D 2009
%P 523--527
%I IEEE Computer Society
%C Tianjian, China
%K genetic algorithms, genetic programming, gene expression programming
%8 14-16  August

%A Lei Duan
%A Changjie Tang
%A Liang Tang
%A Tianqing Zhang
%A Jie Zuo
%T Mining Class Contrast Functions by Gene Expression Programming
%B Proceedings 5th International Conference Advanced Data Mining and Applications ADMA 2009
%S Lecture Notes in Computer Science
%E Ronghuai Huang and Qiang Yang and Jian Pei and Jo\~ao Gama and Xiaofeng Meng and Xue Li
%V 5678
%D 2009
%P 116--127
%I Springer
%C Beijing, China
%K genetic algorithms, genetic programming, gene expression programming
%8 August  17-19

%A Minglei Duan
%A Richard J. Povinelli
%T Estimating Stock Price Predictability Using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 174
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster, time series, data mining, prediction
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Minglei Duan
%A Richard Povinelli
%T Nonlinear Modeling: Genetic Programming vs. Fast Evolutionary Programming
%B Intelligent Engineering Systems Through Artificial Neural Networks (ANNIE 2001)
%E Cihan H. Dagli
%D 2001
%P 171--176
%I 
%I University of Missouri-Rolla, Smart Engineering Systems Laboratory Department of Engineering Management In Cooperation with IEEE Neural Networks Council
%C St. Louis, Missouri, USA
%K genetic algorithms, genetic programming
%U http://povinelli.eece.mu.edu/publications/papers/annie2001a.pdf
%8 4-7  November
%Z cf http://web.umr.edu/~annie/annie01/ ANNIE01 session TP3.3C Marquette University, Milwaukee, WI, USA GPsys \citequreshi:thesis (java) Sunspot; Mackey-glass; Compaq (NYSE)
   and microsoft (NASDAQ) stock prices. GP >= EP

%A Minglei Duan
%A Richard Povinelli
%T Estimating Time Series Predictability Using Genetic Programming
%B Intelligent Engineering Systems Through Artificial Neural Networks (ANNIE 2001)
%E Cihan H. Dagli
%D 2001
%P 215--220
%I 
%I University of Missouri-Rolla, Smart Engineering Systems Laboratory Department of Engineering Management In Cooperation with IEEE Neural Networks Council
%C St. Louis, Missouri, USA
%K genetic algorithms, genetic programming
%U http://povinelli.eece.mu.edu/publications/papers/annie2001b.pdf
%8 4-7  November
%Z cf http://web.umr.edu/~annie/annie01/ ANNIE01 session WP1.3C Marquette University, Milwaukee, WI, USA GPsys \citequreshi:thesis (java) Compaq (NYSE) and general eletric GE
   1999 stock prices

%A Renata Dubcakova
%T Eureqa: software review
%J Genetic Programming and Evolvable Machines
%V 12
%N 2
%D 2011
%P 173--178
%I 
%K genetic algorithms, genetic programming
%8 June

%A Marc Dubreuil
%A Christian Gagne
%A Marc Parizeau
%T Analysis of a Master-Slave Architecture for Distributed Evolutionary Computations
%J IEEE Transactions on Systems, Man, and Cybernetics: Part B - Cybernetics
%V 36
%N 1
%D 2006
%P 229--235
%I 
%K genetic algorithms, genetic programming, Master-Slave Architecture, Evolutionary Computations, Distributed BEAGLE, C++ language, client-server systems, evolutionary
   computation, workstation clusters, C++ framework, distributed evolutionary computation, local area workstation networks
%U http://vision.gel.ulaval.ca/~parizeau/Publications/SMC06.pdf
%X a new mathematical model of the master-slave architecture for distributed evolutionary computations (EC). This model is validated using a concrete implementation based on
   the Distributed BEAGLE C++ framework. Results show that contrary to (current) popular belief, master-slave architectures are able to scale well over local area networks of
   workstations using off-the-shelf networking equipment. The main properties of the master-slave are also compared with those of the more mainstream island-model.
%8 February

%A David J. Duerrenmatt
%A Willi Gujer
%T Automatic reactor model synthesis with genetic programming
%J Water Science \& Technology
%V 65
%N 4
%D 2012
%P 765--772
%I 
%K genetic algorithms, genetic programming, grammar-based genetic programming, hydraulic reactor systems, modelling, operating data
%U http://www.iwaponline.com/wst/06504/0765/065040765.pdf
%X Successful modelling of waste water treatment plant (WWTP) processes requires an accurate description of the plant hydraulics. Common methods such as tracer experiments are
   difficult and costly and thus have limited applicability in practice; engineers are often forced to rely on their experience only. An implementation of grammar-based
   genetic programming with an encoding to represent hydraulic reactor models as program trees should fill this gap: The encoding enables the algorithm to construct arbitrary
   reactor models compatible with common software used for WWTP modeling by linking building blocks, such as continuous stirred-tank reactors. Discharge measurements and
   influent and effluent concentrations are the only required inputs. As shown in a synthetic example, the technique can be used to identify a set of reactor models that
   perform equally well. Instead of being guided by experience, the most suitable model can now be chosen by the engineer from the set. In a second example, temperature
   measurements at the influent and effluent of a primary clarifier are used to generate a reactor model. A virtual tracer experiment performed on the reactor model has good
   agreement with a tracer experiment performed on-site.
%Z Sewage treatment plant

%A John Duffy
%A Jim Engle-Warnick
%T Using Symbolic Regression to Infer Strategies from Experimental Data
%B Fifth International Conference: Computing in Economics and Finance
%E David A. Belsley and Christopher F. Baum
%D 1999
%P 150
%I 
%C Boston College, MA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/304022.html
%X We propose the use of a new technique -- symbolic regression -- as a method for inferring the strategies that are being played by subjects in economic decision making
   experiments. We begin by describing symbolic regression and our implementation of this technique using genetic programming. We provide a brief overview of how our algorithm
   works and how it can be used to uncover simple data generating functions that have the flavor of strategic rules. We then apply symbolic regression using genetic
   programming to experimental data from the ultimatum game. We discuss and analyze the strategies that we uncover using symbolic regression and we conclude by arguing that
   symbolic regression techniques should at least complement standard regression analyses of experimental data.
%O Book of Abstracts
%8 24-26  June
%Z CEF'99 See also \citeduffy:1999:srised http://fmwww.bc.edu/cef99/sess/chen.cfp.html

%A John Duffy
%A Jim Engle-Warnick
%T Using Symbolic Regression to Infer Strategies from Experimental Data
%B Evolutionary Computation in Economics and Finance
%S Studies in Fuzziness and Soft Computing
%E Shu-Heng Chen
%V 100
%D 2002
%P 61--84
%I Physica Verlag
%K genetic algorithms, genetic programming
%U http://www.pitt.edu/~jduffy/docs/Usr.ps
%X We propose the use of a new technique -- symbolic regression -- as a method for inferring the strategies that are being played by subjects in economic decision making
   experiments. We begin by describing symbolic regression and our implementation of this technique using genetic programming. We provide a brief overview of how our algorithm
   works and how it can be used to uncover simple data generating functions that have the flavor of strategic rules. We then apply symbolic regression using genetic
   programming to experimental data from the ultimatum game. We discuss and analyze the strategies that we uncover using symbolic regression and we conclude by arguing that
   symbolic regression techniques should at least complement standard regression analyses of experimental data.
%O 4
%8 2002
%Z Presented at CEF'99 (see \citeduffy:1999:CEF) http://fmwww.bc.edu/cef99/sess/chen.cfp.html
   http://btobsearch.barnesandnoble.com/booksearch/isbnInquiry.asp?sourceid=00395996645644787198&btob=Y&endeca=1&isbn=3790814768&itm=2
%@ 3-7908-1476-8

%A Grzegorz Dulewicz
%A Olgierd Unold
%T Evolving Natural Language Parser with Genetic Programming
%B 2001 International Workshop on Hybrid Intelligent Systems
%S LNCS
%E Ajith Abraham and Mario Koppen
%D 2001
%P 361--378
%I Springer-Verlag Berlin
%C Adelaide, Australia
%K genetic algorithms, genetic programming, natural language processing, edge encoding
%U http://www.amazon.com/Hybrid-Information-Systems-Ajith-Abraham/dp/3790814806/ref=sr_1_8?s=books&ie=UTF8&qid=1326475568&sr=1-8
%X 1 Introduction When we try to deal with natural language processing (NLP) we have to start with a grammar of a natural language. But the grammars described in linguistic
   literature have an informal form and many exceptions. Thus, they are not useful to create final formal models of grammars, which make machine processing of sentences
   possible. These grammars can be a starting point for the attempts to create basic models of natural language grammar at the most. However, it requires expert knowledge.
   Machine learning based on a set of sample sentences can be the better way to find the grammar rules. This kind of learning allows to avoid the preparation of knowledge
   about the language for the NLP system. The examples of correct and incorrect sentences allow the NLP systems with the self-evolutionary parser to try to find the right
   grammar. This self-evolutionary parser can be improved on basis of new examples. Thus, the knowledge acquired in this way is flexible and easyly modifiable.
%8 11-12  Decemeber
%Z HIS01
%@ 3-7908-1480-6

%A G. Dumont
%A Frederic Chapelle
%A O. Chocron
%A Philippe Bidaud
%T Prototypage virtuel d'un micro-endoscope
%B Journee thematique PRIMECA
%D 2000
%I 
%C Valenciennes, France
%K genetic algorithms
%O in french
%8 March

%A G. Dumont
%A Frederic Chapelle
%T Simulation multi-physique pour la conception en micro-robotique
%B Journees du Pole Micro-robotique
%D 2000
%I 
%C Cachan, France
%K genetic algorithms
%O in french
%8 June

%A Georges Dumont
%A Frederic Chapelle
%A Philippe Bidaud
%T Toward virtual prototyping of active endoscopes
%B International Symposium on Robotics (ISR'01)
%D 2001
%P 821--826
%I 
%I International Federation of Robotics
%C Seoul, Korea
%K genetic algorithms
%8 19-20  April
%Z http://isr2001.kist.re.kr/Teams/isr2001/sessionprogram.htm

%A Bertrand Daniel Dunay
%A Frederick E. Petry
%A Bill P Buckles
%T Regular language induction with genetic programming
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 396--400
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming S-expressions, computational difficulties, deterministic finite automata, editing, formal language accepters, inductive inference,
   informant, population pressure, reachable states, regular language induction, renumbering, run-time determined solution size, sample strings, transition tables,
   translation, deterministic automata, finite automata, formal languages, inference mechanisms
%X In this research, inductive inference is done with an informant on the class of regular languages. The approach is to evolve formal language accepters which are consistent
   with a set of sample strings from the language, and a set of sample strings known not to be in the language. Deterministic finite automata (DFA) were chosen as the formal
   language accepters to alleviate the computational difficulties of nondeterministic constructs such as rewrite grammars. Genetic programming (GP) offers two significant
   improvements for regular language induction over genetic algorithms. First, GP allows the size of the solution (the DFA) to be determined at run time in response to
   population pressure. Second, GP's potential for assuring correct dependencies in complex individuals can be exploited to assure that all states in a DFA are reachable from
   the start state. The contribution of this research is the effective translation of DFAs to S-expressions, the application of renumbering, and of editing to the problem of
   language induction. DFAs or transition tables form the basis of many problems. By using the techniques found in this paper, many of these problems can be directly
   translated into the domain of genetic programming
%8 27-29  June
%Z Considers two classes of regular language (NB series and Tomita) which can be recognised or accpeted by deterministic finite automata (Finite state machines). Can translate
   from DFA to tree structure. Trees are not executable programs but represent languages. crossover on trees defined. GP able to define a language given examples of it. Works
   on simplier examples but has difficulties with 8b, 9b, 10b and TL5.

%A Bertrand Daniel Dunay
%A Frederic E. Petry
%T Solving Complex Problems with Genetic Algorithms
%B Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)
%E Larry J. Eshelman
%D 1995
%P 264--270
%I Morgan Kaufmann San Francisco, CA, USA
%C Pittsburgh, PA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/dunay_1995_scpga.pdf
%X Using GA to evolve Turing machines which recognise languages from the Chomsky heirarchy. Example for regular languages (awb), context free languages (a**nb**n) and context
   sensitive languages (a**nb**na**n).
%8 15-19  July
%@ 1-55860-370-0

%A Enrique Dunn
%A Gustavo Olague
%A Evelyne Lutton
%T Parisian camera placement for vision metrology
%J Pattern Recognition Letters
%V 27
%N 11
%D 2006
%P 1209--1219
%I 
%K genetic algorithms, genetic programming, Camera placement, Accurate 3D reconstruction, Photogrammetric network design, Evolutionary computation, Parisian approach
%U http://www.sciencedirect.com/science/article/B6V15-4HX477K-2/2/e82b5b25f9a7a82607ac4b30c9fb9c45
%X This paper presents a novel camera network design methodology based on the Parisian evolutionary computation approach. This methodology proposes to partition the original
   problem into a set of homogeneous elements, whose individual contribution to the problem solution can be evaluated separately. A population comprised of these homogeneous
   elements is evolved with the goal of creating a single solution by a process of aggregation. The goal of the Parisian evolutionary process is to locally build better
   individuals that jointly form better global solutions. The implementation of the proposed approach requires addressing aspects such as problem decomposition and
   representation, local and global fitness integration, as well as diversity preservation mechanisms. The benefit of applying the Parisian approach to our camera placement
   problem is a substantial reduction in computational effort expended in the evolutionary optimization process. Moreover, experimental results coincide with previous state of
   the art photogrammetric network design methodologies, while incurring in only a fraction of the computational cost.
%O Evolutionary Computer Vision and Image Understanding

%A Ted E. Dunning
%A Mark W. Davis
%T Evolutionary Algorithms for Natural Language Processing
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 16--23
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming, NLP
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-201031-7

%A R. Dupas
%A G. Cavory
%A G. Goncalves
%T Real-World Applications. Optimising the throughput of a manufacturing production line using a genetic algortihm
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1775
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-717.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jean-Francois Dupuis
%A Marc Parizeau
%T Evolving a Vision-Based Line-Following Robot Controller
%B The 3rd Canadian Conference on Computer and Robot Vision (CRV'06)
%D 2006
%P 75
%I IEEE Computer Society
%K genetic algorithms, genetic programming
%U http://vision.gel.ulaval.ca/~jfdupuis/pubs/jfdupuisCRV2006.pdf
%X framework for evolving a vision-based mobile robot controller using genetic programming. This framework is built on the Open BEAGLE framework for the evolutionary
   computations, and on OpenGL for simulating the visual environment of a physical mobile robot. The feasibility of this framework is demonstrated through a simple, yet
   non-trivial, line following problem.
%@ 0-7695-2542-3

%A Jean-Francois Dupuis
%T Automated Design of Hybrid Systems Using Evolutionary Computation
%R Ph.D. Thesis
%D 2011
%I 
%I Department of Management Engineering, Engineering Design and Product Development (K\&P), Technical University of Denmark
%C Lyngby, Denmark
%K genetic algorithms, genetic programming, bond graphs
%U http://www.jfdupuis.info/files/Dupuis2011ab.pdf
%X The study of hybrid systems is becoming increasingly popular. They have enjoyed a particular growth in interest since the 1990s. Most of the focus on the subject has been
   oriented toward the design of controllers and on the development of a complete control theory. However, this work looks at hybrid systems from a synthesis point of view.
   More precisely, it aims at developing an automated design synthesis method to the design of hybrid mechatronic systems. In order to achieve that, hybrid bond graphs are
   used to model the physical systems, and evolutionary computation is used to explore the search space. The study of hybrid systems is becoming increasingly popular. They
   have enjoyed a particular growth in interest since the 1990s. Most of the focus on the subject has been oriented toward the design of controllers and on the development of
   a complete control theory. However, this work looks at hybrid systems from a synthesis point of view. More precisely, it aims at developing an automated design synthesis
   method to the design of hybrid mechatronic systems. In order to achieve that, hybrid bond graphs are used to model the physical systems, and evolutionary computation is
   used to explore the search space.
%8 April
%Z Three-tank system, DC-DC converter

%A Jean-Francois Dupuis
%A Zhun Fan
%A Erik D. Goodman
%T Evolutionary Design of Both Topologies and Parameters of a Hybrid Dynamical System
%J IEEE Transactions on Evolutionary Computation
%I 
%K genetic algorithms, genetic programming, Embryo, Encoding, Junctions, Mechatronics, Switches, Automated design, bond graphs, evolutionary design, hybrid mechatronic systems
%X This paper investigates the issue of evolutionary design of open-ended plants for hybrid dynamical systems, i.e., both their topologies and parameters. Hybrid bond graphs
   (HBGs) are used to represent dynamical systems involving both continuous and discrete system dynamics. Genetic programming, with some special mechanisms incorporated, is
   used as a search tool to explore the open-ended design space of hybrid bond graphs. Combination of these two tools, i.e., HBGs and genetic programming, leads to an approach
   called HBGGP that can automatically generate viable design candidates of hybrid dynamical systems that fulfill predefined design specifications. A comprehensive
   investigation of a case study of DC-DC converter design demonstrates the feasibility and effectiveness of the HBGGP approach. Important characteristics of the approach are
   also discussed, with some future research directions pointed out.
%O Accepted for future publication
%Z also known as \cite6045329

%A Nicolas Durand
%A Jean-Marc Alliot
%T Genetic crossover operator for partially separable functions
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 487--494
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Greg Durrett
%A Frank Neumann
%A Una-May O'Reilly
%T Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics
%D 2010
%I 
%K genetic algorithms, genetic programming, Computational Complexity, Data Structures and Algorithms
%U http://arxiv.org/pdf/1007.4636v1
%X Analysing the computational complexity of evolutionary algorithms for binary search spaces has significantly increased their theoretical understanding. With this paper, we
   start the computational complexity analysis of genetic programming. We set up several simplified genetic programming algorithms and analyze them on two separable model
   problems, ORDER and MAJORITY, each of which captures an important facet of typical genetic programming problems. Both analyses give first rigorous insights on aspects of
   genetic programming design, highlighting in particular the impact of accepting or rejecting neutral moves and the importance of a local mutation operator.
%O arXiv:1007.4636v1
%8 27  July
%Z See \citeDurrett:2011:foga

%A Greg Durrett
%A Frank Neumann
%A Una-May O'Reilly
%T Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics
%B Foundations of Genetic Algorithms
%E Hans-Georg Beyer and W. B. Langdon
%D 2011
%P 69--80
%I ACM
%I SigEvo
%C Schwarzenberg, Austria
%K genetic algorithms, genetic programming, Genetic Programming Theory, Computational Complexity, Hill Climbing
%X Analysing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this
   paper, we start the computational complexity analysis of genetic programming (GP). We set up several simplified GP algorithms and analyse them on two separable model
   problems, ORDER and MAJORITY, each of which captures a relevant facet of typical GP problems. Both analyses give first rigorous insights into aspects of GP design,
   highlighting in particular the impact of accepting or rejecting neutral moves and the importance of a local mutation operator.
%8 5-9  January
%Z See \citeDurrett:2010:ccaGP2pmips ACM order number 910114

%A Korneel Duyvesteyn
%A Uzay Kaymak
%T Genetic Programming in Economic Modelling
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 2
%D 2005
%P 1025--1031
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X Typically, economists develop models by first selecting a model structure based on theoretical considerations and equilibrium conditions, followed by parameter estimation
   from available data. As more and more data become available about economic processes, the question arises whether it is possible to obtain models in which "data speak for
   themselves", where both the model structure and the parameter values are identified directly from the data. In this paper, we discuss how genetic programming might be used
   for this purpose. We propose a framework to formulate a genetic programming search for suitable economic models. We also study a simple case and discuss future directions
   of research for developing the genetic programming methodology for economic modelling.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Garett Dworman
%A Steven Kimbrough
%A James Laing
%T An Application of Genetic Programming to Bargaining in a Three-Agent Coalition Game
%R Technical Report 95-01-04
%D 1995
%I 
%I Department of Operations and Information Management, The Wharton School, University of Pennsylvania
%C Philadelphia PA 19104-6366, USA
%K genetic algorithms, genetic programming
%U http://opim.wharton.upenn.edu/risk/downloads/archive/arch62.pdf
%Z file cogs9506 30 Jan 1995 See also \citedworman:1995:b3acg

%A Garett Dworman
%A Steven O. Kimbrough
%A James D. Laing
%T Bargaining in a Three-Agent Coalition Game: An Application of Genetic Programming
%B Working Notes for the AAAI Symposium on Genetic Programming
%E E. V. Siegel and J. R. Koza
%D 1995
%P 9--16
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025, USA
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Library/Symposia/Fall/fs95-01.php
%X We are conducting a series of investigations whose primary objective is to demonstrate that boundedly rational agents, operating with fairly elementary computational
   mechanisms, can adapt to achieve approximately optimal strategies for bargaining with other agents in complex and dynamic environments of multilateral negotiations that
   humans find challenging. In this paper, we present results from an application of genetic programming (Koza, 1992) to model the co-evolution of simple artificial agents
   negotiating coalition agreements in a three agent cooperative game.
%8 10--12  November
%Z AAAI-95f GP. Part of \citesiegel:1995:aaai-fgp. See also \citeDworman:95-01-04. \em Telephone: 415-328-3123 \em Fax: 415-321-4457 \em email info@aaai.org \em URL:
   http://www.aaai.org/

%A Garett Dworman
%A Steve O. Kimbrough
%A James D. Laing
%T Implementation of a Genetic Programming System in a Game-Theoretic Context
%R working paper 95-01-02
%D 1995
%I 
%I University of Pennsylvania, Department of Operations and Information Management
%K genetic algorithms, genetic programming
%U http://opim.wharton.upenn.edu/home/wp/

%A Garett Dworman
%A Steve O. Kimbrough
%A James D. Laing
%T On Automated Discovery of Models Using Genetic Programming in Game-Theoretic Contexts
%B Proceedings of the 28th Hawaii International Conference on System Sciences, Volume III: Information Systems: Decision Support and Knowledge-based Systems
%E Jay F. Nunamaker Jr. and Ralph H. Sprague Jr.
%D 1995
%P 428--438
%I IEEE Computer Society Press Los Alamitos, CA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/dworman95automated.html
%X The creation of mathematical, as well as qualitative (or rule-based), models is difficult, time-consuming, and expensive. Recent developments in evolutionary computation
   hold out the prospect that, for many problems of practical import, machine learning techniques can be used to discover useful models automatically. These prospects are
   particularly bright, we believe, for such automated discoveries in the context of game theory. This paper reports on a series of successful experiments in which we used a
   genetic programming regime to discover high-quality negotiation policies. The game-theoretic context in which we conducted these experiments-a three-player coalitions game
   with sidepayments-is considerably more complex and subtle than any reported in the literature on machine learning applied to game theory.
%8 January

%A Garett Dworman
%A Steven O. Kimbrough
%A James D. Laing
%T Bargaining by Artificial Agents in Two Coalition Games: A Study in Genetic Programming for Electronic Commerce
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 54--62
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A J. M. Dwyer
%A I. R. Mackay
%T ANTIGEN-BINDING LYMPHOCYTES IN HUMAN FETAL THYMUS
%J The Lancet
%V 295
%N 7658
%D 1970
%P 1199--1202
%I 
%U http://www.sciencedirect.com/science/article/B6T1B-498RPPJ-1MK/2/6cc03de5ebb144b1c653e0ffdc1720e8
%Z Not on GP

%A Saso Dzeroski
%A Ljupeo Todorovski
%A Igor Petrovski
%T Dynamical System Identification with Machine Learning
%B Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications
%E Justinian P. Rosca
%D 1995
%P 50--63
%I 
%C Tahoe City, California, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/dzeroski_1995_dsiml.pdf
%X LAGRANGE algorithm described, brusselator, volterra-lotka model of population dynamics, monod equations, pole balancing, system identification.
%8 9  July
%Z part of \citerosca:1995:ml

%A E. William East
%T Infrastructure Work Order Planning Using Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1510--1516
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-728.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) Seeding initial
   population Order-based chromosome
%@ 1-55860-611-4

%A Timothy M. D. Ebbels
%A Rachel Cavill
%T Bioinformatic methods in NMR-based metabolic profiling
%J Progress in Nuclear Magnetic Resonance Spectroscopy
%V 55
%N 4
%D 2009
%P 361--374
%I 
%K genetic algorithms, genetic programming, Metabonomics, Metabolomics, Metabolic profiling, Bioinformatics, Statistical methods, Modelling, Machine learning, Pattern
   recognition
%U http://www.sciencedirect.com/science/article/B6THC-4WTRS75-2/2/f678fb62de29b228e8d54c803da32b57

%A Roland H. Ebel
%A William Wagoner
%A Henry F. Hrubecky
%T Get ready for the L-bomb: A preliminary social assessment of longevity technology
%J Technological Forecasting and Social Change
%V 13
%N 2
%D 1979
%P 131--148
%I 
%U http://www.sciencedirect.com/science/article/B6V71-45P0D4G-2X/2/89b1c10893ac90101be199e8be7a7a53
%Z not on GP

%A Eugene Eberbach
%T Enhancing Genetic Programming by \$-calculus
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 88
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Eberbach_1997_eGPdc.pdf
%8 13-16  July
%Z GP-97

%A Eugene Eberbach
%T Expressing Evolutionary Computation, Genetic Programming, Artificial Life, Autonomous Agents and DNA-Based Computing in l-Calculus
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB see updated version \citeeberbach:2000:eecgpalaadc

%A Eugene Eberbach
%T Expressing Evolutionary Computation, Genetic Programming, Artificial Life, Autonomous Agents, and DNA-Based Computing in \$-Calculus
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 1361--1368
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, new paradigms
%U http://citeseer.ist.psu.edu/491674.html
%X Genetic programming, autonomous agents, artificial life and evolutionary computation share many common ideas. They generally investigate distributed complex processes,
   perhaps with the ability to interact. It seems to be natural to study their behavior using process algebras, which were designed to handle distributed interactive systems.
   \$-calculus is a higher-order polyadic process algebra for resource bounded computation. It has been designed to handle autonomous agents, evolutionary computing, neural
   nets, expert systems, machine learning, and distributed interactive AI systems, in general. \$-calculus has built-in cost-optimisation mechanism allowing to deal with
   nondeterminism, incomplete and uncertain information. In this paper, we express in \$-calculus several subareas of evolutionary computation, including genetic programming,
   artificial life, autonomous agents and DNA-based computing.
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
   Revision of \citeeberbach:1998:xECGPALAADNAClc \$-calculus == cost-calculus
%@ 0-7803-6375-2

%A Eugene Eberbach
%T The \$-calculus process algebra for problem solving: A paradigmatic shift in handling hard computational problems
%J Theoretical Computer Science
%V 383
%N 2-3
%D 2007
%P 200--243
%I 
%K genetic algorithms, genetic programming, Problem solving, Process algebras, Anytime algorithms, SuperTuring models of computation, Bounded rational agents, $-calculus,
   Intractability, Undecidability, Completeness, Optimality, Search optimality, Total optimality
%U http://www.sciencedirect.com/science/article/B6V1G-4NGKWGF-7/2/07c09787a0b898de98e171ac414f6ddc
%X The $-calculus is the extension of the [pi]-calculus, built around the central notion of cost and allowing infinity in its operators. We propose the $-calculus as a more
   complete model for problem solving to provide a support to handle intractability and undecidability. It goes beyond the Turing Machine model. We define the semantics of the
   $-calculus using a novel optimization method (the k[Omega]-optimization), which approximates a nonexisting universal search algorithm and allows the simulation of many
   other search methods. In particular, the notion of total optimality has been used to provide an automatic way to deal with intractability of problem solving by optimizing
   together the quality of solutions and search costs. The sufficient conditions needed for completeness, optimality and total optimality of problem solving search are
   defined. A very flexible classification scheme of problem solving methods into easy, hard and solvable in the limit classes has been proposed. In particular, the third
   class deals with non-recursive solutions of undecidable problems. The approach is illustrated by solutions of some intractable and undecidable problems. We also briefly
   overview two possible implementations of the $-calculus.
%O Complexity of Algorithms and Computations
%Z GP one technique in many

%A Eugene Eberbach
%A Mark Burgin
%T Evolutionary Automata as Foundation of Evolutionary Computation: Larry Fogel Was Right
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P -
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, EP
%X In this paper we study expressiveness of evolutionary computation. To do so we introduce evolutionary automata and define their several subclasses. To our surprise, we got
   the result that evolving finite automata by finite automata leads outside its class, and allows to express for example pushdown automata or Turing machines. This explains
   partially why Larry Fogel restricted representation in Evolutionary Programming to finite state machines only. The power of evolution is enormous indeed!
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A David S. Ebert
%A F. Kenton Musgrave
%A Darwyn Peachey
%A Ken Perlin
%A Steven Worley
%T Texturing and Modeling, a Procedural Approach
%D 2002
%I Morgan Kaufmann
%C 3
%K genetic algorithms, genetic programming, genetic textures
%O 19
%Z http://www.texturingandmodeling.com/ART/MUSGRAVE/CH19/Chapter19Art.html
%@ 1-55860-848-6

%A Marc Ebner
%T Evolution of Hierarchical Translation-Invariant Feature Detectors with an Application to Character Recognition
%B Mustererkennung 1997, 19. DAGM-Symposium
%S Informatik Aktuell
%E Erwin Paulus and Friedrich M. Wahl
%D 1997
%P 456--463
%I Springer-Verlag Berlin
%C Braunschweig
%K genetic algorithms, genetic programming, evolution strategies, structure evolution, feature detection
%U http://wwwi2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/evolve.ps.gz
%8 15-17  September
%@ 3-540-63426-6

%A Marc Ebner
%T On the Evolution of Edge Detectors for Robot Vision using Genetic Programming
%B Workshop SOAVE '97 - Selbstorganisation von Adaptivem Verhalten, VDI Reihe 8 Nr. 663
%E Horst-Michael Gro\ss
%D 1997
%P 127--134
%I VDI Verlag
%C D\"usseldorf
%K genetic algorithms, genetic programming, edge detection
%U http://www.ra.cs.uni-tuebingen.de/mitarb/ebner/research/publications/uniTu/gpedge.ps.gz
%X Genetic programming has been applied to the task of evolving edge detectors... Canny ...
%@ 3-18-366308-2

%A Marc Ebner
%T On the Evolution of Interest Operators using Genetic Programming
%B Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming
%E Riccardo Poli and W. B. Langdon and Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf
%D 1998
%P 6--10
%I CSRP-98-10, The University of Birmingham, UK School of Computer Science
%C Paris, France
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/158450.html
%X Interest operators play an important role in computer vision. Depending on the type of the environment some features may prove to be more advantageous than others. Thus
   detection of interesting features has to be made adaptive such that the best features according to some measure are extracted. We are trying to evolve such feature
   detectors using genetic programming. In this paper we describe our results where the desired operator, which is a Moravec interest operator, is directly specified. We show
   that the problem is a rather difficult one. Only an approximation to the Moravec operator could be evolved using several sets of elementary functions. 1 Motivation Interest
   operators play an important role in computer vision [8]. They highlight points which can be found easily using simple correlation methods. They can be used to calculate
   accurate distance information and for map building [23]. However no interest operator is suitable for all types of environments. A mobile robot which ma...
%8 14-15  April
%Z EuroGP'98LB part of \citePoli:1998:egplb

%A Marc Ebner
%T Evolution of a control architecture for a mobile robot
%B Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware (ICES 98)
%S LNCS
%E Moshe Sipper and Daniel Mange and Andres Perez-Uribe
%V 1478
%D 1998
%P 303--310
%I Springer Verlag Berlin
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/512626.html
%X Programming a robot to perform a desired task in an unknown environment is a difficult task. Due to unexpected interactions between the environment and the robot many
   iterations of program development are often required. Using genetic programming the robots themselves may search the space of possible programs. In an experiment which was
   conducted over a period of two months we evolved a behavior-based control architecture for a large sized mobile robot, a Real World Interface B21. This is the first time
   that a large mobile robot was used in evolutionary robotics using tree-based genetic programming. In addition, our architecture uses conditional statements to build up a
   hierarchical reactive control structure. Sonar sensors are used to sense the environment. Because the robot is able to exert a considerable force if it crashes into an
   object, safety measures had to be taken to automatically monitor the run.
%8 23-25  September
%Z ICES98
%@ 3-540-64954-9

%A Marc Ebner
%T Evolving an Environment Model for Robot Localization
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 184--192
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=184
%X The use of an evolutionary method for robot localization is explored. We use genetic programming to evolve an inverse function mapping sensor readings to robot locations.
   This inverse function is an internal model of the environment. The robot senses its environment using dense distance information which may be obtained from a laser range
   finder. Moments are calculated from the distance distribution. These moments are used as terminal symbols in the evolved function. Arithmetic, trigonometric functions and a
   conditional statement are used as primitive functions. Using this representation we evolved an inverse function to localize a robot in a simulated office environment. We
   also analyzed the accuracy of the resulting function. This research was done at the University of Tübingen, Wilhelm-Schickard-Institute for Computer Science, Computer
   Architecture (Prof. Zell).
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP
%@ 3-540-65899-8

%A Marc Ebner
%A Andreas Zell
%T Evolving a Task Specific Image Operator
%B Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99
%S LNCS
%E Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty
%V 1596
%D 1999
%P 74--89
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/392593.html
%X Image processing is usually done by chaining a series of well known image processing operators. Using evolutionary methods this process may be automated. In this paper we
   address the problem of evolving task specific image processing operators. In general, the quality of the operator depends on the task and the current environment. Using
   genetic programming we evolved an interest operator which is used to calculate sparse optical flow. To evolve the interest operator we define a series of criteria which
   need to be optimized. The different criteria are combined into an overall fitness function. Finally, we present experimental results on the evolution of the interest
   operator.
%8 28-29  May
%Z EvoIASP99'99
%@ 3-540-65837-8

%A Marc Ebner
%A Andreas Zell
%T Evolving a behavior-based control architecture- From simulations to the real world
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1009--1014
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-414.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Marc Ebner
%T On the Search Space of Genetic Programming and Its Relation to Nature's Search Space
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 2
%D 1999
%P 1357--1361
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, models of evolutionary computation
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143
%@ 0-7803-5537-7 (Microfiche)

%A Marc Ebner
%T Steuerung eines mobilen Roboters mit evolvierten Merkmalsdetektoren
%R Ph.D. Thesis
%D 1999
%I 
%I Eberhard-Karls-Universit\"at T\"abingen
%K genetic algorithms, genetic programming, computer vision, biologically inspired systems
%U http://www2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/diss.ps.gz

%A Marc Ebner
%T Evolving Color Constancy for an Artificial Retina
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 11--22
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Color Constancy, Artificial Retina, Image Processing
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=11
%X Objects retain their color in spite of changes in the wavelength and energy composition of the light they reflect. This phenomenon is called color constancy and plays an
   important role in computer vision research. We have used genetic programming to automatically search the space of programs to solve the problem of color constancy for an
   artificial retina. This retina consists of a two dimensional array of elements each capable of exchanging information with its adjacent neighbors. The task of the program
   is to compute the intensities of the light illuminating the scene. These intensities are then used to calculate the reflectances of the object. Randomly generated color
   Mondrians were used as fitness cases. The evolved program was tested on artificial Mondrians and natural images.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Marc Ebner
%T A Three-Dimensional Environment for Self-Reproducing Programs
%B Advances in Artificial Life, Proceedings 6th European Conference, ECAL 2001
%S Lecture Notes in Computer Science
%E Jozef Kelemen and Petr Sosik
%V 2159
%D 2001
%P 306--315
%I Springer-Verlag
%C Prague, Czech Republic
%K genetic algorithms, genetic programming, self-reproducing programs, artificial life
%U http://link.springer.de/link/service/series/0558/bibs/2159/21590306.htm
%X Experimental results with a three-dimensional environment for self-reproducing programs are presented. The environment consists of a cube of virtual CPUs each capable of
   running a single process. Each process has access to the memory of 7 CPUs, to its own as well as to the memory of 6 neighbouring CPUs. Each CPU has a particular orientation
   which may be changed using special opcodes of the machine language. An additional opcode may be used to move the CPU. We have used a standard machine language with two
   operands. Constants are coded in a separate section of each command and a special mutation operator is used to ensure strong causality. This type of environment sets itself
   apart from other types of environments in the use of redundant mappings. Individuals have read as well as write access to neighboring CPUs and reproduce by copying their
   genetic material. They need to move through space in order to spawn new individuals and avoid overwriting their own offspring. After a short time all CPUs are filled by
   self-reproducing individuals and competition between individuals sets in which results in an increased rate of speciation.
%8 September  10-14
%@ 3-540-42567-5

%A Marc Ebner
%A Adrian Grigore
%A Alexander Heffner
%A J{\"u}rgen Albert
%T Coevolution Produces an Arms Race Among Virtual Plants
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 316--325
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/publications/uniWu/evoPlant.ps.gz
%X Creating interesting virtual worlds is a difficult task. We are using a variant of genetic programming to automatically create plants for a virtual environment. The plants
   are represented as context-free Lindenmayer systems. OpenGL is used to visualize and evaluate the plants. Our plants have to collect virtual sunlight through their leaves
   in order to reproduce successfully. Thus we have realized an interaction between the plant and its environment. Plants are either evaluated separately or all individuals of
   a population at the same time. The experiments show that during coevolution plants grow much higher compared to rather bushy plants when plants are evaluated in isolation.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Marc Ebner
%T Evolutionary Design of Objects Using Scene Graphs
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 47--58
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=47
%X One of the main issues in evolutionary design is how to create three-dimensional shape. The representation needs to be general enough such that all possible shapes can be
   created, yet it has to be evolvable. That is, parent and offspring must be related. Small changes to the genotype should lead to small changes of the fitness of an
   individual. We have explored the use of scene graphs to evolve three-dimensional shapes. Two different scene graph representations are analyzed, the scene graph
   representation used by OpenInventor and the scene graph representation used by VRML. Both representations use internal floating point variables to specify three-dimensional
   vectors, rotation axes and rotation angles. The internal parameters are initially chosen at random, then remain fixed during the run. We also experimented with an evolution
   strategy to adapt the internal variables. Experimental results are presented for the evolution of a wind turbine. The VRML representation produced better results.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003 overview http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/evoRotor/evoRotor.html
%@ 3-540-00971-X

%A Marc Ebner
%T Evolution and Growth of Virtual Plants
%B Advances in Artificial Life. 7th European Conference on Artificial Life
%S Lecture Notes in Artificial Intelligence
%E Wolfgang Banzhaf and Thomas Christaller and Peter Dittrich and Jan T. Kim and Jens Ziegler
%V 2801
%D 2003
%P 228--237
%I Springer
%C Dortmund, Germany
%K genetic algorithms, genetic programming, virtual plants, L-systems, co-evolution
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2801&spage=228
%X According to the Red Queen hypothesis, an evolving population may be improving some trait, even though its fitness remains constant. We have created such a scenario with a
   population of coevolving plants. Plants are modelled using Lindenmayer systems and rendered with OpenGL. The plants consist of branches and leaves. Their reproductive
   success depends on their ability to catch sunlight as well as their structural complexity. All plants are evaluated inside the same environment, which means that one plant
   is able to cover other plants leaves. Leaves which are placed in the shadow of other plants do not catch any sunlight. The shape of the plant also determines the area where
   offspring can be placed. Offspring can only be placed in the vicinity of a plant. A number of experiments were performed in different environments. The Red Queen effect was
   seen in all cases.
%8 14-17  September
%Z ECAL-2003
%@ 3-540-20057-6

%A Marc Ebner
%T Book Review: Illustrating Evolutionary Computation with Mathematica
%J Genetic Programming and Evolvable Machines
%V 4
%N 3
%D 2003
%P 291--294
%I 
%K genetic algorithms, genetic programming
%8 September
%Z Review of \citejacob:2001:iecm Article ID: 5141126

%A Marc Ebner
%A Markus Reinhardt
%A J{\"u}rgen Albert
%T Evolution of Vertex and Pixel Shaders
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 261--270
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming, GPU, linear GP
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=261
%X In real-time rendering, objects are represented using polygons or triangles. Triangles are easy to render and graphics hardware is highly optimised for rendering of
   triangles. Initially, the shading computations were carried out by dedicated hardwired algorithms for each vertex and then interpolated by the rasterizer. Todays graphics
   hardware contains vertex and pixel shaders which can be reprogrammed by the user. Vertex and pixel shaders allow almost arbitrary computations per vertex respectively per
   pixel. We have developed a system to evolve such programs. The system runs on a variety of graphics hardware due to the use of NVIDIA's high level Cg shader language.
   Fitness of the shaders is determined by user interaction. Both fixed length and variable length genomes are supported. The system is highly customisable. Each individual
   consists of a series of meta commands. The resulting Cg program is translated into the low level commands which are required for the particular graphics hardware.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005 examples
   http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/evoShader/evoShader.html Cg
%@ 3-540-25436-6

%A Marc Ebner
%T Coevolution and the Red Queen effect shape virtual plants
%J Genetic Programming and Evolvable Machines
%V 7
%N 1
%D 2006
%P 103--123
%I 
%K genetic algorithms, genetic programming, Red Queen effect, Coevolution, Lindenmayer systems, Artificial plants
%X According to the Red Queen hypothesis a population of individuals may be improving some trait even though fitness remains constant. We have tested this hypothesis using a
   population of virtual plants. The plants have to compete with each other for virtual sunlight. Plants are modelled using Lindenmayer systems and rendered with OpenGL.
   Reproductive success of a plant depends on the amount of virtual light received as well as on the structural complexity of the plant. We experiment with two different modes
   of evaluation. In one experiment, plants are evaluated in isolation, while in other experiments plants are evaluated using coevolution. When using coevolution plants have
   to compete with each other for sunlight inside the same environment. Coevolution produces much thinner and taller plants in comparison to bush-like plants which are
   obtained when plants are evaluated in isolation. The presence of other individuals leads to an evolutionary arms race. Because plants are evaluated inside the same
   environment, the leaves of one plant may be shadowed by other plants. In an attempt to gain more sunlight, plants grow higher and higher. The Red Queen effect was observed
   when individuals of a single population were coevolving.
%8 March
%Z L-Systems p103 'continued opened evolution'. Plants grow near parents on 2-D surface. Multiple genetic operations. Z-buffer rendering light gives (part of) fitness, other
   part from resources to grow plant. P112 Perlin noise 1998.

%A Marc Ebner
%T Evolving color constancy
%J Pattern Recognition Letters
%V 27
%N 11
%D 2006
%P 1220--1229
%I 
%K genetic algorithms, genetic programming, Colour constancy, Local space average colour
%X The ability to compute colour constant descriptors of objects in view irrespective of the light illuminating the scene is called color constancy. We have used genetic
   programming to evolve an algorithm for colour constancy. The algorithm runs on a grid of processing elements. Each processing element is connected to neighbouring
   processing elements. Information exchange can therefore only occur locally. Randomly generated colour Mondrians were used as test cases. The evolved individual was tested
   on synthetic as well as real input images. Encouraged by these results we developed a parallel algorithm for colour constancy. This algorithm is based on the computation of
   local space average colour. Local space average colour is used to estimate the illuminant locally for each image pixel. Given an estimate of the illuminant, we can compute
   the reflectances of the corresponding object points. The algorithm can be easily mapped to a neural architecture and could be implemented directly in CCD or CMOS chips used
   in todays cameras.
%O Evolutionary Computer Vision and Image Understanding
%8 August

%T Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%I Springer Berlin Heidelberg NewYork
%C Valencia, Spain
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/content/978-3-540-71602-0/
%8 11-13  April
%Z EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Marc Ebner
%T Color Constancy
%S Imaging Science and Technology
%D 2007
%P 198--204
%I John Wiley \& Sons
%K genetic algorithms, genetic programming
%8 April
%@ 0-470-05829-3

%A Marc Ebner
%T A Genetic Programming Approach to Deriving the Spectral Sensitivity of an Optical System
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 61--72
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Marc Ebner
%T An Adaptive On-Line Evolutionary Visual System
%B Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW 2008
%E E. Hart and B. Paechter and J. Willies
%D 2008
%P 84--89
%I IEEE Press
%I IEEE
%C Venice
%K genetic algorithms, genetic programming, GPU, adaptive online evolutionary visual system, evolutionary computer vision, training images, adaptive systems, computer vision,
   evolutionary computation
%X In evolutionary computer vision, algorithms are usually evolved which address one particular computer vision problem. Quite often, a set of training images is used to
   evolve an algorithm. Another set of images is then used to evaluate the performance of those algorithms. In contrast of this standard form of algorithm evolution, it is
   proposed to develop a vision system which continuously evolves algorithms based on the task at hand. This adaptation of computer vision algorithms would happen on-line for
   every image which is presented to the system. Such a system would continuously adapt to new environmental conditions.
%8 20-24  October
%Z Workshop on Pervasive Adaptation. Also known as \cite4800658

%A Marc Ebner
%T A Real-Time Evolutionary Object Recognition System
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 268--279
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming, poster
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Marc Ebner
%A Thorsten Tiede
%T Evolving driving controllers using Genetic Programming
%B IEEE Symposium on Computational Intelligence and Games, CIG 2009
%D 2009
%P 279--286
%I 
%K genetic algorithms, genetic programming, computational gaming, computational learning approaches, computer gaming, driving controllers, manually crafted race car driver,
   virtual drivers, computer games, control engineering computing, driver information systems, learning (artificial intelligence), virtual reality
%X Computational gaming requires the automatic generation of virtual opponents for different game levels. We have turned to artificial evolution to automatically generate such
   game players. In particular, we have used genetic programming to automatically evolve computer programs for computer gaming. With genetic programming, in theory, it is
   possible to generate any kind of program. The programs are not constrained as much as they are in other computational learning approaches, e.g. neural networks. We show how
   genetic programming improved upon a manually crafted race car driver (proportional controller). The open race car simulator TORCS was used to evaluate the virtual drivers.
%8 September
%Z Also known as \cite5286465

%A Marc Ebner
%T Engineering of Computer Vision Algorithms Using Evolutionary Algorithms
%B Proceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009
%S Lecture Notes in Computer Science
%E Jacques Blanc-Talon and Wilfried Philips and Dan Popescu and Paul Scheunders
%V 5807
%D 2009
%P 367--378
%I Springer
%C Bordeaux, France
%K genetic algorithms, genetic programming, Cartesian genetic programming, GPU, OpenGLSL
%U http://www.ra.cs.uni-tuebingen.de/mitarb/ebner/research/publications/uniTu2/EvoCVengineering.pdf
%X Computer vision algorithms are currently developed by looking up the available operators from the literature and then arranging those operators such that the desired task
   is performed. This is often a tedious process which also involves testing the algorithm with different lighting conditions or at different sites. We have developed a system
   for the automatic generation of computer vision algorithms at interactive frame rates using GPU accelerated image processing. The user simply tells the system which object
   should be detected in an image sequence. Simulated evolution, in particular Genetic Programming, is used to automatically generate and test alternative computer vision
   algorithms. Only the best algorithms survive and eventually provide a solution to the user's image processing task.
%8 September  28-  October  2
%Z Interactive evolution of image processing software. Realtime 30 seconds. OpenGL shader language. mip mapping. nVidia GeForce 9600 GT/PCI/SEE2

%A Marc Ebner
%T Towards Automated Learning of Object Detectors
%B EvoIASP
%S LNCS
%E Cecilia Di Chio and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and
   Mike Preuss and Julian Togelius and Georgios N. Yannakakis
%V 6024
%D 2010
%P 231--240
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming, cartesian genetic programming
%X Recognizing arbitrary objects in images or video sequences is a difficult task for a computer vision system. We work towards automated learning of object detectors from
   video sequences (without user interaction). Our system uses object motion as an important cue to detect independently moving objects in the input sequence. The largest
   object is always taken as the teaching input, i.e. the object to be extracted. We use Cartesian Genetic Programming to evolve image processing routines which deliver the
   maximum output at the same position where the detected object is located. The graphics processor (GPU) is used to speed up the image processing. Our system is a step
   towards automated learning of object detectors.
%8 7-9  April
%Z EvoIASP'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Marc Ebner
%T Evolving Object Detectors with a GPU Accelerated Vision System
%B Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010
%S Lecture Notes in Computer Science
%E Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller
%V 6274
%D 2010
%P 109--120
%I Springer
%C York
%K genetic algorithms, genetic programming
%X Using GPU processing, it is now possible to develop an evolutionary vision system working at interactive frame rates. Our system uses motion as an important cue to evolve
   detectors which are able to detect an object when this cue is not available. Object detectors consist of a series of high level operators which are applied to the input
   image. A matrix of low level point operators are used to recombine the output of the high level operators. With this contribution, we investigate, which image processing
   operators are most useful for object detection. It was found that the set of image processing operators could be considerably reduced without reducing recognition
   performance. Reducing the set of operators lead to an increase in speedup compared to a standard CPU implementation.
%8 September  6-8

%A Michael J. Ebstyne
%T Musings on Syncopation and Machines
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 36--46
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%X music
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A William Edelson
%A Michael L. Gargano
%T Efficient Calculation of Compute-Intensive Fitness In Genetic Computations Using A Survival Indicator For Population Members
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 784
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, classifier systems, poster papers
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Andrew N. Edmonds
%A Diana Burkhardt
%A Osei Adjei
%T Genetic Programming of Fuzzy Logic Production Rules
%B 1995 IEEE Conference on Evolutionary Computation
%V 2
%D 1995
%P 765
%I IEEE Press Piscataway, NJ, USA
%C Perth, Australia
%K genetic algorithms, genetic programming
%U http://ieeexplore.ieee.org/iel2/3507/10438/00487482.pdf
%X John Koza has demonstrated that a form of machine learning can be constructed by using the techniques of Genetic Programming using LISP statements. We describe here an
   extension to this principle using Fuzzy Logic sets and operations instead of LISP expressions. We show that Genetic programming can be used to generate trees of fuzzy logic
   statements, the evaluation of which optimise some external process, in our example financial trading. We also show that these trees can be simply converted to natural
   language rules, and that these rules are easily comprehended by a lay audience. This clarity of internal function can be compared to Black Box non-parametric modelling
   techniques such as Neural Networks. We then show that even with minimal data preparation the technique produces rules with good out of sample performance on a range of
   different financial instruments.
%8 29  November  - 1  Decemeber
%Z ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html NNCM95.pdf is ten page
   version?

%A Bruce Edmonds
%A Scott Moss
%T Modelling of Boundedly Rational Agents using Evolutionary Programming Techniques
%B Evolutionary Computing
%S LNCS
%E David Corne and Jonathan L. Shapiro
%V 1305
%D 1997
%P 31--42
%I Springer-Verlag
%C University of Manchester, UK
%K genetic algorithms, genetic programming
%U http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-63476-2
%X A technique for the credible modelling of economic agents with bounded rationality based on the evolutionary techniques is described. The genetic programming paradigm is
   most suited due to its meaningful and flexible genome. The fact we are aiming to model agents with real characteristics implies a different approach from those evolutionary
   algorithms designed to efficiently solve specific problems. Some of these are that we use very small populations, it is based on different operators and uses a breeding
   selection mechanism. It is precisely some of the "pathological" features of this algorithm that capture the target behaviour. Some possibilities for integration of
   deductive logic-based approaches and the GP paradigm are suggested. An example application of an agent seeking to maximise its utility by modelling its own utility function
   is briefly described.
%8 7-8  April
%Z Papers in AISB-97 Evolutionaru computation workshop proceedings may be revised before final publication. http://www.cs.man.ac.uk/ai/AISB97/text.html#evolut Former soviet
   union. Strictly declarative modelling language SDML. 3 sets of runs, agents have memory of different sizes, space for 10, 20 or 30 models. http://www.fmb.mmu.ac.uk
%@ 3-540-63476-2

%A Bruce Edmonds
%T Meta-Genetic Programming: Co-evolving the Operators of Variation
%R CPM Report 98-32
%D 1998
%I 
%I Centre for Policy Modelling, Manchester Metropolitan University, UK
%C Aytoun St., Manchester, M1 3GH. UK
%K genetic algorithms, genetic programming, automatic programming, genetic operators, co-evolution
%U http://www.cpm.mmu.ac.uk/cpmrep32.html
%X The standard Genetic Programming approach is augmented by co-evolving the genetic operators. To do this the operators are coded as trees of indefinite length. In order for
   this technique to work, the language that the operators are defined in must be such that it preserves the variation in the base population. This technique can varied by
   adding further populations of operators and changing which populations act as operators for others, including itself, thus to provide a framework for a whole set of
   augmented GP techniques. The technique is tested on the parity problem. The pros and cons of the technique are discussed.
%8 January
%Z see \citeedmonds:2001:mGPcov

%A Bruce Edmonds
%T Gossip, Sexual Recombination and the El Farol Bar: modelling the emergence of heterogeneity
%B Proceedings of the 1998 Conference on Computation in Economics, Finance and Engineering
%D 1998
%I 
%C Cambridge
%K genetic algorithms, genetic programming
%U http://cogprints.ecs.soton.ac.uk/archive/00000514/
%X Brian Arthur's `El Farol Bar' model is extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process
   acting upon a population of mental models inside each agent. The evolutionary process is based on a Genetic Programming algorithm. Each gene is composed of two
   tree-structures: one to control its action and one to determine its communication. A detailed case-study from the simulations show how the agents have differentiated so
   that by the end of the run they had taken on very different roles. Thus the introduction of a flexible learning process and an expressive internal representation has
   allowed the emergence of heterogeneity. agents also learn and communicate. Each gene is composed of two tree-structures: one to control its actions and one to determine
   communication.
%8 June
%Z coevolution, bounded rationality. Communicate (talk) one branch first. Then action (go to bar OR not go). STGP. page 3 "total population was 5 in this example". SDML.
   problem specific terminal and function sets (different for two branches) See \citeedmonds:1999:gsrefb

%A Bruce Edmonds
%T The Uses of Genetic Programming in Social Simulation: A Review of Five Books
%J Journal of Artificial Societies and Social Simulation
%V 1
%N 4
%D 1998
%I 
%K genetic algorithms, genetic programming
%U http://jasss.soc.surrey.ac.uk/2/1/review1.html
%X Genetic Programming: On the Programming of Computers by Natural Selection John R. Koza Cambridge, MA: The M.I.T. Press 1992 Cloth: ISBN 0-262-11170-5 Genetic Programming
   II: Automatic Discovery of Reusable Programs John R. Koza Cambridge, MA: The M.I.T. Press, A Bradford Book 1994 Cloth: ISBN 0-262-11189-6 Advances in Genetic Programming
   Edited by Kenneth E. Kinnear Jr. Cambridge, MA: The M.I.T. Press, A Bradford Book 1994 Cloth: ISBN 0-262-11188-8 Advance in Genetic Programming Volume 2 Edited by Peter J.
   Angeline and Kenneth E. Kinnear Jr. Cambridge, MA: The M.I.T. Press, A Bradford Book 1996 Cloth: ISBN 0-262-01158-1 Genetic Programming and Data Structures William B.
   Langdon Dordrecht: Kluwer Academic Publishers 1998 Cloth: ISBN 0-792-38135-1
%O Book review
%8 31-  October

%A Bruce Edmonds
%T Gossip, Sexual Recombination and the El Farol bar: modelling the emergence of heterogeneity
%J Journal of Artificial Societies and Social Simulation
%V 2
%N 3
%D 1999
%I 
%K genetic algorithms, genetic programming, differentiation, El Farol, evolution, co-evolution, emergence, heterogeneity, society, roles, social structure, SDML, naming,
   creativity
%U http://cogprints.ecs.soton.ac.uk/archive/00001775/
%X An investigation into the conditions conducive to the emergence of heterogeneity among agents is presented. This is done by using a model of creative artificial agents to
   investigate some of the possibilities. The simulation is based on Brian Arthur's 'El Farol Bar' model but extended so that the agents also learn and communicate. The
   learning and communication is implemented using an evolutionary process acting upon a population of strategies inside each agent. This evolutionary learning process is
   based on a Genetic Programming algorithm. This is chosen to make the agents as creative as possible and thus allow the outside edge of the simulation trajectory to be
   explored. A detailed case study from the simulations show how the agents have differentiated so that by the end of the run they had taken on qualitatively different roles.
   It provides some evidence that the introduction of a flexible learning process and an expressive internal representation has facilitated the emergence of this
   heterogeneity.
%Z See also \citeedmonds:1998:gsrefb

%A Bruce Edmonds
%T The Uses of Genetic Programming in Social Simulation: A Review of Five Books
%J The Journal of Artificial Societies and Social Simulation
%V 2
%N 1
%D 1999
%I 
%K genetic algorithms, genetic programming
%U http://www.soc.surrey.ac.uk/JASSS/2/1/review1.html
%X Moderately extensive introduction to GP followed by review of the following five books from the perspective of Social Simulation: Genetic Programming: On the Programming of
   Computers by Natural Selection John R. Koza Cambridge, MA: The M.I.T. Press 1992 \citekoza:book Genetic Programming II: Automatic Discovery of Reusable Programs John R.
   Koza Cambridge, MA: The M.I.T. Press, A Bradford Book 1994 \citekoza:gp2 Advances in Genetic Programming Edited by Kenneth E. Kinnear Jr. Cambridge, MA: The M.I.T. Press, A
   Bradford Book 1994 \citekinnear:book Advance in Genetic Programming Volume 2 Edited by Peter J. Angeline and Kenneth E. Kinnear Jr. Cambridge, MA: The M.I.T. Press, A
   Bradford Book 1996 \citebook:1996:aigp2 Genetic Programming and Data Structures William B. Langdon Dordrecht: Kluwer Academic Publishers 1998 \citelangdon:book
%8 January
%Z JASSS

%A Bruce Edmonds
%T A Review of the ``Advances in Genetic Programming'' Series (Volumes 1, 2 and 3)
%J Genetic Programming and Evolvable Machines
%V 1
%N 3
%D 2000
%P 289--296
%I 
%K genetic algorithms, genetic programming
%8 July
%Z \citekinnear:book \citebook:1996:aigp2 \citebook:1999:aigp3 Article ID: 264705

%A Bruce Edmonds
%T Learning Appropriate Contexts
%B Modelling and Using Context: Third International and Interdisciplinary Conference, CONTEXT
%S LNAI
%E Varol Akman and Paolo Bouquet and Richard Thomason and Roger Young
%V 2116
%D 2001
%P 143--155
%I Springer-Verlag Berlin / Heidelberg
%C Dundee, UK
%K genetic algorithms, genetic programming, learning, conditions of application, context, evolutionary computing, error
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2116&spage=143
%X Genetic Programming is extended so that the solutions being evolved do so in the context of local domains within the total problem domain. This produces a situation where
   different "species" of solution develop to exploit different "niches" of the problem indicating exploitable solutions. It is argued that for context to be fully learnable a
   further step of abstraction is necessary. Such contexts abstracted from clusters of solution/model domains make sense of the problem of how to identify when it is the
   content of a model is wrong and when it is the context. Some principles of learning to identify useful contexts are proposed.
%8 27-30  July
%Z Volume in the proceedings of the 3rd International and interdisciplinary conference, CONTEXT 2001, Dundee, UK, July 2001
   http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-42379-6 Cited by \citeulgtsdl. Centre for Policy Modelling, Manchester Metropolitan University, Aytoun Building,
   Autoun Street, Manchester, M1 3GH, UK. b.edmonds@mmu.ac.uk http://www.cpm.mmu.ac.uk/ bruce Feb 2007 duplicate entry \citeEdmonds:2001:CONTEXT combined with edmonds:2001:MUC
%@ 3-540-42379-6

%A Bruce Edmonds
%A Scott Moss
%T The Importance of Representing Cognitive Processes in Multi-agent Models
%B Artificial Neural Networks - ICANN 2001 : International Conference, Proceedings
%S Lecture Notes in Computer Science
%E G. Dorffner and H. Bischof and K. Hornik
%V 2130
%D 2001
%P 759--766
%I 
%C Vienna, Austria
%K genetic algorithms, genetic programming, modelling, methodology, agent, economics, neural net, representation, prediction, explanation, cognition, stock market, negotiation
%U http://link.springer-ny.com/link/service/series/0558/papers/2130/21300759.pdf", acknowledgement = ack-nhfb
%X We distinguish between two main types of model: predictive and explanatory. It is argued (in the absence of models that predict on unseen data) that in order for a model to
   increase our understanding of the target system the model must credibly represent the structure of that system, including the relevant aspects of agent cognition. Merely
   plugging in an existing algorithm for the agent cognition will not help in such understanding. In order to demonstrate that the cognitive model matters, we compare two
   multi-agent stock market models that differ only in the type of algorithm used by the agents to learn. We also present a positive example where a neural net is used to
   model an aspect of agent behaviour in a more descriptive manner.
%8 August  21-25

%A Bruce Edmonds
%T Meta-Genetic Programming: Co-evolving the Operators of Variation
%J Elektrik
%V 9
%N 1
%D 2001
%P 13--29
%I 
%K genetic algorithms, genetic programming, automatic programming, genetic operators, co-evolution
%U http://cogprints.ecs.soton.ac.uk/archive/00001776/00/mgp.pdf
%X The standard Genetic Programming approach is augmented by co-evolving the genetic operators. To do this the operators are coded as trees of indefinite length. In order for
   this technique to work, the language that the operators are defined in must be such that it preserves the variation in the base population. This technique can varied by
   adding further populations of operators and changing which populations act as operators for others, including itself, thus to provide a framework for a whole set of
   augmented GP techniques. The technique is tested on the parity problem. The pros and cons of the technique are discussed.
%O Turkish Journal Electrical Engineering and Computer Sciences
%8 May
%Z Elektrik http://www.tubitak.gov.tr/journals/elektrik/ see \citeedmonds:1998:mGPcov

%A Bruce Edmonds
%T Using Localised 'Gossip' to Structure Distributed Learning
%R CPM Report CPM-04-142
%D 2005
%I 
%I Centre for Policy Modelling, Manchester Metropolitan University Business School
%C UK
%K genetic algorithms, genetic programming
%U http://cfpm.org/cpmrep142.html
%X The idea of a "memetic" spread of solutions through a human culture in parallel to their development is applied as a distributed approach to learning. Local parts of a
   problem are associated with a set of overlapping localities in a space and solutions are then evolved in those localities. Good solutions are not only crossed with others
   to search for better solutions but also they propagate across the areas of the problem space where they are relatively successful. Thus the whole population co-evolves
   solutions with the domains in which they are found to work. This approach is compared to the equivalent global evolutionary computation approach with respect to predicting
   the occurrence of heart disease in the Cleveland data set. It greatly outperforms the global approach, but the space of attributes within which this evolutionary process
   occurs can effect its efficiency.
%8 15th  May
%Z 'geographic separation' in space of inputs. How this is done has dramatic effect on effectiveness of this approach. 'exact distance metric did not noticeable effect the
   results'. Global GP only using 10 percent of training data.

%A Bruce Edmonds
%T Using Localised 'Gossip' to Structure Distributed Learning
%B AISB'05: Proceedings of the Joint Symposium on Socially Inspired Computing (Engineering with Social Metaphors)
%E Bruce Edmonds and Nigel Gilbert and Steven Gustafson and David Hales and Natalio Krasnogor
%D 2005
%P 127--134
%I 
%I AISB
%C University of Hertfordshire, Hatfield, UK
%K genetic algorithms, genetic programming
%O SSAISB 2005 Convention
%8 12-15  April
%Z see also CPM rep 142 \citeulgtsdl. In the joint-symposium ``Socially Inspired Computing'', in the AISB 2005 Convention ``Social Intelligence and Interaction in Animals,
   Robots and Agents''. http://aisb2005.feis.herts.ac.uk/

%A James Edmondson
%A Douglas Schmidt
%T Multi-agent distributed adaptive resource allocation (MADARA)
%J International Journal of Communication Networks and Distributed Systems
%V 5
%N 3
%D 2010
%P 229--245
%I 
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.5912
%X The component placement problem involves mapping a component to a particular location and maximising component utility in grid and cloud systems. It is also an NP hard
   resource allocation and deployment problem, so many common grid and cloud computing libraries, such as MPICH and Hadoop, do not address this problem, even though large
   performance gains can occur by optimising communications between nodes. This paper provides four contributions to research on the component placement problem for grid and
   cloud computing environments. First, we present the multi-agent distributed adaptive resource allocation (MADARA) toolkit, which is designed to address grid and cloud
   allocation and deployment needs. Second, we present a heuristic called the comparison-based iteration by degree (CID) heuristic, which we use to approximate optimal
   deployments in MADARA. Third, we analyse the performance of applying the CID heuristic to approximate common grid and cloud operations, such as broadcast, gather and
   reduce. Fourth, we evaluate the results of applying genetic programming mutation to improve our CID heuristic.

%A Stian Edvardsen
%T Classification of Images using Color, CBIR Distance Measures and Genetic Programming: An evolutionary Experiment
%R Undergraduate thesis Masteroppgave-level
%D 2006
%I 
%K genetic algorithms, genetic programming
%U http://urn.ub.uu.se/resolve?urn=urn:nbn:no:ntnu:diva-1132
%O Undergraduate Theses from Norwegian University of Science and Technology. Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Computer
   and Information Science
%8 June

%A A. W. F. Edwards
%T Forced Evolution
%J Nature
%V 375
%D 1995
%P 11
%I 
%8 6  July
%Z Notes "Professor of speculative learning" at the "Grand academy of Lagado" visited by Captain Lemuel Gulliver in his travels. Cites work claiming this travelogue was read
   by Charles Darwin in 1840.

%A J. Eggermont
%A A. E. Eiben
%A J. I. {van Hemert}
%T Adapting the Fitness Function in GP for Data Mining
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 193--202
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming, data mining
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=193
%X We describe how the Stepwise Adaptation of Weights (SAW) technique can be applied in genetic programming. The SAW-ing mechanism has been originally developed for and
   successfully used in constraint satisfaction problems. Here we identify the very basic underlying ideas behind SAW-ing and point out how it can be used for different types
   of problems. In particular, SAW-ing is well suited for data mining task s where the fitness of a candidate solution is composed by `local scores' on data records. We
   evaluate the power of the SAW-ing mechanism on a number of benchmark classification data sets. The results indicate that extending the GP with the SAW-ing feature increases
   its performance when different types of misclassifications are not weighted differently, but leads to worse results when they are.
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP
%@ 3-540-65899-8

%A Jeroen Eggermont
%A Agoston E. Eiben
%A Jano I. {van Hemert}
%T A comparison of genetic programming variants for data classification
%B Advances in Intelligent Data Analysis, Third International Symposium, IDA-99
%S LNCS
%E David J. Hand and Joost N. Kok and Michael R. Berthold
%V 1642
%D 1999
%P 281--290
%I Springer-Verlag Berlin
%C Amsterdam, The Netherlands
%K genetic algorithms, genetic programming, classification, data mining
%U http://www.vanhemert.co.uk/publications/ida99.A_comparison_of_genetic_programming_variants_for_data_classification.ps.gz
%X We report a comparative study on different variations of genetic programming applied on binary data classification problems. The first genetic programming variant is
   weighting data records for calculating the classification error and modifying the weights during the run. Hereby the algorithm is defining its own fitness function in an
   on-line fashion giving higher weights to `hard' records. Another novel feature we study is the atomic representation, where `Booleanization' of data is not performed at the
   root, but at the leafs of the trees and only Boolean functions are used in the trees' body. As a third aspect we look at generational and steady-state models in combination
   of both features.
%8 9--11  August
%Z IDA-99, Booleanization of inputs, ML: Australian credit, German Credit, Heart Disease, Pima. steady state. SAW-ing
%@ 3-540-66332-0

%A J. Eggermont
%A A. E. Eiben
%A J. I. {van Hemert}
%T A comparison of genetic programming variants for data classification
%B Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
%E Eric Postma and Marc Gyssens
%D 1999
%P 253--254
%I 
%I BNVKI, Dutch and the Belgian AI Association
%C Kasteel Vaeshartelt, Maastricht, Holland
%K genetic algorithms, genetic programming, data mining, classification
%U http://www.vanhemert.co.uk/publications/bnaic99.shortpaper.Comparing_genetic_programming_variants_for_data_classification.ps.gz
%X This article is a combined summary of two papers written by the authors. Binary data classification problems (with exactly two disjoint classes) form an important
   application area of machine learning techniques, in particular genetic programming (GP). We compare a number of different variants of GP applied to such problems whereby we
   investigate the effect of two significant changes in a fixed GP setup in combination with two different evolutionary models
%8 3-4  November
%Z resubmission of \citeEEH99b http://www.cs.unimaas.nl/~bnvki/

%A J. Eggermont
%A J. I. {van Hemert}
%T Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming
%B Proceedings of the Twelveth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'00)
%E Antal van den Bosch and Hans Weigand
%D 2000
%P 259--266
%I 
%I BNVKI, Dutch and the Belgian AI Association
%C De Efteling, Kaatsheuvel, Holland
%K genetic algorithms, genetic programming, data mining
%U http://citeseer.ist.psu.edu/374087.html
%X In this paper we continue study on the Stepwise Adaptation of Weights (SAW) technique. Previous studies on constraint satisfaction and data classification have indicated
   that SAW is a promising technique to boost the performance of evolutionary algorithms. Here we use SAW to boost performance of a genetic programming algorithm on simple
   symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression problems.
%8 1-2  November

%A Jeroen Eggermont
%A Jano I. {van Hemert}
%T Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 23--35
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Adaptation, Symbolic Regression, Problem Generator, Program Trees, data mining
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=23
%X In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (SAW) to boost performance of a genetic programming algorithm on
   simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression problems from
   literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three GP variants.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Jeroen Eggermont
%A Tom Lenaerts
%A Sanna Poyhonen
%A Alexandre Termier
%T Raising the Dead; Extending Evolutionary Algorithms with a Case-based Memory
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 280--290
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Dynamic Fitness, Global Memory
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=280
%X In dynamically changing environments, the performance of a standard evolutionary algorithm deteriorates. This is due to the fact that the population, which is considered to
   contain the history of the evolutionary process, does not contain enough information to allow the algorithm to react adequately to changes in the fitness landscape.
   Therefore, we added a simple, global case-based memory to the process to keep track of interesting historical events. Through the introduction of this memory and a storing
   and replacement scheme we were able to improve the reaction capabilities of an evolutionary algorithm with a periodically changing fitness function.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Jeroen Eggermont
%T Evolving Fuzzy Decision Trees with Genetic Programming and Clustering
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 71--82
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780071.pdf
%X In this paper we present a new fuzzy decision tree representation for n-category data classification using genetic programming. The new fuzzy representation uses fuzzy
   clusters for handling continuous attributes. To make optimal use of the fuzzy classifications of this representation an extra fitness measure is used. The new fuzzy
   representation will be compared, using several machine learning data sets, to a similar non-fuzzy representation as well as to some other evolutionary and non-evolutionary
   algorithms from literature.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A J. Eggermont
%T Evolving Fuzzy Decision Trees for Data Classification
%B Proceedings of the 14th Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02)
%E Hendrik Blockeel and Marc Denecker
%D 2002
%I 
%I BNVKI, Dutch and the Belgian AI Association
%C Leuven, Belgium
%K genetic algorithms, genetic programming
%8 21-22  October
%Z Katholieke Universiteit Leuven and Universite Libre de Bruxelles in collaboration with PharmaDM and under the auspices of BNVKI/AIABN (the Belgian-Dutch Association for
   Artificial Intelligence), SIKS (School for Information and Knowledge Systems), and SNN (the Foundation for Neural Networks).

%A J. Eggermont
%A T. Lenaerts
%T Dynamic Optimization using Evolutionary Algorithms with a Case-based Memory
%B Proceedings of the 14th Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02)
%E Hendrik Blockeel and Marc Denecker
%D 2002
%I 
%I BNVKI, Dutch and the Belgian AI Association
%C Leuven, Belgium
%K genetic algorithms, genetic programming, evolutionary algorithms
%U http://www.liacs.nl/~jeggermo/publications/bnaic02-dynamic.ps.gz
%X Dynamic environments form a dicult class of problems for evolutionary algorithms to solve. In this paper we propose a new evolutionary algorithm for this class in which we
   combine a case-based memory with a meta-learner.
%8 21-22  October
%Z Katholieke Universiteit Leuven and Universite Libre de Bruxelles in collaboration with PharmaDM and under the auspices of BNVKI/AIABN (the Belgian-Dutch Association for
   Artificial Intelligence), SIKS (School for Information and Knowledge Systems), and SNN (the Foundation for Neural Networks).

%A J. Eggermont
%A J. N. Kok
%A W. A. Kosters
%T Genetic Programming for Data Classification: Partitioning the Search Space
%B Proceedings of the 2004 Symposium on Applied Computing (ACM SAC'04)
%D 2004
%P 1001--1005
%I 
%I ACM
%C Nicosia, Cyprus
%K genetic algorithms, genetic programming, data classification
%U http://www.liacs.nl/~kosters/SAC2003final.pdf
%X When Genetic Programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. We present several methods using techniques
   from the field of machine learning to refine and thereby reduce the search space sizes for decision tree evolvers. We will show that these refinement methods improve the
   classification performance of our algorithms.
%8 14-17  March

%A J. Eggermont
%A J. N. Kok
%A W. A. Kosters
%T Genetic Programming for Data Classification: Refining the Search Space
%B Proceedings of the Fivteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'03)
%E T. Heskes and P. Lucas and L. Vuurpijl and W. Wiegerinck
%D 2003
%P 123--130
%I 
%I BNVKI, Dutch and the Belgian AI Association
%C Nijmegen, The Netherlands
%K genetic algorithms, genetic programming
%U http://www.liacs.nl/home/kosters/bnaic03-eggermont.ps
%8 23-24  October
%Z C4.5 ID3

%A J. Eggermont
%A J. N. Kok
%A W. A. Kosters
%T Genetic Programming for Data Classification: Partitioning the Search Space
%B Proceedings of the 2004 Symposium on applied computing (ACM SAC'04)
%D 2004
%P 1001--1005
%I 
%C Nicosia, Cyprus
%K genetic algorithms, genetic programming
%8 14-17  March

%A Jeroen Eggermont
%A Joost N. Kok
%A Walter A. Kosters
%T Detecting and Pruning Introns for Faster Decision Tree Evolution
%B Parallel Problem Solving from Nature - PPSN VIII
%S LNCS
%E Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\vno Ata Kab\'an and Hans-Paul
   Schwefel
%V 3242
%D 2004
%P 1071--1080
%I Springer-Verlag Berlin
%C Birmingham, UK
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=1071
%X We show how the understandability and speed of genetic programming classification algorithms can be improved, without affecting the classification accuracy. By analysing
   the decision trees evolved we can remove the unessential parts, called introns, from the discovered decision trees. Since the resulting trees contain only useful
   information they are smaller and easier to understand. Moreover, by using these pruned decision trees in a fitness cache we can significantly reduce the number of
   unnecessary fitness calculations.
%8 18-22  September
%Z PPSN-VIII
%@ 3-540-23092-0

%A Jeroen Eggermont
%T Data Mining using Genetic Programming: Classification and Symbolic Regression
%R Ph.D. Thesis
%D 2005
%I 
%I Institute for Programming research and Algorithmics, Leiden Institute of Advanced Computer Science, Faculty of Mathematics \& Natural Sciences, Leiden University
%C The Netherlands
%K genetic algorithms, genetic programming, data mining
%U https://openaccess.leidenuniv.nl/dspace/bitstream/1887/3393/1/proefschriftppi-eggermont.pdf
%X Sir Francis Bacon said about four centuries ago: "Knowledge is Power". If we look at today's society, information is becoming increasingly important. According to [73]
   about five exabytes (5 Ś 1018 bytes) of new information were produced in 2002, 92% of which on magnetic media (e.g., hard-disks). This was more than double the amount of
   information produced in 1999 (2 exabytes). However, as Albert Einstein observed: "Information is not Knowledge". One of the challenges of the large amounts of information
   stored in databases is to find or extract potentially useful, understandable and novel patterns in data which can lead to new insights. To quote T.S. Eliot: "Where is the
   knowledge we have lost in information ?" [35]. This is the goal of a process called Knowledge Discovery in Databases (KDD) [36]. The KDD process consists of several phases:
   in the Data Mining phase the actual discovery of new knowledge takes place. The outline of the rest of this introduction is as follows. We start with an introduction of
   Data Mining and more specifically the two subject areas of Data Mining we will be looking at: classification and regression. Next we give an introduction about evolutionary
   computation in general and tree-based genetic programming in particular. In Section 1.4 we give our motivation for using genetic programming for Data Mining. Finally, in
   the last sections we give an overview of the thesis and related publications.
%8 14  September
%Z IPA 1887/3393
%@ 90-9019760-5

%A Jeroen Eggermont
%T Juan Romero and Penousal Machado (eds): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music Natural Computing Series, Springer Science+Business Media,
   2008, 460 pp, 169 illustrations, 91 in colour, Hard Cover with DVD, ISBN: 978-3-540-72876-4
%J Genetic Programming and Evolvable Machines
%V 10
%N 1
%D 2009
%P 95--96
%I 
%K genetic algorithms, genetic programming
%8 March

%A Jason T. Eglit
%T Trend Prediction in Financial Time Series
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 31--40
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 Decemeber
%Z This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-187263-3

%A Toru Eguchi
%A Kotaro Hirasawa
%A Jinglu Hu
%A Junichi Murata
%T Multiagent Systems with Symbiotic Learning and Evolution Using Genetic Network Programming
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 130--137
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp

%A Toru Eguchi
%A Kotaro Hirasawa
%A Jinglu Hu
%A Sandor Markon
%T Elevator Group Supervisory Control Systems Using Genetic Network Programming
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 1661--1667
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Real-world applications, Theory of evolutionary algorithms
%X Genetic Network Programming (GNP) has been proposed and studied as a new method of evolutionary computations. Until now, the applicability and availability of GNP to the
   real-world applications have not been studied. In this paper, Elevator Group Supervisory Control Systems (EGSCSs) are considered as the real- world application for GNP, and
   it is reported that the design of a controller of EGSCSs has been studied using GNP. From simulations, it is clarified that better solutions are obtained by using GNP than
   other conventional methods and the availability of GNP to real-world applications is confirmed.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Toru Eguchi
%A Kotaro Hirasawa
%A Jinglu Hu
%A Sandor Markon
%T Elevator Group Supervisory Control System Using Genetic Network Programming with Functional Localization
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 1
%D 2005
%P 328--335
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X Genetic Network Programming (GNP) whose gene consists of directed graphs has been proposed as a new method of evolutionary computations, and it is recently applied to the
   Elevator Group Supervisory Control System (EGSCS), a real world problem, to confirm its effectiveness. In the previous study, although the flow of traffic in the elevator
   system is known and fixed, it is changed dynamically with time in real elevator systems. Therefore, the EGSCS with an adaptive control should be studied considering such
   changes for practical applications. In this paper, the GNP with functional localisation is applied to the EGSCS to construct such an adaptive system. In the proposed
   method, the switching GNP can switch the functionally localised GNPs (assigning GNPs) fitted to several kinds of traffic by detecting the change of the flow of traffic.
   From the simulations, the adaptability and effectiveness of the proposed method are clarified using the traffic data of a day in an office building.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Toru Eguchi
%A Kotaro Hirasawa
%A Jinglu Hu
%A Nathan Ota
%T A study of evolutionary multiagent models based on symbiosis
%J IEEE Transactions on Systems, Man, and Cybernetics, Part B
%V 36
%N 1
%D 2006
%P 179--193
%I 
%K genetic algorithms, genetic programming, decision making, evolutionary computation, graph theory, learning (artificial intelligence), multi-agent systems, directed graph,
   evolutionary multiagent models, genetic network programming, match type tile-world, nash equilibria, symbiosis multiagent systems, symbiotic evolution, symbiotic learning,
   virtual model, Evolutionary computation, multiagent systems, symbiosis, tile-world
%X Multiagent Systems with Symbiotic Learning and Evolution (Masbiole) has been proposed and studied, which is a new methodology of Multiagent Systems (MAS) based on symbiosis
   in the ecosystem. Masbiole employs a method of symbiotic learning and evolution where agents can learn or evolve according to their symbiotic relations toward others, i.e.,
   considering the benefits/losses of both itself and an opponent. As a result, Masbiole can escape from Nash Equilibria and obtain better performances than conventional MAS
   where agents consider only their own benefits. This paper focuses on the evolutionary model of Masbiole, and its characteristics are examined especially with an emphasis on
   the behaviours of agents obtained by symbiotic evolution. In the simulations, two ideas suitable for the effective analysis of such behaviors are introduced; "Match Type
   Tile-world (MTT)" and "Genetic Network Programming (GNP)". MTT is a virtual model where tile-world is improved so that agents can behave considering their symbiotic
   relations. GNP is a newly developed evolutionary computation which has the directed graph type gene structure and enables to analyse the decision making mechanism of agents
   easily. Simulation results show that Masbiole can obtain various kinds of behaviours and better performances than conventional MAS in MTT by evolution.
%8 February

%A Tobin Ehlis
%T Evolution of Intelligent Task Prioritization in a Dynamic Randomly Updated Environment
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 125--134
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Tobin Ehlis
%T Application of Genetic Programming to the ``Snake Game''
%J Gamedev.Net
%N 175
%D 2000
%I 
%K genetic algorithms, genetic programming, game strategy
%U http://www.gamedev.net/reference/articles/article1175.asp
%X This paper describes the evolution of a genetic program to optimise a problem featuring task prioritisation in a dynamic, randomly updated environment. The specific problem
   approached is the "snake game" in which a snake confined to a rectangular board attempts to avoid the walls and its own body while eating pieces of food. The problem is
   particularly interesting because as the snake eats the food, its body grows, causing the space through which the snake can navigate to become more confined. Furthermore,
   with each piece of food eaten, a new piece of food is generated in a random location in the playing field, adding an element of uncertainty to the program. This paper will
   focus on the development and analysis of a successful function set that will allow the evolution of a genetic program that causes the snake to eat the maximum possible
   pieces of food.
%Z this article was posted to GameDev.net: 8/10/2000 Cited by \citeCS310GeneticAlgsProject http://www.dcs.warwick.ac.uk/~csvnw/CS310GeneticAlgsProject.pdf "Evolving Ghosts in
   Pacman Using Evolutionary Algorithms" A 3rd year project report by James Hume [8] Ehlis Tobin. Application of Genetic programming to the "Snake Game". Available from the
   World Wide Web: http://www.gamedev.net/reference/articles/article1175.asp (Accessed 9 th October 2003). Accessed 18 Dec 2005

%A Rachel Ehrenberg
%T Software Scientist
%J Science News
%V 181
%D 2012
%P 20
%I 
%K genetic algorithms, genetic programming, Eureqa
%U http://www.sciencenews.org/view/feature/id/337207/title/Software_Scientist
%X With a little data, Eureqa generates fundamental laws of nature
%8 14  January
%Z ...'Eureqa has many more papers with many different authors to its name. The program is openly available online and has been downloaded more than 25,000 times'. ...'Deep
   Thought in Douglas Adams' The Hitchhiker's Guide to the Galaxy.... [GP] ...gave for the meaning of everything: 42'. See also \citeDubcakova:2011:GPEM

%A Herman H. Ehrenburg
%A H. A. N. {van Maanen}
%T A Finite Automaton Learning System Using Genetic Programming
%R NeuroColt Tech Rep CS-R9458
%D 1994
%I 
%I Department of Computer Science, CWI, Centrum voor Wiskunde en Informmatica
%C CWI, P.O. Box 94079, 1090 GB Amsterdam, The Netherlands
%K genetic algorithms, genetic programming, Evolutionary Computing, finite automata
%U http://citeseer.ist.psu.edu/427245.html
%X This report describes the Finite Automaton Learning System (FALS), an evolutionary system that is designed to find small digital circuits that duplicate the behavior of a
   given finite automaton. FALS is developed with the aim to get a better insight in learning systems. It is also targeted to become a general purpose automatic programming
   system. The system is based on the genetic programming approach to evolve programs for tasks instead of explicitly programming them. A representation of digital circuits
   suitable for genetic programming is given as well as an extended crossover operator that alleviates the need to specify an upper bound for the number of states in advance.
%Z Also available as NC-TR-95-009

%A Herman Ehrenburg
%T Improved Directed Acyclic Graph Handling and the Combine Operator in Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 285--291
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming, DAG
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 'combine' genetic operator, ancestor information. p289 Does not 'make use of the 32-fold speedup be evaluating 32 fitness cases in parallel'. p290 'Martin C. Martin'
   'CMU 32-parallelization trick'.
%@ 0-262-61127-9

%A A. E. Eiben
%A T. J. Euverman
%A W. Kowalczyk
%A E. Peelen
%A F. Slisser
%A J. A. M. Wesseling
%T Comparing Adaptive and Traditional Techniques for Direct Marketing
%B Proceedings of the 4th European Congress on Intelligent Techniq ues and Soft Computing
%E H.-J. Zimmermann
%D 1996
%P 434--437
%I Verlag Mainz Aachen, Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/eiben96comparing.html
%X he paper contains results of a research project aimed at application and evaluation of modern data analysis techniques in the field of marketing. The investigated
   techniques were: neural networks, evolutionary algorithms, CHAID and logistic regression analysis. All techniques were applied to the problem of making optimal selections
   for direct mailing and the resulting models were compared w.r.t. accuracy, interpretability, transparency and time and expertise needed for their construction.

%A Gusz Eiben
%T GP in Leiden
%D 1997
%I 
%K genetic algorithms, genetic programming
%O electronic communication
%8 10  November
%Z Organisation: Leiden University, Dept. of Mathematics & Computer Science, The Netherlands Dear GP-ers, Following Michele's example on > everybody could just give a list of
   keywords, > describing > - the main technical focus of the person or the team > - the applications. here is the item on the Leiden group. Mainly applications and
   application oriented research in the filed of marketing and financial services. Alas, this implies that many of our projects are confidential. Even if we are allowed to
   submit, we need to leave out so many technical details that the reviewers find it unacceptable. I hope you can use some of this info. <snip> Cheers, Gusz
   -------------------------------------------------------------------- Topics, resp. applications: - Direct marketing application for a big multinational computer
   manufacturer (see the publication below) - Credit-Score-Card application for a medium size Dutch bank - Creditability evaluation application for a major Dutch bank - Data
   mining feature selection application for a small Dutch software house - Customer retention modelling for a major Dutch investment fund see \citeEEKS98 Master Theses
   co-supervised by our group members (not published) M. Keijzer, Representing Computer Programs in Genetic Programming, 1995 (in English). Supervised by A.E. Eiben and M.
   Gerrets. S. da Silva, Go and Genetic Programming: Playing Go with Filter Functions, 1996 (in English). Supervised by A.E. Eiben and H.J.M. Goeman. H.D. Sneep, A Genetic
   Algorithms for the Development of a Credit-Score-Card, 1994. Supervised by A.E. Eiben and H.J. Gaaikema. C. van Straten, Predictive Power of Genetic Programming, 1995.
   Supervised by A.E. Eiben and J.A.M. Wesseling. C.J. Veenman, Positional Genetic Programming, 1996 (in English). Supervised by A.E. Eiben and W.J. Kowalczyk (see
   \citeveennan:mastersthesis ) D. de Vries, Seeking for the Reliable Custumer with Darwin, 1994. Supervised by A.E. Eiben and B. Kersten. D.L.T. Zwietering, Genetic Selection
   of Relevant Features, 1995. Supervised by A.E. Eiben, E. Lebert and D. Thierens. Cheers

%A A. E. Eiben
%A T. J. Euverman
%A W. Kowalczyk
%A F. Slisser
%T Modelling Customer Retention with Statistical Techniques, Rough Data Models and Genetic Programming
%B Rough-Fuzzy Hybridization: A New Trend in Decision Making Fuzzy Sets, Rough Sets and Decision Making Processes
%E Sankar K. Pal and Andrzej Skowron
%D 1998
%P 330--345
%I Springer-Verlag
%C Berlin
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=DF680800F7770919CB85C7A704F50DC9?doi=10.1.1.55.7177&rep=rep1&type=pdf
%X This paper contains results of a research project aiming at modelling the phenomenon of customer retention. Historical data from a database of a big mutual fund investment
   company have been analysed with three techniques: logistic regression, rough data models, and genetic programming. Models created by these techniques were used to gain
   insights into factors influencing customer behaviour and to make predictions on ending the relationship with the company in question. Because the techniques were applied
   independently of each other, it was possible to make a comparison of their basic features in the context of data mining.
%Z http://www.amazon.com/Rough-Fuzzy-Hybridization-Decision-Making/dp/9814021008
%@ 9814021008

%A A. E. Eiben
%A A. E. Koudijs
%A F. Slisser
%T Genetic Modelling of Customer Retention
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 178--186
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%X This paper contains results of a research project aiming at the application and evaluation of modern data analysis techniques in the field of marketing. The investigated
   techniques are: genetic programm ing, rough data analysis, CHAID and logistic regression analysis. All four techniques are applied independently to the problem of customer
   retention modelling, using a database of a financial company. Models created by these techniques are used to gain insights into factors influencing customer behaviour and
   to make predictions on ending the relationship with the company in question. Comparing the predictive power of the obtained models shows that the genetic technology offers
   the highest performance.
%8 14-15  April
%Z EuroGP'98
%@ 3-540-64360-5

%A A. E. Eiben
%A D. Elia
%A J. I. van Hemert
%T Population dynamics and emerging mental features in AEGIS
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1257--1264
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-038.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Agoston Endre Eiben
%A Robert Hinterding
%A Zbigniew Michalewicz
%T Parameter Control in Evolutionary Algorithms
%J IEEE Transations on Evolutionary Computation
%V 3
%N 2
%D 1999
%P 124--141
%I 
%K evolutionary strategies, genetic algorithms, evolutionary computation, self-adjusting systems, control mechanisms, evolutionary algorithms, parameter control,
   self-adaptation
%X The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation:
   it has a potential of adjusting the algorithm to the problem while solving the problem. In the paper we: 1) revise the terminology, which is unclear and confusing, thereby
   providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent
   years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research
%8 July
%Z Some mention of GP, particularly Peter Angeline's work. Reference Cited: 144 CODEN: ITEVF5 Inspec Accession Number: 6290502

%A A. E. Eiben
%A M. Schoenauer
%T Evolutionary computing
%J Information Processing Letters
%V 82
%N 1
%D 2002
%P 1--6
%I 
%K genetic algorithms, genetic programming, Evolutionary computing, Evolution strategies, Evolutionary programming
%U http://www.sciencedirect.com/science/article/B6V0F-44YWS0J-1/2/a93e1d8b3c96d1cb1a32da104588a569
%X Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of
   natural selection. we briefly introduce the main concepts behind evolutionary computing. We present the main components all evolutionary algorithms (EAs), sketch the
   differences between different types of EAs and survey application areas ranging from optimisation, modelling and simulation to entertainment.

%A A. E. Eiben
%A J. E. Smith
%T Introduction to Evolutionary Computing
%D 2003
%I Springer
%K genetic algorithms, genetic programming
%U http://www.cs.vu.nl/~gusz/ecbook/ecbook.html
%Z Chapter list 1. Introduction 2. What is an Evolutionary Algorithm? 3. Genetic Algorithms 4. Evolution Strategies 5. Evolutionary Programming 6. Genetic Programming 7.
   Learning Classifier Systems 8. Parameter Control in Evolutionary Algorithms 9. Multi-Modal Problems and Spatial Distribution 10. Hybridisation with Other Techniques:
   Memetic Algorithms 11. Theory 12. Constraint Handling 13. Special Forms of Evolution 14. Working with Evolutionary Algorithms 15. Summary 16. Appendices 17. Index 18.
   References
%@ 3-540-40184-9

%A Gusz Eiben
%A Joeri Bekker
%A Robert Griffioen
%A Evert Haasdijk
%T Balancing quality and quantity in evolving agent systems
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 1
%D 2007
%P 335--335
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware: Poster, multiagent system, NEW TIES, quality bias,
   quantity bias, varying population size
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p335.pdf
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Jacob Eisenstein
%T Genetic Algorithms and Incremental Learning
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 47--56
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming, seeding
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Jacob Eisenstein
%T Evolving Robocode Tank Fighters
%R AI Memo 2003-023
%D 2003
%I 
%I Computer Science and Artificial Intelligence Laboratory, MIT
%C Cambridge, MA 02139, USA
%K genetic algorithms, genetic programming
%U ftp://publications.ai.mit.edu/ai-publications/2003/AIM-2003-023.ps
%X In this paper, I describe the application of genetic programming to evolve a controller for a robotic tank in a simulated environment. The purpose is to explore how genetic
   techniques can best be applied to produce controllers based on subsumption and behavior oriented languages such as REX. As part of my implementation, I developed TableRex,
   a modification of REX that can be expressed on a fixed-length genome. Using a fixed subsumption architecture of TableRex modules, I evolved robots that beat some of the
   most competitive hand-coded adversaries.
%8 28  October

%A Aniko Ekart
%T Generating Class Descriptions of Four Bar Linkages
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/465578.html
%X Kinematic synthesis of four bar mechanisms is a design problem that is difficult to solve by generative methods. The present approach is a variant based method that
   combines the genetic programming and decision tree learning methods. The aim of the research is to give a structural description for the class of mechanisms that produce
   desired coupler curves. For finding and characterizing feasible regions of the design space constructive induction is used. The new features are created by genetic
   programming.
%8 22-25  July
%Z GP-98LB See also \citeekart:1999:ASI

%A Aniko Ekart
%T Controlling Code Growth in Genetic Programming by Mutation
%B Late-Breaking Papers of EuroGP-99
%E W. B. Langdon and Riccardo Poli and Peter Nordin and Terry Fogarty
%D 1999
%P 3--12
%I 
%I EvoGP
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.ps.Z
%X In the paper a method that moderate code growth in genetic programming is presented. The addressed problem is symbolic regression. A special mutation operator is used for
   the simplification of programs. If every individual program in each generation is simplified, then performance of the genetic programming system is worsened. But if
   simplification is applied as a mutation operator, more compact solutions of the same or better accuracy can be obtained
%8 26-27  May
%Z EuroGP'99LB part of \citelangdon:1999:egplb

%A Aniko Ekart
%A Andras Markus
%T Decision Trees and Genetic Programming in Synthesis of Four Bar Mechanisms
%B Life Cycle Approaches to Production Systems, Proceedings of the Advanced Summer Institute-ASI'99
%D 1999
%P 210--208
%I 
%C Leuven
%K genetic algorithms, genetic programming
%8 22-24  September
%Z http://www.lar.ee.upatras.gr/icims/asi/asi99/asi99.htm See also \citeekart:1998:gcd4bl Nice fusion of C4.5 and GP.
%@ 960-530-040-0

%A Aniko Ekart
%T Shorter Fitness Preserving Genetic Programs
%B Artificial Evolution. 4th European Conference, AE'99, Selected Papers
%S LNCS
%E C. Fonlupt and J.-K. Hao and E. Lutton and E. Ronald and M. Schoenauer
%V 1829
%D 2000
%P 73--83
%I 
%C Dunkerque, France
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/496596.html
%8 3-5  November
%Z http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67846-8 "Simplification is implemented in Prolog and consists of approximately 250 clauses." Fig 4. plots of
   fitness (RMS error) times program size
%@ 3-540-67846-8

%A Aniko Ekart
%A S. Z. Nemeth
%T A metric for genetic programs and fitness sharing
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 259--270
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=259
%X In the paper a metric for genetic programs is constructed. This metric reflects the structural difference of the genetic programs. It is used then for applying fitness
   sharing to genetic programs, in analogy with fitness sharing applied to genetic algorithms. The experimental results for several parameter settings are discussed. We
   observe that by applying fitness sharing the code growth of genetic programs could be limited.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Aniko Ekart
%A S. Z. Nemeth
%T Selection Based on the Pareto Nondomination Criterion for Controlling Code Growth in Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 2
%N 1
%D 2001
%P 61--73
%I 
%K genetic algorithms, genetic programming, code growth, selection scheme, multiobjective optimization
%X The rapid growth of program code is an important problem in genetic programming systems. In the present paper we investigate a selection scheme based on multiobjective
   optimization. Since we want to obtain accurate and small solutions, we reformulate this problem as multiobjective optimization. We show that selection based on the Pareto
   nondomination criterion reduces code growth and processing time without significant loss of solution accuracy.
%8 March
%Z Article ID: 319813

%A Aniko Ekart
%A S. Z. Nemeth
%T Stability of Tree Based Decision Principles
%B EURO Summer Institute (ESI) XIX, Decision Analysis and Artificial Intellience
%D 2001
%I 
%C Toulouse, France
%K genetic algorithms, genetic programming
%8 9-22  September

%A Aniko Ekart
%T Genetic programming: new performance improving methods and applications
%R Ph.D. Thesis
%D 2001
%I 
%I E\"otv\"os Lorand University
%K genetic algorithms, genetic programming
%X Genetic programming is the newest form of evolutionary computation that was conceived in the late 1980's as a possible means for automatic programming. Genetic programming
   performs an evolutionary search in the space of computer programs and selects the program that solves a given task according to certain criteria. In the first part of the
   dissertation we give an overview of evolutionary computation and in particular genetic programming. We raise key issues for genetic programming: code growth, diversity,
   real world applications. In the second part we present our contribution to the theory of genetic programming. We demonstrate two methods for limiting the code growth. The
   first method consists in applying an additional mutation operator that simplifies the structure of a genetic program without altering its behavior. The second method
   applies multiobjective optimization for the objectives of fitness and program size. We show that both methods are successful in reducing code growth without significant
   loss of accuracy. We then define a distance metric for genetic programs and use it for applying the fitness sharing technique. We propose a simple diversity measure based
   on our metric and study the effects of fitness sharing with the help of this diversity measure. In the third part we show the application of genetic programming in two
   complex real world problems. The first problem comes from mechanical engineering. Four bar mechanisms play a very important role in practical mechanism design. We describe
   our four bar mechanism design system. We demonstrate how genetic programming can be a vital component of a complex design system. We integrate genetic programming with
   decision trees into a powerful learning machine. The second problem belongs to the decision support domain of economics. The decision-makers have to make many subjective
   decisions. Consequently, the final decision is sensitive to even small changes in these subjective values. We present our genetic programming system that helps the
   decision-makers to arrive at stable decisions. That is, for small variations in the values of the involved variables, the final decision remains unchanged.

%A Anik\'o Ek\'art
%A Sandor Zoltan N\'emeth
%T Maintaining the Diversity of Genetic Programs
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 162--171
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://www.sztaki.hu/~ekart/eurgp2.ps
%X An important problem of evolutionary algorithms is that throughout evolution they loose genetic diversity. Many techniques have been developed for maintaining diversity in
   genetic algorithms, but few investigations have been done for genetic programs. We define here a diversity measure for genetic programs based on our metric for genetic
   trees. We use this distance measure for studying the effects of fitness sharing. We then propose a method for adaptively maintaining the diversity of a population during
   evolution.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Anik\'o Ek\'art
%A S. Z. N\'emeth
%T Stability analysis of tree structured decision functions
%J European Journal of Operational Research
%V 160
%N 3
%D 2005
%P 676--695
%I 
%K genetic algorithms, genetic programming, Decision support systems, Evolutionary computation, Stability analysis, Decision functions
%U http://www.sciencedirect.com/science/article/B6VCT-4B6CR54-4/2/8de1437b694f9e2060da541ad1b175be
%X In multicriteria decision problems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to
   subjective criteria. Since these assignments are approximate, it is very important to analyze the sensitivity of results when small modifications of the assignments are
   made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. We propose here a method
   based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies.
%8 1  February

%A Aniko Ekart
%A Andras Markus
%T Using Genetic Programming and Decision Trees for Generating Structural Descriptions of Four Bar Mechanisms
%J Artificial Intelligence for Engineering Design, Analysis and Manufacturing
%V 17
%N 3
%D 2003
%P 205--220
%I 
%K genetic algorithms, genetic programming, decision trees, four bar mechanism synthesis, machine learning
%X Four bar mechanisms are basic components of many important mechanical device. The kinematic synthesis of four bar mechanisms is a difficult design problem. We present here
   a novel method that combines the genetic programming and decision tree learning methods. We give a structural description for the class of mechanisms that produce desired
   coupler curves. For finding and characterising feasible regions of the design space constructive induction is used. Decision trees constitute the learning engine and the
   new features are created by genetic programming.
%Z http://journals.cambridge.org/action/displayJournal?jid=AIE

%A Aniko Ekart
%A Steven Gustafson
%T A Data Structure for Improved GP Analysis via Efficient Computation and Visualisation of Population Measures
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 35--46
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-itree-2004.pdf
%X Population measures for genetic programs are defined and analysed in an attempt to better understand the behaviour of genetic programming. Some measures are simple, but do
   not provide sufficient insight. The more meaningful ones are complex and take extra computation time. Here we present a unified view on the computation of population
   measures through an information hyper-tree (iTree). The iTree allows for a unified and efficient calculation of population measures via a basic tree traversal.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Aniko Ekart
%T Analysing the Emerging Properties of Genetic Programs through the iTrees of Populations
%B Proceedings of the 5th International Workshop on Emergent Synthesis IWES'04
%D 2004
%P 61--66
%I 
%I Computer and Automation Research Institute. Hungarian Academy of Sciences
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%8 May  24-25
%Z http://www.sztaki.hu/IWES04/

%A Aniko Ekart
%T Evolution of lace knitting stitch patterns by genetic programming
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2007)
%E Peter A. N. Bosman
%D 2007
%P 2457--2461
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, creativity, evaluation, representation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2457.pdf
%X In this paper we study the generation of lace knitting stitch patterns by using genetic programming. We devise a genetic representation of knitting charts that accurately
   reflects their usage for hand knitting the pattern. We apply a basic evolutionary algorithm for generating the patterns, where the key of success is evaluation. We propose
   automatic evaluation of the patterns, without interaction with the user. We present some patterns generated by the method and then discuss further possibilities for
   bringing automatic evaluation closer to human evaluation.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A E. Eklund
%T A Massively Parallel GP Architecture
%B Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems
%E K. C. Giannakoglou and D. T. Tsahalis and J. P\'eriaux and K. D. Papailiou and T. Fogarty
%D 2001
%P 103--108
%I International Center for Numerical Methods in Engineering (Cmine) Gran Capitan s/n, 08034 Barcelona, Spain
%C Athens, Greece
%K genetic algorithms, genetic programming
%8 19-21  September
%Z Proceedings of the EUROGEN2001 Conference
%@ 84-89925-97-6

%A Sven E. Eklund
%T A Massively Parallel Architecture for Linear Machine Code Genetic Programming
%B Evolvable Systems: From Biology to Hardware: Proceedings of 4th International Conference, ICES 2001
%S Lecture Notes in Computer Science
%E Yong Liu and Kiyoshi Tanaka and Masaya Iwata and Tetsuya Higuchi and Moritoshi Yasunaga
%V 2210
%D 2001
%P 216--224
%I Springer-Verlag Heidelberg
%C Tokyo, Japan
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2210&spage=216", acknowledgement = ack-nhfb
%X Over the last decades Genetic Algorithms (GA) and Genetic Programming (GP) have proven to be efficient tools for a wide range of applications. However, in order to solve
   human-competitive problems they require large amounts of computational power, particularly during fitness calculations. In this paper I propose the implementation of a
   massively parallel model in hardware in order to speed up GP. This fine-grained diffusion architecture has the advantage over the popular Island model of being
   VLSI-friendly and is therefore small and portable, without sacrificing scalability and effectiveness. The diffusion architecture consists of a large amount of independent
   processing nodes, connected through an X-net topology, that evolve a large number of small, overlapping sub-populations. Every node has its own embedded CPU, which executes
   a linear machine code representation of the individuals. Preliminary simulation results (low-level VHDL simulation) indicate a performance of 10.000 generations per second
   (depending on the application). One node requires 10-20.000 gates including the CPU (also application dependent), which makes it possible to fit up to 2.000 individuals in
   one FPGA (Virtex XC2V10000).
%8 October  3-5
%Z ICES-2001 A1 Dalarna University, Sweden sven.eklund@ieee.org

%A Sven E. Eklund
%T A Massively Parallel GP Engine in VLSI
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 629--633
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%X In this paper we propose the implementation of a massively parallel GP model in hardware in order to speed up the genetic algorithm. This fine-grained diffusion
   architecture consists of a large amount of independent processing nodes that evolve a large number of small, overlapping subpopulations. Every node has an embedded CPU that
   executes a linear machine code GP representation at a rate of up to 20,000 generations per second.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Sven E Eklund
%T Time series forecasting using massively parallel genetic programming
%B Proceedings of Parallel and Distributed Processing International Symposium
%D 2003
%P 143--147
%I 
%I IEEE
%K genetic algorithms, genetic programming, EHW, FPGA, Virtex XC2V10000, wolfe sunspot
%U http://dalea.du.se/research/?itemId=147
%X a massively parallel GP model in hardware as an efficient,flexible and scaleable machine learning system.This fine-grained diffusion architecture consists of a large amount
   of independent processing nodes that evolve a large number of small, overlapping subpopulations.Every node has an embedded CPU that executes a linear machine code GP
   representation at a rate of up to 20,000 generations per second.Besides being efficient,implementing the system in VLSI makes it highly portable and makes it possible to
   target mobile,n-line applications.The SIMD-like architecture also makes the system scalable so that larger problems can be addressed with a system with more processing
   nodes.Finally,the use of GP representation and VHDL modeling makes the system highly flexible and easy to adapt to different applications.We demonstrate the effectiveness
   of the system on a time series forecasting application.
%8 22-26  April
%Z outperforms SETAR but not best ANN

%A Sven E. Eklund
%T Handwritten Character Recognition using a massively parallel GP engine in VLSI
%B IFAC International Conference on Intelligent Control Systems and Signal Processing
%E Peter J. Fleming
%D 2003
%I 
%I IFAC
%C Faro, Portugal
%K genetic algorithms, genetic programming
%8 April  08-11
%Z http://www.sciference.com/icons03/main.py/pre_programme

%A Sven E. Eklund
%T A massively parallel architecture for distributed genetic algorithms
%J Parallel Computing
%V 30
%N 5-6
%D 2004
%P 647--676
%I 
%K genetic algorithms, genetic programming, Parallel architecture, Diffusion model, FPGA, Classification, Time series forecasting, Regression
%U http://www.sciencedirect.com/science/article/B6V12-4CDS49V-1/2/5ba1531eae2c9d8b336f1e90cc0ba5e9
%X Genetic algorithms are a group of stochastic search algorithms with a broad field of application. Although highly successful in many fields, genetic algorithms in general
   suffer from long execution times. we describe parallel models for genetic algorithms in general and the massively parallel Diffusion Model in particular, in order to
   speedup the execution.Implemented in hardware, the Diffusion Model constitutes an efficient, flexible, scalable and mobile machine learning system. This fine-grained system
   consists of a large number of processing nodes that evolve a large number of small, overlapping subpopulations. Every processing node has an embedded CPU that executes a
   linear machine code representation at a rate of up to 20,000 generations per second.Besides being efficient, implemented in hardware this model is highly portable and
   applicable to mobile, on-line applications. The architecture is also scalable so that larger problems can be addressed with a system with more processing nodes. Finally,
   the use of linear machine code as genetic programming representation and VHDL as hardware description language, makes the system highly flexible and easy to adapt to
   different applications.Through a series of experiments we determine the settings of the most important parameters of the Diffusion Model. We also demonstrate the
   effectiveness and flexibility of the architecture on a set of regression problems, a classification application and a time series forecasting application.

%A Magnus Ekman
%A Peter Nordin
%T Evolvable Hardware using State-machines
%B Graduate Student Workshop
%E Conor Ryan
%D 2001
%P 409--412
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming
%8 7  July
%Z GECCO-2001WKS Part of heckendorn:2001:GECCOWKS

%A Salah Yaseen El-Bakry
%A Amr Radi
%T Genetic Programming approach for electron-alkali-metal atom collisions
%J International Journal of Modern Physics B
%V 20
%N 32
%D 2006
%P 5463--5471
%I 
%K genetic algorithms, genetic programming, Condensed Matter Physics, Statistical Physics, Applied Physics, electron scattering, alkali atoms, total cross sections, dipole
   polarizability
%U http://www.genetic-programming.org/hc2007/09-Radi/Radi-Paper-A.pdf
%X New technique is presented for modelling the total cross sections of electron scattering by Na, K, Rb and Cs atoms in the low and intermediate energy regions. The
   calculations have been performed in the framework of genetic programming (GP) technique. The GP has been running based on the experimental data of the total collisional
   cross sections to produce the total cross sections for each target atom. The incident energy and atomic number as well as the static dipole polarisability have been used as
   input variables to find the functions that describe the total collisional cross sections of the scattering of electrons by alkali atoms. The experimental, calculated and
   predicted total collisional cross sections are compared. The discovered functions show a good match to the experimental data.
%8 Decemeber
%Z IJMPB 2007 HUMIES GECCO-2007 Physics Department, Faculty of Science, Taibah University, Madinah Munawwarah, P. O. Box 344, Kingdom of Saudi Arabia Physics Department,
   Faculty of Science, Ain Shams University, Abbassia, Cairo, Egypt

%A Mostafa Y. El-Bakry
%A Amr Radi
%T Genetic programming approach for flow of steady state fluid between two eccentric spheres
%J Applied Rheology
%V 17
%N 6
%D 2007
%P 68210
%I Kerschensteiner Verlag, Germany
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/hc2007/09-Radi/Radi-Paper-C.pdf
%X Genetic Programming (GP) is used to estimate the functions that describe the torque and the force acting on the external sphere due to steady state motion of viscoelastic
   fluid between two eccentric spheres. The GP has been running based on experimental data of the torque at different eccentricities to produce torque for each target
   eccentricity. The angular velocity of the inner sphere and the eccentricity of the two spheres have been used as input variables to find the discovered functions. The
   experimental, calculated and predicted torque and forces are compared. The discovered function shows a good match to the experimental data.We find that the GP technique is
   a good new mechanism of determination of the force and torque of fluid in eccentric sphere model.
%Z Mostafa Elbakry http://www.appliedrheology.org/ 2007 HUMIES GECCO-2007

%A Mostafa Y. El-Bakry
%A Amr Radi
%T Genetic Programming for Hadronic Interactions at High Energies
%J International Journal of Modern Physics C, Computational Physics and Physical Computation
%V 18
%N 3
%D 2007
%P 329--334
%I 
%K genetic algorithms, genetic programming, hadron-hadron interactions, Pseudo-rapidity distribution, proton2proton interaction at high energies
%X Genetic programming (GP) has been used to discover a function that describes pseudo-rapidity distribution of created pions from proton-proton (p-p) interactions at high and
   ultra-high energies. The predicted distributions from the GP-based model are compared with the experimental data. The discovered function of GP model has proven matching
   better for experimental data.
%Z IJMPC On leave from Faculty of Education, Physics Department, Ain Shams University, Egypt. Faculty of Science, P. O. Box 838, Dammam 31113, Saudi Arabia Faculty of Science,
   Ain Shams University, Egypt. Faculty of Science, P. O. Box 838, Dammam 31113, Saudi Arabia

%A Salah Yaseen El-Bakry
%A Amr Radi
%T Discovered Function for Positron Collisions with Alkali-Metal Atoms using Genetic Programming
%J International Journal of Modern Physics C, Computational Physics and Physical Computation
%V 18
%N 3
%D 2007
%P 351--358
%I 
%K genetic algorithms, genetic programming, positron collisions, alkali-metal atoms, total collisional cross sections
%X Genetic programming (GP) has been used to discover the function that describes the collisions of positrons with sodium, potassium, rubidium and caesium atoms at low and
   intermediate energies. The GP has been running based on experimental data of the total collisional cross sections to produce the total cross sections for each target atom.
   The incident energy and the static dipole polarizability of the alkali target atom have been used as input variables to find the discovered function. The experimental,
   calculated and predicted total collisional cross sections are compared. The discovered function shows a good match to the experimental data. We find that the GP technique
   is able to improve upon more traditional methods. To our knowledge, this is the first application of the GP technique to the data of positron collisions with alkali atoms
   at low and intermediate energies.
%Z IJMPC Physics Department, Taibah University, Madinah Munawwarah, P. O. Box 344, Saudi Arabia Physics Department, Ain Shams University, Abbassia, Cairo, Egypt

%A I. El-Baroudy
%A A. Elshorbagy
%A S. K. Carey
%A O. Giustolisi
%A D. Savic
%T Comparison of three data-driven techniques in modelling the evapotranspiration process
%J Journal of Hydroinformatics
%V 12
%N 4
%D 2010
%P 365--379
%I 
%K genetic algorithms, genetic programming, EPR, actual evapotranspiration, data driven techniques, eddy covariance, evolutionary polynomial regression, neural networks
%U http://www.iwaponline.com/jh/012/0365/0120365.pdf
%X Evapotranspiration is one of the main components of the hydrological cycle as it accounts for more than two-thirds of the precipitation losses at the global scale. Reliable
   estimates of actual evapotranspiration are crucial for effective watershed modelling and water resource management, yet direct measurements of the evapotranspiration losses
   are difficult and expensive. This research explores the utility and effectiveness of data-driven techniques in modelling actual evapotranspiration measured by an eddy
   covariance system. The authors compare the Evolutionary Polynomial Regression (EPR) performance to Artificial Neural Networks (ANNs) and Genetic Programming (GP).
   Furthermore, this research investigates the effect of previous states (time lags) of the meteorological input variables on characterising actual evapotranspiration. The
   models developed using the EPR, based on the two case studies at the Mildred Lake mine, AB, Canada provided comparable performance to the models of GP and ANNs. Moreover,
   the EPR provided simpler models than those developed by the other data-driven techniques, particularly in one of the case studies. The inclusion of the previous states of
   the input variables slightly enhanced the performance of the developed model, which in turn indicates the dynamic nature of the evapotranspiration process.

%A Mohammed A. El-Beltagy
%A Prasanth B. Nair
%A Andy J. Keane
%T Metamodeling Techniques For Evolutionary Optimization of Computationally Expensive Problems: Promises and Limitations
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 196--203
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-854.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Ayman El-Sawah
%A Chris Joslin
%A Nicolas D. Georganas
%A Emil M. Petriu
%T A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming
%B Fourth Canadian Conference on Computer and Robot Vision, CRV '07
%D 2007
%P 495--502
%I IEEE Computer Society
%C Montreal
%K genetic algorithms, genetic programming, VR, 3D hand tracking, 3D hand vision-based posture hypothesis, dynamic Bayesian network model, fuzzy set theory, geometric
   transformation, gesture recognition, image plane, kinematics inverse transformation, probabilistic observation model, single camera, soft computing, Bayes methods, cameras,
   computer vision, optical tracking, pose estimation, probability
%X In this paper we present a framework for 3D hand tracking and dynamic gesture recognition using a single camera. Hand tracking is performed in a two step process: we first
   generate 3D hand posture hypothesis using geometric and kinematics inverse transformations, and then validate the hypothesis by projecting the postures on the image plane
   and comparing the projected model with the ground truth using a probabilistic observation model. Dynamic gesture recognition is performed using a Dynamic Bayesian Network
   model. The framework uses elements of soft computing to resolve the ambiguity inherent in vision-based tracking by producing a fuzzy hand posture output by the hand
   tracking module and feeding back potential posture hypothesis from the gesture recognition module.
%8 28-30  May
%Z Univ. of Ottawa, Ottawa Almost no description of GP used

%A Craig Eldershaw
%A Stephen Cameron
%T Real-world applications: Motion planning using GAs
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1776
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-768.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Khalid Eldrandaly
%A Abdel-Azim Negm
%T Performance Evaluation of Gene Expression Programming for Hydraulic Data Mining
%J The International Arab Journal of Information Technology
%V 5
%N 2
%D 2008
%P 126--131
%I 
%K genetic algorithms, genetic programming, gene expression programming, GEP, Data mining, multiple linear regression, MLR, hydraulic jump.
%U http://www.ccis2k.org/iajit/PDF/vol.5,no.2/4-103.pdf
%X Predication is one of the fundamental tasks of data mining. In recent years, Artificial Intelligence techniques are widely being used in data mining applications where
   conventional statistical methods were used such as Regression and classification. The aim of this work is to show the applicability of Gene Expression Programming (GEP), a
   recently developed AI technique, for hydraulic data prediction and to evaluate its performance by comparing it with Multiple Linear Regression (MLR). Both GEP and MLR were
   used to model the hydraulic jump over a roughened bed using very large series of experimental data that contain all the important flow and roughness parameters such as the
   initial Froude number, the height of roughness ratio, the length of roughness ratio, the initial length ratio (from the gate) and the roughness density. The results show
   that GEP is a promising AI approach for hydraulic data prediction.
%8 April
%Z Information Systems Department, College of Computers, Zagazig University, Egypt http://www.iajit.org/

%A Khalid A. Eldrandaly
%T Integrating Gene Expression Programming and Geographic Information Systems for Solving a Multi Site Land Use Allocation Problem
%J American Journal of Applied Sciences
%D 2009
%I Science Publications
%K genetic algorithms, genetic programming, gene expression programming, Multi site land use allocation, GIS, SDSS
%U http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=15469239\&date=2009\&volume=6\&issue=5\&spage=1021
%X Problem statement: Land use planning may be defined as the process of allocating different activities or uses to specific units of area within a region. Multi sites Land
   Use Allocation Problems (MLUA) refer to the problem of allocating more than one land use type in an area. MLUA problem is one of the truly NP Complete (combinatorial
   optimization) problems. Approach: To cope with this type of problems, intelligent techniques such as genetic algorithms and simulated annealing, have been used. In this
   study a new approach for solving MLUA problems was proposed by integrating Gene Expression Programming (GEP) and GIS. The feasibility of the proposed approach in solving
   MLUA problems was checked using a fictive case study. Results: The results indicated clearly that the proposed approach gives good and satisfactory results.
   Conclusion/Recommendation: Integrating GIS and GEP is a promising and efficient approach for solving MLUA problems. This research focused on minimizing the development
   costs and maximizing the compactness of the allocated land use. The optimization model can be extended in the future to maximize also the spatial contiguity of the
   allocated land use.
%Z Faculty of Computers and Informatics, Zagazig University, Egypt

%A Khalid Eldrandaly
%T A GEP-based spatial decision support system for multisite land use allocation
%J Applied Soft Computing
%V 10
%N 3
%D 2009
%P 694--702
%I 
%K genetic algorithms, genetic programming, Spatial decision support systems, Multisite land use allocation, GIS, Gene expression programming
%U http://www.sciencedirect.com/science/article/B6W86-4X2DCVV-2/2/c8addfbfae7f3e5035dc45213f378416
%X Multisite Land Use Allocation Problem (MLUA) refers to the problem of allocating more than one land use type in an area. MLUA problem is one of the truly NP Complete
   (combinatorial optimisation) problems. To cope with this type of problems, intelligent techniques such as genetic algorithms, and simulated annealing, have been used.
   Research in the area of Spatial Decision Support Systems (SDSS) for resource allocation issues, a new scientific area of information system applications developed to
   support semi-structured or unstructured spatial decisions, has recently generated attention for integrating Artificial Intelligence (AI) techniques with Geographic
   Information Systems (GIS). In this paper we demonstrate how GIS can be integrated with Gene Expression Programming (GEP), a recently developed AI approach, for solving MLUA
   problems. The feasibility of the proposed approach in solving MLUA problems was checked using a fictive case study. The results indicated that the proposed approach gives
   good and satisfactory results.
%8 June
%Z King Abdulaziz University, P.O. Box 80105, Jeddah 21589, Saudi Arabia

%A Stefan Elfwing
%T Embodied Evolution of Learning Ability
%R Ph.D. Thesis Doctoral Thesis
%D 2007
%I 
%I KTH School of Computer Science and Communication
%C SE-100 44 Stockholm, Sweden
%K genetic algorithms, genetic programming, Embodied Evolution, Evolutionary Robotics, Reinforcement Learning, Shaping Rewards, Meta-parameters, Hierarchical Reinforcement
   Learning, Learning and Evolution. Meta-learning, Baldwin Effect, Lamarckian Evolution
%U http://www.irp.oist.jp/nc/elfwing/Elfwing_thesis_final_electronic.pdf
%X Embodied evolution is a methodology for evolutionary robotics that mimics the distributed, asynchronous, and autonomous properties of biological evolution. The evaluation,
   selection, and reproduction are carried out by cooperation and competition of the robots, without any need for human intervention. An embodied evolution framework is
   therefore well suited to study the adaptive learning mechanisms for artificial agents that share the same fundamental constraints as biological agents: self-preservation
   and self-reproduction. The main goal of the research in this thesis has been to develop a framework for performing embodied evolution with a limited number of robots, by
   using time-sharing of subpopulations of virtual agents inside each robot. The framework integrates reproduction as a directed autonomous behaviour, and allows for learning
   of basic behaviors for survival by reinforcement learning. The purpose of the evolution is to evolve the learning ability of the agents, by optimising meta-properties in
   reinforcement learning, such as the selection of basic behaviours, meta-parameters that modulate the efficiency of the learning, and additional and richer reward signals
   that guides the learning in the form of shaping rewards. The realization of the embodied evolution framework has been a cumulative research process in three steps: 1)
   investigation of the learning of a cooperative mating behaviour for directed autonomous reproduction; 2) development of an embodied evolution framework, in which the
   selection of pre-learned basic behaviours and the optimisation of battery recharging are evolved; and 3) development of an embodied evolution framework that includes
   meta-learning of basic reinforcement learning behaviors for survival, and in which the individuals are evaluated by an implicit and biologically inspired fitness function
   that promotes reproductive ability. The proposed embodied evolution methods have been validated in a simulation environment of the Cyber Rodent robot, a robotic platform
   developed for embodied evolution purposes. The evolutionarily obtained solutions have also been transferred to the real robotic platform. The evolutionary approach to
   meta-learning has also been applied for automatic design of task hierarchies in hierarchical reinforcement learning, and for co-evolving meta-parameters and potential-based
   shaping rewards to accelerate reinforcement learning, both in regards to finding initial solutions and in regards to convergence to robust policies.
%8 November
%Z TRITA-CSC-A 2007:16 ISSN-1653-5723 ISRN-KTH/CSC/A--07/16--SE Akademisk avhandling som med tillstand av Kungliga Tekniska hogskolan framlagges till offentlig granskning for
   avlaggande av teknologie doktorsexamen mandagen den 12 november 2007 kl. 10.00 i sal F3, Lindstedtsvagen 26, Kungliga Tekniska hogskolan, Stockholm. Stefan Elfwing, 2007
   Tryck: Universitetsservice US AB

%A Stefan Elfwing
%A Eiji Uchibe
%A Kenji Doya
%A Henrik I. Christensen
%T Evolutionary Development of Hierarchical Learning Structures
%J IEEE Transactions on Evolutionary Computation
%V 11
%N 2
%D 2007
%P 249--264
%I 
%K genetic algorithms, genetic programming, learning (artificial intelligence), Lamarckian evolutionary development, MAXQ hierarchical RL method, foraging task, genetic
   programming, hierarchical learning structures, hierarchical reinforcement learning, task decomposition
%X Hierarchical reinforcement learning (RL) algorithms can learn a policy faster than standard RL algorithms. However, the applicability of hierarchical RL algorithms is
   limited by the fact that the task decomposition has to be performed in advance by the human designer. We propose a Lamarckian evolutionary approach for automatic
   development of the learning structure in hierarchical RL. The proposed method combines the MAXQ hierarchical RL method and genetic programming (GP). In the MAXQ framework,
   a subtask can optimise the policy independently of its parent task's policy, which makes it possible to reuse learned policies of the subtasks. In the proposed method, the
   MAXQ method learns the policy based on the task hierarchies obtained by GP, while the GP explores the appropriate hierarchies using the result of the MAXQ method. To show
   the validity of the proposed method, we have performed simulation experiments for a foraging task in three different environmental settings. The results show strong
   interconnection between the obtained learning structures and the given task environments. The main conclusion of the experiments is that the GP can find a minimal strategy,
   i.e., a hierarchy that minimises the number of primitive subtasks that can be executed for each type of situation. The experimental results for the most challenging
   environment also show that the policies of the subtasks can continue to improve, even after the structure of the hierarchy has been evolutionary stabilised, as an effect of
   Lamarckian mechanisms
%8 April

%A Salah Elhaggaz
%A Brian Turton
%A John Brown
%T Evolutionary algorithm for phased network topology design
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 80--87
%I 
%C Orlando, Florida, USA
%8 13  July
%Z GECCO-99LB

%A David I. Ellis
%A David Broadhurst
%A Douglas B. Kell
%A Jem J. Rowland
%A Royston Goodacre
%T Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning
%J Applied and Environmental Microbiology
%V 68
%N 6
%D 2002
%P 2822--2828
%I 
%K genetic algorithms, genetic programming
%U http://dbkgroup.org/Papers/app_%20env_microbiol_68_(2822).pdf
%X Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show
   here that this technique can be used directly on the surface of food to produce biochemically interpretable ?fingerprints.? Spoilage in meat is the result of decomposition
   and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat
   substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil
   at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were
   obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of
   bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10 7 bacteria.g 1 the main
   biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial
   loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point
   process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.
%8 June
%Z American Society for Microbiology PMID: 12039738

%A David I. Ellis
%A David Broadhurst
%A Royston Goodacre
%T Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning
%J Analytica Chimica Acta
%V 514
%N 2
%D 2004
%P 193--201
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6TF4-4CDJJ78-5/2/63df147cb89407ac7ac8bf9d093580f7
%X Beef is a commercially important and widely consumed muscle food and central to the protein intake of many societies. In the food industry no technology exists for the
   rapid and accurate detection of microbiologically spoiled or contaminated beef. Fourier transform infrared (FT-IR) spectroscopy is a rapid, reagentless and non-destructive
   analytical technique whose continued development is resulting in manifold applications across a wide range of biosciences. FT-IR was exploited to measure biochemical
   changes within the fresh beef substrate, enhancing and accelerating the detection of microbial spoilage. Separately packaged fresh beef rump steaks were purchased from a
   national retailer, comminuted for 15 s and left to spoil at ambient room temperature for 24 h. Every hour, FT-IR measurements were collected directly from the sample
   surface using attenuated total reflectance, in parallel the total viable counts of bacteria were obtained by classical microbiological plating methods. Quantitative
   interpretation of FT-IR spectra was undertaken using partial least squares regression and allowed for accurate estimates of bacterial loads to be calculated directly from
   the meat surface in 60 s. Machine learning methods in the form of genetic algorithms and genetic programming were used to elucidate the wavenumbers of interest related to
   the spoilage process. The results obtained demonstrated that using FT-IR and machine learning it was possible to detect bacterial spoilage rapidly in beef and that the most
   significant functional groups selected could be directly correlated to the spoilage process which arose from proteolysis, resulting in changes in the levels of amides and
   amines.

%A A. El-Nofely
%A L. Sadek
%A N. Soliman
%T Spacing in the human deciduous dentition in relation to tooth size and dental arch size
%J Archives of Oral Biology
%V 34
%N 6
%D 1989
%P 437--441
%I 
%U http://www.sciencedirect.com/science/article/B6T4J-4BWHJWH-10R/2/d3ad580204c24fb1b0297899cd63dc6d
%Z Not on GP

%A Justin Elsey
%A Jorg Riepenhausen
%A Ben McKay
%A Geoffrey W. Barton
%T Dynamic Modelling of a Cooking Extruder
%B Chemeca 96: Excellence in Chemical Engineering; 24th Australian and New Zealand Chemical Engineering Conference and Exhibition; Proceedings
%E Gordon Weiss
%V 2
%D 1996
%P 43--48
%I 
%I Institution of Engineers, Australia
%C Barton, ACT, Australia
%K genetic algorithms, genetic programming
%U http://search.informit.com.au/documentSummary;dn=893841670974616;res=IELENG
%X A dynamic model of a twin-screw cooking extruder suitable for process optimisation and control purposes was implemented in MATLAB. The model is capable of predicting
   pressure, temperature and starch gelatinisation profiles, as well as the residence time distribution and the specific mechanical energy expended on the product. Two
   different rheological models were considered for their suitability in fitting experimental data. It was shown that the model proposed by Kulshreshtha et al. (1991) more
   accurately described the rheological behaviour of extruded starch than that used by Vergnes et al. (1987), although the latter model did provide a better prediction of the
   general trends observable in the data. The relevant model parameters were determined from experimental data using a least-square optimisation routine. The model predictions
   compared favourably with measured residence time distribution data.
%Z National conference publication (Institution of Engineers, Australia) ; no. 96/13. cited 20 Dec 11
%@ 0-85825-658-4

%A Justin Rae Elsey
%T Dynamic Modelling, Measurement and Control of Co-rotating Twin-Screw Extruders
%R Ph.D. Thesis
%D 2002
%I 
%I Department of Chemical Engineering, University of Sydney
%C Australia
%K genetic algorithms, genetic programming, twin-screw extrusion, extruder geometry, dynamic modelling, process control, acoustic sensors, image analysis, bubble growth
%U http://hdl.handle.net/2123/687
%X Co-rotating twin-screw extruders are unique and versatile machines that are used widely in the plastics and food processing industries. Due to the large number of operating
   variables and design parameters available for manipulation and the complex interactions between them, it cannot be claimed that these extruders are currently being
   optimally utilised. The most significant improvement to the field of twin-screw extrusion would be through the provision of a generally applicable dynamic process model
   that is both computationally inexpensive and accurate. This would enable product design, process optimisation and process controller design to be performed cheaply and more
   thoroughly on a computer than can currently be achieved through experimental trials. This thesis is divided into three parts: dynamic modelling, measurement and control.
   The first part outlines the development of a dynamic model of the extrusion process which satisfies the above mentioned criteria. The dynamic model predicts quasi-3D
   spatial profiles of the degree of fill, pressure, temperature, specific mechanical energy input and concentrations of inert and reacting species in the extruder. The
   individual material transport models which constitute the dynamic model are examined closely for their accuracy and computational efficiency by comparing candidate models
   amongst themselves and against full 3D finite volume flow models. Several new modelling approaches are proposed in the course of this investigation. The dynamic model
   achieves a high degree of simplicity and flexibility by assuming a slight compressibility in the process material, allowing the pressure to be calculated directly from the
   degree of over-fill in each model element using an equation of state. Comparison of the model predictions with dynamic temperature, pressure and residence time distribution
   data from an extrusion cooking process indicates a good predictive capability. The model can perform dynamic step-change calculations for typical screw configurations in
   approximately 30 seconds on a 600 MHz Pentium 3 personal computer. The second part of this thesis relates to the measurement of product quality attributes of extruded
   materials. A digital image processing technique for measuring the bubble size distribution in extruded foams from cross sectional images is presented. It is recognised that
   this is an important product quality attribute, though difficult to measure accurately with existing techniques. The present technique is demonstrated on several different
   products. A simulation study of the formation mechanism of polymer foams is also performed. The measurement of product quality attributes such as bulk density and hardness
   in a manner suitable for automatic control is also addressed. This is achieved through the development of an acoustic sensor for inferring product attributes using the
   sounds emanating from the product as it leaves the extruder. This method is found to have good prediction ability on unseen data. The third and final part of this thesis
   relates to the automatic control of product quality attributes using multivariable model predictive controllers based on both direct and indirect control strategies. In the
   given case study, indirect control strategies, which seek to regulate the product quality attributes through the control of secondary process indicators such as temperature
   and pressure, are found to cause greater deviations in product quality than taking no corrective control action at all. Conversely, direct control strategies are shown to
   give tight control over the product quality attributes, provided that appropriate product quality sensors or inferential estimation techniques are available.
%8 25  August
%Z Uses GP, eg in chapter 6. See also his publications pages iv-v

%A Amin Elshorbagy
%A Ibrahim El-Baroudy
%T Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content
%J Journal of Hydroinformatics
%V 11
%N 3-4
%D 2009
%P 237--251
%I 
%K genetic algorithms, genetic programming, evolutionary polynomial regression, EPR, prediction, soil moisture, tool uncertainty
%U http://www.iwaponline.com/jh/011/0237/0110237.pdf
%X Soil moisture has a crucial role in both the global energy and hydrological cycles; it affects different ecosystem processes. Spatial and temporal variability of soil
   moisture add to its complex behaviour, which undermines the reliability of most current measurement methods. In this paper, two promising evolutionary data-driven
   techniques, namely (i) Evolutionary Polynomial Regression and (ii) Genetic Programming, are challenged with modelling the soil moisture response to the near surface
   atmospheric conditions. The utility of the proposed models is demonstrated through the prediction of the soil moisture response of three experimental soil covers, used for
   the restoration of watersheds that were disturbed by the mining industry. The results showed that the storage effect of the soil moisture response is the major challenging
   factor; it can be quantified using cumulative inputs better than time-lag inputs, which can be attributed to the effect of the soil layer moisture-holding capacity. This
   effect increases with the increase in the soil layer thickness. Three different modelling tools are tested to investigate the tool effect in data-driven modelling. Despite
   the promising results with regard to the prediction accuracy, the study demonstrates the need for adopting multiple data-driven modelling techniques and tools (modelling
   environments) to obtain reliable predictions.
%Z Laucelli EPR toolbox, South Bison Hill, oil sands reclamation, 1 foot or more peat layer, AB Canada, Discipulus \citefrancone:manual p242 'Discipulus produced better models
   than EPR'. p246 EPR provides insight. p258 GPLAB always evolved constants (not formulae).

%A A. Elshorbagy
%A G. Corzo
%A S. Srinivasulu
%A D. P. Solomatine
%T Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology
%J Hydrology and Earth System Sciences
%V 14
%N 10
%D 2010
%P 1931--1941
%I Copernicus GmbH
%K genetic algorithms, genetic programming
%U www.hydrol-earth-syst-sci.net/14/1931/2010/
%X A comprehensive data driven modelling experiment is presented in a two-part paper. In this first part, an extensive data-driven modeling experiment is proposed. The most
   important concerns regarding the way data driven modelling (DDM) techniques and data were handled, compared, and evaluated, and the basis on which findings and conclusions
   were drawn are discussed. A concise review of key articles that presented comparisons among various DDM techniques is presented. Six DDM techniques, namely, neural
   networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and K-nearest neighbours are proposed and explained. Multiple
   linear regression and na\"ive models are also suggested as baseline for comparison with the various techniques. Five datasets from Canada and Europe representing
   evapotranspiration, upper and lower layer soil moisture content, and rainfall-runoff process are described and proposed, in the second paper, for the modelling experiment.
   Twelve different realisations (groups) from each dataset are created by a procedure involving random sampling. Each group contains three subsets; training,
   cross-validation, and testing. Each modelling technique is proposed to be applied to each of the 12 groups of each dataset. This way, both prediction accuracy and
   uncertainty of the modelling techniques can be evaluated. The description of the data sets, the implementation of the modeling techniques, results and analysis, and the
   findings of the modelling experiment are deferred to the second part of this paper.
%8 14  October
%Z See also \citeElshorbagy:2010a:HESS Published in Hydrol. Earth Syst. Sci. Discuss.: 19 November 2009 \citeoai:doaj-articles:09c2f3076a15547532440e3ac274c044 and
   \citeoai:doaj-articles:90e50b27744c40b3f9d0243d0896b665 http://www.hydrol-earth-syst-sci-discuss.net/6/7055/2009/hessd-6-7055-2009.pdf citehessd-6-7055-2009. 1 Centre for
   Advanced Numerical Simulation (CANSIM), Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada 2 Department of
   Hydroinformatics and Knowledge Management, UNESCO-IHE Institute for Water Education, Delft, The Netherlands 3 Water Resources Section, Delft University of Technology,
   Delft, The Netherlands

%A A. Elshorbagy
%A G. Corzo
%A S. Srinivasulu
%A D. P. Solomatine
%T Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application
%J Hydrology and Earth System Sciences
%V 14
%N 10
%D 2010
%P 1943--1961
%I 
%K genetic algorithms, genetic programming
%U http://www.hydrol-earth-syst-sci.net/14/1943/2010/hess-14-1943-2010.pdf
%X In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case
   studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on
   previous studies or using the mutual information content. Twelve groups (realisations) were randomly generated from each data set by randomly sampling without replacement
   from the original data set. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5),
   K-nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive
   accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals,
   and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique.
   Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual
   evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the model ed data. EPR
   performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of
   SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn,
   and GP could be more successful than other modelling techniques. K-nn is also successful in linear situations, and it should not be ignored as a potential modelling
   technique for hydrological applications.
%8 14  October
%Z See also \citeElshorbagy:2010:HESS

%A M. E. El-Telbany
%T The egyptian stock market return prediction: a genetic programming approach
%B International Conference on Electrical, Electronic and Computer Engineering, ICEEC-04
%E Abdel-Moniem Wahdan and Ahmed Amer and Hani Fikry and Ashraf Salem
%D 2004
%P 161--164
%I 
%C Ain Shams University, Cairo, Egypt
%K genetic algorithms, genetic programming
%8 5-7  September
%Z details from ieee

%A Maria Cl{\'a}udia Figueiredo Pereira Emer
%A Silvia Regina Vergilio
%T GPTesT: A Testing Tool Based On Genetic Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 1343--1350
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, search-based software engineering, fault-based testing, induction of programs, mutation analysis, software test criteria
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-25.pdf
%X Genetic Programming (GP) has recently been applied to solve problems in several areas. It has the goal of inducing programs from test cases by using the concepts of
   Darwin's evolution theory. On the other hand, software testing, that is a fundamental and expensive activity for software quality assurance, has the objective of generating
   test cases from the program being tested. In this sense, a symmetry between induction of programs based on GP and testing is noticed. Based on such symmetry, this work
   presents GPTesT, a testing tool based on GP. Fault-based testing criteria generally derive test data using a set of mutant operators to produce alternatives that differ
   from the program under testing by a simple modification. GPtesT uses a set of alternatives genetically derived, which allow the test of interactions between faults. GPTesT
   implements two test procedures respectively for guiding the selection and evaluation of test data sets. Examples with these procedures show that the approach can be used as
   a testing criterion.
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Maria Claudia Emer
%A Silvia Regina Vergilio
%T Selection and evaluation of test data sets based on genetic programming
%B XVI Simposio Brasileiro de Engenharia de Software
%D 2002
%I 
%C Gramado, Rio Grande do Sul, Brasil
%K genetic algorithms, genetic programming

%A Maria Claudia F. P. Emer
%A Silvia Regina Vergilio
%T Selection and Evaluation of Test Data Based on Genetic Programming
%J Software Quality Journal
%V 11
%N 2
%D 2003
%P 167--186
%I 
%K genetic algorithms, genetic programming, evolutionary computation, testing criteria, mutation analysis SBSE, software engineering
%X In the literature, we find several criteria that consider different aspects of the program to guide the testing, a fundamental activity for software quality assurance. They
   address two important questions: how to select test cases to reveal as many fault as possible and how to evaluate a test set T and end the test. Fault-based criteria, such
   as mutation testing, use mutation operators to generate alternatives for the program P being tested. The goal is to derive test cases capable of producing different
   behaviors in P and its alternatives. However, this approach usually does not allow the test of interaction between faults since the alternative differs from P by a simple
   modification. This work explores the use of Genetic Programming (GP), a field of Evolutionary Computation, to derive alternatives for testing P and introduces two GP-based
   procedures for selection and evaluation of test data. The procedures are related to the above questions, usually addressed by most testing criteria and tools. A tool, named
   GPTesT, is described and results from an experiment using this tool are also presented. The results show the applicability of our approach and allow comparison with
   mutation testing.
%8 June
%Z Article ID: 5122058 Interactive tool incorporating GP. GPTesT (C++ UML). Chameleon \citeSpinosa:2001:gtgp grammar based generates C programs. "Control over anomalous code
   (overflow, infinite loop among others)" p171. "divide by zero" p177. v. Proteum (71 SE mutation operators) GPBT. cmm (common multiple), fat (factorial), max, cmd (common
   divisor) Computer Science Department, Federal University of Parana?UFPR CP: 19081, 81531-970, Curitiba, Brazil mpereira@inf.ufpr.br

%A Ken Endo
%A Funinori Yamasaki
%A Takashi Maeno
%A Hiroaki Kitano
%T Co-evolution of Morphology and Controller for Biped Humanoid Robot
%B RoboCup 2002: Robot Soccer World Cup VI
%S Lecture Notes in Artificial Intelligence
%E Gal A. Kaminka and Pedro U. Lima and Raul Rojas
%V 2752
%D 2002
%P 327--341
%I Springer-Verlag Berlin and Heidelberg
%K genetic algorithms
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2752&spage=327
%X we present a method for co-evolving structures and control circuits of bi-ped humanoid robots. Currently, biped walking humanoid robots are designed manually on
   trial-and-error basis. Although certain control theory exists, such as zero moment point (ZMP) compensation, these theories does not constrain design space of humanoid
   robot morphology or detailed control. Thus, engineers has to design control program for apriori designed morphology, neither of them shown to be optimal within a large
   design space. We propose evolutionary approaches that enables: (1) automated design of control program for a given humanoid morphology, and (2) co-evolution of morphology
   and control. An evolved controller has been applied to a humanoid PINO, and attained more stable walking than human designed controller. Coevolution was achieved in a
   precision dynamics simulator, and discovered unexpected optimal solutions. This indicate that a complex design task of bi-ped humanoid can be performed automatically using
   evolution-based approach, thus varieties of humanoid robots can be design in speedy manner. This is a major importance to the emerging robotics industries.
%Z GA, not a GP approach
%@ 3-540-40666-2

%A David Engel
%T Evolving Effective Solutions in Effective Amounts of Time
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 76--85
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A A. P. Engelbrecht
%A L. Schoeman
%A Sonja Rouwhorst
%T A Building Block Approach to Genetic Programming for Rule Discovery
%B Data Mining: A Heuristic Approach
%E Hussein A. Abbass and Charles S. Newton and Ruhul Sarker
%D 2002
%P 174--190
%I IGI-global
%C 701 E Chocolate Avenue, Hershey PA 17033, USA
%K genetic algorithms, genetic programming
%U http://www.igi-global.com/chapter/building-block-approach-genetic-programming/7589
%X Genetic programming has recently been used successfully to extract knowledge in the form of IF-THEN rules. For these genetic programming approaches to knowledge extraction
   from data, individuals represent decision trees. The main objective of the evolutionary process is therefore to evolve the best decision tree, or classifier, to describe
   the data. Rules are then extracted, after convergence, from the best individual. The current genetic programming approaches to evolve decision trees are computationally
   complex, since individuals are initialised to complete decision trees. This chapter discusses a new approach to genetic programming for rule extraction, namely the building
   block approach. This approach starts with individuals consisting of only one building block, and adds new building blocks during the evolutionary process when the
   simplicity of the individuals cannot account for the complexity in the underlying data. Experimental results are presented and compared with that of C4.5 and CN2. The
   chapter shows that the building block approach achieves very good accuracies compared to that of C4.5 and CN2. It is also shown that the building block approach extracts
   substantially less rules.
%O 9
%Z A. P. Engelbrecht (University of Pretoria, South Africa), L. Schoeman (University of Pretoria, South Africa) and Sonja Rouwhorst (Vrije Universiteit Amsterdam, The
   Netherlands)

%A Barbara Engelhardt
%T Learning a Bayesian Network from Data Samples Using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 1--10
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Joseph Engler
%T Optimization of test engineering utilizing evolutionary computation
%B IEEE AUTOTESTCON, 2009
%D 2009
%P 447--452
%I 
%K genetic algorithms, genetic programming, SBSE, adaptive memory, automated station software generation, evolutionary computation, genetic programming algorithm, test
   engineering optimization, test station software creation, testing requirements, automatic test pattern generation, automatic test software
%X Test engineering often experiences pressures to produce test stations and software in a short time frame with constrained budgets. Since test is a negative influence
   towards product costs, it is crucial to optimize the processes of test station software creation as well as the configuration of the test station itself. This paper
   introduces novel methodologies for optimized station configuration and automated station software generation. These two optimizations use evolutionary computation to
   automatically generate software for the test station and to offer optimal configurations of the station based upon testing requirements. Presented is a modified genetic
   programming algorithm for the creation of test station software (e.g. COTS software drivers). The genetic algorithm is improved through use of adaptive memory to recall
   historic schemas of high fitness. From the automated software generation an optimal station configuration is produced based upon the requirements of the testing to be
   performed. This system has been implemented in industry and an actual industrial case study is presented to illustrate the efficiency of this novel optimization technique.
   Comparisons with standard genetic programming techniques are offered to further illustrate the efficiency of this methodology.
%8 September
%Z Also known as \cite5314025

%A Milo Engoren
%A Jeffrey A. Kline
%T Use of genetic programming to diagnose venous thromboembolism in the emergency department
%J Genetic Programming and Evolvable Machines
%V 9
%N 1
%D 2008
%P 39--51
%I 
%K genetic algorithms, genetic programming, Pulmonary embolism, Venous thromboembolic disease, Capnometry, Oximetry
%X Pulmonary thromboembolism as a cause of respiratory complaints is frequently undiagnosed and requires expensive imaging modalities to diagnose. The objective of this study
   was to determine if genetic programming could be used to classify patients as having or not having pulmonary thromboembolism using exhaled ventilatory and gas indices as
   genetic material. Using a custom-built exhaled oxygen and carbon dioxide analyser; exhaled flows, volumes, and gas partial pressures were recorded from patients for a
   series of deep exhalation and 30 seconds tidal volume breathing. A diagnosis of pulmonary embolism was made by contrast-enhanced computerised tomography angiography of the
   chest and indirect venography supplemented by 90-day follow-up. Genetic programming developed a series of genomes comprising genes of exhaled CO2, O2, flow, volume, vital
   signs, and patient demographics from these data and their predictions were compared to the radiological results. We found that 24 of 178 patients had pulmonary embolism.
   The best genome consisted of four genes: the minimum flow rate during the third 30 s period of tidal breathing, the average peak exhaled CO2 during the first 30 s period of
   tidal breathing, the average peak of the exhaled O2 during the first 30 s period of tidal breathing, and the average peak exhaled CO2 during the fourth period of tidal
   breathing, which immediately followed a deep exhalation. This had 100percent sensitivity and 91percent specificity on the construction population and 100percent and
   82percent, respectively when tested on the separate validation population. Genetic programming using only data obtained from exhaled breaths was very accurate in
   classifying patients with suspected pulmonary thromboembolism.
%8 March
%Z Continuous variables converted to independent 11 deciles. Explicit representation of missing data via 11th decile. No concent of adjacency between deciles. Possibility of
   gaps between deciles. Fortran, windowsXP, 500 generations. At most 4 genes (to prevent over fitting). ROC

%A Margaret J. Eppstein
%A Joshua L. Payne
%A Bill C. White
%A Jason H. Moore
%T Hill-climbing through "random chemistry" for detecting epistasis
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006)
%E J\"orn Grahl
%D 2006
%I 
%C Seattle, WA, USA
%K genetic algorithms, genetic programming, Population based optimisation, epistasis, SNPs, data mining.
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp111.pdf
%X There are estimated to be on the order of 1000000 single nucleotide polymorphisms (SNPs) existing as standing variation in the human genome. Certain combinations of these
   SNPs can interact in complex ways to predispose individuals for a variety of common diseases, even though individual SNPs may have no ill effects. Detecting these epistatic
   combinations is a computationally daunting task. Trying to use individual or growing subsets of SNPs as building blocks for detection of larger combinations of purely
   epistatic SNPs (e.g., via genetic algorithms or genetic programming) is no better than random search, since there is no predictive power in subsets of the correct set of
   epistatically interacting SNPs. Here, we explore the potential for hill-climbing from the other direction; that is, from large sets of candidate SNPs to smaller ones. This
   approach was inspired by Kauffman's "random chemistry" approach to detecting small autocatalytic sets of molecules from within large sets. Preliminary results from
   synthetic data sets show that the resulting algorithm can detect epistatic pairs from up to 1000 candidate SNPs in O(log N) fitness evaluations, although success rate
   degrades as heritability declines. The results presented herein are offered as proof of concept for the random chemistry approach.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2006

%A Margaret J. Eppstein
%A Joshua L. Payne
%A Bill C. White
%A Jason H. Moore
%T Genomic mining for complex disease traits with ``random chemistry''
%J Genetic Programming and Evolvable Machines
%V 8
%N 4
%D 2007
%P 395--411
%I 
%K Evolutionary algorithms, Epistasis, Single nucleotide polymorphisms, Data mining, Genome-wide association studies, Complex traits, Feature selection
%X Our rapidly growing knowledge regarding genetic variation in the human genome offers great potential for understanding the genetic etiology of disease. This, in turn, could
   revolutionise detection, treatment, and in some cases prevention of disease. While genes for most of the rare monogenic diseases have already been discovered, most common
   diseases are complex traits, resulting from multiple gene-gene and gene-environment interactions. Detecting epistatic genetic interactions that predispose for disease is an
   important, but computationally daunting, task currently facing bioinformaticists. Here, we propose a new evolutionary approach that attempts to hill-climb from large sets
   of candidate epistatic genetic features to smaller sets, inspired by Kauffman's ``random chemistry'' approach to detecting small auto-catalytic sets of molecules from
   within large sets. Although the algorithm is conceptually straightforward, its success hinges upon the creation of a fitness function able to discriminate large sets that
   contain subsets of interacting genetic features from those that don't. Here, we employ an approximate and noisy fitness function based on the ReliefF data mining algorithm.
   We establish proof-of-concept using synthetic data sets, where individual features have no marginal effects. We show that the resulting algorithm can successfully detect
   epistatic pairs from up to 1,000 candidate single nucleotide polymorphisms in time that is linear in the size of the initial set, although success rate degrades as
   heritability declines. Research continues into seeking a more accurate fitness approximator for large sets and other algorithmic improvements that will enable us to extend
   the approach to larger data sets and to lower heritabilities.
%O special issue on medical applications of Genetic and Evolutionary Computation
%8 Decemeber
%Z SNP, ROC, AUC

%A Massimiliano Erba
%A Roberto Rossi
%A Valentino Liberali
%A Andrea Tettamanzi
%T An Evolutionary Approach to Automatic Generation of VHDL Code for Low-Power Digital Filters
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 36--50
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Evolvable Hardware, Evolutionary Algorithms, Electronic Design, Digital Filters, VHDL
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=36
%X An evolutionary algorithm is used to design a finite impulse response digital filter with reduced power consumption. The proposed design approach combines genetic
   optimization and simulation methodology, to evaluate a multi-objective fitness function which includes both the suitability of the filter transfer function and the
   transition activity of digital blocks. The proper choice of fitness function and selection criteria allows the genetic algorithm to perform a better search within the
   design space, thus exploring possible solutions which are not considered in the conventional structured design methodology. Although the evolutionary process is not
   guaranteed to generate a filter fully compliant to specifications in every run, experimental evidence shows that, when specifications are met, evolved filters are much
   better than classical designs both in terms of power consumption and in terms of area, while maintaining the same performance.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Roger Eriksson
%A Bj{\"{o}}rn Olsson
%T Cooperative Coevolution in Inventory Control Optimisation
%B Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97
%E George D. Smith and Nigel C. Steele and Rudolf F. Albrecht
%D 1997
%I Springer-Verlag
%C University of East Anglia, Norwich, UK
%K genetic algorithms
%O published in 1998
%Z ICANNGA97
%@ 3-211-83087-1

%A R. Eriksson
%A B. Olsson
%T Adapting genetic regulatory models by genetic programming
%J Biosystems
%V 76
%N 1-3
%D 2004
%P 217--227
%I 
%K genetic algorithms, genetic programming, Gene networks, Evolutionary algorithms, Machine learning
%U http://www.sciencedirect.com/science/article/B6T2K-4D09KY2-7/2/1abfe196bb4afc60afc3311cadb75d66
%X we focus on the task of adapting genetic regulatory models based on gene expression data from microarrays. Our approach aims at automatic revision of qualitative regulatory
   models to improve their fit to expression data. We describe a type of regulatory model designed for this purpose, a method for predicting the quality of such models, and a
   method for adapting the models by means of genetic programming. We also report experimental results highlighting the ability of the methods to infer models on a number of
   artificial data sets. In closing, we contrast our results with those of alternative methods, after which we give some suggestions for future work.
%Z Papers presented at the Fifth International Workshop on Information Processing in Cells and Tissues PMID: 15351145 [PubMed - indexed for MEDLINE]

%A Cathy Escazut
%A Terence C. Fogarty
%T Coevolving Classifier Systems to Control Traffic Signals
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Gabi Escuela
%A Gabriela Ochoa
%A Natalio Krasnogor
%T Evolving L-Systems to Capture Protein Structure Native Conformations
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 74--84
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=74
%X A protein is a linear chain of amino acids, that under certain physical conditions, folds into a unique functional structure, called its native state or tertiary structure.
   In this state, proteins show repeated substructures like alpha helices and beta sheets. This observation suggests that native structures may be captured by the formalism
   known as Lindenmayer systems (L-systems). In this paper an evolutionary algorithm is used as the inference procedure for folded structures under the HP model in 2D
   lattices. The EA searches in the space of possible L-systems which are then executed to obtain the phenotype, thus our approach is close to that of Grammatical Evolution.
   The problem is to find a set of rewriting rules that represents a target native structure on the selected lattice model. The proposed approach has produced promising
   results for short sequences under the 2D square lattice. Thus the foundations are set for a novel encoding based on L-systems for evolutionary approaches to both the
   Protein Structure Prediction and Inverse Folding Problems.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Akbar Esfahanipour
%A Somaye Mousavi
%T Genetic programming application to generate technical trading rules in stock markets
%J International Journal of Reasoning-based Intelligent Systems
%V 2
%N 3/4
%D 2010
%P 244--250
%I 
%K genetic algorithms, genetic programming, technical trading rules, stock markets, tehran stock exchange, TSE, Iran, decision making, stock trading
%U http://www.inderscience.com/link.php?id=36870
%X Technical trading rules can be generated from historical data for decision making in stock trading. In this study, genetic programming (GP) as an evolutionary algorithm has
   been applied to automatically generate such technical trading rules on individual stocks. In order to obtain more realistic trading rules, we have included transaction
   costs, dividends and splits in our GP model. Our model has been applied for nine Iranian companies listed on different activity sectors of Tehran Stock Exchange (TSE). Our
   results show that this model could generate profitable trading rules in comparison with buy and hold strategy for companies having frequent trading in the market. Also, the
   effect of the above mentioned parameters on trading rule's profitability are evaluated using three separate models.

%A Akbar Esfahanipour
%A Somayeh Mousavi
%T A genetic programming model to generate risk-adjusted technical trading rules in stock markets
%J Expert Systems with Applications
%V 38
%N 7
%D 2011
%P 8438--8445
%I 
%K genetic algorithms, genetic programming, Technical trading rules, Risk-adjusted measures, Conditional Sharpe ratio, Tehran Stock Exchange (TSE)
%U http://www.sciencedirect.com/science/article/B6V03-52178YW-J/2/5208571320b6e5c08daf35597b9f81f4
%X Technical trading rules can be generated from historical data for decision making in stock markets. Genetic programming (GP) as an artificial intelligence technique is a
   valuable method to automatically generate such technical trading rules. In this paper, GP has been applied for generating risk-adjusted trading rules on individual stocks.
   Among many risk measures in the literature, conditional Sharpe ratio has been selected for this study because it uses conditional value at risk (CVaR) as an optimal
   coherent risk measure. In our proposed GP model, binary trading rules have been also extended to more realistic rules which are called trinary rules using three signals of
   buy, sell and no trade. Additionally we have included transaction costs, dividend and splits in our GP model for calculating more accurate returns in the generated rules.
   Our proposed model has been applied for 10 Iranian companies listed in Tehran Stock Exchange (TSE). The numerical results showed that our extended GP model could generate
   profitable trading rules in comparison with buy and hold strategy especially in the case of risk adjusted basis.

%A Murat Eskil
%A Erdogan Kanca
%T A new formulation for martensite start temperature of Fe-Mn-Si shape memory alloys using genetic programming
%J Computational Materials Science
%V 43
%N 4
%D 2008
%P 774--784
%I 
%K genetic algorithms, genetic programming, Martensite start temperature, Fe-Mn-Si alloys, Shape memory effect, Formulation and modelling
%U http://www.sciencedirect.com/science/article/B6TWM-4S1BT8K-1/2/8c255199aba8337ed54aa30bf0ec4ab4
%X This study presents genetic programming (GP) soft computing technique as a new tool for the formulation of martensite start temperature (Ms) of Fe-Mn-Si shape memory alloys
   for various compositions and heat treatments. The objective of this study is to provide a different formulation to design composition at certain ranges and to verify the
   robustness of GP for the formulation of such characterization problems. The training and testing patterns of the proposed GP formulation is based on well established
   experimental results from the literature. The GP based formulation results are compared with experimental results and found to be quite reliable.

%A E. Eskin
%A Eric V. Siegel
%T Genetic Programming Applied to Othello: Introducing Students to Machine Learning Research
%B 30th Technical Symposium of the ACM Special Interest Group in Computer Science Education
%S SIGCSE Bulletin
%E Daniel Joyce
%V 31.1
%D 1999
%P 242--246
%I ACM Press
%C New Orleans, LA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.columbia.edu/~evs/papers/sigcse-paper.ps
%X In this paper we describe and analyze a three week assignment that was given in a Machine Learning course at Columbia University. The assignment presented students with an
   introduction to machine learning research. The assignment required students to apply Genetic Programming to evolve algorithms that play the board game Othello. The students
   were provided with an implemented experimental approach as a starting point. The students were required to perform their own experimental modifications corresponding to
   research issues in machine learning. The results of student experiments were good both in terms of research and in terms of student learning. All relevant code,
   documentation and information about GPOthello is available at the following url: http://www.cs.columbia.edu/~evs/ml/othello.html .
%8 24-28  March

%A Brent Eskridge
%A Dean Hougen
%T Imitating Success: A Memetic Crossover Operator for Genetic Programming
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 809--815
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Theory of evolutionary algorithms, Poster Session
%X For some problem domains, the evaluation of individuals is significantly more expensive than the other steps in the evolutionary process. Minimizing these evaluations is
   vital if we want to make genetic programming a viable strategy. In order to minimize the required evaluations, we need to maximize the amount learned from each evaluation.
   To accomplish this we introduce a new crossover operator for genetic programming, memetic crossover, that allows individuals to imitate the observed success of others. An
   individual that has done poorly in some parts of the problem may then imitate an individual that did well on those same parts. This results in an intelligent search of the
   feature-space and, therefore, fewer evaluations.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Brent E. Eskridge
%A Dean F. Hougen
%T Memetic Crossover for Genetic Programming: Evolution Through Imitation
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 459--470
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030459.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Brent E. Eskridge
%A Dean F. Hougen
%T An Analysis of Memetic Crossover's Impact on a Population
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 6844--6850
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X In problem domains such as robotic control, where the evaluation of an individual significantly dominates the rest of the evolutionary process with respect to time, the
   viability of an evolutionary approach can be called into question. In an effort to minimise the number of evaluations by maximising the learning that takes place during an
   evaluation, a new crossover operator for genetic programming, memetic crossover, was recently introduced. This work analyses the genealogical impact of this operator at
   varying levels. Although diversity, both in terms of individuals and nodes, is reduced in memetic crossover, we show that memetic crossover is capable of working with
   standard
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Guilherme Esmeraldo
%A Edna Barros
%T A Genetic Programming based approach for efficiently exploring architectural communication design space of MPSoCs
%B VI Southern Programmable Logic Conference (SPL 2010)
%D 2010
%P 29--34
%I 
%C Ipojuca, Brazil
%K genetic algorithms, genetic programming, MPSoC, architectural communication design space, generalised linear models, mixed approach, multiprocessor system-on-chip,
   simulation models, static analysis based approach, multiprocessing systems, system-on-chip
%X New integrated circuits technologies and the demand for more complex applications have created Multi-Processor System-on-Chip (MPSoC). MPSoC is a complex integrated
   circuit, which can be composed of microprocessors, buses, memories and others computational system components. As the number and variety of components of today's MPSoC is
   increasing, its communication architecture is becoming a limiting factor for applications performance and power consumption. Thus, techniques have been created for
   exploring the design space in order to find out the best communication architecture for a given application. Such techniques, however, are either inaccurate (by using
   static analysis based approaches) or very time consuming since each communication configuration of the design space must be simulated (by using simulation models) or
   estimated (using mixed approaches). This paper presents a new approach to explore the design space of bus-based communication architectures of MPSoCs using Generalised
   Linear Models and Genetic Programming. By using the proposed approach, some experiments show that it was possible to explore a subset of the design space and to identify
   the best communication configuration for a given application reducing 90percent of the exploration time with less of 3,8percent mean global error.
%8 24-26  March
%Z Also known as \cite5483006

%A K. C. Sharman
%A A. I. Esparcia-Alcazar
%A Y. Li
%T Evolving Digital Signal Processing Algorithms by Genetic Programming
%R Technical Report CSC-95012
%D 1995
%I 
%I Faculty of Engineering
%C Glasgow G12 8QQ, Scotland
%K genetic algorithms, genetic programming, simulated annealing, digital signal processing, neural networks
%U http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs95012.html
%X We introduce a novel genetic programming (GP) technique to evolve both the structure and parameters of adaptive digital signal processing algorithms. This is accomplished
   by defining a set of node functions and terminals to implement the basic operations commonly used in a large class of DSP algorithms. In addition, we show how simulated
   annealing may be employed to assist the GP in optimising the numerical parameters of expression trees. The concepts are illustrated by using GP to evolve high performance
   algorithms for detecting binary data sequences at the output of a noisy, non-linear communications channel.
%8 31  March
%Z Also submitted to: Proc. First IEE/IEEE Int. Conf. on GA in Eng. Syst.: Innovations and Appl., Sheffield, Sept. 1995, pp.473-480.

%A Anna I. Esparcia-Alcazar
%A Ken C. Sharman
%T Evolving Recurrent Neural Network Architectures by Genetic Programming
%R Technical Report CSC-96009
%D 1996
%I 
%I Faculty of Engineering
%C Glasgow G12 8QQ, Scotland
%K genetic algorithms, genetic programming, Recurrent Neural Networks, Simulated annealing, Digital Signal Processing
%U http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs96009.html
%X We propose a novel design paradigm for recurrent neural networks. This employs a two-stage Genetic Programming / Simulated Annealing hybrid algorithm to produce a neural
   network which satisfies a set of design constraints. The Genetic Programming part of the algorithm is used to evolve the general topology of the network, along with
   specifications for the neuronal transfer functions, while the Simulated Annealing component of the algorithm adapts the network's connection weights in response to a set of
   training data. Our approach offers important advantages over existing methods for automated network design. Firstly, it allows us to develop recurrent network connections;
   secondly, we are able to employ neurons with arbitrary transfer functions, and thirdly, the approach yields an efficient and easy to implement on-line training algorithm.
   The procedures involved in using the GP/SA hybrid algorithm are illustrated by using it to design a neural network for adaptive filtering in a signal processing
   application.

%A Anna I. Esparcia-Alcazar
%A Ken C. Sharman
%T Application of Genetic Programming to Signal Processing Problems
%R Technical Report CSC-96010
%D 1996
%I 
%I Faculty of Engineering
%C Glasgow G12 8QQ, Scotland
%K genetic algorithms, genetic programming, Digital Signal Processing Simulated Annealing, Adaptive Filtering
%U http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs96010.html
%X The field of Digital Signal Processing (DSP) is concerned with the restoration of signals which have undergone distortion and interference or noise corruption as a result
   of being transmitted. The usual way to recover such a signal is by adaptive filtering. Designing adaptive filters is not an easy task. It usually involves complicated
   algorithms whose performance depends on the skill of the designer as well as the power of the computer used. The aim of the present work is to provide a way of automating
   such process by means of a black box technique. In this approach, both the structure and the parameters of adaptive filters are evolved. The former is done by Genetic
   Programming (GP) and the latter is done by Simulated Annealing (SA). The power of the hybrid GP/SA is demonstrated with some results on three interesting DSP applications:
   channel equalisation, noise cancellation and interference removal.
%Z Also submitted to: Late-breaking papers at the Genetic Programming 96 Conference, Stanford, USA, July 1996 \citeesparcia:1996:GPdsp

%A Anna I. {Esparcia Alcazar}
%A Ken C. Sharman
%T Some Applications of Genetic Programming in Digital Signal Processing
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 24--31
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming, DSP
%U http://www.iti.upv.es/~anna/papers/someappsgp96.ps
%8 28--31  July
%Z GP-96LB, recursive, memory The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 accepted for GP'96
   but, due to a number of circumstances, never appeared in the proceedings. It was presented at the conference See \citeesparcia:1996:96010
%@ 0-18-201031-7

%A Anna I. Esparcia-Alcazar
%A Kenneth C. Sharman
%T Genetic Programming Techniques that Evolve Recurrent Neural Networks Architectures for Signal Processing
%B IEEE Workshop on Neural Networks for Signal Processing
%D 1996
%P 139--148
%I IEEE
%C Seiko, Kyoto, Japan
%K genetic algorithms, genetic programming, adaptive filtering, arbitrary transfer functions, design constraints, genetic programming techniques, neuronal transfer functions,
   online training algorithm, recurrent neural network architectures, signal processing, simulated annealing, adaptive filters, geometric programming, neural net architecture,
   recurrent neural nets, signal processing, simulated annealing, transfer functions
%X We propose a novel design paradigm for recurrent neural networks. This employs a two-stage genetic programming/simulated annealing hybrid algorithm to produce a neural
   network which satisfies a set of design constraints. The genetic programming part of the algorithm is used to evolve the general topology of the network, along with
   specifications for the neuronal transfer functions, while the simulated annealing component of the algorithm adapts the network's connection weights in response to a set of
   training data. Our approach offers important advantages over existing methods for automated network design. Firstly, it allows us to develop recurrent network connections;
   secondly, we are able to employ neurones with arbitrary transfer functions, and thirdly, the approach yields an efficient and easy to implement on-line training algorithm.
   The procedures involved in using the GP/SA hybrid algorithm are illustrated by using it to design a neural network for adaptive filtering in a signal processing application
%8 4-6  September

%A Anna I. Esparcia-Alcazar
%A Ken Sharman
%T Evolving Recurrent Neural Network Architectures by Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 89--94
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.iti.upv.es/~anna/papers/gp-rnn97.ps
%8 13-16  July
%Z GP-97

%A Anna I. Esparcia-Alcazar
%A Ken Sharman
%T Learning Schemes for Genetic Programming
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 57--65
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.iti.upv.es/~anna/papers/learningGP97.ps
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Anna I Esparcia-Alcazar
%T An investigation into a Genetic Programming Technique for Adaptive Signal Processing
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 290
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Anna I. Esparcia-Alcazar
%T Genetic Programming for Adaptive Signal Processing
%R Ph.D. Thesis
%D 1998
%I 
%I Electronics and Electrical Engineering, University of Glasgow
%K genetic algorithms, genetic programming
%U http://www.iti.upv.es/~anna/papers/Thesis.zip
%X a new way of handling numerical parameters in GP, node gains. A node gain is a numerical parameter associated to a node that multiplies its output value. This concept was
   introduced by Sharman and Esparcia-Alcazar (1993) and is fully developed here. The motivation for a parameterised GP is addressed, together with an overview of how it has
   been addressed by other authors. The drawbacks of these methods are highlighted: there is no established way of determining the number of parameters to use and their
   placement; further, unused parameters can be unnecessarily adapted while, on the other hand, useful ones might be eliminated. The way in which node gains overcome these
   problems is explained. An extra advantage is the possibility of expressing complex systems in a compact way, which is labelled "compacting effect" of node gains. The costs
   of node gains are also pointed out: increase in the degrees of freedom and increased complexity. This, in theory, results in an increase of computational expense, due to
   the handling of more complex nodes and to the fact that an extra multiplication is needed per node. These costs, however, are expected to be of, at most, the same order of
   magnitude as those of the alternatives. Experimental analysis shows that random node gains may not be able to achieve all the potential benefits expected. It is conjectured
   that optimisation of the values is needed in order to attain the full benefits of node gains, which brings along the next contribution. a mathematical model is given for an
   adaptive GP. As concluded from the previous point, adaptation of the values of the node gains is needed in order to take full advantage of them. A Simulated Annealing (SA)
   algorithm is introduced as the adaptation algorithm. This is put in the context of an analogy: the adaptation of the gains by SA is equivalent to the learning process of an
   individual during its lifetime. This analogy gives way to the introduction of two learning schemes, labelled Lamarckian and Darwinian, which refer to the possibility of
   inheriting the learned gains. The Darwinian and Lamarckian learning schemes for GP are compared to the standard GP technique and also to GP with random node gains.
   Statistical analysis, done for both fixed and time-varying environments, shows the superiority of both learning methods over the non-learning ones, although it is not
   possible at this stage to determine which of the two provides a better performance. a number of interesting results in the channel equalisation problem. These are compared
   to those obtained by other techniques and it is concluded that the proposed method obtains better or similar performance when extreme values (maximum fitness or minimum
   error) are considered.
%8 July

%A Anna Esparcia-Alcazar
%A Ken Sharman
%T Phenotype Plasticity in Genetic Programming: A Comparison of Darwinian and Lamarckian Inheritance Schemes
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 49--64
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=49
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP Combination of GP and Simulated Annealing. Performs experiments were SA produced changes (ie new constants) are incorporated into genes
   (Lamarckian inheritance, also known as "repair" in GA circles) compared to not writing back. SA+GP claimed to be good (often).
%@ 3-540-65899-8

%A Anna Esparcia-Alcazar
%A Ken Sharman
%T Genetic Programming for Channel Equalisation
%B Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99
%S LNCS
%E Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty
%V 1596
%D 1999
%P 126--137
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/286482.html
%X This paper is devoted to providing a comparison between classical and neural channel equalisation techniques and node gain Genetic Programming enhanced with Simulated
   Annealing (or GP+SA). Firstly, the shortcomings of existing techniques are exposed and the main requirements to demand of a new method enumerated. A description of the
   problem is followed by an account of particular cases of equalisation, exemplified by three channels, both linear and nonlinear. Results are obtained for these channels
   both with the proposed method and a classical technique, the Recursive Least Squares (RLS) algorithm, and they are further compared to those existing in the literature. The
   comparison shows the great potential of GP+SA, especially in the case of nonlinear channels. The main disadvantage of the proposed method, the computational effort
   involved, is also pointed out and it is concluded that, upon the whole, the method deserves further investigation.
%8 28-29  May
%Z EvoIASP99'99
%@ 3-540-65837-8

%T GECCO '09: Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%I ACM New York, NY, USA
%I SigEVO
%C Montreal, Qu\'ebec, Canada
%K genetic algorithms, genetic programming
%8 8-12  July
%Z GECCO 2009 workshops

%T Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%8 7-9  April
%Z EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Pedro G. Espejo
%A Cesar Hervas
%A Sebastian Ventura
%A Cristobal Romero
%T Eleccion de Operadores Logicos para la Induccion de Conocimiento Comprensible
%J Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial
%V 29
%D 2006
%P 19--30
%I 
%K genetic algorithms, genetic programming, Grammatical Evolution, Mineria de datos, Clasificacion, Comprensibilidad, Programacion genetica gramatical
%U http://sci2s.ugr.es/keel/pdf/keel/articulo/1cr-1r-2r.pdf
%X In data mining, the quality of induced knowledge is determined by several features. The focus has been usually placed on accuracy, paying much less attention to
   comprehensibility. In this paper, we present a rule-based data mining system for classification. Our main goal is the analysis of the trade-off between accuracy and
   comprehensibility, but we approach comprehensibility from a novel point of view: we are interested in gaining insight into how the use of logical operators affects
   comprehensibility. In addition, we study the suitability of grammar-based genetic programming for data mining
%O Ejemplar dedicado a: Mineria de Datos
%Z c AEPIA (http://www.aepia.dsic.upv.es/) In Spanish

%A Pedro G. Espejo
%A Sebastian Ventura
%A Francisco Herrera
%T A Survey on the Application of Genetic Programming to Classification
%J IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
%V 40
%N 2
%D 2010
%P 121--144
%I 
%K genetic algorithms, genetic programming, Classification, decision trees, ensemble classifiers, feature construction, feature selection, rule-based systems
%X Classification is one of the most researched questions in machine learning and data mining. A wide range of real problems have been stated as classification problems, for
   example credit scoring, bankruptcy prediction, medical diagnosis, pattern recognition, text categorization, software quality assessment, and many more. The use of
   evolutionary algorithms for training classifiers has been studied in the past few decades. Genetic programming (GP) is a flexible and powerful evolutionary technique with
   some features that can be very valuable and suitable for the evolution of classifiers. This paper surveys existing literature about the application of genetic programming
   to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers.
%8 March
%Z Also known as \cite5340522

%A Floriana Esposito
%A Nicola Fanizzi
%A Claudia d'Amato
%T Conceptual Clustering Applied to Ontologies by means of Semantic Discernability
%B ECML/PKDD Workshop on Prior Conceptual Knowledge in Machine Learning and Knowledge Discovery, PriCKL'07
%D 2007
%I 
%C Warsaw, Poland
%K genetic algorithms, genetic programming
%U http://www.ecmlpkdd2007.org/CD/workshops/PRICKLWM2/P_Fan/PriCKL07/PriCkl2007-final.pdf
%X A clustering method is presented which can be applied to relational knowledge bases to discover interesting groupings of resources through their annotations expressed in
   the standard languages of the Semantic Web. The method exploits a simple (yet effective and language-independent) semi-distance measure for individuals, that is based on
   the semantics of the resources w.r.t. a number of dimensions corresponding to a set of concept descriptions (discriminating features). The algorithm adapts the classic
   BISECTING K-MEANS to work with medoids. A final experiment demonstrates the validity of the approach using absolute quality indices
%8 September  21
%Z Says based on GP and Simulated Annealing Dipartimento di Informatica, Universit`a degli Studi di Bari Campus Universitario, Via Orabona 4, 70125 Bari, Italy

%A Daryl Essam
%A R. I. Bob McKay
%T Adaptive Control of Partial Functions in Genetic Programming
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 895--901
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, Partial Functions, Fitness Evaluation
%U http://www.cs.adfa.edu.au/~rim/PAPERS/CEC01final.pdf
%X The paper investigates the use of partial functions in genetic programming. Previous work (R.I. McKay, 2000), has shown that the convergent behaviour of populations of
   partial functions is very similar to that of populations of total functions. However the convergence rates of populations of partial functions have been slower. The results
   presented demonstrate a significant improvement in the rate of convergence of populations of partial functions, and indicate that partial functions represent a realistic
   alternative to total functions for a range of problems
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . Convergence
   of populations of partial functions. recursion list membership, 6-multiplexor, 11-mux. undef, DCTG-GP cf. \citeross:1999:LGPDCTG fitness sharing mitigated by non-undef. "A
   partial function is a function whose value is not defined for some argument values", ie "undef". Tree GP. Grammar DCTG-GP. Infinite recursion prevented by a depth limit of
   20.
%@ 0-7803-6658-1

%A Daryl Essam
%T Book Review: Blondie24: Playing at the Edge of AI
%J Genetic Programming and Evolvable Machines
%V 3
%N 4
%D 2002
%P 389--390
%I 
%8 Decemeber
%Z Article ID: 5103876

%A Daryl Essam
%A R I (Bob) McKay
%T Heritage Diversity in Genetic Programming
%B The 5th International Conference on Simulated Evolution And Learning (SEAL'04)
%D 2004
%I 
%C Busan, Korea
%K genetic algorithms, genetic programming, diversity
%8 October  26-29

%A C{\'e}sar Est{\'e}banez
%A Jos{\'e} Mar\'{\i}a Valls
%A Ricardo Aler
%A In{\'e}s Mar\'{\i}a Galv{\'a}n
%T A First Attempt at Constructing Genetic Programming Expressions for EEG Classification
%B Artificial Neural Networks: Biological Inspirations - ICANN 2005, 15th International Conference, 2005, Proceedings, Part I
%S Lecture Notes in Computer Science
%E Wlodzislaw Duch and Janusz Kacprzyk and Erkki Oja and Slawomir Zadrozny
%V 3696
%D 2005
%P 665--670
%I Springer
%C Warsaw, Poland
%K genetic algorithms, genetic programming, EEG, BCI, brain computer interface, projection
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3696&spage=665
%X In BCI (Brain Computer Interface) research, the classification of EEG signals is a domain where raw data has to undergo some preprocessing, so that the right attributes for
   classification are obtained. Several transformational techniques have been used for this purpose: Principal Component Analysis, the Adaptive Autoregressive Model, FFT or
   Wavelet Transforms, etc. However, it would be useful to automatically build significant attributes appropriate for each particular problem. we use Genetic Programming to
   evolve projections that translate EEG data into a new vectorial space (coordinates of this space being the new attributes), where projected data can be more easily
   classified. Although our method is applied here in a straightforward way to check for feasibility, it has achieved reasonable classification results that are comparable to
   those obtained by other state of the art algorithms. In the future, we expect that by choosing carefully primitive functions, Genetic Programming will be able to give
   original results that cannot be matched by other machine learning classification algorithms.
%8 11-15  September
%@ 3-540-28752-3

%A Cesar Estebanez
%A Ricardo Aler
%A Jose Maria Valls
%T Genetic Programming Based Data Projections for Classification Tasks
%B International Enformatika Conference, IEC'05
%E Cemal Ardil
%V 7
%D 2005
%P 56--61
%I Enformatika, \cCanakkale, Turkey
%I World Enformatika Society
%C Prague, Czech Republic
%K genetic algorithms, genetic programming
%O CDROM
%8 August  26-28
%Z http://www.enformatika.org/proceedings.html
%@ 975-98458-6-5

%A C\'esar Est\'ebanez
%A Jos\'e M. Valls
%A Ricardo Aler
%T Projecting Financial Data using Genetic Programming in Classification and Regression Tasks
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 202--212
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050202.pdf
%X The use of Constructive Induction (CI) methods for the generation of high-quality attributes is a very important issue in Machine Learning. In this paper, we present a CI
   method based in Genetic Programming (GP). This method is able to evolve projections that transform the dataset, constructing a new coordinates space in which the data can
   be more easily predicted. This coordinates space can be smaller than the original one, achieving two main goals at the same time: on one hand, improving classification
   tasks; on the other hand, reducing dimensionality of the problem. Also, our method can handle classification and regression problems. We have tested our approach in two
   financial prediction problems because their high dimensionality is very appropriate for our method. In the first one, GP is used to tackle prediction of bankruptcy of
   companies (classification problem). In the second one, an IPO Underpricing prediction domain (a classical regression problem) is confronted. Our method obtained in both
   cases competitive results and, in addition, it drastically reduced dimensionality of the problem.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Cesar Estebanez
%A Julio Cesar Hernandez-Castro
%A Arturo Ribagorda
%A Pedro Isasi
%T Evolving hash functions by means of genetic programming
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 2
%D 2006
%P 1861--1862
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Real-World Applications: Poster, avalanche effect, hash functions
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1861.pdf
%X The design of hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this work, we use Genetic Programming (GP) to evolve robust
   and fast hash functions. We use a fitness function based on a non-linearity measure, producing evolved hashes with a good degree of Avalanche Effect. Efficiency is assured
   by using only very fast operators (both in hardware and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we
   call gp-hash, that is able to outperform a set of five human-generated, widely-used hash functions.
%8 8-12  July
%Z lilgp GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference
   (GP-2006). ACM Order Number 910060
%@ 1-59593-186-4

%A Cesar Estebanez
%A Julio Cesar Hernandez-Castro
%A Arturo Ribagorda
%A Pedro Isasi
%T Finding State-of-the-Art Non-cryptographic Hashes with Genetic Programming
%B Parallel Problem Solving from Nature - PPSN IX
%S LNCS
%E Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao
%V 4193
%D 2006
%P 818--827
%I Springer-Verlag Berlin
%C Reykjavik, Iceland
%K genetic algorithms, genetic programming
%X The design of non-cryptographic hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this paper, we use the Genetic
   Programming paradigm to evolve collision free and fast hash functions. For achieving robustness against collision we use a fitness function based on a non-linearity
   concept, producing evolved hashes with a good degree of Avalanche Effect. The other main issue, efficiency, is assured by using only very fast operators (both in hardware
   and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we call gp-hash, that is able to outperform a set of five
   human-generated, widely-used hash functions.
%8 9-13  September
%Z PPSN-IX
%@ 3-540-38990-3

%A Cesar Estebanez
%A Ricardo Aler
%A Jose Maria Valls
%T A Method Based on Genetic Programming for Improving the Quality of Datasets in Classification Problems
%J International Journal of Computer Science and Applications
%V 4
%N 1
%D 2007
%P 69--80
%I 
%K genetic algorithms, genetic programming, Classification, projections
%U http://www.tmrfindia.org/ijcsa/V4I17.pdf
%X The problem of the representation of data is a key issue in the Machine Learning (ML) field. ML tries to automatically induct knowledge from a set of examples or instances
   of a problem, learning how to distinguish between the different classes. It is known that inappropriate representations of the data can drastically limit the performance of
   ML algorithms. On the other hand, a high-quality representation of the same data, can produce a strong improvement in classification rates. In this work we present a
   GP-based method for automatically evolve projections. These projections change the data space of a classification problem into a higher-quality one, thus improving the
   performance of ML algorithms. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. We have tested our approach in four
   domains. The experiments show that it obtains good results, compared to other ML approaches that do not use our projections, while reducing dimensionality in many cases.
%Z Including Mini Special Issue based on extended versions of selected papers presented during the First International Multiconference on Computer Science and Information
   Technology (FIMCSIT), which took place in Wisla, Poland, on November 6-10, 2006. Guest Editors: Maria Ganzha and Marcin Paprzycki Ripley Data Set, Pima Indians Diabetes,
   NIPS 2001 Brain Computer Interface Workshop

%A Cesar Estebanez
%A Ricardo Aler
%A Jose M. Valls
%A Pablo Alonso
%T An experimental study on fitness distributions of tree shapes in GP with One-Point Crossover
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 244--255
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A P. A. Estevez
%A N. Becerra-Yoma
%A N. Boric
%A J. A. Ramirez
%T Genetic programming-based voice activity detection
%J Electronics Letters
%V 41
%D 2005
%P 1141--1143
%I 
%K genetic algorithms, genetic programming
%X A voice activity detection (VAD) algorithm is generated by using genetic programming (GP). The inputs of this VAD are the parameters extracted from the speech signals
   according to the ITU-T G.729B VAD standard. The GP-based VAD algorithm (GP-VAD) is evaluated using the AURORA-2 database. It is shown that the GP-VAD achieves approximately
   the same behaviour as the G.729B standard with a high artificial-to-intelligence ratio.
%8 29  September
%Z Dept. of Electr. Eng., Univ. de Chile, Santiago, Chile

%A Pablo A. Estevez
%T Russel C. Eberhart, Yuhui Shi: Computational Intelligence: Concepts to Implementation
%J Genetic Programming and Evolvable Machines
%V 9
%N 4
%D 2008
%P 367--369
%I 
%K genetic algorithms, genetic programming
%X Book review
%8 Decemeber

%A Jesus K. Estrada-Gil
%A Juan C. Fernandez-Lopez
%A Enrique Hernandez-Lemus
%A Irma Silva-Zolezzi
%A Alfredo Hidalgo-Miranda
%A Gerardo Jimenez-Sanchez
%A Edgar E. Vallejo-Clemente
%T GPDTI: A Genetic Programming Decision Tree Induction method to find epistatic effects in common complex diseases
%J Bioinformatics
%V 13
%N 13
%D 2007
%P i167--i174
%I 
%K genetic algorithms, genetic programming
%X Motivation: The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that
   common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting
   interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms
   of genetic disease by detecting interactions even in the presence of very low heritabilities. Results: We have implemented a method using Genetic Programming that is able
   to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and
   tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming
   environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated
   errors. Availability: Information on the generated data sets and executable code is available upon request.
%Z PMID: 17646293 [PubMed - in process]

%A Hossein Etemadi
%A Ali Asghar Anvary Rostamy
%A Hassan Farajzadeh Dehkordi
%T A genetic programming model for bankruptcy prediction: Empirical evidence from Iran
%J Expert Systems with Applications
%V 36
%N 2, Part 2
%D 2009
%P 3199--3207
%I 
%K genetic algorithms, genetic programming, Bankruptcy prediction, Financial ratios, Multiple discriminant analysis, Iranian companies
%U http://www.sciencedirect.com/science/article/B6V03-4RSRDDN-4/2/acecffea7c551388162fae4dfbe2a6e2
%X Prediction of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms, and governments alike. Timely identification of firms'
   impending failure is indeed desirable. By this time, several methods have been used for predicting bankruptcy but some of them suffer from underlying shortcomings. In
   recent years, Genetic Programming (GP) has reached great attention in academic and empirical fields for efficient solving high complex problems. GP is a technique for
   programming computers by means of natural selection. It is a variant of the genetic algorithm, which is based on the concept of adaptive survival in natural organisms. In
   this study, we investigated application of GP for bankruptcy prediction modeling. GP was applied to classify 144 bankrupt and non-bankrupt Iranian firms listed in Tehran
   stock exchange (TSE). Then a multiple discriminant analysis (MDA) was used to benchmarking GP model. Genetic model achieved 94percent and 90percent accuracy rates in
   training and holdout samples, respectively; while MDA model achieved only 77percent and 73percent accuracy rates in training and holdout samples, respectively. McNemar test
   showed that GP approach outperforms MDA to the problem of corporate bankruptcy prediction.

%A Shinji Eto
%A Kotaro Hirasawa
%A Jinglu Hu
%T Functional Localization of Genetic Network Programming and its Application to a Pursuit Problem
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 683--690
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Evolutionary intelligent agents, Poster Session
%X According to the knowledge of brain science, it is suggested that there exists cerebral functional localization, which means that a specific part of the cerebrum is
   activated depending on various kinds of information human receives. The aim of this paper is to build an artificial model to realize functional localization based on
   Genetic Network Programming (GNP), a new evolutionary computation method recently developed. GNP has a directed graph structure suitable for realizing functional
   localization brain has. We studied the basic characteristics of the proposed system by making GNP work in a functionally localized way.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Shinji Eto
%A Shingo Mabu
%A Kotaro Hirasawa
%A Jinglu Hu
%T Evolutionary method of Genetic Network Programing Considering Breadth and Depth
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006)
%E J\"orn Grahl
%D 2006
%I 
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp119.pdf
%X Many methods of generating behaviour sequences of agents by evolution have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has
   also been developed recently along with these trends. In this paper, a new method for evolving GNP considering Breadth and Depth is proposed.The performance of the proposed
   method is shown from simulations using garbage collector problem.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2006

%A Shinji Eto
%A Shingo Mabu
%A Kotaro Hirasawa
%A Takayuki Huruzuki
%T Genetic Network Programming with Control Nodes
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 1023--1028
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Many methods of generating behaviour sequences of agents by evolution have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has
   been also developed recently along with these trends. GNP has a directed graph structure and the search for obtaining optimal GNP becomes difficult when the scale of GNP is
   large. The aim of this paper is to find a well structured GNP considering Breadth and Depth of GNP searching. It has been shown that the proposed method is efficient
   compared with conventional GNPs from simulations using a garbage collector problem.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A C. Evans
%A P. J. Fleming
%A D. C. Hill
%A J. P. Norton
%A I. Pratt
%A D. Rees
%A K. Rodriguez-Vazquez
%T Application of system identification techniques to aircraft gas turbine engines
%J Control Engineering Practice
%V 9
%N 2
%D 2001
%P 135--148
%I 
%K genetic algorithms, genetic programming, Gas turbines, System identification, Frequency domain, Multisine signals, Least-squares estimation, Time-varying systems, Structure
   selection
%U http://www.sciencedirect.com/science/article/B6V2H-4280YP2-3/1/24d44180070f91dea854032d98f9187a
%X A variety of system identification techniques are applied to the modelling of aircraft gas turbine dynamics. The motivation behind the study is to improve the efficiency
   and cost-effectiveness of system identification techniques currently used in the industry. Three system identification approaches are outlined in this paper. They are based
   upon: multisine testing and frequency-domain identification, time-varying models estimated using extended least squares with optimal smoothing, and multiobjective genetic
   programming to select model structure.
%8 February

%A H. Evans
%A Mengjie Zhang
%T Particle swarm optimisation for object classification
%B 23rd International Conference Image and Vision Computing New Zealand, IVCNZ 2008
%D 2008
%P 1--6
%I 
%K genetic algorithms, genetic programming, PSO, feature partitioning, noise factor, object classification, optimal partition matrix, particle swarm optimisation, weight
   matrix, feature extraction, image classification, object detection, particle swarm optimisation
%X This paper describes a new approach to the use of particle swarm optimisation (PSO) for object classification problems. Instead of using PSO to evolve only a set of good
   parameter values for another machine learning method for object classification, the new approach developed in this paper can be used as a stand alone method for
   classification. Two new methods are developed in the new approach. The first new PSO method treats all different features equally important and finds an optimal partition
   matrix to separate a data set into distinct class groups. The second new PSO method considers the relative importance of each feature with the noise factor, and evolves a
   weight matrix to mitigate the effects of noisy partitions and feature dimensions. The two methods are examined and compared with a popular method using PSO combined with
   the nearest centroid and another evolutionary computing method, genetic programming, on three image data sets of increasing difficulty. The results suggest that the new
   weighted PSO method outperforms these existing methods on these object classification problems.
%8 November
%Z Refers to \citezhang:2004:eurogp Also known as \cite4762143

%A Ian W. Evett
%A E. J. Spiehler
%T Rule Induction in Forensic Science
%B KBS in Goverment
%D 1987
%P 107--118
%I Online Publications Pinner, UK
%K genetic algorithms, genetic programming, BEAGLE
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/evett_1987_rifs.pdf
%Z British Library shelfmark 5088.238300 BEAGLE trial by UK Home Office forensic scientist to give binary and three way classification of class samples based on its refractive
   index and its composition (8 elements obtianed by scanning electron microscope). In blind trial (ten cases) "The results reported from BEAGLE rules gave the lowest error
   rate." [page 116] when compared to two standard techniques, neighest 3 neighbours (cartestian distance) and Statistical Package for the Social Sciences (SPSS). Is this the
   source of UCI glass dataset?

%A Matthew Evett
%A Taghi Khoshgoftaar
%A Pei-der Chien
%A Edward Allen
%T Modelling software quality with GP
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1232
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, poster papers, SBSE
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-462.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A M. Evett
%A T. Fernandez
%T A Distributed System for Genetic Programming that Dynamically Allocates Processors
%R Technical Report
%D 1997
%I 
%I Dept. Computer Science and Engineering, Florida Atlantic University
%C Boca Raton, FL, USA
%K genetic algorithms, genetic programming
%O AGPS
%Z See \citeEvett:1997:aaaiMAL. parallel GP system, AGPS, is based on MPI, not PVM

%A Matthew Evett
%A Thomas Fernandez
%T A Distributed System for Genetic Programming that Dynamically Allocates Processors
%B Papers from the AAAI Workshop on Building Resource-Bounded Reasoning Systems
%E Shlomo Zilberstein and Louis Hoebel
%D 1997
%P 43--48
%I 
%I AAAI
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Papers/Workshops/1997/WS-97-06/WS97-06-008.pdf
%X AGPS is a portable, distributed genetic programming system, implemented on MPI. AGPS views processors as a bounded resource and optimises the use of that resource by
   dynamically varying the number of processors that it uses during execution, adapting to the external demand for those processors. AGPS also attempts to optimize the use of
   available processors by automatically terminating a genetic programming run when it appears to have stalled in a local minimum so that another run can begin.
%O Published in AAAI Technical Report WS-97-06
%Z http://www.aaai.org/Library/Workshops/ws97-06.php

%A Matthew Evett
%A Taghi Khoshgoftar
%A Pei-der Chien
%A Edward Allen
%T GP-based software quality prediction
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 60--65
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, SBSE
%U http://www.emunix.emich.edu/~evett/Publications/gp98-se.pdf
%X Software development managers use software quality prediction methods to determine to which modules expensive reliability techniques should be applied. In this paper we
   describe a genetic programming (GP) based system for targeting software modules for reliability enhancement. The paper describes the GP system, and provides a case study
   using software quality data from two actual industrial projects. The system is shown to be robust enough for use in industrial domains.
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Matthew Evett
%A Thomas Fernandez
%T Numeric Mutation Improves the Discovery of Numeric Constants in Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 66--71
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://www.emunix.emich.edu/~evett/Publications/gp98-nm.pdf
%X Genetic programming suffers difficulty in discovering useful numeric constants for the terminal nodes of its sexpression trees. In earlier work we postulated a solution to
   this problem called numeric mutation. Here, we provide empirical evidence to demonstrate that this method provides a statistically significant improvement in GP system
   performance on a variety of problems.
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Matthew Evett
%A Taghi Khoshgoftaar
%A Pei-der Chien
%A Ed Allen
%T Using genetic programming to determine software quality
%B Proceedings of the Twelfth International FLAIRS Conference
%D 1999
%P 113--117
%I AAAI
%K genetic algorithms, genetic programming, SBSE
%U http://www.aaai.org/Papers/FLAIRS/1999/FLAIRS99-020.pdf
%X Software development managers use software quality prediction methods to determine to which modules expensive reliability techniques should be applied. In this paper we
   describe a genetic programming (GP) based system that classifies software modules as "faulty" or "Not faulty", allowing the targeting of modules for reliability
   enhancement. The paper describes the GP system, and provides a case study using software quality data from a very large industrial project. The demonstrated quality of the
   system is such that plans are under way to integrate it into a commercial software quality management system.
%Z lil-gp p117 "Indeed, our current work involves the integration of this GP system into the existing EMERALD industrial software management system." Department of Computer
   Science and Engineering Florida Atlantic University Boca Raton, Florida 33431, USA Evolved non-linear model could use EMERALD metrics: FILINCUQT, LGPATHT, VARSPNMX, USAGEA,
   BETA_PR, BETA_FIX, CUST_FIX, SRC_GRON, UNQ_DENS, UPD_CATR, VLO_UPD

%T MOLE at City University
%J EvoNEWS
%V 11
%D 1999
%P 2--3
%I 
%K genetic algorithms, genetic programming
%U http://evonet.lri.fr/evoweb/files/evonews/evonews11.pdf
%X Profile of research group. Introns Peter Smith application of GP to MRI brain tumors+Principal Component Analysis, NMR Helen Gray and Peter W. H. Smith (NMR in Biomedicine,
   11)
%8 summer

%T Evol-artists - a new breed entirely
%J EvoNEWS
%V 11
%D 1999
%P 7--10
%I 
%K genetic algorithms, genetic programming
%U http://evonet.lri.fr/evoweb/files/evonews/evonews11.pdf
%X I CANT STOP. There is something compelling about this process. It feels as though the images are trying to break out of their hyperspace into the physical world. Sometimes
   I'll be two or three days into a run dozens of generations with one or two hundred individuals in the population when Wham! there's something familiar staring back at me
   from out of the computer screen, demanding to be made real.
%8 summer
%Z Steven Rooke. Richard Dawkins Biomorphs. Jeffrey Ventrella. Mattias Fagerlund http://www.acacia.se/Mattias/WebGP/ Ken Musgrave. Karl Sims. Dr. Mutatis an evolutionary art
   tool. Jano I. van Hemert --- Pieter Mondriaan. Pensousal Machado -- NEvAr

%A Igor V. Evstigneev
%A Thorsten Hens
%A Klaus Reiner Schenk-Hoppe
%T Evolutionary Finance
%B Handbook of Financial Markets: Dynamics and Evolution
%E Thorsten Hens and Klaus Reiner Schenk-Hoppe
%D 2009
%P 507--566
%I North-Holland
%C San Diego
%U http://www.sciencedirect.com/science/article/B8N8N-4W6Y2CK-9/2/d140c798e01e01356572d883e6694255
%X Evolutionary finance studies the dynamic interaction of investment strategies in financial markets. This market interaction generates a stochastic wealth dynamic on a
   heterogenous population of traders through the fluctuation of asset prices and their random payoffs. Asset prices are endogenously determined through short-term market
   clearing. Investors' portfolio choices are characterized by investment strategies that provide a descriptive model of decision behavior. The mathematical framework of these
   models is given by random dynamical systems. This chapter surveys the recent progress made by the authors in the theory and applications of evolutionary finance models. An
   introduction to and the motivation of the modeling approach is followed by a theoretical part that presents results on the market selection (and coexistence) of investment
   strategies, discusses the relation to the Kelly Rule and implications for asset-pricing theory, and introduces a continuous-time mathematical finance version. Applications
   are concerned with simulation studies of market dynamics, empirical estimation of asset prices and their dynamics, and evolution of investment strategies using genetic
   programming.

%A Francesc Xavier Llora i Fabrega
%A Josep Maria Garrell i Guiu
%T GENIFER: A Nearest Neighbour based Classifier System using GA
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 797
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-321.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A David Fagan
%A Michael O'Neill
%A Edgar Galvan-Lopez
%A Anthony Brabazon
%A Sean McGarraghy
%T An Analysis of Genotype-Phenotype Maps in Grammatical Evolution
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 62--73
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X We present an analysis of the genotype-phenotype map in Grammatical Evolution (GE). The standard map adopted in GE is a depth-first expansion of the non-terminal symbols
   during the derivation sequence. Earlier studies have indicated that allowing the path of the expansion to be under the guidance of evolution as opposed to a deterministic
   process produced significant performance gains on all of the benchmark problems analysed. In this study we extend this analysis to include a breadth-first and random map,
   investigate additional benchmark problems, and take into consideration the implications of recent results on alternative grammar representations with this new evidence. We
   conclude that it is possible to improve the performance of grammar-based Genetic Programming by the manner in which a genotype-phenotype map is performed.
%8 7-9  April
%Z Typed GP, GEVA, pi-GE, 5-parity, x+x^2+x^3+x^4, Santa Fe trail, Max \citelangdon:1997:MAX. Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with
   EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A David Fagan
%A Miguel Nicolau
%A Michael O'Neill
%A Edgar Galvan-Lopez
%A Anthony Brabazon
%A Sean McGarraghy
%T Investigating Mapping Order in piGE
%B 2010 IEEE World Congress on Computational Intelligence
%D 2010
%P 3058--3064
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Barcelona, Spain
%K genetic algorithms, genetic programming, grammatical evolution
%X We present an investigation into the genotype-phenotype map in Position Independent Grammatical Evolution (piGE). Previous studies have shown piGE to exhibit a performance
   increase over standard GE. The only difference between the two approaches is in how the genotype-phenotype mapping process is performed. GE uses a leftmost non terminal
   expansion, while piGE evolves the order of mapping as well as the content. In this study, we use the idea of focused search to examine which aspect of the piGE mapping
   process provides the lift in performance over standard GE by applying our approaches to four benchmark problems taken from specialised literature. We examined the
   traditional piGE approach and compared it to two setups which examined the extremes of mapping order search and content search, and against setups with varying ratios of
   content and order search. In all of these tests a purely content focused piGE was shown to exhibit a performance gain over the other setups.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586204

%A David Fagan
%A Miguel Nicolau
%A Erik Hemberg
%A Michael O'Neill
%A Anthony Brabazon
%A Sean McGarraghy
%T Investigation of the Performance of Different Mapping Orders for GE on the Max Problem
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 286--297
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming, Grammatical Evolution: poster
%X We present an analysis of how the genotype-phenotype map in Grammatical Evolution (GE) can effect performance on the Max Problem. Earlier studies have demonstrated a
   performance decrease for Position Independent Grammatical Evolution (pige) in this problem domain. In piGE the genotype-phenotype map is changed so that the evolutionary
   algorithm controls not only what the next expansion will be but also the choice of what position in the derivation tree is expanded next. In this study we extend previous
   work and investigate whether the ability to change the order of expansion is responsible for the performance decrease or if the problem is simply that a certain order of
   expansion in the genotype-phenotype map is responsible. We conclude that the reduction of performance in the Max problem domain by pi GE is rooted in the way the
   genotype-phenotype map and the genetic operators used with this mapping interact.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A David Fagan
%A Miguel Nicolau
%A Erik Hemberg
%A Michael O'Neill
%A Anthony Brabazon
%T Dynamic ant: introducing a new benchmark for genetic programming in dynamic environments
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 183--184
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution: Poster
%X In this paper we present a new variant of the Ant Problem in the Dynamic Problem Domain. This approach presents a functional dynamism to the problem landscape, where by the
   behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised
   solution to the problem is possible, thus providing a functional change in behaviour.
%8 12-16  July
%Z Also known as \cite2001961 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A David Fagan
%T Genotype-phenotype mapping in dynamic environments with grammatical evolution
%B GECCO 2011 Graduate students workshop
%E Miguel Nicolau
%D 2011
%P 783--786
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution
%X The application of a genotype-phenotype mapping in Evolutionary Computation is not a new idea, however, how this mapping process is interpreted, and implemented varies
   wildly. In the majority of cases a very simple abstraction of the biological genotype-phenotype mapping is used, but as our understanding of this process increases, the
   deficiencies in current approaches become more evident. In this paper, an outline of what approaches have been taken in the investigation of the genotype-phenotype map in
   Grammatical Evolution are presented and an outline of proposed future work is introduced.
%8 12-16  July
%Z Also known as \cite2002091 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Rodolfo Faglia
%A David Vetturi
%T Motion Planning and Design of CAM Mechanisms by Means of a Genetic Algorithm
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 479--484
%I MIT Press
%C Stanford University, CA, USA
%K Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 GA paper

%A Peter Fairley
%T Germs that build Circuits
%J IEEE Spectrim
%D 2003
%P 36--41
%I 
%K nanotechnology
%U http://ieeexplore.ieee.org/iel5/6/27854/01242955.pdf
%X Circuits With viruses serving as construction crews and DNA as the blueprint, biotechnology may hold the key to postlithography integrated circuits
%8 November
%Z gee wow level but good pointers? Belcher (MIT), protein genetic engineering circuit evolution peptide gallium arsenide, indium phosphate, 7x850nm, quantum dot, solar cells,
   flash memory, laser, semiconductors zinc sulfide and cadmium sulfide, magnetic materials cobalt-platinum and iron-platinum. viralware, fabric treatment and cosmetic
   products. Sensors. Carbon nanotubes. $30m

%A Paolo Falbo
%A Nicola Doninelli
%T "Reverse engineering" of managed fund market timing strategies
%B The Sixteenth Triennial Conference of the International Federation of Operational Research Societies
%D 2002
%I 
%I UK Operational Research Society
%C University of Edinburgh
%K genetic algorithms, genetic programming
%X In market timing studies the sensitivity of fund returns to the payoff of perfect market timing strategies is usually provided. Nothing is said about the nature of the
   trading strategies implemented by fund managers. In this work we present a novel method to identify timing activity more than timing ability based on genetic programming
   and the Henriksson-Merton model. While timing ability is necessarily associated to superior forecasting, timing activity is not. Therefore, we're not testing the EMH from
   the supply side but attempt to address a slightly different question: do mutual funds use timing strategies? This is an intriguing problem given that we focus on investment
   style more than on the average profits of market timing.
%O Conference theme: OR in a globalised, networked world economy, Invited session
%8 8-12  July
%Z University of Brescia, Italy http://meetings.informs.org/IFORS2002/working_files/program.pdf

%A Wes Faler
%T Automatic Algorithm Invention with GPU
%B 28th Chaos Communication Congress
%D 2011
%P ID 4764
%I 
%C Berlin
%K genetic algorithms, genetic programming, GPU, Cartesian Genetic Programming
%U http://events.ccc.de/congress/2011/Fahrplan/attachments/2029_AutomaticAlgorithmInvention.pdf
%X You write software. You test software. You know how to tell if the software is working. Automate your software testing sufficiently and you can let the computer do the
   writing for you! 'Genetic Programming', especially 'Cartesian Genetic Programming' (CGP), is a powerful tool for creating software and designing physical objects. See how
   to do CGP as we invent image filters for the Part Time Scientists' 3D cameras. Danger: Actual code will be shown!
%8 27-30  Decemeber
%Z Hell Yeah, it's rocket science. Slides only? http://events.ccc.de/congress/2011/ https://mail.google.com/mail/h/wbzd6ywtnwil/?&v=c&th=1354023091e04a31
   http://wesfaler.wordpress.com/2011/12/29/algorithm-invention-with-cartesian-genetic-programming/

%A Wes Faler
%T Evolving custom communication protocols
%B 28th Chaos Communication Congress
%D 2011
%P ID 4818
%I 
%C Berlin
%K genetic algorithms, genetic programming, GPU, Cartesian Genetic Programming
%U http://events.ccc.de/congress/2011/Fahrplan/attachments/2054_EvolvingCustomCommunicationProtocols.pdf
%X Even after years of committee review, communication protocols can certainly be hacked, sometimes highly entertainingly. What about creating a protocol the opposite way?
   Start with all the hacks that can be done and search for a protocol that gets around them all. Is it even possible? Part Time Scientists has used a GPU to help design our
   moon mission protocols and we'll show you the what and how. Danger: Real code will be shown!
%8 27-30  Decemeber
%Z Hell Yeah, it's rocket science. Slides only? http://events.ccc.de/congress/2011/ https://mail.google.com/mail/h/wbzd6ywtnwil/?&v=c&th=1354023091e04a31

%A John L. Fan
%T Design of an Adaptive Detector for Digital Communications using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 11--19
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Weiguo Fan
%A Michael D. Gordon
%A Praveen Pathak
%T Automatic generation of matching functions by genetic programming for effective information retrieval
%B Proceedings of the 1999 Americas Conference on Information Systems
%E W. David Haseman and Derek L. Nazareth
%D 1999
%P 49--51
%I 
%I Association for Information Systems
%C Milwaukee, WI, USA
%K genetic algorithms, genetic programming
%U http://filebox.vt.edu/users/wfan/paper/Amcis_final.pdf
%X With the advent of the Internet, online resources are increasingly available. Many users choose popular search engines to perform an online search to satisfy their
   information need. However, these search engines tend to turn up many non-relevant documents, which make their retrieval precision very low. How to find appropriate ranking
   metrics to retrieve more relevant documents and fewer non-relevant documents for users remains a big challenge to the information retrieval community. In this paper, we
   propose a new framework that combines the merits of genetic programming and relevance feedback techniques to automatically generate and refine the matching functions used
   for document ranking. This approach overcomes the shortcoming of traditional ranking algorithms using a fixed ranking strategy. It also gives some new ideas and hints for
   information retrieval professionals.
%8 13-15  August
%Z AMCIS99 https://commerce.mindspring.com/www.icisnet.org/proc.html Prototype implemented in C. Fitness based on user feedback Duplicate entry \citeFan:1999:AMCIS removed 21
   Oct 2006

%A Weiguo Fan
%A Michael D. Gordon
%A Praveen Pathak
%T Personalization of Search Engine Services for Effective Retrieval and Knowledge Management
%B The Proceedings of the International Conference on Information Systems 2000
%D 2000
%P 20--34
%I 
%K genetic algorithms, genetic programming, information retrieval
%U http://filebox.vt.edu/users/wfan/paper/icis_final.pdf
%X The Internet and corporate intranets provide far more information than anybody can absorb. People use search engines to find the information they require. However, these
   systems tend to use only one fixed term weighting strategy regardless of the context to which it applies, posing serious performance problems when characteristics of
   different users, queries, and text collections are taken into consideration. In this paper, we argue that the term weighting strategy should be context specific, that is,
   different term weighting strategies should be applied to different contexts, and we propose a new systematic approach that can automatically generate term weighting
   strategies for different contexts based on genetic programming (GP). The new proposed framework was tested on TREC data and the results are very promising.

%A Weiguo Fan
%A Michael D. Gordon
%A Praveen Pathak
%T Discovery of context-specific ranking functions for effective information retrieval using genetic programming
%J IEEE Transactions on Knowledge and Data Engineering
%V 16
%N 4
%D 2004
%P 523--527
%I 
%K genetic algorithms, genetic programming, data mining, information retrieval, search engines, tree data structures, Internet, TREC data, context-specific ranking function
   discovery, corporate intranets, fixed ranking strategy, information routing, intelligent contextual information retrieval, search engines, term weighting strategy, text
   mining
%X The Internet and corporate intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to
   use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text
   collections are taken into account. We argue that the ranking strategy should be context specific and we propose a , new systematic method that can automatically generate
   ranking strategies for different contexts based on genetic programming (GP). The new method was tested on TREC data and the results are very promising.
%8 April
%Z http://filebox.vt.edu/users/wfan/pub_area.html

%A Weiguo Fan
%A Michael D. Gordon
%A Praveen Pathak
%T A generic ranking function discovery framework by genetic programming for information retrieval
%J Information Processing and Management
%V 40
%N 4
%D 2003
%P 587--602
%I 
%K genetic algorithms, genetic programming, Information retrieval; Ranking function, Text mining
%U http://www.sciencedirect.com/science/article/B6VC8-49J8S58-2/2/158a3713b59ef9defad7d00e81707f66
%X Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available
   in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections,
   users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we
   propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking
   function design/discovery process.

%A Weiguo Fan
%A Michael D. Gordon
%A Praveen Pathak
%A Wensi Xi
%A Edward A. Fox
%T Ranking Function Optimization For Effective Web Search By Genetic Programming: An Empirical Study
%B Proceedings of 37th Hawaii International Conference on System Sciences
%D 2004
%P 105--112
%I IEEE
%C Hawaii
%K genetic algorithms, genetic programming
%X Web search engines have become indispensable in our daily life to help us find the information we need. Although search engines are very fast in search response time, their
   effectiveness in finding useful and relevant documents at the top of the search hit list needs to be improved. In this paper, we report our experience applying Genetic
   Programming (GP) to the ranking function discovery problem leveraging the structural information of HTML documents. Our empirical experiments using the web track data from
   recent TREC conferences show that we can discover better ranking functions than existing well-known ranking strategies from IR, such as Okapi, Ptfidf. The performance is
   even comparable to those
%8 5-8  January
%Z http://filebox.vt.edu/users/wfan/pub_area.html

%A Weiguo Fan
%A Michael D. Gordon
%A Praveen Pathak
%T A two stage integrated model for intelligent information routing
%J Decision Support Systems
%V 42
%N 1
%D 2006
%P 362--374
%I 
%K genetic algorithms, genetic programming, Information Routing, Information Retrieval, Personalization, Text Mining
%U http://filebox.vt.edu/users/wfan/pub_area.html
%X A recent surge of subscriptions to online news services exemplifies the fact that people and organizations constantly need up-to-date information to stay competitive and
   make better informed decisions. However, many of these news services often require users to either manually input their profiles or subscribe to existing news channel. This
   results in lack of intelligence and personalization, and thus make them less attractive to users. In this paper, an integrated model that combines query expansion with
   ranking function adaptation for online information routing is proposed and tested using two different large scale corpora. The experimental results show that this new model
   can deliver much better quality information than existing models.
%8 October

%A Weiguo Fan
%A Edward A. Fox
%A Praveen Pathak
%A Harris Wu
%T The effects of fitness functions on genetic programming-based ranking discovery for web search
%J Journal of the American Society for Information Science and Technology
%V 55
%N 7
%D 2004
%P 628--636
%I 
%K genetic algorithms, genetic programming, ranking function, text mining, web search, information retrieval
%U http://filebox.vt.edu/users/wfan/paper/ARRANGER/JASIST2004.pdf
%X Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the
   1980s. Recently, GP has been applied to a new IR task- discovery of ranking functions for Web search-and has achieved very promising results. However, in our prior
   research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search,
   especially since it is well known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this
   article, we report our experience in contrasting different fitness function designs on GP-based learning using a very large Web corpus. Our results indicate that the design
   of fitness functions is instrumental in performance improvement. We also give recommendations on the design of fitness functions for genetic-based information retrieval
   experiments.

%A Weiguo Fan
%A Ming Luo
%A Li Wang
%A Wensi Xi
%A Edward A. Fox
%T Tuning before feedback: combining ranking function discovery and blind feedback for robust retrieval
%B the Proceedings of the 27th Annual International ACM SIGIR Conference
%D 2004
%I ACM
%C U.K.
%K genetic algorithms, genetic programming, intelligent information retrieval, search engine, ranking function discovery, information retrieval, blind feedback
%U http://filebox.vt.edu/users/wfan/paper/ARRANGER/p52-Fan.pdf
%X Both ranking functions and user queries are very important factors affecting a search engine's performance. Prior research has looked at how to improve ad-hoc retrieval
   performance for existing queries while tuning the ranking function, or modify and expand user queries using a fixed ranking scheme using blind feedback. However, almost no
   research has looked at how to combine ranking function tuning and blind feedback together to improve ad-hoc retrieval performance. In this paper, we look at the performance
   improvement for ad-hoc retrieval from a more integrated point of view by combining the merits of both techniques. In particular, we argue that the ranking function should
   be tuned first, using user-provided queries, before applying the blind feedback technique. The intuition is that highly-tuned ranking offers more high quality documents at
   the top of the hit list, thus offers a stronger baseline for blind feedback. We verify this integrated model in a large scale heterogeneous collection and the experimental
   results show that combining ranking function tuning and blind feedback can improve search performance by almost 30 percent over the baseline Okapi system.

%A Weiguo Fan
%A Praveen Pathak
%A Linda Wallace
%T Nonlinear ranking function representations in genetic programming-based ranking discovery for personalized search
%J Decision Support Systems
%V 42
%N 3
%D 2006
%P 1338--1349
%I 
%K genetic algorithms, genetic programming, Information routing, Information retrieval, Ranking function
%X Ranking function is instrumental in affecting the performance of a search engine. Designing and optimising a search engine's ranking function remains a daunting task for
   computer and information scientists. Recently, genetic programming (GP), a machine learning technique based on evolutionary theory, has shown promise in tackling this very
   difficult problem. Ranking functions discovered by GP have been found to be significantly better than many of the other existing ranking functions. However, current GP
   implementations for ranking function discovery are all designed using the Vector Space model in which the same term weighting strategy is applied to all terms in a
   document. This may not be an ideal representation scheme at the individual query level considering the fact that many query terms should play different roles in the final
   ranking. In this paper, we propose a novel nonlinear ranking function representation scheme and compare this new design to the well-known Vector Space model. We
   theoretically show that the new representation scheme subsumes the traditional Vector Space model representation scheme as a special case and hence allows for additional
   flexibility in term weighting. We test the new representation scheme with the GP-based discovery framework in a personalised search (information routing) context using a
   TREC web corpus. The experimental results show that the new ranking function representation design outperforms the traditional Vector Space model for GP-based ranking
   function discovery.
%8 Decemeber

%A Weiguo Fan
%A Praveen Pathak
%A Mi Zhou
%T Genetic-based approaches in ranking function discovery and optimization in information retrieval -- A framework
%J Decision Support Systems
%V 47
%N 4
%D 2009
%P 398--407
%I 
%K genetic algorithms, genetic programming, Information retrieval, Artificial intelligence, Evolutionary computations, Data fusion
%U http://www.sciencedirect.com/science/article/B6V8S-4W2W5G2-2/2/891e4aeaad9141e2bfe99d4477f96c1a
%X An Information Retrieval (IR) system consists of document collection, queries issued by users, and the matching/ranking functions used to rank documents in the predicted
   order of relevance for a given query. A variety of ranking functions have been used in the literature. But studies show that these functions do not perform consistently
   well across different contexts. In this paper we propose a two-stage integrated framework for discovering and optimising ranking functions used in IR. The first stage,
   discovery process, is accomplished by intelligently leveraging the structural and statistical information available in HTML documents by using Genetic Programming
   techniques to yield novel ranking functions. In the second stage, the optimization process, document retrieval scores of various well-known ranking functions are combined
   using Genetic Algorithms. The overall discovery and optimization framework is tested on the well-known TREC collection of web documents for both the ad-hoc retrieval task
   and the routing task. Using our framework we observe a significant increase in retrieval performance compared to some of the well-known stand alone ranking functions.
%O Smart Business Networks: Concepts and Empirical Evidence

%A Xinqiao Fan
%A Yongli Zhu
%T The application of Empirical Mode Decomposition and Gene Expression Programming to short-term load forecasting
%B Sixth International Conference on Natural Computation (ICNC 2010)
%V 8
%D 2010
%P 4331--4334
%I 
%K genetic algorithms, genetic programming, gene expression programming, empirical mode decomposition, intrinsic mode functions, short-term load forecasting, wavelet
   transforms, genetic algorithms, load forecasting, statistical analysis, wavelet transforms
%X A forecasting method of combining Empirical Mode Decomposition(EMD) and Gene Expression Programming(GEP) that's called EMD and GEP method here is suggested, which is
   applied to short-term load forecasting and higher forecasting precision is obtained. The load samples are handled in order to eliminate the pseudo-data, and the intrinsic
   mode functions(IMFs) and the residual trend of different frequency are obtained according to EMD. Then the corresponding load series of the same time but different days in
   the IMFs and the residual trend are chosen as the training samples, and by means of the flexible expressive capacity of GEP, the models of different time points in each IMF
   and the residual trend are evolved according to time-sharing. And the final forecasting result is obtained by reconstructing the models of each IMF and the residual trend.
   The method of EMD overcomes the shortcomings of wavelet transform that it's difficult to select proper wavelet function, and the final result indicates that the IMFs can
   reflect the characteristics of the power load. After comparison with the results forecasted by means of Wavelet and GEP, it proves that the effect of the forecasting method
   of EMD and GEP in short-term load forecasting is better.
%8 10-12  August
%Z also known as \cite5583605

%A Zhun Fan
%A Jianjun Hu
%A Kisung Seo
%A Erik D. Goodman
%A Ronald C. Rosenberg
%A Baihai Zhang
%T Bond Graph Representation and GP for Automated Analog Filter Design
%B 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Erik D. Goodman
%D 2001
%P 81--86
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, STGP, bond graphs, evolutionary synthesis
%U http://citeseer.ist.psu.edu/448346.html
%X We present a novel circuit representation scheme, namely bond graph, along with strong-typed genetic programming for the evolution of analog filter circuits. Bond graph is
   a concise and uniform language for the description of circuit systems and more general engineering systems. Many unique characteristics of bond graph makes it an attractive
   candidate for representing circuit in genetic programming design. The feasibility and efficiency of using bond graph as the representation technique of circuit systems are
   verified in our experiments with automated analogue filter design.
%8 9-11  July
%Z GECCO-2001LB, lilgp

%A Zhun Fan
%A Kisung Seo
%A Ronald C. Rosenberg
%A Jianjun Hu
%A Erik D. Goodman
%T Exploring Multiple Design Topologies Using Genetic Programming And Bond Graphs
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 1073--1080
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, real world applications, bond graphs, design automation, mechatronic system, topology
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf
%X To realize design automation of dynamic systems, there are two major issues to be dealt with: open-topology generation of dynamic systems and simulation or analysis of
   those models. For the first issue, we exploit the strong topology exploration capability of genetic programming to create and evolve structures representing dynamic
   systems. With the help of ERCs (ephemeral random constants) in genetic programming, we can also evolve the sizing of dynamic system components along with the structures.
   The second issue, simulation and analysis of those system models, is made more complex when they represent mixed-energy- domain systems. We take advantage of bond graphs as
   a tool for multi- or mixed-domain modeling and simulation of dynamic systems. Because there are many considerations in dynamic system design that are not completely
   captured by a bond graph, we would like to generate multiple solutions, allowing the designer more latitude in choosing a model to implement. The approach in this paper is
   capable of providing a variety of design choices to the designer for further analysis, comparison and trade-off. The approach is shown to be efficient and effective in an
   example of open-ended real- world dynamic system design application, a printer re-design problem.
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Zhun Fan
%A Kisung Seo
%A Ronald C. Rosenberg
%A Jianjun Hu
%A Erik D. Goodman
%T Computational Synthesis of Multi-Domain Systems
%B Proceedings of the 2003 AAAI Spring Symposium - Computational Synthesis: From Basic Building Blocks to High Level Functionality
%D 2003
%P 59--66
%I 
%I AAAI
%C Stanford, California
%K genetic algorithms, genetic programming, bond graphs, evolutionary synthesis
%U http://www.egr.msu.edu/~ksseo/publication.htm
%X Several challenging issues have to be addressed for automated synthesis of multi-domain systems. First, design of interdisciplinary (multi-domain) engineering systems, such
   as mechatronic systems, differs from design of single-domain systems, such as electronic circuits, mechanisms, and fluid power systems, in part because of the need to
   integrate the several distinct domain characteristics in predicting system behavior. Second, a mechanism is needed to automatically select useful elements from the building
   block repertoire, construct them into a system, evaluate the system and then reconfigure the system structure to achieve better performance. Dynamic system models based on
   diverse branches of engineering science can be expressed using the notation of bond graphs, based on energy and information flow. One may construct models of electrical,
   mechanical, magnetic, hydraulic, pneumatic, thermal, and other systems using only a rather small set of ideal elements as building blocks. Another useful tool, genetic
   programming, is a powerful method for creating and evolving novel design structures in an open-ended manner. Through definition of a set of constructor functions, a
   genotype tree is created for each individual in each generation. The process of evaluating the genotype tree maps the genotype into a phenotype -- i.e., to the abstract
   topological description of the design of a multi-domain system, using a bond graph along with parameters for each component, if needed. Finally, physical realization is
   carried out to relate each abstract element of the bond graph to corresponding components in various physical domains. To implement the above GPBG approach in a specific
   application domain, cautious steps have to be taken to make the evolved design represented by bond graphs realizable and manufacturable. To achieve this, one important step
   is to define appropriate building blocks of the design space and carefully design a realizable function set in genetic programming. We are going to illustrate this in an
   example of behavioral synthesis of an RF MEM circuit C a micro-mechanical band pass filter design. Finally, we have some discussions on how to extend the above approach to
   an integrated evolutionary synthesis environment for MEMS across a variety of design layers.
%8 March

%A Zhun Fan
%A Kisung Seo
%A Jianjun Hu
%A Ronald C. Rosenberg
%A Erik D. Goodman
%T System-Level Synthesis of MEMS via Genetic Programming and Bond Graphs
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 2058--2071
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, Real World Applications
%X Initial results have been achieved for automatic synthesis of MEMS system-level lumped parameter models using genetic programming and bond graphs. This paper first
   discusses the necessity of narrowing the problem of MEMS synthesis into a certain specific application domain, e.g., RF MEM devices. Then the paper briefly introduces the
   flow of a structured MEMS design process and points out that system-level lumped-parameter model synthesis is the first step of the MEMS synthesis process. Bond graphs can
   be used to represent a system-level model of a MEM system. As an example, building blocks of RF MEM devices are selected carefully and their bond graph representations are
   obtained. After a proper and realizable function set to operate on that category of building blocks is defined, genetic programming can evolve both the topologies and
   parameters of corresponding RF MEM devices to meet predefined design specifications. Adaptive fitness definition is used to better direct the search process of genetic
   programming. Experimental results demonstrate the feasibility of the approach as a first step of an automated MEMS synthesis process. Some methods to extend the approach
   are also discussed.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Zhun Fan
%A Erik Goodman
%A Jiachuan Wang
%A Ronald Rosenberg
%A Kisung Seo
%A Jianjun Hu
%T Hierarchical Evolutionary Synthesis of MEMS
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 2320--2327
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Evolutionary design \& evolvable hardware, Real-world applications
%X In this paper, we discuss the hierarchy that is involved in a typical MEMS design and how evolutionary approaches can be used to automate the hierarchical design and
   synthesis process for MEMS. At the system level, the approach combining bond graphs and genetic programming can lead to satisfactory design candidates of system level
   models that meet the predefined behavioral specifications for designers to tradeoff. At the physical layout synthesis level, the selection of geometric parameters for
   component devices is formulated as a constrained optimization problem and addressed using a constrained GA approach. A multiple-resonator microsystem design is used to
   illustrate the integrated design automation idea using evolutionary approaches.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Zhun Fan
%A Jiachuan Wang
%A Kisung Seo
%A Jianjun Hu
%A Ronald Rosenberg
%A Janis Terpenny
%A Erik Goodman
%T Automating the Hierarchical Synthesis of MEMS Using Evolutionary Approaches
%B Evolvable Machines: Theory \& Practice
%S Studies in Fuzziness and Soft Computing
%E Nadia Nedjah and Luiza de Macedo Mourelle
%V 161
%D 2004
%P 129--149
%I Springer
%C Berlin
%K genetic algorithms, genetic programming
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html
%O 6
%Z Springer says published in 2005 but available Nov 2004
%@ 3-540-22905-1

%A Zhun Fan
%T Design Automation of Mechatronic Systems
%R Ph.D. Thesis
%D 2004
%I 
%I Electrical and Computer Engineering, Michigan State University
%C USA
%K genetic algorithms, genetic programming
%U https://www.msu.edu/~fanzhun/Zhun%27s%20Dissertation%20Research.htm
%X Design automation is a difficult task and has been studied for some time by researchers. Most research is quite successful in automating the parameters of a given design
   topology. However, their limitation is that they only accept fixed design topologies. Others can design in topologically unconstrained space, but are limited or specially
   tailored to a single physical domain. The motivation of this research is two-fold. First, we want to find a way to generate a population of topologically open-ended design
   alternatives and provide for the designer, in an automated manner, a variety of satisfactory design candidates to choose among and trade off. Second, we want our method to
   be applicable not only in one physical domain, but in multiple domains or a mixture of them, as is required for design of mechatronic systems. To meet these ends, the
   capability of genetic programming to search automatically in an open-ended search space and the strong capability of bond graphs to represent and model mixed-domain systems
   are studied and ways to blend their merits in one unified approach are investigated. In our research, the BG/GP method, combining bond graphs and genetic programming, has
   been developed to automate the conceptual design process for general multidisciplinary mechatronic systems. Several design problems, in macro- and micro-domains, and in
   different physical domains, have been used as design examples to test the feasibility of the BG/GP approach. The analog electronic filter design problem shows the
   efficiency and effectiveness of the proposed approach. A vibration absorber design for a mechanical printer demonstrates that the approach can also be used for redesign and
   is very effective in exploring in an open-ended topology space and capable of providing designers with a variety of good design candidates for further analysis and
   tradeoff. A pneumatic air pump design shows how to bias design preference and implies the possibility and significance of extracting design heuristics in the evolutionary
   process. Finally, a MEM filter design problem shows that the BG/GP approach can be applied in a very general class of conceptual design problems with severe topology and/or
   parameter constraints. The results show that the BG/GP method is a powerful synergistic approach for automated, mixed-domain, and topologically open-ended design of
   mechatronic systems

%A Zhun Fan
%A Jiachuan Wang
%A Sofiane Achiche
%A Erik Goodman
%A Ronald Rosenberg
%T Structured synthesis of MEMS using evolutionary approaches
%J Applied Soft Computing
%V 8
%N 1
%D 2008
%P 579--589
%I 
%K genetic algorithms, genetic programming, MEMS synthesis, Genetic programming, Bond graphs, Genetic algorithm
%U http://www.sciencedirect.com/science/article/B6W86-4NWCGRR-6/2/6d147c9eb8cc9af8eec68e592dfd22f
%X In this paper, we discuss the hierarchy that is involved in a typical MEMS design and how evolutionary approaches can be used to automate the hierarchical synthesis process
   for MEMS. The paper first introduces the flow of a structured MEMS design process and emphasizes that system-level lumped-parameter model synthesis is the first step of the
   MEMS synthesis process. At the system level, an approach combining bond graphs and genetic programming can lead to satisfactory design candidates as system-level models
   that meet the predefined behavioral specifications for designers to trade off. Then at the physical layout synthesis level, the selection of geometric parameters for
   component devices and other design variables is formulated as a constrained optimization problem and addressed using a constrained genetic algorithm approach. A
   multiple-resonator microsystem design is used to illustrate the integrated design automation idea using these evolutionary approaches.

%A Zhun Fan
%T Mechatronic Design Automation: Emerging Research and Recent Advances
%D 2010
%I Nova publishers
%K genetic algorithms, genetic programming, bond graph
%U http://www.amazon.com/Mechatronic-Design-Automation-Engineering-Applications/dp/1616689560
%X This book proposes a novel design method that combines both genetic programming (GP) to automatically explore the open-ended design space and bond graphs (BG) to unify
   design representations of multi-domain Mechatronic systems. Results show that the method, formally called GPBG method, can successfully design analog filters, vibration
   absorbers, micro-electro-mechanical systems, and vehicle suspension systems, all in an automatic or semi-automatic way. It also investigates the very important issue of
   co-designing body-structures and dynamic controllers in automated design of Mechatronic systems.
%8 April
%Z Technical University of Denmark, Denmark

%A Yongsheng Fang
%A Jun Li
%T A Review of Tournament Selection in Genetic Programming
%B ISICA 2010
%S Lecture Notes in Computer Science
%E Zhihua Cai and Chengyu Hu and Zhuo Kang and Yong Liu
%V 6382
%D 2010
%P 181--192
%I Springer
%K genetic algorithms, genetic programming
%X This paper provides a detailed review of tournament selection in genetic programming. It starts from introducing tournament selection and genetic programming, followed by a
   brief explanation of the popularity of the tournament selection in genetic programming. It then reviews issues and drawbacks in tournament selection, followed by analysis
   of and solutions to these issues and drawbacks. It finally points out some interesting directions for future work.

%A Nicola Fanizzi
%A Claudia d'Amato
%A Floriana Esposito
%T Clustering Individuals in Ontologies: a Distance-based Evolutionary Approach
%B Proceedings of the third ECML/PKDD international workshop on Mining Complex Data
%E Zbigniew W. Ras and Djamel Zighed and Shusaku Tsumoto
%D 2007
%P 197--208
%I 
%C Warsaw
%K genetic algorithms, genetic programming
%U http://www.ecmlpkdd2007.org/CD/workshops/MCDM/18_Fanizzi/mcdws2007-final.pdf
%X A clustering method is presented which can be applied to semantically annotated resources in the context of ontological knowledge bases. This method can be used to discover
   interesting groupings of structured objects through expressed in the standard languages employed for modeling concepts in the Semantic Web. The method exploits an effective
   and language-independent semidistance measure over the space of resources, that is based on their semantics w.r.t. a number of dimensions corresponding to a committee of
   features represented by a group of concept descriptions (discriminating features). A maximally discriminating group of features can be constructed through a feature
   construction method based on genetic programming. The evolutionary clustering algorithm employed is based on the notion of medoids applied to relational representations. It
   is able to induce a set of clusters by means of a proper fitness function based on a discernibility criterion. An experimentation with some ontologies proves the
   feasibility of our method.
%8 17 and 21  September
%Z LACAM Dipartimento di Informatica, Universit`a degli Studi di Bari Campus Universitario, Via Orabona 4 70125 Bari, Italy

%A Nicola Fanizzi
%A Claudia d'Amato
%A Floriana Esposito
%T Metric-based stochastic conceptual clustering for ontologies
%J Information Systems
%V 34
%N 8
%D 2009
%P 792--806
%I 
%K genetic algorithms, genetic programming, Conceptual clustering
%U http://www.sciencedirect.com/science/article/B6V0G-4W3HXC0-1/2/95a1535c9097d816c4ec5ad804772c4b
%X A conceptual clustering framework is presented which can be applied to multi-relational knowledge bases storing resource annotations expressed in the standard languages for
   the Semantic Web. The framework adopts an effective and language-independent family of semi-distance measures defined for the space of individual resources. These measures
   are based on a finite number of dimensions corresponding to a committee of discriminating features represented by concept descriptions. The clustering algorithm expresses
   the possible clusterings in terms of strings of central elements (medoids, w.r.t. the given metric) of variable length. The method performs a stochastic search in the space
   of possible clusterings, exploiting a technique based on genetic programming. Besides, the number of clusters is not necessarily required as a parameter: a natural number
   of clusters is autonomously determined, since the search spans a space of strings of different length. An experimentation with real ontologies proves the feasibility of the
   clustering method and its effectiveness in terms of standard validity indices. The framework is completed by a successive phase, where a newly constructed intensional
   definition, expressed in the adopted concept language, can be assigned to each cluster. Finally, two possible extensions are proposed. One allows the induction of
   hierarchies of clusters. The other applies clustering to concept drift and novelty detection in the context of ontologies.
%O Sixteenth ACM Conference on Information Knowledge and Management (CIKM 2007)

%A K. M. Faraoun
%A A. Boukelif
%T Genetic Programming Approach for Multi-Category Pattern Classification Applied to Network Intrusions Detection
%J International Journal of Computational Intelligence and Applications (IJCIA)
%V 6
%N 1
%D 2006
%P 77--100
%I 
%K genetic algorithms, genetic programming
%U http://www.worldscinet.com/cgi-bin/jform.cgi?/ijcia/mkt/free/S1469026806001812.html
%8 March

%A Antonella Farinaccio
%A Leonardo Vanneschi
%A Mario Giacobini
%A Giancarlo Mauri
%A Paolo Provero
%T On the use of genetic programming for the prediction of survival in cancer
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 163--170
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, Bioinformatics, computational, systems and synthetic biology, SVM, ANN, MLP, voted percenptron, RBF
%X The classification of cancer patients into risk classes is a very active field of research, with direct clinical applications. We have recently compared several machine
   learning methods on the well known 70-genes signature dataset. In that study, genetic programming showed promising results, given that it outperformed all the other
   techniques. Nevertheless, the study was preliminary, mainly because the validation dataset was preprocessed and all its features binarized in order to use logical operators
   for the genetic programming functional nodes. If this choice allowed simple interpretation of the solutions from the biological viewpoint, on the other hand the
   binarisation of data was limiting, since it amounts to a sizable loss of information. The goal of this paper is to overcome this limitation, using the 70-genes signature
   dataset with real-valued expression data. The results we present show that genetic programming using the number of incorrectly classified instances as fitness function is
   not able to outperform the other machine learning methods. However, when a weighted average between false positives and false negatives is used to calculate fitness values,
   genetic programming obtains performances that are comparable with the other methods in the minimisation of incorrectly classified instances and outperforms all the other
   methods in the minimization of false negatives, which is one of the main goals in breast cancer clinical applications. Also in this case, the solutions returned by genetic
   programming are simple, easy to understand, and they use a rather limited subset of the available features.
%8 7-11  July
%Z NKI 70-gene breast cancer. p168 Implicit feature selection. AF257175, NM_001809. Also known as \cite1830514 GECCO-2010 A joint meeting of the nineteenth international
   conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)

%A J Farringdon
%T Random Effects in Genetic Algorithms and Programming (\& Other Genetic Algorithm Issues)
%R Internal Note IN/96/05
%D 1996
%I 
%I University College London
%C Computer Science, Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/j.farringdon/GP/in-1996-05.html
%X Phenomena known to mathematicians and psychologists seem to be as yet unexploited by genetic algorithms and genetic programming techniques. A number of genetic techniques
   are briefly considered here from a maths and psychology perspective, the most immediately applicable of which is the use of statistical distributions. The statistical
   distributions technique may be implemented by a programmer and produce returns for a user within an hour.
%8 July

%A Steve Farrow
%T GP in 1958!
%D 2004
%I 
%K genetic algorithms, genetic programming
%U http://groups.yahoo.com/group/genetic_programming/message/2492
%X First four members of a series are a, b, c, d. What is the fifth?
%O Peter Bentely, GP mailing list, EC-digest
%8 8  March
%Z LEO II/4

%A K. A. Farry
%A J. S. Graham
%A F. Vilas
%A K. S. Jarvis
%T Automating Asteroid Surface Composition Identification from Reflectance Spectra
%B The 29th Lunar and Planetary Science Conference
%D 1998
%P 1661
%I 
%C Houston, Texas, USA
%K genetic algorithms, genetic programming
%U http://www.lpi.usra.edu/meetings/LPSC98/pdf/1661.pdf
%X We are applying genetic programming, an evolutionary programming technique, to identifying the minerals in spectra of asteroids from telescopes. We have done a basic
   feasibility test of this new identifier concept using US Geological Survey (USGS) spectra of three terrestrial minerals likely to be present in low-albedo asteroid
   regoliths: Antigorite, Hematite, and Jarosite. Initial results are very promising. Functions produced by genetic programming correctly identify 96percent of 140 spectra
   corrupted by measurement noise, scale uncertainty, and background continua removal uncertainty.
%8 16-20  March
%Z http://www.lpi.usra.edu/meetings/LPSC98/ 1 National Research Council (NRC) Research Associate, NASA/JSC/SN3, Houston, TX 77058, farry@farry.com. 2 9311 Tree Branch,
   Houston, TX 77064. 3 NASA/JSC/SN3, Houston, TX 77058. 4 LMSMSS, Houston, TX 77058. See also (abstract only?) Application of Genetic Programming to Identifying Asteroid
   Surface Composition from Reflectance Spectra \cite1997DPS....29.0721F year 1997, series Bulletin of the American Astronomical Society, volume 29, month jul, pages 976-+,
   http://adsabs.harvard.edu/abs/1997DPS....29.0721F

%A Maria Fasli
%A Yevgeniya Kovalchuk
%T Learning approaches for developing successful seller strategies in dynamic supply chain management
%J Information Sciences
%D 2011
%I 
%U http://www.sciencedirect.com/science/article/B6V0C-52M4V3W-4/2/e88e5f17659c1d3f021a4e6052e7b965
%X Variable, dynamic pricing is a key characteristic of the modern electronic trading environments, allowing for prices that change or fluctuate due to uncertainty and
   different conditions and context. Being able to manage dynamic pricing strategies is vital for companies wishing to succeed in the world of modern business. The ability to
   accurately predict selling prices at a given time can help organisations to maximise their profit. This paper addresses the problem of predicting customer order prices and
   choosing the selling strategy which can lead to a greater profit in the context of supply chain management (SCM). The potential of the Neural Networks (NN) and Genetic
   Programming (GP) learning techniques is explored for making price forecasts. In particular, different parameter settings and methods for preprocessing input data are
   investigated in the paper. Although, both techniques showed the potential for dealing with the problem of dynamic pricing in SCM, NN models outperform GP models in the
   context under consideration in terms of accuracy of prediction, complexity of implementation, and execution time.
%O In Press, Corrected Proof

%A Ethan Fast
%A Claire {Le Goues}
%A Stephanie Forrest
%A Westley Weimer
%T Designing better fitness functions for automated program repair
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 965--972
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, SBSE, Software repair, software engineering
%U http://www.cs.virginia.edu/~weimer/p/weimer-gecco2010-preprint.pdf
%X Evolutionary methods have been used to repair programs automatically, with promising results. However, the fitness function used to achieve these results was based on a few
   simple test cases and is likely too simplistic for larger programs and more complex bugs. We focus here on two aspects of fitness evaluation: efficiency and precision.
   Efficiency is an issue because many programs have hundreds of test cases, and it is costly to run each test on every individual in the population. Moreover, the precision
   of fitness functions based on test cases is limited by the fact that a program either passes a test case, or does not, which leads to a fitness function that can take on
   only a few distinct values. This paper investigates two approaches to enhancing fitness functions for program repair, incorporating (1) test suite selection to improve
   efficiency and (2) formal specifications to improve precision. We evaluate test suite selection on 10 programs, improving running time for automated repair by 81percent. We
   evaluate program invariants using the Fitness Distance Correlation (FDC) metric, demonstrating significant improvements and smoother evolution of repairs.
%8 7-11  July
%Z deroff, gcd, look, uniq, and zune nullhttpd, lighttpd, zune, tiff, leukocyte, and imagemagick. SUS. Oracle comparator, sand box, diffX. Daikon, (cites ClearView, Chianti.)
   pop=40. Also known as \cite1830654 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Shaheen Fatima
%A Mohamed Bader-El-Den
%T Co-evolutionary hyper-heuristic method for auction based scheduling
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X In this paper, we present a co-evolutionary hyper-heuristic method for solving a sequential auction based resource allocation problem. The method combines genetic
   programming (GP) for evolving agent's bidding functions for the individual auctions with genetic algorithms (GAs) for evolving an optimal ordering for auctions. The
   framework is evaluated in the context of the exam timetabling problem (ETTP). In this problem, there is a set of exams, which have to be assigned to a predefined set of
   slots. Here, the exam time tabling system is the seller that sells a set of slots in a series of auctions. There is one auction for each slot. The exams are viewed as the
   bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is
   minimised. In order to arrive at such a schedule, we find the bidders optimal bids for an auction using GP. We combine this with a GA that finds an optimal ordering for
   conducting the auctions. The effectiveness of this co-evolutionary method is demonstrated experimentally by comparing it with some existing benchmarks for exam timetabling.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586319

%A Shaheen Fatima
%A Ahmed Kattan
%T Evolving optimal agendas for package deal negotiation
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 505--512
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, Evolutionary combinatorial optimization and metaheuristics
%X This paper presents a hyper GA system to evolve optimal agendas for package deal negotiation. The proposed system uses a Surrogate Model based on Radial Basis Function
   Networks (RBFNs) to speed up the evolution. The negotiation scenario is as follows. There are two negotiators/agents (a and b) and m issues/items available for negotiation.
   But from these m issues, the agents must choose g issues and negotiate on them. The g issues thus chosen form the agenda. The agenda is important because the outcome of
   negotiation depends on it. Furthermore, a and b will, in general, get different utilities/profits from different agendas. Thus, for competitive negotiation (i.e.,
   negotiation where each agent wants to maximise its own utility), each agent wants to choose an agenda that maximizes its own profit. However, the problem of determining an
   agent's optimal agenda is complex, as it requires combinatorial search. To overcome this problem, we present a hyper GA method that uses a Surrogate Model based on Radial
   Basis Function Networks (RBFNs) to find an agent's optimal agenda. The performance of the proposed method is evaluated experimentally. The results of these experiments
   demonstrate that the surrogate assisted algorithm, on average, performs better than standard GA and random search.
%8 12-16  July
%Z Also known as \cite2001646 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Pedro Fazenda
%A James McDermott
%A Una-May O'Reilly
%T A Library to Run Evolutionary Algorithms in the Cloud using MapReduce
%B Applications of Evolutionary Computing, EvoApplications2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC
%S LNCS
%E Cecilia Di Chio and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. Fernandez de Vega and Gianni A. Di Caro and Rolf Drechsler and Aniko Ekart and Anna I
   Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero
   and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis
%V 7248
%D 2012
%P 416--425
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, MapReduce, Hadoop, EC, Amazon EC2, FlexEA
%X We discuss ongoing development of an evolutionary algorithm library to run on the cloud. We relate how we have used the Hadoop open-source MapReduce distributed data
   processing framework to implement a single `island' with a potentially very large population. The design generalises beyond the current, one-off kind of MapReduce
   implementations. It is in preparation for the library becoming a modelling or optimization service in a service oriented architecture or a development tool for designing
   new evolutionary algorithms.
%8 11-13  April
%Z EDO-Lib, Reporter, Island Model, HDFS, p419 'FitnessEvaluator can be set by injection'. Matlab. Does not give execution time in terms of GP operation per second. Population
   up to 1 million. XEN.org p423 'takes much longer to design a MapReduce implementation than it would to develop a socket or MPI model.' p424 'It also results in a code base
   which requires more effort to support and maintain which impacts research agility.' Part of \citeDiChio:2012:EvoApps EvoApplications2012 held in conjunction with
   EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012

%A Francine Federman
%A Susan Fife Dorchak
%T A Study of Classifier Length and Population Size
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 629--634
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, classifiers
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Francine Federman
%A Gayle Sparkman
%A Stephanie Watt
%T Representation of Music in a Learning Classifier System Utilizing Bach Chorales
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 785
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Garry Fehr
%T Spontaneous Emergence of Multicellular Organisms From Unicellular Ancestors
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 28--34
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford
   University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Peter Feiler
%A Richard P. Gabriel
%A John Goodenough
%A Rick Linger
%A Tom Longstaff
%A Rick Kazman
%A Mark Klein
%A Linda Northrop
%A Douglas Schmidt
%A Kevin Sullivan
%A Kurt Wallnau
%T Ultra-Large-Scale Systems -- The Software Challenge of the Future
%R Technical Report
%D 2006
%I 
%I software engineering institute, Carnegie Mellon University
%C Pittsburgh, PA 15213-3890, USA
%K genetic algorithms, genetic programming
%U www.sei.cmu.edu/library/assets/ULS_Book20062.pdf
%X The U. S. Department of Defense (DoD) has a goal of information dominance-to achieve and exploit superior collection, fusion, analysis, and use of information to meet
   mission objectives. This goal depends on increasingly complex systems characterised by thousands of platforms, sensors, decision nodes, weapons, and war fighters connected
   through heterogeneous wired and wireless networks. These systems will push far beyond the size of today's systems and systems of systems by every measure: number of lines
   of code; number of people employing the system for different purposes; amount of data stored, accessed, manipulated, and refined; number of connections and
   interdependencies among software components; and number of hardware elements. They will be ultra-largescale (ULS) systems.
%8 June
%Z GP described as digital evolution. USA Federal Government Contract Number FA8721-05-C-0003.
%@ 0-9786956-0-7

%A Udo Feldkamp
%A Hilmar Rauhe
%A Wolfgang Banzhaf
%T Software Tools for DNA Sequence Design
%J Genetic Programming and Evolvable Machines
%V 4
%N 2
%D 2003
%P 153--171
%I 
%K DNA computing, DNA nanotechnology, molecular self-assembly, sequence design, specific hybridization
%U http://www.cs.mun.ca/~banzhaf/papers/softwaretools.pdf
%X The design of DNA sequences is a key problem for implementing molecular self-assembly with nucleic acid molecules. These molecules must meet several physical, chemical and
   logical requirements, mainly to avoid mishybridization. Since manual selection of proper sequences is too time-consuming for more than a handful of molecules, the aid of
   computer programs is advisable. In this paper two software tools for designing DNA sequences are presented, the DNASequenceGenerator and the DNASequenceCompiler. Both
   employ an approach of sequence dissimilarity based on the uniqueness of overlapping subsequences and a graph based algorithm for sequence generation. Other sequence
   properties like melting temperature or forbidden subsequences are also regarded, but not secondary structure errors or equilibrium chemistry. Fields of application are DNA
   computing and DNA-based nanotechnology. In the second part of this paper, sequences generated with the DNASequenceGenerator are compared to those from several publications
   of other groups, an example application for the DNASequenceCompiler is presented, and the advantages and disadvantages of the presented approach are discussed.
%8 June
%Z Special Issue on Biomolecular Machines and Artificial Evolution Article ID: 5122743

%A Robert Feldt
%T An experiment on using genetic programming to develop multiple diverse software variants
%R Technical Report 98-13
%D 1998
%I 
%I Department of Computer Engineering, Chalmers University of Technology
%C Gothenburg, Sweden
%K genetic algorithms, genetic programming
%8 September
%Z Included also in \citefeldt:1998:midthesis

%A Robert Feldt
%T A survey of the concept of diversity in genetic programming and software fault tolerance
%R Technical Report 98-15
%D 1998
%I 
%I Department of Computer Engineering, Chalmers University of Technology
%C Gothenburg, Sweden
%K genetic algorithms, genetic programming
%8 October
%Z Included also in \citefeldt:1998:midthesis

%A Robert Feldt
%T Generating Multiple Diverse Software Versions with Genetic Programming
%B Proceedings of the 24th EUROMICRO Conference, Workshop on Dependable Computing Systems
%D 1998
%P 387--396
%I 
%C Vaesteraas, Sweden
%K genetic algorithms, genetic programming
%U http://www.amp.york.ac.uk/external/sweden/sweden.htm
%X Software fault tolerance schemes often employ multiple software versions developed to meet the same specification. If the versions fail independently of each other, they
   can be combined to give high levels of reliability. While design diversity is a means to develop these versions, it has been questioned because it increases development
   costs and because reliability gains are limited by common-mode failures. We propose the use of genetic programming to generate multiple software versions and postulate that
   these versions can be forced to differ by varying parameters to the genetic programming algorithm. This might prove a cost-effective approach to obtain forced diversity and
   make possible controlled experiments with large numbers of diverse development methodologies. This paper qualitatively compares the proposed approach to design diversity
   and its sources of diversity. An experiment environment to evaluate whether significant diversity can be generated is outlined.
%8 25-27th  August
%Z described in \citefeldt:1998:midthesis

%A Robert Feldt
%T Generating Diverse Software Versions with Genetic Programming: an Experimental Study
%J IEE Proceedings - Software Engineering
%V 145
%N 6
%D 1998
%P 228--236
%I 
%K genetic algorithms, genetic programming, SBSE, aircraft control, program testing, programming environments, software reliability, aircraft arrestment system, aircraft
   controller, common-mode failure, design diversity, experimental study, multiple software versions, software development costs, software fault-tolerance, software
   reliability, software version generation, specification, navy aircraft carrier
%U http://www.iee.org.uk/publish/journals/profjrnl/cntnsen.html#SENDecember1998
%X Software fault-tolerance schemes often employ multiple software versions developed to meet the same specification. If the versions fail independently of each other, they
   can be combined to give high levels of reliability. Although design diversity is a means to develop these versions, it has been questioned because it increases development
   costs and because reliability gains are limited by common-mode failures. The use of genetic programming is proposed to generate multiple software versions by varying
   parameters of the genetic programming algorithm. An environment is developed to generate programs for a controller in an aircraft arrestment system. Eighty programs have
   been developed and tested on 10000 test cases. The experimental data show that failure diversity is achieved, but for the top performing programs its levels are limited
%O Special issue on Dependable Computing Systems
%8 Decemeber
%Z See Workshop: Managing and Optimising Multiplicity Computing, 22-23 March 2012 http://crest.cs.ucl.ac.uk/cow/18/ described in \citefeldt:1998:midthesis. Also known as
   \cite765682 CODEN: IPSEFU INSPEC Accession Number:6150266

%A Robert Feldt
%T Using Genetic Programming to Systematically Force Software Diversity
%R Technical Report 296L
%D 1998
%I 
%I Department of Computer Engineering, Chalmers University of Technology
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%8 November
%Z licentiate of Engineering thesis
%@ 91-7197-740-6

%A Robert Feldt
%T Genetic Programming as an Explorative Tool in Early Software Development Phases
%B Proceedings of the 1st International Workshop on Soft Computing Applied to Software Engineering
%E Conor Ryan and Jim Buckley
%D 1999
%P 11--20
%I Limerick University Press
%I SCARE
%C University of Limerick, Ireland
%K genetic algorithms, genetic programming
%U http://drfeldt.googlepages.com/feldt_1999_gp_as_explorative_tool.pdf
%X Early in a software development project the developers lack knowledge about the problem to be solved by the software. Any knowledge that can be gained at an early stage can
   reduce the risk of making erroneous decisions and injecting defects that can be expensive to eliminate in later phases. This paper presents the idea of using genetic
   programming to explore the difficulty of different input data in the input space, determine the effects of different requirements and identify design trade-offs inherent in
   the problem. Data from a pilot experiment is analysed and the knowledge gained is used to question and prioritize the requirements on the target system. Coping with
   high-dimensional input spaces and establishing the relationship between GP- and human-developed programs are identified as the major outstanding problems. An extended
   experimental environment is proposed based on techniques for visual database exploration.
%8 12-14  April
%Z http://scare.csis.ul.ie/scase99/ SCASE'99 USAF aircraft arresting system (landing on carriers) used as example. Java GPsys.
%@ 1-874653-52-6

%A Robert Feldt
%A Peter Nordin
%T Using Factorial Experiments to Evaluate the Effect of Genetic Programming Parameters
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 271--282
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=271
%X Statistical techniques for designing and analyzing experiments are used to evaluate the individual and combined effects of genetic programming parameters. Three binary
   classification problems are investigated in a total of seven experiments consisting of 1108 runs of a machine code genetic programming system. The parameters having the
   largest effect in these experiments are the population size and the number of generations. A large number of parameters have negligible effects. The experiments indicate
   that the investigated genetic programming system is robust to parameter variations, with the exception of a few important parameters.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Robert Feldt
%A Michael O'Neill
%A Conor Ryan
%A Peter Nordin
%A William B. Langdon
%T GP-Beagle: A Benchmarking Problem Repository for the Genetic Programming Community
%B Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference
%E Darrell Whitley
%D 2000
%P 90--97
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/302050.html
%X Experimental studies in genetic programming often only use a few, artical problems. The results thus obtained may not be typical and may not reect performance on problems
   met in the real world. To change this we propose the use of common suites of benchmark problems and introduce a benchmarking problem repository called GP-Beagle. The basic
   entities in the repository are problems, problem instances, problem suites and usage information. We give examples of problems and suites that can be found in the
   repository and identify its WWW site location.
%8 8  July
%Z Part of \citewhitley:2000:GECCOlb

%A Robert Feldt
%T An Interactive Software Development Workbench based on Biomimetic Algorithms
%R Technical Report
%D 2002
%I 
%I Department of Computer Engineering, Chalmers University of Technology
%C Gothenburg, SWEDEN
%K genetic algorithms, genetic programming, simulated annealing, multi-agent, SBSE, Ruby
%U http://drfeldt.googlepages.com/feldt_2002_wise_tech_report.pdf
%X Based on a theory for software development that focus on the internal models of the developer this paper presents a design for an interactive workbench to support the
   iterative refinement of developers models. The goal for the workbench is to expose unknown features of the software being developed so that the developer can check if they
   correspond to his expectations. The workbench employs a biomimetic search system to find tests with novel features. The search system assembles test templates from small
   pieces of test code and data packaged into a cell.We describe a prototype of the workbench implemented in Ruby and focus on the module used for evolving tests.A case study
   show that the prototype supports development of tests that are both diverse, complete and have a meaning to the developer. Furthermore, the system can easily be extended by
   the developer when he comes up with new test strategies.
%8 November

%A Robert Feldt
%T Biomimetic Software Engineering Techniques for Dependability
%R Ph.D. Thesis
%D 2002
%I 
%I Department of Computer Engineering, Chalmers University of Technology
%C Gothenburg, Sweden
%K genetic algorithms, genetic programming, SBSE
%8 Decemeber

%A Michael J. Felton
%T Survival of the Fittest in Drug Design
%J Modern Drug Discovery
%V 3
%N 9
%D 2000
%P 49--50
%I American Chemical Society
%K genetic algorithms, genetic programming
%U http://pubs.acs.org/subscribe/journals/mdd/v03/i09/html/felton.html
%8 November /  Decemeber
%Z magazine

%A Xia-Ting Feng
%A Bing-Rui Chen
%A Chengxiang Yang
%A Hui Zhou
%A Xiuli Ding
%T Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm
%J International Journal of Rock Mechanics and Mining Sciences
%V 43
%N 5
%D 2006
%P 789--801
%I 
%K genetic algorithms, genetic programming, Visco-elastic models, Rock, Evolutionary algorithm, Particle swarm optimisation
%X The response of rocks to stress can be highly non-linear, so sometimes it is difficult to establish a suitable constitutive model using traditional mechanics methods. It is
   appropriate, therefore, to consider modelling methods developed in other fields in order to provide adequate models for rock behaviour, and this particularly applies to the
   time-dependent behavior of rock. Accordingly, a new system identification method, based on a hybrid genetic programming with the improved particle swarm optimization (PSO)
   algorithm, for the simultaneous establishment of a visco-elastic rock material model structure and the related parameters is proposed. The method searches for the optimal
   model, not among several known models as in previous methods proposed in the literatures, but in the whole model space made up of elastic and viscous elementary components.
   Genetic programming is used for exploring the model's structure and the modified PSO is used to identify parameters (coefficients) in the provisional model. The evolution
   of the provisional models (individuals) is driven by the fitness based on the residual sum of squares of the behaviour predicted by the model and the actual behaviour of
   the rock given by a set of mechanical experiments. Using this proposed algorithm, visco-elastic models for the celadon argillaceous rock and fuchsia argillaceous rock in
   the Goupitan hydroelectric power station, China, are identified. The results show that the algorithm is feasible for rock mechanics use and has a useful ability in finding
   potential models. The algorithm enables the identification of models and parameters simultaneously and provides a new method for studying the mechanical characteristics of
   visco-elastic rocks.
%8 July
%Z a Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China b School of Resources and Civil Engineering, Northeastern University, Shenyang
   110004, China c Yangtze River Scientific Research Institute, Wuhan 430010, China

%A Yanghe Feng
%A Chaofan Dai
%A Jianmai Shi
%A Liang Mu
%T An Automatic Model Selection Algorithm Based Genetic Programming
%B 2nd International Symposium on Information Engineering and Electronic Commerce (IEEC 2010)
%D 2010
%I 
%K genetic algorithms, genetic programming, automatic model selection algorithm, genetic operations, metamodels, model-aided decision usability, metacomputing
%X The usability of model-aided decision relies on intellectualized level of model selection. An algorithm of Model selection based sample data is proposed in the paper. The
   meta-models are classified by characters of the sample data, and the assembled models are built as tree format. The genetic operations are performed under several
   restrictions to provide the model selection scheme. Its process hardly depends on user's knowledge on domain.
%8 July
%Z Also known as \cite5533243

%A Lavinia Ferariu
%A Alina Patelli
%T Multiobjective Genetic Programming for Nonlinear System Identification
%B 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009
%S Lecture Notes in Computer Science
%E Mikko Kolehmainen and Pekka Toivanen and Bartlomiej Beliczynski
%V 5495
%D 2009
%P 233--242
%I Springer
%C Kuopio, Finland
%K genetic algorithms, genetic programming, multiobjective optimisation, nonlinear system identification
%X The paper presents a novel identification method, which makes use of genetic programming for concomitant flexible selection of models structure and parameters. The case of
   nonlinear models, linear in parameters is addressed. To increase the convergence speed, the proposed algorithm considers customized genetic operators and a local
   optimisation procedure, based on QR decomposition, able to efficiently exploit the linearity of the model subject to its parameters. Both the model accuracy and parsimony
   are improved via a multiobjective optimization, considering different priority levels for the involved objectives. An enhanced Pareto loop is implemented, by means of a
   special fitness assignment technique and a migration mechanism, in order to evolve accurate and compact representations of dynamic nonlinear systems. The experimental
   results reveal the benefits of the proposed methodology within the framework of an industrial system identification.
%O Revised selected papers
%8 23-25  April
%Z ICANNGA 2009

%A L. Ferariu
%A A. Patelli
%T Migration-based multiobjective genetic programming for nonlinear system identification
%B 5th International Symposium on Applied Computational Intelligence and Informatics, SACI '09
%D 2009
%P 475--480
%I 
%K genetic algorithms, genetic programming, QR decomposition, adaptive threshold, convergence speed, dominance analysis, flexible model structure selection, migration-based
   multiobjective genetic programming, nonlinear system identification, optimization algorithm, quasi independent subpopulation, tree encoding, identification, nonlinear
   control systems, trees (mathematics)
%X Nonlinear system identification is addressed by means of genetic programming. For a flexible selection of model structure and parameters, a multiobjective optimization of
   the tree encoded individuals is carried out, in terms of accuracy and parsimony. The paper suggests a new optimization algorithm based on the evolvement of two
   quasi-independent subpopulations, which makes use of a flexible migration scheme with adaptive thresholds and multiple rates. By efficiently exploiting the concept of
   dominance analysis, the algorithm is able to select compact and accurate models, with good generalization capabilities. The approach is compliant with nonlinear models,
   linear in parameters. That permits the hybridization with QR decomposition and the use of enhanced genetic operators, aimed to increase the algorithm convergence speed. The
   performances of the suggested design procedure are illustrated by the identification of two nonlinear industrial subsystems.
%8 May
%Z Also known as \cite5136295

%A L. Ferariu
%A B. Burlacu
%T Graph genetic programming for hybrid neural networks design
%B International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI)
%D 2010
%P 547--552
%I 
%K genetic algorithms, genetic programming
%X This paper presents a novel approach devoted to the design of feed forward hybrid neural models. Graph genetic programming techniques are used to provide a flexible
   construction of partially interconnected neural structures with heterogeneous layers built as combinations of local and global neurons. By exploiting the inner modularity
   and the parallelism of the neural architectures, the approach suggests the encryption of the potential mathematical models as directed acyclic graphs and defines a
   minimally sufficient set of functions which guarantees that any combination of primitives encodes a valid neural model. The exploration capabilities of the algorithm are
   heightened by means of customised crossovers and mutations, which act both at the structural and the parametric level of the encrypted individuals, for producing offspring
   compliant with the neural networks' formalism. As the parameters of the models become the parameters of the primitive functions, the genetic operators are extended to
   manage the inner configuration of the functional nodes in the involved hierarchical individuals. The applicability of the proposed design algorithm is discussed on the
   identification of an industrial nonlinear plant.
%8 May
%Z Also known as \cite5491213

%A Carlos Fernandes
%A Joao Paulo Caldeira
%A Fernando Melicio
%A Agostinho Rosa
%T Evolutionary Algorithm for School Timetabling
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1777
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-743.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Alberto Fernandez
%A Francisco Jose Berlanga
%A Maria Jose {del Jesus}
%A Francisco Herrera
%T Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets
%B Proceedings of the Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference
%E Jo\~ao Paulo Carvalho and Didier Dubois and Uzay Kaymak and Jo\~ao Miguel da Costa Sousa
%D 2009
%P 42--47
%I 
%C Lisbon, Portugal
%K genetic algorithms, genetic programming, Fuzzy Rule-Based Classification Systems, Genetic Fuzzy Systems, imbalanced Data-Sets, Interpretability
%U http://www.eusflat.org/publications/proceedings/IFSA-EUSFLAT_2009/pdf/tema_0042.pdf
%X Classification in imbalanced domains is an important problem in Data Mining. We refer to imbalanced classification when data presents many examples from one class and few
   from the other class, and the less representative class is the one which has more interest from the point of view of the learning task. The aim of this work is to study the
   behaviour of the GP-COACH algorithm in the scenario of data-sets with high imbalance, analysing both the performance and the interpretability of the obtained fuzzy models.
   To develop the experimental study we will compare this approach with a well-known fuzzy rule learning algorithm, the Chi et al.'s method, and an algorithm of reference in
   the field of imbalanced data-sets, the C4.5 decision tree.
%8 July  20-24
%Z 1.Department of Computer Science and Artificial Intelligence, University of Granada Granada, Spain 2.Department of Computer Science and Systems Engineering, University of
   Zaragoza Zaragoza, Spain 3.Department of Computer Science, University of Jaen Spain

%A F. Fernandez
%A J. M. Sanchez
%A M. Tomassini
%A J. A. Gomez
%T A Parallel Genetic Programming Tool based on PVM
%B Recent Advances in Parallel Virtual Machine and Message Passing Interface, Proceedings of the 6th European PVM/MPI Users' Group Meeting
%S Lecture Notes in Computer Science
%E J. Dongarra and E. Luque and T. Margalef
%V 1697
%D 1999
%P 241--248
%I Springer-Verlag
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%8 September
%@ 3-540-66549-8

%A F. Fernandez
%A M. Tomassini
%A L. Vanneschi
%A L. Bucher
%T A Distributed Computing Environment for Genetic Programming using MPI
%B Recent advances in parallel virtual machine and message passing interface: 7th European PVM\slash MPI Users' Group Meeting
%S Lecture Notes in Computer Science
%E J. J. Dongarra and Peter Kacsuk and Norbert Podhorszki
%V 1908
%D 2000
%P 322--329
%I Springer-Verlag
%C Balatonfured, Hungary
%K genetic algorithms, genetic programming
%8 10-13  September
%@ 3-540-41010-4 (softcover)

%A Francisco Fernandez
%A Marco Tomassini
%A J. M. Sanchez
%T Solving the Ant and the Even Parity-5 problems by means of parallel genetic programming
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 88--92
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%8 13  July
%Z GECCO-99LB

%A F. Fernandez
%A M. Tomassini
%A W. F. {Punch III}
%A J. M. Sanchez
%T Experimental Study of Multipopulation Parallel Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 283--293
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=283
%X The parallel execution of several populations in evolutionary algorithms has usually given good results. Nevertheless, researchers have to date drawn conflicting
   conclusions when using some of the parallel genetic programming models. One aspect of the conflict is population size, since published GP works do not agree about whether
   to use large or small populations. This paper presents an experimental study of a number of common GP test problems. Via our experiments, we discovered that an optimal
   range of values exists. This assists us in our choice of population size and in the selection of an appropriate parallel genetic programming model. Finding efficient
   parameters helps us to speed up our search for solutions. At the same time, it allows us to locate features that are common to parallel genetic programming and the classic
   genetic programming technique.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A F. {Fernandez de Vega}
%A Laura M. Roa
%A Marco Tomassini
%A J. M. Sanchez
%T Multipopulation Genetic Programing Applied to Burn Diagnosing
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 1292--1296
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, novel applications i
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Francisco Fernandez
%A Marco Tomassini
%T Genetic programming and reconfigurable hardware: A proposal for solving the problem of placement and routing
%B Graduate Student Workshop
%E Conor Ryan and Una-May O'Reilly and William B. Langdon
%D 2000
%P 265--268
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%8 8  July
%Z GECCO-2000WKS Part of \citewu:2000:GECCOWKS

%A Francisco Fernandez
%A Marco Tomassini
%A William Punch
%A J. M. Sanchez
%T Experimental Study of Isolated Multipopulation Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 536
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP159.ps
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A F. Fernandez
%A M. Tomassini
%A J. M. Sanchez
%T Experimental Study of Isolated Multipopulation Genetic Programming
%B Proceedings of the 26th Annual Conference of the IEEE Industrial Electronics Society
%V 1697
%D 2000
%P 2672--2677
%I IEEE Press
%C Nagoya, Japan
%K genetic algorithms, genetic programming
%U http://fp.ieeexplore.ieee.org/iel5/7662/20956/00972420.pdf?isNumber=20956&prod=CNF&arnumber=00972420
%X In this paper we present results obtained when comparing classic genetic programming (GP) with the isolated multipopulation version. Our first discovery was that sometimes,
   given a certain number of individuals, it is useful to distribute them among several populations even when no communication is allowed. This consequently lead to research
   concentrating on three main questions: firstly, how to distribute individuals according to the problem in hand; secondly, how many populations must be employed in
   proportion to the effort and fitness involved when solving a problem; and finally, how to use isolated multipopulation GP in the classification of problems.
%8 October
%@ 0-7803-6456-2

%A Francisco Fernandez
%A Marco Tomassini
%A Leonardo Vanneschi
%T Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 51--63
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Distributed Genetic Programming, Parallelism, Multipopulation structures, Parallel evolutionary algorithms
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=51
%X In this paper we present a systematic experimental study of some of the parameters influencing parallel and distributed genetic programming (PADGP) by using three benchmark
   problems. We first present results on the system's communication topology and then we study the parameters governing individual migration between subpopulations: the number
   of individuals sent and the frequency of exchange. Our results suggest that fitness evolution is more sensitive to the migration factor than the communication topology.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A F. Fernandez
%A M. Tomassini
%T Studying the Optimal Parameter Range of Values in PADGP by Means of Real-life Problems
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 436--441
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, Parallel Genetic Programming, FPGA
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =
%@ 0-7803-6658-1

%A F. Fernandez
%A J. M. Sanchez
%A M. Tomassini
%T A new methodology for the Placement and Routing problem based on PADGP
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 175
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster, Parallel Evolutionary Algorithms, Evolvable Hardware
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A F. Fernandez
%A J. M. Sanchez
%A M. Tomassini
%T Placing and Routing Circuits on FPGAs by Means of Parallel and Distributed Genetic Programming
%B Evolvable Systems: From Biology to Hardware, Proceedings of the 4th International Conference, ICES 2001
%S Lecture Notes in Computer Science
%E Yong Liu and Kiyoshi Tanaka and Masaya Iwata and Tetsuya Higuchi and Moritoshi Yasunaga
%V 2210
%D 2001
%P 204--214
%I Springer-Verlag
%C Tokyo, Japan
%K genetic algorithms, genetic programming, evolvable hardware, PADGP
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2210&spage=204
%X We present results on the application of a new methodology based on Parallel and Distributed Genetic Programming (PADGP). The aim for the methodology we present is to
   automatically perform the placement and routing of circuits on reconfigurable hardware. The system has been successfully applied to some benchmark problems. For each of the
   problems we have dealt with, the methodology is capable of finding several solutions. The results show the methodology's feasibility for addressing the problem of placement
   and routing on FPGAs.
%8 3-5 Octpber
%Z island-based FPGAs eg Xilinx. Digital circuits. Connecting circuits given by syntax of evolved GP tree.
%@ 3-540-42671-X

%A Francisco Fernandez
%A G. Galeano
%A J. A. Gomez
%T Comparing Synchronous and Asynchronous Parallel and Distributed GP Models
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 326--335
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780326.pdf
%X In this paper we present a study that analyses the respective advantages and disadvantages of the synchronous and asynchronous versions of island-based genetic programming.
   We also look at different measuring systems for comparing parallel genetic programming with panmitic model. At the same time we show an interesting relationship between the
   bloat phenomenon and the number of individuals we use. Finally, we study a relationship between the number of subpopulations in parallel GP and the advantages of the
   asynchronous model.
%8 3-5  April
%Z EuroGP'2002, part of lutton:2002:GP. Santa Fe Ant, even-5-parity. padgp
%@ 3-540-43378-3

%A Francisco Fernandez
%A Marco Tomassini
%A Leonardo Vanneschi
%T An Empirical Study of Multipopulation Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 4
%N 1
%D 2003
%P 21--51
%I 
%K genetic algorithms, genetic programming, distributed evolutionary algorithms, parallel algorithms, structured populations
%X This paper presents an experimental study of distributed multipopulation genetic programming. Using three well-known benchmark problems and one real-life problem, we
   discuss the role of the parameters that characterise the evolutionary process of standard panmictic and parallel genetic programming. We find that distributing individuals
   between subpopulations offers in all cases studied here an advantage both in terms of the quality of solutions and of the computational effort spent, when compared to
   single populations. We also study the influence of communication patterns such as the communication topology, the number of individuals exchanged and the frequency of
   exchange on the evolutionary process. We empirically show that the topology does not have a marked influence on the results for the test cases studied here, while the
   frequency and number of individuals exchanged are related and there exists a suitable range for those parameters which is consistently similar for all the problems studied.
%8 March
%Z Article ID: 5113071

%A Francisco Fernandez
%A Leonardo Vanneschi
%A Marco Tomassini
%T The Effect of Plagues in Genetic Programming: A Study of Variable-Size Populations
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 317--326
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=317
%X A study on the effect of variable size populations in genetic programming is presented in this work. We apply the idea of plague (high desease of individuals). We show that
   although plagues are generally considered as negative events, they can help populations to save computing time and at the same time surviving individuals can reach high
   peaks in the fitness landscape.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Francisco {Fernandez de Vega}
%T Distributed Genetic Programming Models with Application to Logic Synthesis on FPGAs
%R Ph.D. Thesis
%D 2001
%I 
%I University of Extremadura
%K genetic algorithms, genetic programming, reconfigurable hardware
%U http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/thesis/phd.html
%Z For Spanish version see \citefernandez:thesis:espanol CONCLUSIONS AND FINAL REMARKS We have presented a new implementation of GP - based on MPI - which allows us to make
   use of parallelism as well as experimenting with different communication topologies and GP parameters We have compared performances of this methodology ?PADGP ? with
   classic GP. The tool was applied to the study of two important parameters that affect convergence results on PADGP: the number and size of populations. By means of this
   study, we have observed the existence of a region of effort which defines the best number of individuals we must use when employing a given number of populations with
   PADGP. This region of effort has been detected both in benchmark problems and in ?real life? problems. We have also presented random topology as a way of improving
   convergence when using PADGP. We have used PADGP with random topology and compared it to classic GP. This comparison showed that the former gives better results. We have
   also compared random topology and grid topology and we have shown that results are similar. Nevertheless random topology requires a smaller amount of communication
   processes. We have presented a methodology that is based on PADGP, and which aids medical diagnosing. We used this problem to check the validity of results obtained in the
   benchmark problem, while we also proposed PADGP as an appropriate methodology for extracting medical knowledge. We have studied isolated subpopulations (IMGP) as a limit
   case of PADGP and we have experimentally seen that IMGP obtains similar convergence results than GP; sometimes results are even better if the total number of individuals is
   high. We have then dealt with an optimisation problem: the problem of placement and routing on FPGAs. We have developed a new methodology based on GP, and this allows us to
   represent circuits by means of GP trees. Furthermore, the methodology achieved the proposed goal: finding several ways of placing and routing circuits on reconfigurable
   hardware. The problem was later used for checking the conclusions which had been reached in the first part of this research. All statistical results obtained are in
   agreement with those obtained from benchmark problems. We think that the main goals we established at the beginning have been achieved: checking the usefulness of PADGP
   with random communications and developing a methodology for logic synthesis on FPGAs. In the researching process we discovered the concept of region of effort and we
   obtained interesting conclusions via the use of IMGP. Results we obtained during our research have been published in the main conferences and reviews that deal with the
   different topics addressed in this thesis (see References).

%A Francisco {Fernandez de Vega}
%T Modelos de Programacion Genetica Paralela y Distribuida con aplicaciones a la Sintesis Logica en FPGAs
%R Ph.D. Thesis
%D 2001
%I 
%I University of Extremadura
%K genetic algorithms, genetic programming, reconfigurable hardware
%U http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/thesis/phd.html
%Z version espa\~nol. for english version see \citefernandez:thesis

%A Francisco {Fernandez de Vega}
%T Estudio de Poblaciones de tama\~no variable en Programacion Genetica
%B Actas del II Congreso Espa\~nol sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados
%D 2003
%P 424--428
%I 
%K genetic algorithms, genetic programming, bloat
%U http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/papers/maeb04.pdf
%X En este trabajo presentamos un estudio sobre el efecto de poblaciones de tama\~no variable en Programacion Genetica. Por medio de una serie de experimentos mostramos que la
   supresion sistematica de un n\'umero fijo de individuos a lo largo de varias generaciones puede ayudar a reducir el esfuerzo computacional requerido en la b\'usqueda de
   soluciones a problemas. Por otro lado, la calidad de las soluciones encontradas no se ve afectada de forma significativa por la eliminacion de un n\'umero peque\~no de
   individuos en cada generacion.
%8 February
%Z in spanish

%A F. Fernandez
%A M. Tomassini
%A L. Vanneschi
%T Saving computational effort in genetic programming by means of plagues
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 2042--2049
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%X A new technique for saving computing resources when using Genetic Programming is presented in this work. Instead of directly fighting bloat -the main factor explaining the
   large computational cost required for the evaluation of generations- by acting on individuals, we apply a new operator to the whole population: the plague. By removing some
   individuals every generation, we compensate for the increase in size of individuals, thus saving computing time when looking for solutions.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Francisco Fernandez
%A Aida Martin
%T Saving Effort in Parallel GP by means of Plagues
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 269--278
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=269
%X Plague, a new technique that allows Genetic Programming to save computing resources, has been proposed. By removing some individuals every generation, plague aims at
   compensating for the increase in size of individuals, thus saving computing time when looking for solutions. By means of some test problems, we show that the technique is
   also useful when employing a parallel version of GP, such as that based on the island model.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Francisco {Fernandez de Vega}
%A J. I. Hidalgo
%A J. Lanchares
%A J. M. Sanchez
%T A methodology for reconfigurable hardware design based upon evolutionary computation
%J Microprocessors and Microsystems
%V 28
%N 7
%D 2004
%P 363--371
%I 
%K genetic algorithms, genetic programming, reconfigurable hardware, Field programmable gate arrays, Compact genetic algorithm, Configurable logic blocks
%U http://www.sciencedirect.com/science/article/B6V0X-4C4BWW7-1/2/815fe7c17a6207d7a31f8046e4e2a5d1
%X We present a methodology for Multi-FPGA systems (MFS) design. MFSs are used for a great variety of applications, including dynamically re-configurable hardware
   applications, digital circuit emulation, and numerical computation. There are a great variety of boards for MFS implementation. We have employed a set of techniques based
   on evolutionary algorithms, and we show that they are capable of solving all of the design tasks (partitioning placement and routing). Firstly a hybrid compact genetic
   algorithm solves the partitioning problem and then genetic programming is used to obtain a solution for the two other tasks.
%8 September

%A Francisco Fernandez-de-Vega
%A German Galeano Gil
%A Juan Antonio Gomez Pulido
%A Jose Luis Guisado
%T Control of bloat in Genetic Programming by means of the Island Model
%B Parallel Problem Solving from Nature - PPSN VIII
%S LNCS
%E Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\vno Ata Kab\'an and Hans-Paul
   Schwefel
%V 3242
%D 2004
%P 263--271
%I Springer-Verlag Berlin
%C Birmingham, UK
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=263
%X a new proposal for reducing bloat in Genetic Programming. This proposal is based in a well-known parallel evolutionary model: the island model. We firstly describe the
   theoretical motivation for this new approach to the bloat problem, and then we present a set of experiments that gives us evidence of the findings extracted from the
   theory. The experiments have been performed on a representative problem extracted from the GP field: the even parity 5 problem. We analyse the evolution of bloat employing
   different settings for the parameters employed. The conclusion is that the Island Model helps to prevent the bloat phenomenon.
%8 18-22  September
%Z PPSN-VIII
%@ 3-540-23092-0

%A F. Fernandez
%A J. I. Hidalgo
%A J. M. Sanchez
%A J. Lanchares
%T An Evolutionary Approach to Multi-FPGAs System Synthesis
%B Evolvable Machines: Theory \& Practice
%S Studies in Fuzziness and Soft Computing
%E Nadia Nedjah and Luiza de Macedo Mourelle
%V 161
%D 2004
%P 151--177
%I Springer
%C Berlin Hidelberg Germany
%K genetic algorithms, genetic programming, reconfigurable hardware
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html
%O 7
%Z Springer says published in 2005 but available Nov 2004
%@ 3-540-22905-1

%A Francisco Fernandez
%A Giandomenico Spezzano
%A Marco Tomassini
%A Leonardo Vanneschi
%T Parallel Genetic Programming
%B Parallel Metaheuristics
%S Parallel and Distributed Computing
%E Enrique Alba
%D 2005
%P 127--153
%I Wiley-Interscience
%C Hoboken, New Jersey, USA
%K genetic algorithms, genetic programming, island model, grid cellular structure, placement FPGA, EHW, cellular genetic programming, ensemble of classifiers, CGPC, bagCGPC
%O 6
%Z Last example uses UCI cens (299285 tuples), 16 linux myrinet pentium III nodes
%@ 0-471-67806-6

%A Francisco {Fernandez de Vega}
%T Parallel Genetic Programming: Methodology, History, and Application to Real-Life Problems
%B Handbook of Bioinspired Algorithms and Applications
%S Computer \& Information Science Series
%E Stephan Olariu and Albert Y. Zomaya
%D 2005
%P 5--65--5--84
%I Chapman and Hall/CRC
%K genetic algorithms, genetic programming
%O 5
%Z Reviewed by Kushal Chakrabarti, The Book Review Column 40(4), 2009, William Gasarch, http://www.cs.umd.edu/~gasarch/bookrev/

%A Francisco {Fernandez de Vega}
%A Erick Cantu-Paz
%T Introduction to Special Issue on Parallel Bioinspired Algorithms
%J Journal of Parallel and Distributed Computing
%V 66
%N 8
%D 2006
%P 989--990
%I 
%K genetic algorithms, genetic programming, Parallel EAs
%U http://portal.acm.org/citation.cfm?id=1161625.1161626&coll=&dl=ACM
%8 August

%A Francisco {Fernandez de Vega}
%A Erick Cantu-Paz
%T Special Issue on Distributed Bioinspired Algorithms
%J Soft Computing
%V 12
%N 12
%D 2008
%P 1143--1144
%I 
%K genetic algorithms, genetic programming, Parallel EAs
%U http://www.springerlink.com/content/h57604076205127u/
%8 October

%T Parallel and Distributed Computational Intelligence
%S Studies in Computational Intelligence
%E Francisco Fernandez de Vega and Erick Cantu-Paz
%V 269
%D 2010
%I Springer
%K genetic algorithms, genetic programming, Parallel Computing, Distributed Computing, Grid Computing
%U http://www.springer.com/engineering/mathematical/book/978-3-642-10674-3
%X The growing success of biologically inspired algorithms in solving large and complex problems has spawned many interesting areas of research. Over the years, one of the
   mainstays in bio-inspired research has been the exploitation of parallel and distributed environments to speedup computations and to enrich the algorithms. From the early
   days of research on bio-inspired algorithms, their inherently parallel nature was recognised and different parallelisation approaches have been explored. Parallel
   algorithms promise reductions in execution time and open the door to solve increasingly larger problems. But parallel platforms also inspire new bio-inspired parallel
   algorithms that, while similar to their sequential counterparts, explore search spaces differently and offer improvements in solution quality. The objective in editing this
   book was to assemble a sample of the best work in parallel and distributed biologically inspired algorithms. The editors invited researchers in different domains to submit
   their work. They aimed to include diverse topics to appeal to a wide audience. Some of the chapters summarise work that has been ongoing for several years, while others
   describe more recent exploratory work. Collectively, these works offer a global snapshot of the most recent efforts of bioinspired algorithms researchers aiming at
   profiting from parallel and distributed computer architectures including GPUs, Clusters, Grids, volunteer computing and p2p networks as well as multi-core processors. This
   volume will be of value to a wide set of readers, including, but not limited to specialists in Bioinspired Algorithms, Parallel and Distributed Computing, as well as
   computer science students trying to figure out new paths towards the future of computational intelligence.

%A Jaime J. Fernandez
%A Kristin A. Farry
%A John B. Cheatham
%T Waveform Recognition Using Genetic Programming: The Myoelectric Signal Recognition Problem
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 63--71
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Thomas Fernandez
%A Matthew Evett
%T Training Period Size and Evolved Trading Systems
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 95
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Fernandez_1997_tpsets.pdf
%8 13-16  July
%Z GP-97

%A Thomas Fernandez
%A Matthew Evett
%T Numeric Mutation as an Improvement to Symbolic Regression in Genetic Programming
%B Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%E V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben
%V 1447
%D 1998
%P 251--260
%I Springer-Verlag Berlin
%C Mission Valley Marriott, San Diego, California, USA
%K genetic algorithms, genetic programming
%8 25-27  March
%Z EP-98. Florida Atlantic University, Boca Raton, FL
%@ 3-540-64891-7

%A Thomas Fernandez
%T Virtual Ramping of Genetic Programming Populations
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 471--482
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030471.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A J. Jaime {Fernandez Jr.}
%A Ian D. Walker
%T A Biologically Inspired Fitness Function for Robotic Grasping
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1517--1522
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-744b.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Antonio Jose {Fernandez Leiva}
%A Jorge L. {O'Valle Barragan}
%T Decision Tree-Based Algorithms for Implementing Bot AI in UT2004
%B Proceedings of the 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, Part I
%S Lecture Notes in Computer Science
%E Jose Manuel Ferrandez and Jose Ramon Alvarez Sanchez and Felix de la Paz and F. Javier Toledo
%V 6686
%D 2011
%P 383--392
%I Springer
%C La Palma, Canary Islands, Spain
%K genetic algorithms, genetic programming
%X This paper describes two different decision tree-based approaches to obtain strategies that control the behaviour of bots in the context of the Unreal Tournament 2004. The
   first approach follows the traditional process existing in commercial video games to program the game artificial intelligence (AI), that is to say, it consists of coding
   the strategy manually according to the AI programmer's experience with the aim of increasing player satisfaction. The second approach is based on evolutionary programming
   techniques and has the objective of automatically generating the game AI. An experimental analysis is conducted in order to evaluate the quality of our proposals. This
   analysis is executed on the basis of two fitness functions that were defined intuitively to provide entertainment to the player. Finally a comparison between the two
   approaches is done following the subjective evaluation principles imposed by the 2k bot prize competition.
%8 May  30-  June  3

%A Jose-Luis {Fernandez-Villacanas Martin}
%A Mark Shackleton
%T Investigation of the importance of the genotype-phenotype mapping in information retrieval
%J Future Generation Computer Systems
%V 19
%N 1
%D 2003
%P 55--68
%I 
%K genetic algorithms, genetic programming, Genotype-phenotype mapping, Information retrieval
%U http://www.sciencedirect.com/science/article/B6V06-478HYP6-1/2/4edc0c200ae393af0e1c9cb343c0cf5d
%X An investigation of the role of the genotype-phenotype mapping (G-Pm) is presented for an evolutionary optimisation task. A simple genetic algorithm (SGA) plus a mapping
   creates a new mapping genetic algorithm (MGA) that is used to optimize a Boolean decision tree for an information retrieval task, with the tree being created via a
   relatively complex mapping. Its performance is contrasted with that of a genetic programming algorithm, British Telecom Genetic Programming (BTGP) which operates directly
   on phenotypic trees. The mapping is observed to play an important role in the time evolution of the system allowing the MGA to achieve better results than the BTGP. We
   conclude that an appropriate G-Pm can improve the evolvability of evolutionary algorithms.

%A Hans Karl Gustav Fernlund
%T Evolving models from observed human performance
%R Ph.D. Thesis
%D 2004
%I 
%I Electrical Engineering and Computer Science, University of Central Florida
%C Orlando, Fla., USA
%K genetic algorithms, genetic programming, Context based reasoning, CxBR, Human behavioral modeling, Learning by observation, Simulation
%U http://purl.fcla.edu/fcla/etd/CFE0000013
%X To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a
   tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behaviour models for simulated agents by observing human
   performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human
   performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This
   synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behaviour.
   This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents
   show not only the ability/capability to automatically learn and generalise the behavior of the human observed, but they also capture some of the personal behavior patterns
   observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer.
%8 Spring Term
%Z Adviser: Avelino J. Gonzalez http://ucf.catalog.fcla.edu/cf.jsp?Ntt=CF001100798&Ntk=Number&Nty=1&N=29&I=0&V=D OpenGP

%A Hans Fernlund
%A Avelino J. Gonzalez
%T Using GP to Model Contextual Human Behavior - Competitive with Human Modeling Performance
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP015.pdf
%X To create a realistic environment, some simulations require simulated agents with human behaviour pattern. Creating such agents with realistic behavior can be a tedious and
   time consuming work. This paper describes a new approach that automatically builds human behaviour models for simulated agents by observing human performance. With an
   automatic tool that builds human behavioral agents, the development cost and effort could be dramatically reduced. This research synergistically combines Context-Based
   Reasoning (CxBR), a paradigm especially developed to model tactical human performance within simulated agents, with the Genetic Programming machine learning algorithm able
   to construct the behaviour knowledge in accordance to the CxBR paradigm. This synergistic combination of AI methodologies has resulted in a new algorithm that automatically
   builds simulated agents with human behavior. This algorithm was exhaustively tested with five different simulated agents created by observing the performance of five humans
   driving an automobile simulator. The agents show, not only the capabilities to automatically learn and generalise the behaviour of the human observed, but they also
   exhibited a performance that was at least as good as that of agents developed manually by a knowledge engineer.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Hans Fernlund
%A Avelino J. Gonzalez
%T Using GP to Model Contextual Human Behavior
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 704--705
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming, Poster
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030704.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Hans K. G. Fernlund
%A Avelino J. Gonzalez
%A Michael Georgiopoulos
%A Ronald F. DeMara
%T Learning tactical human behavior through observation of human performance
%J IEEE Transactions on Systems, Man and Cybernetics, Part B
%V 36
%N 1
%D 2006
%P 128--140
%I 
%K genetic algorithms, genetic programming, inference mechanisms, knowledge representation, learning (artificial intelligence), software agents, context-based reasoning, human
   performance observation, knowledge acquisition, tactical agent development, tactical human behavioural learning, tactical knowledge elicitation, tactical knowledge
   representation, Context-based reasoning, human behavioral modeling, simulation
%U http://www.cal.ucf.edu/journal/j_fernlund_gonzalez_itsmc_04.pdf
%X It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tactical behaviours has been one limiting factor in the development of
   simulated agents representing adversaries and teammates in military and game simulations. Several researchers have addressed this problem with varying degrees of success.
   The problem mostly lies in the fact that tactical knowledge is difficult to elicit and represent through interactive sessions between the model developer and the subject
   matter expert. This paper describes a novel approach that employs genetic programming in conjunction with context-based reasoning to evolve tactical agents based upon
   automatic observation of a human performing a mission on a simulator. we describe the process used to carry out the learning. A prototype was built to demonstrate
   feasibility and it is described herein. The prototype was rigorously and extensively tested. The evolved agents exhibited good fidelity to the observed human performance,
   as well as the capacity to generalise from it.
%8 February
%Z INSPEC Accession Number:8736964 Dept. of Culture, Dalarna Univ., Borlange, Sweden

%A Candida Ferreira
%T Gene Expression Programming: a New Adaptive Algorithm for Solving Problems
%D 2000
%I 
%K genetic algorithms, genetic programming
%U http://www.gene-expression-programming.com/webpapers/GEP.pdf
%X Gene expression programming, a genome/phenome genetic algorithm (linear and non-linear), is presented here for the first time as a new technique for creation of computer
   programs. Gene expression programming uses character linear chromosomes composed of genes structurally organised in a head and a tail. The chromosomes function as a genome
   and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, 1-point and 2-point recombination. The
   chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct functions allows
   the algorithm to perform with high efficiency: in the symbolic regression, sequence induction and block stacking problems it surpasses genetic programming in more than two
   orders of magnitude, whereas in the density-classification problem it surpasses genetic programming in more than four orders of magnitude. The suite of problems chosen to
   illustrate the power and versatility of gene expression programming includes, besides the above mentioned problems, two problems of Boolean concept learning: the
   11-multiplexer and the GP rule problem.
%O rejected for publication
%Z Date: Tue, 14 Nov 2000 21:04:44 -0100 To: genetic-programming <genetic-programming@cs.stanford.edu> From: Candida Ferreira <candida.f@mail.telepac.pt> Subject: GP: Paper on
   gene expression programming Hi all, My paper on gene expression programming is now available as a pdf for download at my site: http://www.gene-expression-programming.com Be
   advised that different versions of this paper were submitted and rejected by Nature and Genetic Programming and Evolvable Machines. One of the reasons one anonymous
   reviewer from GPEM gave was that The performance of the GEP algorithm compared to GP seems too good to be true to me. As I really want to see other scientists using GEP in
   other applications, I decided to publish my paper on the web in order to make this powerful algorithm available to all. Remember, though, that there is a patent pending and
   GEP can not be used commercially. Best regards, Candida Ferreira

%A Candida Ferreira
%T GEP tutorial
%D 2001
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.gene-expression-programming.com/webpapers/GEPtutorial.pdf
%O WSC6 tutorial
%8 September
%Z WSC6, 6th World Conference on Soft Computing in Industrial Applications presentation: http://www.gene-expression-programming.com/webpapers/slideShow.pdf See discussion eg
   http://groups.yahoo.com/group/genetic_programming/message/68

%A Candida Ferreira
%T Gene Expression Programming in Problem Solving
%B Soft Computing and Industry Recent Applications
%E Rajkumar Roy and Mario K\"oppen and Seppo Ovaska and Takeshi Furuhashi and Frank Hoffmann
%D 2001
%P 635--654
%I Springer-Verlag
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.gene-expression-programming.com/webpapers/ferreira-WSC6.pdf
%O Published 2002
%8 10--24  September
%Z WSC6 Out of print? http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394
%@ 1-85233-539-4

%A C\^andida Ferreira
%T Gene Expression Programming: A New Adaptive Algorithm for Solving Problems
%J Complex Systems
%V 13
%N 2
%D 2001
%P 87--129
%I 
%K genetic algorithms, genetic programming, GEP
%U http://arXiv.org/abs/cs/0102027
%X Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of
   computer programs. Gene expression programming uses character linear chromosomes composed of genes structurally organized in a head and a tail. The chromosomes function as
   a genome and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, and one- and two-point
   recombination. The chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct
   functions allows the algorithm to perform with high efficiency that greatly surpasses existing adaptive techniques. The suite of problems chosen to illustrate the power and
   versatility of gene expression programming includes symbolic regression, sequence induction with and without constant creation, block stacking, cellular automata rules for
   the density-classification problem, and two problems of boolean concept learning: the 11-multiplexer and the GP rule problem.
%Z Portuguese translation http://www.gene-expression-programming.com/webpapers/GEPPort.pdf

%A C\^andida Ferreira
%T Discovery of the Boolean Functions to the Best Density-Classification Rules Using Gene Expression Programming
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 50--59
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%X Cellular automata are idealized versions of massively parallel, decentralized computing systems capable of emergent behaviours. These complex behaviors result from the
   simultaneous execution of simple rules at multiple local sites. A widely studied behavior consists of correctly determining the density of an initial configuration, and
   both human and computer-written rules have been found that perform with high efficiency at this task. However, the two best rules for the density-classification task,
   Coevolution1 and Coevolution2, were discovered using a coevolutionary algorithm in which a genetic algorithm evolved the rules and, therefore, only the output bits of the
   rules are known. However, to understand why these and other rules perform so well and how the information is transmitted throughout the cellular automata, the Boolean
   expressions that orchestrate this behaviour must be known. The results presented in this work are a contribution in that direction.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Candida Ferreira
%T Mutation, Transposition, and Recombination: An Analysis of the Evolutionary Dynamics
%B 4th International Workshop on Frontiers in Evolutionary Algorithms
%E Manuel Grana Romay and Richard Duro
%D 2002
%I 
%C North Carolina, USA
%K genetic algorithms, genetic programming, gene expression programming
%U http://www.gene-expression-programming.com/webpapers/ferreira-FEA02.pdf
%X Gene expression programming (GEP) uses mutation, transposition, and crossover to create variation. Although there exists a large body of work in genetic algorithms
   concerning the roles of mutation and recombination, these results not only do not apply to GEP due to the genotype/phenotype representation but also seem to contradict the
   GEP experience. Therefore, and given the diversity of GEP operators, it is convenient to develop some kind of understanding of their power. The aim of this work is to help
   develop such an understanding and to show the evolutionary dynamics and the transforming power of each genetic operator, with their advantages and limitations.
%8 8-14  March
%Z Sat, 23 Mar 2002 17:52:10 GMT genetic_programming@yahoogroups.com FEA2002 In conjunction with Sixth Joint Conference on Information Sciences
%@ 0-9707890-1-7

%A Candida Ferreira
%T Combinatorial Optimization by Gene Expression Programming: Inversion Revisited
%B Proceedings of the Argentine Symposium on Artificial Intelligence
%E J. M. Santos and A. Zapico
%D 2002
%P 160--174
%I 
%C Santa Fe, Argentina
%K genetic algorithms, genetic programming, GEP
%U http://www.gene-expression-programming.com/webpapers/ferreira-ASAI02.pdf
%X Combinatorial optimisation problems require combinatorial-specific search operators so that populations of candidate solutions can evolve efficiently. Indeed, several
   researchers created modifications to the basic genetic operators of mutation and recombination in order to create high performing combinatorial-specific operators. However,
   it is not known which operators perform better as no systematic comparisons have been done. In this work, a new algorithm that explores a new chromosomal organisation based
   on multigene families is used. This new organization together with several combinatorial-specific search operators, namely, inversion, gene and sequence deletion/insertion,
   and restricted and generalised permutation, allow the algorithm to perform with high efficiency. The performance of the new algorithm is empirically compared on the 13- and
   19-cities tour travelling salesperson problem, showing that the long abandoned inversion operator is by far the most efficient of the combinatorial operators. The
   efficiency and potentialities of the new algorithm are further demonstrated by solving a simple task assignment problem.
%Z ASAI02 http://www.dc.uba.ar/people/profesores/santos/asai2002.html

%A C\^andida Ferreira
%T Function Finding and the Creation of Numerical Constants in Gene Expression Programming
%B 7th Online World Conference on Soft Computing in Industrial Applications
%D 2002
%I 
%K genetic algorithms, genetic programming, gene expression programming
%U http://www.gene-expression-programming.com/webpapers/Ferreira-WSC7.pdf
%X Gene expression programming is a genotype/phenotype system that evolves computer programs of different sizes and shapes (the phenotype) encoded in linear chromosomes of
   fixed length (the geno-type). The chromosomes are composed of multiple genes, each gene encoding a smaller sub-program. Furthermore, the structural and functional
   organization of the linear chromosomes allows the uncon-strained operation of important genetic operators such as mutation, transposition, and recombination. In this work,
   three function finding problems, including a high dimensional time series prediction task, are analyzed in an attempt to discuss the question of constant creation in
   evolutionary computation by comparing two different approaches to the problem of constant creation. The first algorithm involves a facility to manipulate random numerical
   constants, whereas the second finds the numerical constants on its own or invents new ways of representing them. The results presented here show that evolutionary
   algorithms perform considerably worse if numerical constants are explicitly used.
%O on line
%8 September  23 -  October  4
%Z WSC7 http://wsc7.ugr.es/

%A C. Ferreira
%T Genetic Representation and Genetic Neutrality in Gene Expression Programming
%J Advances in Complex Systems
%V 5
%N 4
%D 2002
%P 389--408
%I 
%K genetic algorithms, genetic programming, GEP, Genetic neutrality, gene expression programming, evolutionary computation
%U http://www.gepsoft.com/gep/webpapers/abstracts.asp#09
%X The neutral theory of molecular evolution states that the accumulation of neutral mutations in the genome is fundamental for evolution to occur. The genetic representation
   of gene expression programming, an artificial genotype/phenotype system, not only allows the existence of non-coding regions in the genome where neutral mutations can
   accumulate but also allows the controlled manipulation of both the number and the extent of these non-coding regions. Therefore, gene expression programming is an ideal
   artificial system where the neutral theory of evolution can be tested in order to gain some insights into the workings of artificial evolutionary systems. The results
   presented in this work show beyond any doubt that the existence of neutral regions in the genome is fundamental for evolution to occur efficiently.
%Z Tue, 18 Mar 2003 20:01:57 GMT Wed, 28 Apr 2004 16:00:48 BST

%A Candida Ferreira
%T Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence
%D 2002
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.gene-expression-programming.com/gep/Books/index.asp
%Z Tue, 30 May 2006 11:21:33 BST to GP list replaced by \citeFerreira:book2 cf email Sun, 06 Jul 2003 18:40:43 BST GP list
%@ 972-95890-5-4

%A Candida Ferreira
%T Analyzing the Founder Effect in Simulated Evolutionary Processes Using Gene Expression Programming
%B Soft Computing Systems: Design, Management and Applications
%E A. Abraham and J. Ruiz-del-Solar and M. K\"oppen
%D 2002
%P 153--162
%I IOS Press
%I Gepsoft
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.gene-expression-programming.com/webpapers/ferreira-his02.pdf
%X Gene expression programming is a genotype/phenotype system that evolves computer programs encoded in linear chromosomes of fixed length. The interplay between genotype
   (chromosomes) and phenotype (expression trees) is made possible by the structural and functional organisation of the linear chromosomes. This organization allows the
   unconstrained operation of important genetic operators such as mutation, transposition, and recombination. Although simple, the genotype/phenotype system of gene expression
   programming can provide some insights into natural evolutionary processes. In this work the question of the initial diversity in evolving populations of computer programs
   is addressed by analysing populations undergoing either mutation or recombination. The results presented here show that populations undergoing mutation recover practically
   undisturbed from evolutionary bottlenecks whereas populations undergoing recombination alone depend considerably on the size of the founder population and are unable to
   evolve efficiently if subjected to really tight bottlenecks.
%@ 1-58603-297-6

%A Candida Ferreira
%T Gene expression programming and the automatic evolution of computer programs
%B Recent Developments in Biologically Inspired Computing
%E Leandro N. de Castro and Fernando J. Von Zuben
%D 2004
%P 82--103
%I Idea Group Publishing
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.gene-expression-programming.com/gep/webpapers/abstracts.asp#11
%X In this chapter an artificial problem solver inspired in natural genotype/phenotype systems gene expression programming is presented. As an introduction, the fundamental
   differences between gene expression programming and its predecessors, genetic algorithms and genetic programming, are briefly summarised so that the evolutionary advantages
   of gene expression programming are better understood. The work proceeds with a detailed description of the architecture of the main players of this new algorithm
   (chromosomes and expression trees), focusing mainly on the interactions between them and how the simple yet revolutionary structure of the chromosomes allows the efficient,
   unconstrained exploration of the search space. And finally, the chapter closes with an advanced application in which gene expression programming is used to evolve computer
   programs for diagnosing breast cancer.
%O 6
%Z http://www.idea-group.com/books/details.asp?id=4376 http://groups.yahoo.com/group/genetic_programming/message/3551
   http://groups.yahoo.com/group/genetic_programming/message/3549
%@ 1-59140-312-X

%A Candida Ferreira
%T Designing Neural Networks Using Gene Expression Programming
%B 9th Online World Conference on Soft Computing in Industrial Applications
%E Ajith Abraham and Mario K\"oppen
%D 2004
%P Paper No. 14
%I 
%I World Federation on Soft Computing (WFSC)
%C On the World Wide Web
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.gene-expression-programming.com/webpapers/Ferreira-WSC9.pdf
%X An artificial neural network with all its elements is a rather complex structure, not easily constructed and/or trained to perform a particular task. Consequently, several
   researchers used Genetic Algorithms to evolve partial aspects of neural networks, such as the weights, the thresholds, and the network architecture. Indeed, over the last
   decade many systems have been developed that perform total network induction. In this work it is shown how the chromosomes of Gene Expression Programming can be modified so
   that a complete neural network, including the architecture, the weights and thresholds, could be totally encoded in a linear chromosome. It is also shown how this
   chromosomal organization allows the training/adaptation of the network using the evolutionary mechanisms of selection and modification, thus providing an approach to the
   automatic design of neural networks. The workings and performance of this new algorithm are tested on the 6-multiplexer and on the classical exclusive-or problems.
%8 20  September  - 8  October
%Z WSC9

%A C\^{a}ndida Ferreira
%T Automatically Defined Functions in Gene Expression Programming
%B Genetic Systems Programming: Theory and Experiences
%S Studies in Computational Intelligence
%E Nadia Nedjah and Ajith Abraham and Luiza de Macedo Mourelle
%V 13
%D 2006
%P 21--56
%I Springer
%C Germany
%K genetic algorithms, genetic programming, gene expression programming, ADF
%U http://www.gene-expression-programming.com/webpapers/Ferreira-GSP2006.pdf
%X In this chapter it is shown how Automatically Defined Functions are encoded in the genotype/phenotype system of Gene Expression Programming. As an introduction, the
   fundamental differences between Gene Expression Programming and its predecessors, Genetic Algorithms and Genetic Programming, are briefly summarized so that the
   evolutionary advantages of Gene Expression Programming are better understood. The introduction proceeds with a detailed description of the architecture of the main players
   of Gene Expression Programming (chromosomes and expression trees), focusing mainly on the interactions between them and how the simple, yet revolutionary, structure of the
   chromosomes allows the efficient, unconstrained exploration of the search space. The work proceeds with an introduction to Automatically Defined Functions and how they are
   implemented in Gene Expression Programming. Furthermore, the importance of Automatically Defined Functions in Evolutionary Computation is thoroughly analyzed by comparing
   the performance of sophisticated learning systems with Automatically Defined Functions with much simpler ones on the sextic polynomial problem.
%Z http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html
%@ 3-540-29849-5

%A Candida Ferreira
%T Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence
%D 2006
%I Springer
%K genetic algorithms, genetic programming, gene expression programming
%8 May
%Z Tue, 30 May 2006 11:21:33 BST Genetic_Programming@Yahoogroups.Com 'This second edition' of \citeFerreira:book 'has been substantially revised and extended with five new
   chapters, including a new chapter describing two new algorithms for inducing decision trees with nominal and numeric/mixed attributes.'
%@ 3-540-32796-7

%A C. Ferreira
%T Designing Neural Networks Using Gene Expression Programming
%B Applied Soft Computing Technologies: The Challenge of Complexity
%E A. Abraham and B. de Baets and M. Koppen and B. Nickolay
%D 2006
%P 517--536
%I Springer-Verlag
%C WWW
%K genetic algorithms, genetic programming, gene expression programming
%U http://www.gene-expression-programming.com/webpapers/abstracts.asp#14
%X An artificial neural network with all its elements is a rather complex structure, not easily constructed and/or trained to perform a particular task. Consequently, several
   researchers used genetic algorithms to evolve partial aspects of neural networks, such as the weights, the thresholds, and the network architecture. Indeed, over the last
   decade many systems have been developed that perform total network induction. In this work it is shown how the chromosomes of Gene Expression Programming can be modified so
   that a complete neural network, including the architecture, the weights and thresholds, could be totally encoded in a linear chromosome. It is also shown how this
   chromosomal organisation allows the training/adaptation of the network using the evolutionary mechanisms of selection and modification, thus providing an approach to the
   automatic design of neural networks. The workings and performance of this new algorithm are tested on the 6-multiplexer and on the classical exclusive-or problems.
%Z genetic_programming@yahoogroups.com Wed, 15 Aug 2007 09:27:52 BST This volume presents the proceedings of the 9th Online World Conference on Soft Computing in Industrial
   Applications (WSC9), September 20th - October 08th, 2004, held on the World Wide Web.

%A Cristiano D. Ferreira
%A Ricardo {da Silva Torres}
%A Marcos Andre Goncalves
%A Weiguo Fan
%T Image Retrieval with Relevance Feedback based on Genetic Programming
%B XXIII Simp\'osio Brasileiro de Banco de Dados
%E Sandra de Amo
%D 2008
%P 120--134
%I SBC
%C Campinas, S\~ao Paulo, Brasil
%K genetic algorithms, genetic programming
%U http://www.lbd.dcc.ufmg.br:8080/colecoes/sbbd/2008/009.pdf
%X This paper presents a new content-based image retrieval framework with relevance feedback. This framework employs Genetic Programming to discover a combination of
   descriptors that better characterizes the user perception of image similarity. Several experiments were conducted to validate the proposed framework. These experiments
   employed three different image databases and colour, shape, and texture descriptors to represent the content of database images. The proposed framework was compared with
   three other relevance feedback methods regarding their efficiency and effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed
   method.
%8 13-15  October
%Z SBBD 2008. CBIR, COREL

%A C. D. Ferreira
%A J. A. Santos
%A R. {da S. Torres}
%A M. A. Goncalves
%A R. C. Rezende
%A Weiguo Fan
%T Relevance feedback based on genetic programming for image retrieval
%J Pattern Recognition Letters
%V 32
%N 1
%D 2011
%P 27--37
%I 
%K genetic algorithms, genetic programming, Relevance feedback, Content-based image retrieval
%U http://www.sciencedirect.com/science/article/B6V15-504123K-4/2/d925135e9c62c6da92ea517f2451d3bf
%X This paper presents two content-based image retrieval frameworks with relevance feedback based on genetic programming. The first framework exploits only the user indication
   of relevant images. The second one considers not only the relevant but also the images indicated as non-relevant. Several experiments were conducted to validate the
   proposed frameworks. These experiments employed three different image databases and colour, shape, and texture descriptors to represent the content of database images. The
   proposed frameworks were compared, and outperformed six other relevance feedback methods regarding their effectiveness and efficiency in image retrieval tasks.
%O Image Processing, Computer Vision and Pattern Recognition in Latin America

%A Gabriel J. Ferrer
%A Worthy N. Martin
%T Using Genetic Programming to Evolve Board Evaluation Functions for a Boardgame
%B 1995 IEEE Conference on Evolutionary Computation
%V 2
%D 1995
%P 747
%I IEEE Press Piscataway, NJ, USA
%C Perth, Australia
%K genetic algorithms, genetic programming, Senet
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/senet.ps.gz
%X In this paper, we employ the genetic programming paradigm to enable a computer to learn to play strategies for the ancient Egyptian boardgame Senet by evolving board
   evaluation functions. Formulating the problem in terms of board evaluation functions made it feasible to evaluate the fitness of game playing strategies by using
   tournament-style fitness evaluation. The game has elements of both strategy and chance. Our approach learns strategies which enable the computer to play consistently at a
   reasonably skillful level.
%8 29  November  - 1  Decemeber
%Z ICEC-95 http://www.io.org/~causal/c_p/cpec95.htm Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at
   http://ciips.ee.uwa.edu.au/~dorota/icnn95.html Fitness given by knockout tournament, rank-proprtionate selection, mutation and crossover, generational, non-standard random
   initial population creation/mutation/crossover, no size limit on programs. 2 non-seeded runs, 2 seeded runs (504 random + 8 different hand-coded). No discussion of
   statistical significance of results.

%A Filomena Ferrucci
%A Carmine Gravino
%A Rocco Oliveto
%A Federica Sarro
%T Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions
%B Proceedings of the 2nd International Symposium on Search Based Software Engineering (SSBSE '10)
%E Massimiliano Di Penta and Simon Poulding and Lionel Briand and John Clark
%D 2010
%P 89--98
%I IEEE
%C Benevento, Italy
%K genetic algorithms, genetic programming, SBSE
%X Context: The use of search-based methods has been recently proposed for software development effort estimation and some case studies have been carried out to assess the
   effectiveness of Genetic Programming (GP). The results reported in the literature showed that GP can provide an estimation accuracy comparable or slightly better than some
   widely used techniques and encouraged further research to investigate whether varying the fitness function the estimation accuracy can be improved. Aim: Starting from these
   considerations, in this paper we report on a case study aiming to analyse the role played by some fitness functions for the accuracy of the estimates. Method: We performed
   a case study based on a publicly available dataset, i.e., Desharnais, by applying a 3-fold cross validation and employing summary measures and statistical tests for the
   analysis of the results. Moreover, we compared the accuracy of the obtained estimates with those achieved using some widely used estimation methods, namely Case-Based
   Reasoning (CBR) and Manual Step Wise Regression (MSWR). Results: The obtained results highlight that the fitness function choice significantly affected the estimation
   accuracy. The results also revealed that GP provided significantly better estimates than CBR and comparable with those of MSWR for the considered dataset.
%8 7-9  September
%Z http://www.ssbse.org/program.php

%A Filomena Ferrucci
%A Carmine Gravino
%A Federica Sarro
%T How Multi-Objective Genetic Programming Is Effective for Software Development Effort Estimation?
%B Search Based Software Engineering
%S Lecture Notes in Computer Science
%E Myra Cohen and Mel O'Cinneid
%V 6956
%D 2011
%P 274--275
%I Springer
%C Szeged, Hungary
%K genetic algorithms, genetic programming, SBSE: Poster
%X The idea of exploiting search-based methods to estimate development effort is based on the observation that the effort estimation problem can be formulated as an
   optimisation problem. As a matter of fact, among possible estimation models, we have to identify the best one, i.e., the one providing the most accurate estimates.
   Nevertheless, in the context of effort estimation there does not exist a unique measure that allows us to compare different models and consistently derives the best one
   [1]. Rather, several evaluation criteria (e.g., MMRE and Pred(25)) covering different aspects of model performances (e.g., underestimating or overestimating) are used to
   assess and compare estimation models [1]. Thus, considering the effort estimation problem as an optimisation problem we should search for the model that optimises several
   measures. From this point of view, the effort estimation problem is inherently multi-objective. Nevertheless, all the studies that have been carried
%8 10-12  September

%A Sevan Gregory Ficici
%T Solution Concepts in Coevolutionary Algorithms
%R Ph.D. Thesis
%D 2004
%I 
%I Computer Science Department, Brandeis University
%C USA
%K genetic algorithms, Coevolutionary Algorithms, Evolutionary Game Theory, Machine Learning
%U http://www.demo.cs.brandeis.edu/papers/long.html#ficici_thesis_04
%X Inspired by the principle of natural selection, coevolutionary algorithms are search methods in which processes of mutual adaptation occur amongst agents that interact
   strategically. The outcomes of interaction reveal a reward structure that guides evolution towards the discovery of increasingly adaptive behaviors. Thus, coevolutionary
   algorithms are often used to search for optimal agent behaviors in domains of strategic interaction. Coevolutionary algorithms require little a priori knowledge about the
   domain. We assume the learning task necessitates the algorithm to 1) discover agent behaviors, 2) learn the domain's reward structure, and 3) approximate an optimal
   solution. Despite the many successes of coevolutionary optimization, the practitioner frequently observes a gap between the properties that actually confer agent adaptivity
   and those expected (or desired) to yield adaptivity, or optimality. This gap is manifested by a variety of well-known pathologies, such as cyclic dynamics, loss of fitness
   gradient, and evolutionary forgetting. This dissertation examines the divergence between expectation and actuality in coevolutionary algorithms---why selection pressures
   fail to conform to our beliefs about adaptiveness, or why our beliefs are evidently erroneous. When we confront the pathologies of coevolutionary algorithms as a
   collection, we find that they are essentially epiphenomena of a single fundamental problem, namely a lack of rigor in our solution concepts. A solution concept is a
   formalism with which to describe and understand the incentive structures of agents that interact strategically. All coevolutionary algorithms implement some solution
   concept, whether by design or by accident, and optimize according to it. Failures to obtain the desiderata of "complexity" or "optimality" often indicate a dissonance
   between the implemented solution concept and that required by our envisaged goal. We make the following contributions: 1) We show that solution concepts are the critical
   link between our expectations of coevolution and the outcomes actually delivered by algorithm operation, and are therefore crucial to explicating the divergence between the
   two, 2) We provide analytic results that show how solution concepts bring our expectations in line with algorithmic reality, and 3) We show how solution concepts empower us
   to construct algorithms that operate more in line with our goals.
%8 May
%Z Available as Computer Science Department Technical Report CS-03-243 Download this paper as: Postscript (ficici_thesis_04.ps) Gzipped Postscript (ficici_thesis_04.ps.gz) PDF
   (ficici_thesis_04.pdf)

%A Mirko Ficko
%A Miha Kovacic
%A Miran Brezocnik
%T Genetic algorithms : a useful optimization method for manufacturing problems
%J Academic Journal of Manufacturing Engineering
%V 2
%N 1
%D 2004
%P 21--26
%I 
%K genetic algorithms, genetic programming, optimisation, facility layout, flexible manufacturing systems
%X a very useful method for solving g the manufacturing problems, and optimising the manufacturing process, i.e. the genetic algorithms (GAs). The well-known basic knowledge
   of the conventional GAs is briefly presented. The second part of the paper discusses an example of optimisation of the design of the flexible manufacturing system (FMS) in
   one row with GAs. First the reasons for studying the layout of devices in the FMS are discussed. The GA model, the most suitable way of coding the solutions into the
   organisms and the selected evolutionary and genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS
   were used. In this connection, the most favourable sequence of devices in the row is established by means of GAs. In the end the test results of the application made and
   the analysis are discussed.
%Z http://www.eng.utt.ro/auif/rev/issue/no-05/no-05.html University of Maribor, Faculty for Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems

%A Mirko Ficko
%A Miran Brezocnik
%A Joze Balic
%T Designing the layout of single- and multiple-rows flexible manufacturing system by genetic algorithms
%J Journal of Materials Processing Technology
%V 157-158
%D 2004
%P 150--158
%I 
%K genetic algorithms, genetic programming, Flexible manufacturing systems (FMS), Optimisation, Facility layout
%X model of designing of the flexible manufacturing system (FMS) in one or multiple rows with genetic algorithms (GAs). First the reasons for studying the layout of devices in
   the FMS are discussed. After studying the properties of the FMS and perusing the methods of layout designing the genetic algorithms methods was selected as the most
   suitable method for designing the FMS. The genetic algorithm model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and
   genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS were used. In this connection, the most
   favourable number of rows and the sequence of devices in the individual row are established by means of genetic algorithms. In the end the test results of the application
   made and the analysis are discussed.
%8 20  Decemeber
%Z Special issue "Achievements in Mechanical and Materials Engineering Conference" Edited by L. A. Dobranski

%A M. Ficko
%A I. Drstvensek
%A M. Brezocnik
%A J. Balic
%A B. Vaupotic
%T Prediction of total manufacturing costs for stamping tool on the basis of CAD-model of finished product
%J Journal of Materials Processing Technology
%V 164-165
%D 2005
%P 1327--1335
%I 
%K genetic algorithms, genetic programming, Prediction of costs, Tool-making, Stamping, CAD-model, Intelligent systems
%U http://www.sciencedirect.com/science/article/B6TGJ-4FJKWTY-D/2/17df3b2567564f2d6c9d8fdcb041d0e9
%X One of the orientations of the tool-making industry is towards shortening the time from enquiry to the supply of tools. The tool-making shops must prepare within the
   shortest possible time an offer for the manufacturer of the tool based on the enquiry in the form of the CAD-model of the final product. For preparation of a proper offer,
   the values of certain technological features occurring in the manufacture of the tool are needed. Most frequently the tool manufacturer is interested in total cost for
   manufacture of the tool. Because of lack of time for making a detailed analysis the total costs of tool manufacture are predicted by the expert on the basis of the
   experience gathered during several years of work in this area. In our work, we conceived an intelligent system for predicting of total cost of the tool manufacture. We
   limited ourselves to tools for manufacture of sheet metal products by stamping; the system is based on the concept of case-based reasoning. On the basis of target and
   source cases, the system prepares the prediction of costs. The target case is the CAD-model in whose costs we are interested, whereas the source cases are the CAD-model of
   products, for which the tools had already been made, and the relevant total costs are known. The system first abstracts from CAD-models the geometrical features, and then
   it calculates the similarities between the source cases and target case. Then the most similar cases are used for preparation of prediction by genetic programming method.
   The genetic programming method provides the model connecting the individual geometrical features with total costs searched for. In the experimental work, we made a system
   adapted for predicting of tool costs used for tool manufacture on the basis of a theoretic model. The results show that the quality of predictions made by the intelligent
   system is comparable to the quality assured by the experienced expert.
%8 15  May
%Z AMPT/AMME05 Part 2

%A M. V. Fidelis
%A H. S. Lopes
%A A. A. Freitas
%T Discovering Comprehensible Classification Rules a Genetic Algorithm
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%V 1
%D 2000
%P 805--810
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, data mining
%U http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/cec2000a.zip
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Cyril Fillon
%A Alberto Bartoli
%T A Divide and Conquer strategy for improving efficiency and probability of success in Genetic Programming
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 13--23
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050013.pdf
%X A common method for improving a genetic programming search on difficult problems is either multiplying the number of runs or increasing the population size. We propose a
   new search strategy which attempts to obtain a higher probability of success with smaller amounts of computational resources. We call this model Divide & Conquer since our
   algorithm initially partitions the search space in smaller regions that are explored independently of each other. Then, our algorithm collects the most competitive
   individuals found in each partition and exploits them in order to get a solution. We benchmarked our proposal on three problem domains widely used in the literature. Our
   results show a significant improvement of the likelihood of success while requiring less computational resources than the standard algorithm.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Cyril Fillon
%A Alberto Bartoli
%T Multi-objective Genetic Programming for Improving the Performance of TCP
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 170--180
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X TCP is one of the fundamental components of the Internet. The performance of TCP is heavily dependent on the quality of its round-trip time (RTT) estimator, i.e. the
   formula that predicts dynamically the delay experienced by packets along a network connection. In this paper we apply multi-objective genetic programming for constructing
   an RTT estimator. We used two different approaches for multi-objective optimisation and a collection of real traces collected at the mail server of our University. The
   solutions that we found outperform the RTT estimator currently used by all TCP implementations. This result could lead to several applications of genetic programming in the
   networking field.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Cyril Fillon
%A Alberto Bartoli
%T Symbolic Regression of Discontinuous and Multivariate Functions by Hyper-Volume Error Separation (HVES)
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 23--30
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Symbolic regression is aimed at discovering mathematical expressions, in symbolic form, that fit a given sample of data points. While Genetic Programming (GP) constitutes a
   powerful tool for solving this class of problems, its effectiveness is still severely limited when the data sample requires different expressions in different regions of
   the input space - i.e., when the approximating function should be discontinuous. In this paper we present a new GP-based approach for symbolic regression of discontinuous
   functions in multivariate data-sets. We identify the portions of the input space that require different approximating functions by means of a new algorithm that we call
   Hyper-Volume Error Separation (HVES). To this end we run a preliminary GP evolution and partition the input space based on the error exhibited by the best individual across
   the data-set. Then we partition the data-set based on the partition of the input space and use each such partition for driving an independent, preliminary GP evolution. The
   populations resulting from such preliminary evolutions are finally merged and evolved again. We compared our approach to the standard GP search and to a GP search for
   discontinuous functions in univariate data-sets. Our results show that coupling HVES with GP is an effective approach and provides significant accuracy improvements while
   requiring less computational resources.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Jenny Rose Finkel
%T Using Genetic Programming to Evolve an Algorithm for Factoring Numbers
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 52--60
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/Finkel.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Marion R. {Finley Jr.}
%A Haruo Akimaru
%A Evelyne B. Hausen-Tropper
%T Element of a theoretical model of tele-learning using genetic algorithms
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 93--98
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms
%8 13  July
%Z GECCO-99LB

%A Hiram Firpi
%A Erik D. Goodman
%A Javier Echauz
%T On Prediction of Epileptic Seizures by Computing Multiple Genetic Programming Artificial Features
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 321--330
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=321
%X In this paper, we present a general-purpose, systematic algorithm, consisting of a genetic programming module and a k-nearest neighbour classifier, to automatically create
   multiple artificial features (i.e., features that are computer-crafted and may not have a known physical meaning) directly from EEG signals that reveal patterns predictive
   of epileptic seizures. The algorithm was evaluated in three different patients, with prediction defined over a horizon that varies between 1 and 5 minutes before
   unequivocal electrographic onset. For one patient, a perfect classification was achieved. For the other two patients, a high classification accuracy was reached, predicting
   three seizures out of four for one, and eleven seizures out of fifteen for the other. For the latter, also, only one normal (non-seizure) signal was misclassified.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Hiram Firpi
%A Erik Goodman
%A Javier Echauz
%T Epileptic seizure detection by means of genetically programmed artificial features
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 1
%D 2005
%P 461--466
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Biological Applications, design, epilepsy, feature extraction, seizure detection, state-space reconstruction
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p461.pdf
%X we describe a general-purpose, systematic algorithm, consisting of a genetic programming module and a knearest neighbour classifier to automatically create artificial
   features?features that are computer-crafted and may not have a known physical meaning?directly from the reconstructed statespace trajectories of the EEG signals that reveal
   patterns indicative of epileptic seizure onset. The algorithm was evaluated in three patients and validation experiments were carried out using 267.6 hours of EEG
   recordings. The results with the artificial features compare favourably with previous benchmark work that used a handcrafted feature.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Hiram Firpi
%A Erik Goodman
%A Javier Echauz
%T On Prediction of Epileptic Seizures by Means of Genetic Programming Artificial Features
%J Annals of Biomedical Engineering
%V 34
%N 3
%D 2006
%P 515--529
%I 
%K genetic algorithms, genetic programming, Epilepsy, Seizure prediction, Artificial feature, Feature extraction, State-space reconstruction
%X A general-purpose, systematic algorithm is presented, consisting of a genetic programming module and a k-nearest neighbour classifier to automatically create artificial
   features computer-crafted features possibly without a known physical meaning directly from the reconstructed state-space trajectory of intracranial EEG signals that reveal
   predictive patterns of epileptic seizures. The algorithm was evaluated with IEEG data from seven patients, with prediction defined over a horizon of 1-5 min before
   unequivocal electrographic onset. A total of 59 baseline epochs (nonseizures) and 55 preictal epochs (preseizures) were used for validation purposes. Among the results, it
   is shown that 12 seizures out of 55 were missed while four baseline epochs were misclassified, yielding 79per cent sensitivity and 93per cent specificity.
%8 March

%A Hiram Firpi
%A Erik D. Goodman
%A Javier Echauz
%T Epileptic Seizure Detection Using Genetically Programmed Artificial Features
%J IEEE Transactions on Biomedical Engineering
%V 54
%N 2
%D 2007
%P 212--224
%I 
%K genetic algorithms, genetic programming, diseases, electroencephalography, genetic algorithms, medical signal detection, medical signal processing, signal classification,
   signal reconstruction730.6 hr, epileptic seizure detection, genetic programming, genetically programmed artificial features, k-nearest neighbour classifier,
   patient-specific epilepsy seizure detectors, reconstructed state-space trajectories
%X Patient-specific epilepsy seizure detectors were designed based on the genetic programming artificial features algorithm, a general-purpose, methodic algorithm comprised by
   a genetic programming module and a k-nearest neighbour classifier to create synthetic features. Artificial features are an extension to conventional features, characterised
   by being computer-coded and may not have a known physical meaning. In this paper, artificial features are constructed from the reconstructed state-space trajectories of the
   intracranial EEG signals intended to reveal patterns indicative of epileptic seizure onset. The algorithm was evaluated in seven patients and validation experiments were
   carried out using 730.6 hr of EEG recordings. The results with the artificial features compare favourably with previous benchmark work that used a handcrafted feature.
   Among other results, 88 out of 92 seizures were detected yielding a low false negative rate of 4.35percent
%8 February

%A Hiram Firpi
%A George Vachtsevanos
%T Genetically programmed-based artificial features extraction applied to fault detection
%J Engineering Applications of Artificial Intelligence
%V 21
%N 4
%D 2008
%P 558--568
%I 
%K genetic algorithms, genetic programming, Fault detection, Feature extraction, Artificial feature, Conventional feature
%U http://www.sciencedirect.com/science/article/B6V2M-4PG2RVD-1/2/83e1929229a124416738c8ec59137146
%X This paper presents a novel application of genetically programmed artificial features, which are computer crafted, data driven, and possibly without physical
   interpretation, to the problem of fault detection. Artificial features are extracted from vibration data of an accelerometer sensor to monitor and detect a crack fault or
   incipient failure seeded in an intermediate gearbox of a helicopter's main transmission. Classification accuracies for the artificial feature constructed from raw data
   exceeded 99percent over training and independent validation sets. As a benchmark, GP-based artificial features constructed from conventional ones under performed those
   derived from raw data by over 2percent over the training and over 11percent over the testing data.

%A Ronald F. Fischer
%T Applying Genetic Algorithms to Bitmap Pattern Matching
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 41--48
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, GENESIS
%8 Decemeber
%Z This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-187263-3

%A Petr Fiser
%A Jan Schmidt
%A Zdenek Vasicek
%A Lukas Sekanina
%T On logic synthesis of conventionally hard to synthesize circuits using genetic programming
%B 13th IEEE International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS), 2010
%D 2010
%P 346--351
%I 
%K genetic algorithms, genetic programming, Cartesian genetic programming, circuit optimisation, circuit synthesis, logic synthesis, multilevel logic networks, search
   algorithm, specific synthesis processes, logic design, search problems
%X Recently, it has been shown that synthesis of some circuits is quite difficult for conventional methods. In this paper we present a method of minimisation of multi-level
   logic networks which can solve these difficult circuit instances. The synthesis problem is transformed on the search problem. A search algorithm called Cartesian genetic
   programming (CGP) is applied to synthesise various difficult circuits. Conventional circuit synthesis usually fails for these difficult circuits; specific synthesis
   processes must be employed to obtain satisfactory results. We have found that CGP is able to implicitly discover new efficient circuit structures. Thus, it is able to
   optimise circuits universally, regardless their structure. The circuit optimization by CGP has been found especially efficient when applied to circuits already optimized by
   a conventional synthesis. The total runtime is reduced, while the result quality is improved further more.
%8 April
%Z Also known as \cite5491755

%A Jeannie Fitzgerald
%A Conor Ryan
%T Drawing boundaries: using individual evolved class boundaries for binary classification problems
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1347--1354
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X This paper describes a technique which can be used with Genetic Programming (GP) to reduce implicit bias in binary classification tasks. Arbitrarily chosen class boundaries
   can introduce bias, but if individuals can choose their own boundaries, tailored to their function set, then their outputs are automatically scaled into a suitable range.
   These boundaries evolve over time as the individuals adapt to the data. Our system calculates the Evolved Class Boundary(ECB) for each individual in every generation, with
   the twin aims of reducing training times and improving test fitness. The method is tested on three benchmark binary classification data sets from the medical domain. The
   results obtained suggest that the strategy can improve training, validation and test fitness, and can also result in smaller individuals as well as reduced training times.
   Our approach is compared with a standard benchmark GP system, as well as with over twenty other systems from the literature, many of which use highly tuned, non-EC methods,
   and is shown to yield superior results in many cases.
%8 12-16  July
%Z Also known as \cite2001758 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Jeannie Fitzgerald
%A Conor Ryan
%T Validation Sets for Evolutionary Curtailment with Improved Generalisation
%B 5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011
%S Lecture Notes in Computer Science
%E Geuk Lee and Daniel Howard and Dominik Slezak
%V 6935
%D 2011
%P 282--289
%I Springer
%C Daejeon, Korea
%K genetic algorithms, genetic programming
%X This paper investigates the leveraging of a validation data set with Genetic Programming (GP) to counteract over-fitting. It considers fitness on both training and
   validation fitness, combined with with an early stopping mechanism to improve generalisation while significantly reducing run times. The method is tested on six benchmark
   binary classification data sets. Results of this preliminary investigation suggest that the strategy can deliver equivalent or improved results on test data.
%8 September  22-24
%Z ICHIT (1)

%A Jeannie Fitzgerald
%A Conor Ryan
%T Validation Sets, Genetic Programming and Generalisation
%B Proceedings of the 31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI-2011
%E Max Bramer and Miltos Petridis and Lars Nolle
%D 2011
%P 79--92
%I Springer London
%I BCS special interest group on Artificial Intelligence
%C Cambridge, England
%K genetic algorithms, genetic programming
%X a new application of a validation set when using a three data set methodology with Genetic Programming (GP). Our system uses Validation Pressure combined with Validation
   Elitism to influence fitness evaluation and population structure with the aim of improving the system's ability to evolve individuals with an enhanced capacity for
   generalisation. This strategy facilitates the use of a validation set to reduce over-fitting while mitigating the loss of training data associated with traditional methods
   employing a validation set. The method is tested on five benchmark binary classification data sets and results obtained suggest that the strategy can deliver improved
   generalisation on unseen test data.
%O Research and Development in Intelligent Systems XXVIII, Incorporating Applications and Innovations in Intelligent Systems XIX
%8 Decemeber

%A Robert Flack
%T Evolution of Architectural Floor Plans
%R M.S. Thesis
%D 2011
%I 
%I Brock University
%K genetic algorithms, genetic programming

%A Matthew Flannery
%T The Evolution of Traffic Behavior Patterns on a Macroscopic Level
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 135--142
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Oliver Flasch
%A Olaf Mersmann
%A Thomas Bartz-Beielstein
%T RGP: an open source genetic programming system for the R environment
%B GECCO 2010 Late breaking abstracts
%E Daniel Tauritz
%D 2010
%P 2071--2072
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%X RGP is a new genetic programming system based on the R environment. The system implements classical untyped tree-based genetic programming as well as more advanced variants
   including, for example, strongly typed genetic programming and Pareto genetic programming. It strives for high modularity through a consistent architecture that allows the
   customisation and replacement of every algorithm component, while maintaining accessibility for new users by adhering to the "convention over configuration" principle.
   Typical GP applications are supported by standard R interfaces. For example, symbolic regression via GP is supported by the same "formula interface" as linear regression in
   R. RGP is freely available as an open source R package.
%8 7-11  July
%Z Also known as \cite1830867 Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.

%A Oliver Flasch
%A Thomas Bartz-Beielstein
%A Artur Davtyan
%A Patrick Koch
%A Wolfgang Konen
%A Tosin Daniel Oyetoyan
%A Michael Tamutan
%T Comparing SPO-tuned GP and NARX prediction models for stormwater tank fill level prediction
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e., Neural
   Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their
   parametrisation. We compare different parameter tuning approaches, e.g. neuro-evolution and Sequential Parameter Optimization (SPO). In comparison to NN, GP yields superior
   results. By optimising GP parameters, GP runtime can be significantly reduced without degrading result quality. The SPO-based parameter tuning leads to results with
   significantly lower standard deviation as compared to the GA based parameter tuning. Our methodology can be transferred to other optimisation and simulation problems, where
   complex models have to be tuned.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586172

%A John Flight
%T The Use of Program State by a Genetic Program to Track a Moving Target
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 57-
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%X how a GP might use state variables and feedback from the fitness measure
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Erik D. Flister
%T The Deceptive Problem of Rational Trading and Negotiation Strategies in Artificial Economic Communities
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 66--75
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Alexandru Floares
%T Genetic Programming and Neural Networks Feedback Linearization for Modeling and Controlling Complex Pharmacogenomic Systems
%B Fuzzy Logic and Applications, 6th International Workshop, WILF 2005, Revised Selected Papers
%S Lecture Notes in Computer Science
%E Isabelle Bloch and Alfredo Petrosino and Andrea Tettamanzi
%V 3849
%D 2005
%P 178--187
%I Springer
%C Crema, Italy
%K genetic algorithms, genetic programming
%X Modern pharmacology, combining pharmacokinetic, pharmacodynamic, and pharmacogenomic data, is dealing with high dimensional, nonlinear, stiff systems. Mathematical
   modelling of these systems is very difficult, but important for understanding them. At least as important is to adequately control them through inputs - drugs' dosage
   regimes. Genetic programming (GP) and neural networks (NN) are alternative techniques for these tasks. We use GP to automatically write the model structure in C++ and
   optimise the model's constants. This gives insights into the subjacent molecular mechanisms. We also show that NN feedback linearisation (FBL) can adequately control these
   systems, with or without a mathematical model. The drug dosage regimen will determine the output of the system to track very well a therapeutic objective. To our knowledge,
   this is the first time when a very large class of complex pharmacological problems are formulated and solved in terms of GP modeling and NN modeling and control.
%8 September  15-17
%Z Published 2006?
%@ 3-540-32529-8

%A Alexandru G. Floares
%T Computation Intelligence Tools for Modeling and Controlling Pharmacogenomic Systems: Genetic Programming and Neural Networks
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 7510--7517
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, computational intelligences tools, computation intelligence tools, computer programming language, differential genes expression,
   neural networks, nonlinear coupled ordinary differential equations, pharmacogenomic systems:, genetics, medical control systems, neurocontrollers, nonlinear differential
   equations, nonlinear equations
%X Pharmacogenomic systems (PG) are very high dimensional, nonlinear, and stiff systems. Mathematical modelling of these systems, as systems of nonlinear coupled ordinary
   differential equations (ODE), is considered important for understanding them; unfortunately, it is also a very difficult task. At least as important is to adequately
   control them through inputs, which are drugs' dosage regimes. In this paper, we investigate new approaches based on computational intelligences tools - genetic programming
   (GP), and neural networks (NN) - for these difficult tasks. We use GP to automatically write the model structure in a computer programming language (C+t) and to optimise
   the model's constants. In some circumstances, the proposed methods not only give an accurate mathematical model of the PG system, but they also give insights into the
   subjacent molecular mechanisms. We also show that NN feedback linearisation (FBL) can adequately control these systems, with or without a mathematical model. The drug
   dosage regimen will determine the output of the system to track very well a therapeutic objective. To our knowledge, this is the first time when a very large class of
   complex pharmacological problems are formulated and solved in terms of GP modelling and NN modeling and control.
%8 16-21  July
%Z May 2010 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1716624&tag=1 \citeconf/ijcnn/Floares06 says this is in IJCNN 2006, 3820--3827, but his own IASTED-2007
   ISBN:978-0-88986-694-2 says CEC 7510--7517. WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Alexandru George Floares
%T Automatic Inferring Drug Gene Regulatory Networks with Missing Information Using Neural Networks and Genetic Programming
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 3078--3085
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X Automatically inferring drug gene regulatory networks models from microarray time series data is a challenging task. The ordinary differential equations models are
   sensible, but difficult to build. We extended our reverse engineering algorithm for gene networks (RODES), based on genetic programming, by adding a neural networks
   feedback linearisation component. Thus, RODES automatically discovers the structure, estimate the parameter, and identify the molecular mechanisms, even when information is
   missing from the data. It produces systems of ordinary differential equations from experimental or simulated microarray time series data. On simulated data the accuracy and
   the CPU time were very good. This is due to reducing the reversing of an ordinary differential equations system to that of individual algebraic equations, and to the
   possibility of incorporating common a priori knowledge. To our knowledge, this is the first realistic reverse engineering algorithm, based on genetic programming and neural
   networks, applicable to large gene networks.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET. Also known as \cite4634233

%A Alexandru George Floares
%T A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia
%J Neural Networks
%V 21
%N 2-3
%D 2008
%P 379--386
%I 
%K genetic algorithms, genetic programming, Neural networks, Reverse engineering algorithm, Linear genetic programming, Systems of ordinary differential equations, Basal
   ganglia, Discovery science approach
%U http://www.sciencedirect.com/science/article/B6T08-4RDR1B6-1/2/5aae1d094dbe3fd190fbb3fe9acebe63
%X Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear
   genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural
   connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated
   time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are
   obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering
   individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a
   realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.
%O Advances in Neural Networks Research: IJCNN '07, 2007 International Joint Conference on Neural Networks IJCNN '07

%A Alexandru George Floares
%T A neural networks algorithm for inferring drug gene regulatory networks from microarray time-series with missing transcription factors information
%B International Joint Conference on Neural Networks, IJCNN 2009
%D 2009
%P 848--854
%I 
%K genetic algorithms, genetic programming, algebraic equations, drug gene regulatory networks, feedback linearization, mathematical modeling, microarray time-series, missing
   transcription factors information, neural networks algorithm, ordinary differential equations, reverse engineering algorithm, algebra, biology computing, data handling,
   differential equations, neural nets, reverse engineering, time series
%X Mathematical modeling gene regulatory networks is important for understanding and controlling them, with various drugs and their dosage. The ordinary differential equations
   approach is sensible but also very difficult. Our reverse engineering algorithm (RODES), based on neural networks feedback linearization and genetic programming, takes as
   inputs high-throughput (e.g., microarray) time series data and automatically infer an accurate ordinary differential equations model. The algorithm decouples the systems of
   differential equations, reducing the problem to that of revere engineering individual algebraic equations, and is able to deal with missing information, reconstructing the
   temporal series of the transcription factors or drug related compounds which are usually missing in microarray experiments. It is also able to incorporate common a priori
   knowledge. To our knowledge, this is the first realistic reverse engineering algorithm, based on genetic programming and neural networks, applicable to large gene networks.
%8 June
%Z Also known as \cite5179081

%A Alexandru Floares
%A Ovidiu Balacescu
%A Carmen Floares
%A Loredana Balacescu
%A Tiberiu Popa
%A Oana Vermesan
%T Mining knowledge and data to discover intelligent molecular biomarkers: Prostate cancer i-Biomarkers
%B 4th International Workshop on Soft Computing Applications (SOFA 2010)
%D 2010
%P 113--118
%I 
%K genetic algorithms, genetic programming, PSA, bladder cancer, chronic hepatitis, data mining, evidence based medicine, intelligent clinical decision supports systems,
   intelligent molecular biomarkers, intelligent noninvasive diagnosis systems, knowledge based medicine, knowledge mining, prostate cancer i-biomarkers, serum angiogenic
   molecules, soft computing techniques, data mining, decision support systems, knowledge based systems, medical computing, patient diagnosis, uncertainty handling
%X Currently, there are some paradigm shifts in medicine, from the search for a single ideal biomarker, to the search for panels of molecules, and from a reductionistic to a
   systemic view, placing these molecules on functional networks. There is also a general trend to favour non-invasive biomarkers. Identifying non-invasive biomarkers in
   high-throughput data, having thousands of features and only tens of samples is not trivial. Here, we proposed a methodology and the related concepts to develop intelligent
   molecular biomarkers, via knowledge mining and knowledge discovery in data, illustrated on prostate cancer diagnosis. An informed feature selection is done by mining
   knowledge about pathways involved in prostate cancer, in specialised data bases. A knowledge discovery in data approach, with soft computing methods, is used to identify
   the relevant features and discover their relationships with clinical outcomes. The intelligent non-invasive diagnosis systems, is based on a team of mathematical models,
   discovered with genetic programming, and taking as inputs eight serum angiogenic molecules and PSA. This systems share with other intelligent systems we build, using this
   methodology but different soft computing techniques, and in different clinical settings - chronic hepatitis, bladder cancer, and prostate cancer - the best published
   accuracy, even 100percent. Soft computing could be a strong foundation for the newly emerging Knowledge-Based-Medicine. The impact on medical practice could be enormous.
   Instead of offering just hints to the clinicians, like Evidence-Based-Medicine, Knowledge-Based-Medicine which is made possible and co-exists with Evidence-Based-Medicine,
   offers intelligent clinical decision supports systems.
%8 15-17  July
%Z Discipulus Also known as \cite5565613

%A Dario Floreano
%A Stefano Nolfi
%T God Save the Red Queen! Competition in Co-Evolutionary Robotics
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 398--406
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Artifical life and evolutionary robotics
%U ftp://kant.irmkant.rm.cnr.it/pub/econets/floreano.co-evolution.ps.Z
%8 13-16  July
%Z GP-97

%A Juan J. Flores
%A Mario Graff
%A Erasmo Cadenas
%T Wind Prediction using Genetic Algorithms and Gene Expression Programming
%B Proceedings of the International Conference on Modelling and Simulation in the Enterprises. AMSE 2005
%D 2005
%I 
%C Morelia, Mexico
%K genetic algorithms, genetic programming, Gene Expression Programming
%8 April
%Z information from Juan Flores <juan@cs.uoregon.edu> Thu, 15 Jun 2006 10:14:13 PDT

%A Juan J. Flores
%A Mario Graff
%T System Identification Using Genetic Programming and Gene Expression Programming
%B Proceedings of the 20th International Symposium Computer and Information Sciences - ISCIS 2005
%S Lecture Notes in Computer Science
%E Pinar Yolum and Tunga Gungor and Fikret Gurgen and Can Ozturan
%V 3733
%D 2005
%P 503--511
%I Springer Berlin / Heidelberg
%C Istanbul, Turkey
%K genetic algorithms, genetic programming, Gene Expression Programming
%X This paper describes a computer program called ECSID that automates the process of system identification using Genetic Programming and Gene Expression Programming. ECSID
   uses a function set, and the observed data to determine an ODE whose behaviour is similar to the observed data. ECSID is capable to evolve linear and non-linear models of
   higher order systems. ECSID can also code a higher order system as a set of higher order equations. ECSID has been tested with linear pendulum, non-linear pendulum,
   mass-spring system, linear circuit, etc.
%8 October  26-28
%@ 3-540-29414-7

%A David B. Fogel
%T Advances in genetic programming : Kenneth E. Kinnear, Jr., (ed.), MIT Press, Cambridge, MA, 1994, 518 pp., \$45.00
%J Biosystems
%V 36
%N 1
%D 1995
%P 82--85
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6T2K-4CHS0P6-5/2/2474f3669e7a25204939e72cbb4d7253
%X Genetic programming, the use of genetic algorithms to evolve computer programs, has received considerable attention following the publication of Koza (1992). The edited
   volume Advances in Genetic Programming is the written record of presentations made at a workshop on genetic programming held in July, 1993 at the Fifth International
   Conference on Genetic Algorithms. The book is divided into three sections: 'Introduction' (two papers), 'Increasing the Power of Genetic Programming' (12 papers), and
   'Innovative Applications of Genetic Programming' (10 papers). The book is designed to share recent research in genetic programming with an interdisciplinary audience. Space
   does not permit a careful review of each paper, but I will focus on particular papers and then offer some general observations.
%Z review of \citekinnear:book

%A David B. Fogel
%A Lawrence J. Fogel
%T Preliminary Experiments on Discriminating between Chaotic Signals and Noise Using Evolutionary Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 512--520
%I MIT Press
%C Stanford University, CA, USA
%K Evolutionary Programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 EP paper

%A Gary B. Fogel
%A Dana G. Weekes
%A Gabor Varga
%A Ernst R. Dow
%A Harry B. Harlow
%A Jude E. Onyia
%A Chen Su
%T Discovery of sequence motifs related to coexpression of genes using evolutionary computation
%J Nucleic Acids Research
%V 32
%N 13
%D 2004
%P 3826--3835
%I 
%X Transcription factors are key regulatory elements that control gene expression. Recognition of transcription factor binding site (TFBS) motifs in the upstream region of
   coexpressed genes is therefore critical towards a true understanding of the regulations of gene expression. The task of discovering eukaryotic TFBSs remains a challenging
   problem. Here, we demonstrate that evolutionary computation can be used to search for TFBSs in upstream regions of genes known to be coexpressed. Evolutionary computation
   was used to search for TFBSs of genes regulated by octamer-binding factor and nuclear factor kappa B. The discovered binding sites included experimentally determined known
   binding motifs as well as lists of putative, previously unknown TFBSs. We believe that this method to search nucleotide sequence information efficiently for similar motifs
   will be useful for discovering TFBSs that affect gene regulation.
%Z PMID:

%A Christopher Fogelberg
%A Mengjie Zhang
%T VUWLGP - An ANSI C++ Linear Genetic Programming Package
%R Technical Report CS-TR-05/8
%D 2005
%I 
%I MSCS, Victoria University of Wellington
%C New Zealand
%K genetic algorithms, genetic programming
%U http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-05-08.abs.html
%X Linear Genetic Programming (LGP) is a recently researched form of genetic programming, the automatic evolution of computer programs which can solve problems. Traditionally,
   genetic programs have been represented as function trees. However, LGP programs are linear sequences of instructions (e.g. register machine instructions) and are not best
   represented as a tree of functions and terminals. Few publicly available packages designed to support research into LGP exist and those that do are often incomplete. VUWLGP
   has been written in C++ and is available for use under the GPL. It is designed to be easily customised and tweaked so that slightly different variants of different problems
   can be researched easily.

%A Christopher Fogelberg
%A Mengjie Zhang
%T Linear Genetic Programming for Multi-class Object Classification
%B AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence, Proceedings
%S Lecture Notes in Computer Science
%E Shichao Zhang and Ray Jarvis
%V 3809
%D 2005
%P 369--379
%I Springer
%C Sydney, Australia
%K genetic algorithms, genetic programming
%X Multi-class object classification is an important field of research in computer vision. In this paper basic linear genetic programming is modified to be more suitable for
   multi-class classification and its performance is then compared to tree-based genetic programming. The directed acyclic graph nature of linear genetic programming is
   exploited. The existing fitness function is modified to more accurately approximate the true feature space. The results show that the new linear genetic programming
   approach outperforms the basic tree-based genetic programming approach on all the tasks investigated here and that the new fitness function leads to better and more
   consistent results. The genetic programs evolved by the new linear genetic programming system are also more comprehensible than those evolved by the tree-based system.
%8 Decemeber  5-9
%@ 3-540-30462-2

%A Sergey V. Fogelson
%A Walter D. Potter
%T A formulation for the relative permittivity of water and steam to high temperatures and pressures evolved using genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1335--1336
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, relative permittivity: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1335.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389351

%A Ka-Ling Fok
%A Tien-Tsin Wong
%A Man-Leung Wong
%T Evolutionary Computing on Consumer Graphics Hardware
%J IEEE Intelligent Systems
%V 22
%N 2
%D 2007
%P 69--78
%I 
%K genetic algorithms, GPU, EP, computer graphic equipment, computer graphics, evolutionary computation, parallel algorithms, consumer graphics card, consumer-grade graphics
   hardware, evolutionary computing, high-performance computer, parallel evolutionary algorithm, evolutionary algorithms, parallel algorithm, pervasive computing, scientific
   computing on graphics-processing units, ubiquitous computing, SIMD
%U http://ieeexplore.ieee.org/iel5/9670/4136845/04136862.pdf?tp=&isnumber=4136845&arnumber=4136862&punumber=9670
%X We propose implementing a parallel EA on consumer graphics cards, which we can find in many PCs. This lets more people use our parallel algorithm to solve large-scale,
   real-world problems such as data mining. Parallel evolutionary algorithms run on consumer-grade graphics hardware achieve better execution times than ordinary evolutionary
   algorithms and offer greater accessibility than those run on high-performance computers
%8 March -  April
%Z Chinese Univ. of Hong Kong, Shatin INSPEC Accession Number:9445531 nVidia GeForce 6800 Ultra. GPU wins for populations bigger than 800. Speedup ratio between 0.62 (slower)
   to 5.02

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T A Cellular Genetic Programming Approach to Classification
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1015--1020
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-427.ps
%X A cellular genetic programming approach to data classification is proposed. The method uses cellular automata as a framework to enable a fine-grained parallel
   implementation of GP through the diffusion model. The main advantages to employ the method for classification problems consist in handling large populations in reasonable
   times, enabling fast convergence by reducing the number of iterations and execution time, favouring the cooperation in the search for good solutions, thus improving the
   accuracy of the method.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 294--303
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=294
%X A method for the data mining task of data classification, suitable to be implemented on massively parallel architectures, is proposed. The method combines genetic
   programming and simulated annealing to evolve a population of decision trees. A cellular automaton is used to realise a fine-grained parallel implementation of genetic
   programming through the diffusion model and the annealing schedule to decide the acceptance of a new solution. Preliminary experimental results, obtained by simulating the
   behaviour of the cellular automaton on a sequential machine, show significant better performances with respect to C4.5.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T CAGE: A Tool for Parallel Genetic Programming Applications
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 64--73
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Parallel programming, Cellular model
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=64
%X A new parallel implementation of genetic programming based on the cellular model is presented and compared with the island model approach. Although the widespread belief
   that cellular model is not suitable for parallel genetic programming implementations, experimental results show a better convergence with respect to the island approach, a
   good scale-up behaviour and a nearly linear speed-up.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T Parallel genetic programming for decision tree induction
%B Proceedings of the 13th International Conference on Tools with Artificial Intelligence
%D 2001
%P 129--135
%I IEEE
%C Dallas, TX USA
%K genetic algorithms, genetic programming, decision trees, genetic algorithms, learning (artificial intelligence), parallel programming, J-measure, UCI machine learning
   repository, data sets, decision tree induction, fitness function, grid model, parallel genetic programming, scalability
%U http://www.icar.cnr.it/pizzuti/ictai01.ps
%X A parallel genetic programming approach to induce decision trees in large data sets is presented. A population of trees is evolved by employing the genetic operators and
   every individual is evaluated by using a fitness function based on the J-measure. The method is able to deal with large data sets since it uses a parallel implementation of
   genetic programming through the grid model and an out of core technique for those data sets that do not fit in main memory. Preliminary experiments on data sets from the
   UCI machine learning repository give good classification outcomes and assess the scalability of the method
%8 7-9  November
%Z Inspec Accession Number: 7139478

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T Improving induction decision trees with parallel genetic programming
%B Proceedings 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing
%D 2002
%P 181--187
%I IEEE
%C Canary Islands
%K genetic algorithms, genetic programming, data mining, decision trees, learning by example, parallel programming, J-measure, UCI machine learning repository, fitness
   function, genetic operators, grid model, induction decision trees, large data sets, parallel genetic programming
%X A parallel genetic programming approach to induce decision trees in large data sets is presented. A population of trees is evolved by employing the genetic operators and
   every individual is evaluated by using a fitness function based on the J-measure. The method is able to deal with large data sets since it uses a parallel implementation of
   genetic programming through the grid model. Experiments on data sets from the UCI machine learning repository show better results with respect to C5. Furthermore,
   performance results show a nearly linear speedup
%8 9-11  January
%Z Inspec Accession Number: 7205091

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T A Scalable Cellular Implementation of Parallel Genetic Programming
%J IEEE Transactions on Evolutionary Computation
%V 7
%N 1
%D 2003
%P 37--53
%I 
%K genetic algorithms, genetic programming, Cellular genetic programming model, load balance, parallel processing, scalability
%X A new parallel implementation of genetic programming (GP) based on the cellular model is presented and compared with both canonical GP and the island model approach. The
   method adopts a load-balancing policy that avoids the unequal use of the processors. Experimental results on benchmark problems of different complexity show the superiority
   of the cellular approach with respect to the canonical sequential implementation and the island model. A theoretical performance analysis reveals the high scalability of
   the implementation realized and allows to predict the size of the population when the number of processors and their efficiency are fixed.
%8 February
%Z CAGE

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T Ensemble techniques for Parallel Genetic Programming based Classifiers
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 59--69
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=59
%X An extension of Cellular Genetic Programming for data classification to induce an ensemble of predictors is presented. Each classifier is trained on a different subset of
   the overall data, then they are combined to classify new tuples by applying a simple majority voting algorithm, like bagging. Preliminary results on a large data set show
   that the ensemble of classifiers trained on a sample of the data obtains higher accuracy than a single classifier that uses the entire data set at a much lower
   computational cost.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A G. Folino
%A C. Pizzuti
%A G. Spezzano
%A L. Vanneschi
%A M. Tomassini
%T Diversity analysis in cellular and multipopulation genetic programming
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 305--311
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%U http://www.icar.cnr.it/pizzuti/cec03.pdf
%X parallel genetic programming (GP) models in maintaining diversity in a population. The parallel models used are the cellular and the multipopulation one. Several measures
   of diversity are considered to gain a deeper understanding of the conditions under which the evolution of both models is successful. Three standard test problems are used
   to illustrate the different diversity measures and analyse their correlation with performance. Results show that diversity is not necessarily synonym of good convergence.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T Boosting technique for Combining Cellular GP Classifiers
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 47--56
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=47
%X An extension of Cellular Genetic Programming for data classification with the boosting technique is presented and a comparison with the bagging-like majority voting
   approach is performed. The method is able to deal with large data sets that do not fit in main memory since each classifier is trained on a subset of the overall training
   data. Experiments showed that, by using a sample of reasonable size, the extension with these voting algorithms enhances classification accuracy at a much lower
   computational cost.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T GP Ensembles for improving multi-class prediction problems
%B AI*IA Workshop on Evolutionary Computation, Evoluzionistico GSICE05
%E Sara Manzoni and Matteo Palmonari and Fabio Sartori
%D 2005
%I 
%C University of Milan Bicocca, Italy
%K genetic algorithms, genetic programming, data mining, classification, boosting
%X Cellular Genetic Programming for data classification extended with the boosting technique to induce an ensemble of predictors is presented. The method implements in
   parallel AdaBoost.M2 to efficiently deal with multi-class problems and it is able to manage large data sets that do not fit in main memory since each classifier is trained
   on a subset of the overall training data. Experiments on several data sets show that, by using a training set of reduced size, better classification accuracy can be
   obtained at a much lower computational cost.
%8 20  September
%Z http://www.ce.unipr.it/people/cagnoni/gsice2005/gsice-eng.pdf Workshop proceedings on CD-ROM only. Workshop held in-conjunction with the IX Congress of the Italian
   Association for Artificial Intelligence. In English. ICAR-CNR, Via P.Bucci 41C, Univ. della Calabria 87036 Rende (CS), Italy
%@ 88-900910-0-2

%A Gianluigi Folino
%A Giandomenico Spezzano
%T P-CAGE: An Environment for Evolutionary Computation in Peer-to-Peer Systems
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 341--350
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050341.pdf
%X Solving complex real-world problems using evolutionary computation is a CPU time-consuming task that requires a large amount of computational resources. Peer-to-Peer (P2P)
   computing has recently revealed as a powerful way to harness these resources and efficiently deal with such problems. In this paper, we present a P2P implementation of
   Genetic Programming based on the JXTA technology. To run genetic programs we use a distributed environment based on a hybrid multi-island model that combines the island
   model with the cellular model. Each island adopts a cellular genetic programming model and the migration occurs among neighbouring peers. The implementation is based on a
   virtual ring topology. Three different termination criteria (effort, time and max-gen) have been implemented. Experiments on some popular benchmarks show that the approach
   presents a accuracy at least comparable with classical distributed models, retaining the obvious advantages in terms of decentralisation, fault tolerance and scalability of
   P2P systems.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T Improving cooperative GP ensemble with clustering and pruning for pattern classification
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 791--798
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, classification, data mining, ensemble
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p791.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Gianluigi Folino
%A Agostino Forestiero
%A Giandomenico Spezzano
%T A Jxta Based Asynchronous Peer-to-Peer Implementation of Genetic Programming
%J Journal of Software
%V 1
%N 2
%D 2006
%P 12--23
%I 
%K genetic algorithms, genetic programming
%U http://www.academypublisher.com/jsw/vol01/no02/jsw01021223.html
%X Solving complex real-world problems using evolutionary computation is a CPU time-consuming task that requires a large amount of computational resources. Peer-to-Peer (P2P)
   computing has recently revealed as a powerful way to harness these resources and efficiently deal with such problems. In this paper, we present P-CAGE: a P2P environment
   for Genetic Programming based on the JXTA protocols. P-CAGE is based on a hybrid multi-island model that combines the island model with the cellular model. Each island
   adopts a cellular model and the migration occurs between neighbouring peers placed in a virtual ring topology. Three different termination criteria (effort, time and
   maxgen) have been implemented. Experiments were conducted on some popular benchmarks and scalability, accuracy and the effect of migration have been studied. Performance
   are at least comparable with classical distributed models, retaining the obvious advantages in terms of decentralisation, fault tolerance and scalability of P2P systems. We
   also demonstrated the important effect of migration in accelerating the convergence.
%8 August
%Z JSW

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T Mining Distributed Evolving Data Streams using Fractal GP Ensembles
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 160--169
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X A Genetic Programming based boosting ensemble method for the classification of distributed streaming data is proposed. The approach handles flows of data coming from
   multiple locations by building a global model obtained by the aggregation of the local models coming from each node. A main characteristics of the algorithm presented is
   its adaptability in presence of concept drift. Changes in data can cause serious deterioration of the ensemble performance. Our approach is able to discover changes by
   adopting a strategy based on self-similarity of the ensemble behaviour, measured by its fractal dimension, and to revise itself by promptly restoring classification
   accuracy. Experimental results on a synthetic data set show the validity of the approach in maintaining an accurate and up-to-date GP ensemble.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T StreamGP: tracking evolving GP ensembles in distributed data streams using fractal dimension
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1751--1751
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming: Poster, data mining, distributed streaming data, ensemble
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1751.pdf
%X The paper presents an adaptive GP boosting ensemble method for the classification of distributed homogeneous streaming data that comes from multiple locations. The approach
   is able to handle concept drift via change detection by employing a change detection strategy, based on self-similarity of the ensemble behaviour, and measured by its
   fractal dimension. It is efficient since each node of the network works with its local streaming data, and communicate only the local model computed with the other
   peer-nodes. Furthermore, once the ensemble has been built, it is used to predict the class membership of new streams of data until concept drift is detected. Only in such a
   case the algorithm is executed to generate a new set of classifiers to update the current ensemble. Experimental results on a synthetic and real life data set showed the
   validity of the approach in maintaining an accurate and up-to-date GP ensemble.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T Training Distributed GP Ensemble With a Selective Algorithm Based on Clustering and Pruning for Pattern Classification
%J IEEE Transactions on Evolutionary Computation
%V 12
%N 4
%D 2008
%P 458--468
%I 
%K genetic algorithms, genetic programming, boosting algorithm, cellular genetic programming, decision trees, distributed hybrid environment, fittest trees, pattern
   classification, pruning strategies, training distributed GP ensemble, decision trees, pattern classification
%X A boosting algorithm based on cellular genetic programming (GP) to build an ensemble of predictors is proposed. The method evolves a population of trees for a fixed number
   of rounds and, after each round, it chooses the predictors to include in the ensemble by applying a clustering algorithm to the population of classifiers. Clustering the
   population allows the selection of the most diverse and fittest trees that best contribute to improve classification accuracy. The method proposed runs on a distributed
   hybrid environment that combines the island and cellular models of parallel GP. The combination of the two models provides an efficient implementation of distributed GP,
   and, at the same time, the generation of low sized and accurate decision trees. The large amount of memory required to store the ensemble affects the performance of the
   method. This paper shows that, by applying suitable pruning strategies, it is possible to select a subset of the classifiers without increasing misclassification errors;
   indeed for some data sets, for up to 30percent of pruning, ensemble accuracy increases. Experimental results show that the combination of clustering and pruning enhances
   classification accuracy of the ensemble approach.
%8 August
%Z Also known as \cite4439200

%A Gianluigi Folino
%A Giuseppe Papuzzo
%T Handling Different Categories of Concept Drifts in Data Streams using Distributed GP
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 74--85
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X Using Genetic Programming (GP) for classifying data streams is problematic as GP is slow compared with traditional single solution techniques. However, the availability of
   cheaper and better-performing distributed and parallel architectures make it possible to deal with complex problems previously hardly solved owing to the large amount of
   time necessary. This work presents a general framework based on a distributed GP ensemble algorithm for coping with different types of concept drift for the task of
   classification of large data streams. The framework is able to detect changes in a very efficient way using only a detection function based on the incoming unclassified
   data. Thus, only if a change is detected a distributed GP algorithm is performed in order to improve classification accuracy and this limits the overhead associated with
   the use of a population-based method. Real world data streams may present drifts of different types. The introduced detection function, based on the self-similarity fractal
   dimension, permits to cope in a very short time with the main types of different drifts, as demonstrated by the first experiments performed on some artificial datasets.
   Furthermore, having an adequate number of resources, distributed GP can handle very frequent concept drifts.
%8 7-9  April
%Z BoostCGPC, cellular GP, island model, AdaBoost, Fractal dimension FD3, cloud computing, Minku Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with
   EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Gianluigi Folino
%A Clara Pizzuti
%A Giandomenico Spezzano
%T An ensemble-based evolutionary framework for coping with distributed intrusion detection
%J Genetic Programming and Evolvable Machines
%V 11
%N 2
%D 2010
%P 131--146
%I 
%K genetic algorithms, genetic programming, Intrusion detection, Ensemble classifiers, Distributed evolutionary algorithms
%X A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity is presented. The algorithm is based on
   genetic programming (GP) extended with the ensemble paradigm. GP ensemble is particularly suitable for distributed intrusion detection because it allows to build a network
   profile by combining different classifiers that together provide complementary information. The main novelty of the algorithm is that data is distributed across multiple
   autonomous sites and the learner component acquires useful knowledge from this data in a cooperative way. The network profile is then used to predict abnormal behavior.
   Experiments on the KDD Cup 1999 Data show the capability of genetic programming in successfully dealing with the problem of intrusion detection on distributed data.
%O Special issue on parallel and distributed evolutionary algorithms, part II
%8 June

%A Cyril W. B. Fonlupt
%A Denis Robilliard
%T Genetic Programming with Dynamic Fitness for a Remote Sensing Application
%B Parallel Problem Solving from Nature - PPSN VI 6th International Conference
%S LNCS
%E Marc Schoenauer and Kalyanmoy Deb and G\"unter Rudolph and Xin Yao and Evelyne Lutton and Juan Julian Merelo and Hans-Paul Schwefel
%V 1917
%D 2000
%P 191--200
%I Springer Verlag
%C Paris, France
%K genetic algorithms, genetic programming
%U http://www-lil.univ-littoral.fr/~robillia/Publis/lil-00-2.ps.gz
%8 16-20  September

%A C. Fonlupt
%T Solving the ocean color problem using a genetic programming approach
%J Applied Soft Computing
%V 1
%N 1
%D 2001
%P 63--72
%I 
%K genetic algorithms, genetic programming, Ocean colour problem, Phytoplankton
%U http://www.sciencedirect.com/science/article/B6W86-43S6W98-6/2/ed66cf73aec7cb186639405e4a8801bb
%X The ocean color problem consists in evaluating ocean components concentrations (phytoplankton, sediment and yellow substance) from sunlight reflectance or luminance values
   at selected wavelengths in the visible band. The interest of this application increases with the availability of new satellite sensors. Moreover, monitoring phytoplankton
   concentrations is a key point for a wide set of problems ranging from greenhouse effect to industrial fishing and signaling toxic algae blooms. To our knowledge, it is the
   first attempt at this regression problem with genetic programming (GP). We show that GP outperforms traditional polynomial fits and rivals artificial neural nets in the
   case of open ocean waters. We improve previous works by also solving a range of coastal waters types, providing detailed results on estimation errors. To our knowledge, we
   are the firsts to publish numerical results regarding coastal waters. Experiments were conducted with a dynamic fitness GP algorithm in order to speed up computing time
   through a process of progressive learning.
%8 June

%A Cyril Fonlupt
%T Book Review: Genetic Programming IV: Routine Human-Competitive Machine Intelligence
%J Genetic Programming and Evolvable Machines
%V 6
%N 2
%D 2005
%P 231--233
%I 
%K genetic algorithms, genetic programming
%8 June
%Z Review of \citekoza:gp4 ISBN 1-4020-7446-8 Book authors John R. Koza, Martin A. Keane, Matthew J. Streeter, William Mydlowec, Jessen Yu, Guido Lanza

%A Cyril Fonlupt
%A Denis Robilliard
%T A Continuous Approach to Genetic Programming
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 335--346
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming: poster
%X Differential Evolution (DE) is an evolutionary heuristic for continuous optimisation problems. In DE, solutions are coded as vectors of floats that evolve by crossover with
   a combination of best and random individuals from the current generation. Experiments to apply DE to automatic programming were made recently by Veenhuis, coding full
   program trees as vectors of floats (Tree Based Differential Evolution or TreeDE). In this paper, we use DE to evolve linear sequences of imperative instructions, which we
   call Linear Differential Evolutionary Programming (LDEP). Unlike TreeDE, our heuristic provides constant management for regression problems and lessens the tree-depth
   constraint on the architecture of solutions. Comparisons with TreeDE and GP show that LDEP is appropriate to automatic programming.
%8 27-29  April
%Z refs \citeICSI-TR-95-012, \citeVeenhuis:2009:eurogp, \citeDBLP:conf/icai/ONeillB06, \citelangdon:book \citelangdon:1998:antspace Part of \citeSilva:2011:GP EuroGP'2011 held
   in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Jose M. Font
%A Daniel Manrique
%T Grammar-guided evolutionary automatic system for autonomously building biological oscillators
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X This paper presents a grammar-guided evolutionary automatic system (GGEAS) that is capable of autonomously building special-purpose problem-solving programs. GGEAS uses a
   grammar-guided genetic programming (GGGP) core that generates solutions to a given problem from scratch, evolving them via selection, crossover and replacement to obtain
   the near-optimal solution to that problem. The GGGP core solves the closure problem and avoids code bloat. This core only outputs valid solutions and is able to freely
   determine their size and architecture. GGEAS is supplemented by three external modules that can be configured for any application domain: context-free grammar (CFG)
   generator, semantic checker and fitness module. The context-free grammar (CFG) generator creates the context-free grammar used by the GGEAS core to formalise the problem
   constraints. The semantic checker ensures the validity of the solutions created. Finally, the fitness module directs the population evolution towards an optimal solution to
   the problem. In order to test the effectiveness and the scope of the system, GGEAS has been applied to generate oscillatory biological programs codified in the BlenX
   language. The results show that GGEAS is effective at creating biological oscillators in silico from scratch without any prior knowledge about the solution and under a
   range of environmental conditions.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586377

%A Jose M. Font
%A Daniel Manrique
%A Juan Rios
%T Evolutionary construction and adaptation of intelligent systems
%J Expert Systems with Applications
%V 37
%N 12
%D 2010
%P 7711--7720
%I 
%K genetic algorithms, genetic programming, Evolutionary computation, Intelligent systems, Rule-based systems, Fuzzy rule-based systems, Artificial neural networks, Medical
   prognosis
%U http://www.sciencedirect.com/science/article/B6V03-501FPHF-C/2/9a2d947791e5706c203b3fed536a0e36
%X This paper introduces evolutionary techniques for automatically constructing intelligent self-adapting systems, capable of modifying their inner structure in order to learn
   from experience and self-adapt to a changing environment. These evolutionary techniques comprise an evolutionary system that is engineered by grammar-guided genetic
   programming, enabling the development of sub-symbolic and symbolic intelligent systems: artificial neural networks and knowledge-based systems, respectively. A
   context-free-grammar based codification system for artificial neural networks and rules, an initialisation method and a crossover operator have been designed to properly
   balance the exploration and exploitation capabilities of the proposed system. This speeds up the convergence process and avoids trapping in local optima. This system has
   been applied to a medical domain: the detection of knee injuries from the analysis of isokinetic time series. The results of the evolved symbolic and sub-symbolic
   intelligent systems have been statistically compared with each other as part of a quantitative and qualitative performance analysis.

%A Jose Font
%A Daniel Manrique
%A Eduardo Pascua
%T Grammar-Guided Evolutionary Construction of Bayesian Networks
%B Proceedings of the 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, Part I
%S Lecture Notes in Computer Science
%E Jose Manuel Ferrandez and Jose Ramon Alvarez Sanchez and Felix de la Paz and F. Javier Toledo
%V 6686
%D 2011
%P 60--69
%I Springer
%C La Palma, Canary Islands, Spain
%K genetic algorithms, genetic programming
%X This paper proposes the EvoBANE system. EvoBANE automatically generates Bayesian networks for solving special-purpose problems. EvoBANE evolves a population of individuals
   that codify Bayesian networks until it finds near optimal individual that solves a given classification problem. EvoBANE has the flexibility to modify the constraints that
   condition the solution search space, self-adapting to the specifications of the problem to be solved. The system extends the GGEAS architecture. GGEAS is a general-purpose
   grammar-guided evolutionary automatic system, whose modular structure favours its application to the automatic construction of intelligent systems. EvoBANE has been applied
   to two classification benchmark datasets belonging to different application domains, and statistically compared with a genetic algorithm performing the same tasks. Results
   show that the proposed system performed better, as it manages different complexity constraints in order to find the simplest solution that best solves every problem.
%8 May  30-  June  3

%A Jose M. Font
%T Evolving Third-Person Shooter Enemies to Optimize Player Satisfaction in Real-Time
%B Applications of Evolutionary Computing, EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC
%S LNCS
%E Cecilia Di Chio and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. Fernandez de Vega and Gianni A. Di Caro and Rolf Drechsler and Aniko Ekart and Anna I
   Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero
   and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis
%V 7248
%D 2011
%P 204--213
%I Springer Verlag Berlin
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Evolutionary computation, fuzzy rule based system, grammar-guided genetic programming, player satisfaction
%X A grammar-guided genetic program is presented to automatically build and evolve populations of AI controlled enemies in a 2D third-person shooter called Genes of War. This
   evolutionary system constantly adapts enemy behaviour, encoded by a multi-layered fuzzy control system, while the game is being played. Thus the enemy behaviour fits a
   target challenge level for the purpose of maximising player satisfaction. Two different methods to calculate this challenge level are presented: 'hardwired' that allows the
   desired difficulty level to be programed at every stage of the gameplay, and 'adaptive' that automatically determines difficulty by analysing several features extracted
   from the player's gameplay. Results show that the genetic program successfully adapts armies of ten enemies to different kinds of players and difficulty distributions.
%8 11-13  April
%Z EvoGames Part of \citeDiChio:2012:EvoApps EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012

%A Nate Foreman
%A Matthew Evett
%T Preventing overfitting in GP with canary functions
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1779--1780
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Poster, experimentation, overfitting, performance
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1779.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052 Prechelt, x4+x3+x2+x, cos(3x), generalisation loss
%@ 1-59593-010-8

%A Stephanie Forrest
%A ThanhVu Nguyen
%A Westley Weimer
%A Claire {Le Goues}
%T A genetic programming approach to automated software repair
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 947--954
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Software Engineering, Testing and Debugging, Programming Languages, Syntax, Algorithms, Software repair, software engineering
%U http://www.cs.virginia.edu/~weimer/p/weimer-gecco2009.pdf
%X Genetic programming is combined with program analysis methods to repair bugs in off-the-shelf legacy C programs. Fitness is defined using negative test cases that exercise
   the bug to be repaired and positive test cases that encode program requirements. Once a successful repair is discovered, structural differencing algorithms and delta
   debugging methods are used to minimize its size. Several modifications to the GP technique contribute to its success: (1) genetic operations are localized to the nodes
   along the execution path of the negative test case; (2) high-level statements are represented as single nodes in the program tree; (3) genetic operators use existing code
   in other parts of the program, so new code does not need to be invented. The paper describes the method, reviews earlier experiments that repaired 11 bugs in over 60,000
   lines of code, reports results on new bug repairs, and describes experiments that analyze the performance and efficacy of the evolutionary components of the algorithm.
%O Best paper
%8 8-12  July
%Z Best paper. Gold medal Humie. Autofix zune bug: microsoft Zune media player end of year bug 31 dec 2008. GECCO-2009 A joint meeting of the eighteenth international
   conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.

%A Stephanie Forrest
%T The Case for Evolvable Software
%B ACM International Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH)
%D 2010
%P 1
%I ACM
%C Reno, USA
%K genetic algorithms, genetic programming
%U http://portal.acm.org/ft_gateway.cfm?id=1869539&type=pdf&CFID=114019259&CFTOKEN=22192943
%X As programmers, we like to think of software as the product of our intelligent design, carefully crafted to meet well-specified goals. In reality, software evolves
   inadvertently through the actions of many individual programmers, often leading to unanticipated consequences. Large complex software systems are subject to constraints
   similar to those faced by evolving biological systems, and we have much to gain by viewing software through the lens of evolutionary biology. The talk will highlight recent
   research that applies the mechanisms of evolution quite directly to the problem of repairing software bugs.
%O Keynote
%8 17-21  October
%Z Abstract only

%A Kilian Forster
%A Pascal Brem
%A Daniel Roggen
%A Gerhard Troster
%T Evolving discriminative features robust to sensor displacement for activity recognition in body area sensor networks
%B 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2009
%D 2009
%P 43--48
%I 
%K genetic algorithms, genetic programming
%X Activity and gesture recognition from body-worn acceleration sensors is an important application in body area sensor networks. The key to any such recognition task are
   discriminative and variation tolerant features. Furthermore good features may reduce the energy requirements of the sensor network as well as increase the robustness of the
   activity recognition. We propose a feature extraction method based on genetic programming. We benchmark this method using two datasets and compare the results to a feature
   selection which is typically used for obtaining a set of features. With one extracted feature we achieve an accuracy of 73.4percent on a fitness activity dataset, in
   contrast to 70.1percent using one selected standard feature. In a gesture based HCI dataset we achieved 95.0percent accuracy with one extracted feature. A selection of up
   to five standard features achieved 90.6percent accuracy in the same setting. On the HCI dataset we also evaluated the robustness of extracted features to sensor
   displacement which is a common problem in movement based activity and gesture recognition. With one extracted features we achieved an accuracy of 85.0percent on a displaced
   sensor position. With the best selection of standard features we achieved 55.2percent accuracy. The results show that our proposed genetic programming feature extraction
   method is superior to a feature selection based on standard features.
%8 Decemeber
%Z Also known as \cite5416810

%A Richard Forsyth
%T BEAGLE A Darwinian Approach to Pattern Recognition
%J Kybernetes
%V 10
%N 3
%D 1981
%P 159--166
%I 
%K genetic algorithms, genetic programming, soccer foot ball pools
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/kybernetes_forsyth.pdf
%X BEAGLE (Biological Evolutionary Algorithm Generating Logical Expressions) is a computer package producing decision-rules by induction from a database. It works on the
   principle of naturalistic selection whereby rules that fit the data badly are killed off and replaced by mutations of better rules or by new rules created by mating two
   better adapted rules. The rules are Boolean expressions represented by tree structures. The software consists of two Pascal programs, HERB (Heuristic Evolutionary Rule
   Breeder) and LEAF (Logical Evaluator And Forecaster). HERB improves a given starting set of rules by running over several simulated generations, LEAF uses the rules to
   classify samples from a database where the correct membership may not be known. Preliminary test on three different databases have been carried out -- on hospital
   admissions (classing heart patients as deaths or survivors), on athletic physique (classing Olympic finalists as long-distance runners or sprinters) and on football results
   (categorising games into draws and non-draws) It appears from the tests that the method works better than the standard discriminant analysis technique based on a linear
   discriminant function, and hence that this long-neglected approach warrants further investigation.
%Z Copy from British Library May 1994

%A Richard Forsyth
%A Roy Rada
%T Machine Learning applications in Expert Systems and Information Retrieval
%S Ellis Horwood series in artificial intelligence
%D 1986
%I Ellis Horwood
%C Chichester, UK
%K genetic algorithms, genetic programming
%U http://www.amazon.co.uk/Machine-Learning-Applications-Information-Retrieval/dp/0745800459
%Z Chapters on BEAGLE
%@ 0-7458-0045-9

%A Richard Forsyth
%T The evolution of intelligence
%B Machine Learning, Priciples and Techniques
%E Richard Forsyth
%D 1989
%P 65--82
%I Chapman and Hall
%K genetic algorithms, genetic programming
%O 4
%Z some general stuff on history of GAs, evolution strategy and evolution programming, cf Fogel 1966, description of Goldberg's natural gas pipeline control GA/classifier
   experiments. BEAGLE applied to classifiying countries by their flags etc and brief description of PC/Beagle being applied to forensic science "where a rule set developed
   with the aid of PC/BEAGLE was found to descriminate among glass fragments better than standard statistical procedures" [page 77].
%@ 0-412-30570-4

%A Blair Foster
%A Anil Somayaji
%T Object-level recombination of commodity applications
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 957--964
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, SBSE, software recombination, ObjRecombGA, object-level recombination, commodity programs
%U people.scs.carleton.ca/~soma/pubs/bfoster-gecco-2010.pdf
%X This paper presents ObjRecombGA, a genetic algorithm framework for recombining related programs at the object file level. A genetic algorithm guides the selection of object
   files, while a robust link resolver allows working program binaries to be produced from the object files derived from two ancestor programs. Tests on compiled C programs,
   including a simple web browser and a well-known 3D video game, show that functional program variants can be created that exhibit key features of both ancestor programs.
   This work illustrates the feasibility of applying evolutionary techniques directly to commodity applications
%8 7-11  July
%Z Unix sed (8 version pairs), Dillo (6 version pairs), Quake (6 version pairs). Manual (Human interactive?) fitness. Population=12, =30, =50. Mashup. linear bit string GA,
   each bit refers to a C .o in two ancestor programs. unix bash shell script. GNU ld linker. open source in near future? p962 'we were able to repair bugs and merge
   functionality by recombining programs at the object file level.' Also known as \cite1830653 GECCO-2010 A joint meeting of the nineteenth international conference on genetic
   algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)

%A James A. Foster
%A Terence Soule
%T Comments on the intron/exon distinction as it relates to genetic programming and biology
%D 1997
%I 
%C East Lansing, MI, USA
%K genetic algorithms, genetic programming, introns
%O Position paper at the Workshop on Exploring Non-coding Segments and Genetics-based Encodings at ICGA-97
%8 21  July
%Z http://www.aic.nrl.navy.mil/~aswu/icga97/

%A James A. Foster
%T Review: Discipulus: A Commercial Genetic Programming System
%J Genetic Programming and Evolvable Machines
%V 2
%N 2
%D 2001
%P 201--203
%I 
%K genetic algorithms, genetic programming
%8 June
%Z Article ID: 335720

%A James A. Foster
%A Erick Cantu-Paz
%T Introduction
%J Genetic Programming and Evolvable Machines
%V 6
%N 1
%D 2005
%P 5--6
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%8 March
%Z Special issue best of GECCO-2003 \citeGECCO2003-PartI, \citeGECCO2003-PartII

%A James A. Foster
%A Jason H. Moore
%T GECCO-2006 Highlights: Biological Applications
%J SIGEVOlution
%V 1
%N 3
%D 2006
%P 23
%I 
%K genetic algorithms, genetic programming
%U http://www.sigevolution.org/2006/03/issue.pdf
%8 September

%A Miguel Frade
%A F. {Fernandez de Vega}
%A Carlos Cotta
%T Modelling Video Games' Landscapes by Means of Genetic Terrain Programming - A New Approach for Improving Users' Experience
%B Proceedings of EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops
%S Lecture Notes in Computer Science
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Rolf Drechsler and Anik\'o Ek\'art and Anna Esparcia-Alc\'azar and Muddassar Farooq and
   Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang
%V 4974
%D 2008
%P 485--490
%I Springer
%C Naples
%K genetic algorithms, genetic programming, terrain generation, video games, evolutionary art
%X Terrain generation algorithms can provide a realistic scenario for video game experience and can help keep users interested in playing by providing new landscapes each time
   they play. Nowadays there are a wide range of techniques for terrain generation, but all of them are focused on providing realistic terrains. This paper proposes a new
   technique, Genetic Terrain Programming, based on evolutionary design with GP to allow game designers to evolve terrains according to their aesthetic feelings or desired
   features. The developed application produces Terrains Programs that will always generate different terrains, but consistently with the same features (e.g. valleys, lakes).
%8 26-28  March
%Z GPLAB Matlab, FFT

%A Miguel Frade
%A Francisco {Fernandez de Vega}
%A Carlos Cotta
%T Breeding Terrains with Genetic Terrain Programming: The Evolution of Terrain Generators
%J International Journal of Computer Games Technology
%V 2009
%D 2009
%I 
%K genetic algorithms, genetic programming, Genetic terrain programming, evolutionary systems, terrain generator, level of detail
%U http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2009/125714
%X Although a number of terrain generation techniques have been proposed during the last few years, all of them have some key constraints. Modelling techniques depend highly
   upon designer's skills, time and effort to obtain acceptable results, and cannot be used to automatically generate terrains. The simpler methods allow only a narrow variety
   of terrain types and offer little control on the outcome terrain. The Genetic Terrain Programming technique, based on evolutionary design with Genetic Programming, allows
   designers to evolve terrains according to their aesthetic feelings or desired features. This technique evolves TPs (Terrain Programmes) that are capable of generating a
   family of terrains - different terrains that consistently present the same morphological characteristics. This paper presents a study about the persistence of morphological
   characteristics of terrains generated with different resolutions by a given TP. Results show it is possible to use low resolutions during the evolutionary phase without
   compromising the outcome and that terrain macro-features are scale invariant.
%O Special issue on Artificial Intelligence for Computer Games
%Z Article ID 125714

%A Miguel Frade
%A Francisco {Fernandez de Vega}
%A Carlos Cotta
%T Evolution of Artificial Terrains for Video Games Based on Accessibility
%B EvoGAMES
%S LNCS
%E Cecilia Di Chio and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and
   Mike Preuss and Julian Togelius and Georgios N. Yannakakis
%V 6024
%D 2010
%P 90--99
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming, genetic terrain programming, artificial terrains, video games
%X Diverse methods have been developed to generate terrains under constraints to control terrain features, but most of them use strict restrictions. However, there are
   situations were more flexible restrictions are sufficient, such as ensuring that terrains have enough accessible area, which is an important trait for video games. The
   Genetic Terrain Program technique, based on genetic programming, was used to automatically evolve Terrain Programs (TPs - which are able to generate terrains procedurally)
   for the desired accessibility parameters. Results showed that the accessibility parameters have negligible influence on the evolutionary system and that the terminal set
   has a major role on the terrain look. TPs produced this way are already being used on Chapas video game.
%8 7-9  April
%Z EvoGAMES'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Miguel Frade
%A F. {Fernandez de Vega}
%A Carlos Cotta
%T Evolution of artificial terrains for video games based on obstacles edge length
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Several methods have been developed to generate terrains under constraints to control terrain features, but most of them use strict restrictions. However, there are
   situations were more flexible restrictions are sufficient, such as ensuring that terrains have enough accessible area, which is an important trait for video games. Many
   terrains, generated with Genetic Terrain Program technique, based only on the desired accessibility parameters presented a single large non-accessible area. In an attempt
   to solve this problem a new fitness function, based on obstacles edge length, is presented on this paper. Results showed that the new metric suits our goal and also
   produces many terrains with novelty and aesthetic appeal. Terrains produced this way are already being used on Chapas video game.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586032

%A Tiago Francisco
%A Gustavo Miguel Jorge {dos Reis}
%T Evolving combat algorithms to control space ships in a 2D space simulation game with co-evolution using genetic programming and decision trees
%B GECCO-2008 Workshop: Defense Applications of Computational Intelligence (DAC)
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 1887--1892
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1887.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1388995

%A Tiago Francisco
%A Gustavo Miguel Jorge {dos Reis}
%T Evolving predator and prey behaviours with co-evolution using genetic programming and decision trees
%B GECCO-2008 Workshop: Defense Applications of Computational Intelligence (DAC)
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 1893--1900
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1893.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1388996

%A Frank D. Francone
%A Peter Nordin
%A Wolfgang Banzhaf
%T Benchmarking the Generalization Capabilities of a Compiling Genetic programming System using Sparse Data Sets
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 72--80
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.mun.ca/~banzhaf/papers/benchmarking.pdf
%8 28--31  July
%Z GP-96 Notes based upon version submitted to GP-96 Wed, 17 Apr 1996 09:20:19 PDT When I read your email (koza's), I went back and checked the output on two other problems
   that we ran as part of that paper. Gaussian 3D and Phoneme Classification. Each of these was a two output problem and the way the classification was set up, one would
   expect less than 50% correct classification from a randomly created individual. In those problems, we used 10 different random seeds, 3000 individuals per run. The
   following were the results for the best individual from generation 0's classification rate. Mean Best Worst gauss 0.59 0.64 0.55 iris 0.98 0.99 0.97 phoneme 0.73 0.75 0.71
   Note that these figures represent the results of a random search of 30,000 individuals. As Peter Nordin points out in his email to which this is a reply, on the IRIS
   problem, even the worst figure is very good. In fact it was statistically indistinguishible from a highly optimized KNN beachmark run on twice as large a training set. This
   is because the IRIS problem is trivial. As pointed out in the above referenced paper, IRIS should probably not be used as a measure of the learning ability of any ML
   system, notwithstanding its status as a 'classic' problem. It is probably better characterized as a 'classic' way to make a ML system look good. On the other two problems,
   which were much more difficult, the genetic search improved on the random search considerably. The individuals with the best abilitiy to generalize on the test data set
   were respectively. Best Generalizer Gaussian 3D 72% Phoneme 85% I report these figures here because the generation 0 figures are not reported in the above paper directly.
   Regards Frank Francone

%A Wolfgang Banzhaf
%A Frank D. Francone
%A Peter Nordin
%T The Effect of Extensive Use of the Mutation Operator on Generalization in Genetic Programming Using Sparse Data Sets
%B Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation
%S LNCS
%E Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel
%V 1141
%D 1996
%P 300--309
%I Springer Verlag Heidelberg, Germany
%C Berlin, Germany
%K genetic algorithms, genetic programming
%X Ordinarily, Genetic Programming uses little or no mutation. Crossover is the predominant operator. This study tests the effect of a very aggressive use of the mutation
   operator on the generalisation performance of our Compiling Genetic Programming System (CPGS). We ran our tests on two benchmark classification problems on very sparse
   training sets. In all, we performed 240 complete runs of population 3000 for each of the problems, varying mutation rate between 5percent and 80percent. We found that
   increasing the mutation rate can significantly improve the generalization capabilities of GP. The mechanism by which mutation affects the generalization capability of GP is
   not entirely clear. What is clear is that changing the balance between mutation and crossover effects the course of GP training substantially - for example, increasing
   mutation greatly extends the number of generations for which the GP system can train before the population converges.
%8 22-26  September
%Z http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 machine code GP CGPS used on IRIS, Gaussian 3D and phoneme ELENA classification problems. Iris trivial. On others best
   performance from 50/50 mix of crossover and mutation. Answer extracted via designated hardware register. Stop runs when destructive crossover falls below 10percent (used as
   convergence indicator). Mutation giving rise to more complex introns. GP premature convergence
%@ 3-540-61723-X

%A Wolfgang Banzhaf
%A Frank D. Francone
%A Peter Nordin
%T Some Emergent Properties of Variable Size EAs
%D 1997
%I 
%C East Lansing, MI, USA
%K genetic algorithms, genetic programming, bloat, variable size representation
%O Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97
%8 20  July

%A Wolfgang Banzhaf
%A Peter Nordin
%A Frank D. Francone
%T Why introns in genetic programming grow exponentially
%D 1997
%I 
%C East Lansing, MI, USA
%K genetic algorithms, genetic programming, introns
%O Position paper at the Workshop on Exploring Non-coding Segments and Genetics-based Encodings at ICGA-97
%8 21  July
%Z http://www.aic.nrl.navy.mil/~aswu/icga97/

%A Frank D. Francone
%A Markus Conrads
%A Wolfgang Banzhaf
%A Peter Nordin
%T Homologous Crossover in Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1021--1026
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-463.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Frank D. Francone
%A Peter Nordin
%A Wolfgang Banzhaf
%A Larry M. Deschaine
%T Automatic Induction of Machine Code (AIM) Learning Real Time Adaptive Control Strategies
%D 2000
%I 
%K genetic algorithms, genetic programming, discipulus automatic control, industrial control, model design, machine learning
%U http://pw2.netcom.com/%7elmdmit84/AimProcessControl2000.pdf broken
%O www document
%8 11  May
%Z high level

%A Frank D. Francone
%T Discipulus Owner's Manual
%D 2001
%I 
%I Register Machine Learning Technologies, Inc.
%C 11757 W. Ken Caryl Avenue F, PBM 512, Littleton, Colorado, 80127-3719, USA
%K genetic algorithms, genetic programming
%U http://www.aimlearning.com/Discipulus%20Owners%20Manual.pdf

%A Frank D. Francone
%A Larry M. Deschaine
%T Extending the boundaries of design optimization by integrating fast optimization techniques with machine-code-based, linear genetic programming
%J Information Sciences
%V 161
%N 3-4
%D 2004
%P 99--120
%I 
%K genetic algorithms, genetic programming
%X Optimised models of complex physical systems are difficult to create and time consuming to optimise. The physical and business processes are often not well understood and
   are therefore difficult to model. The models of often too complex to be well optimized with available computational resources. Too often approximate, less than optimal
   models result. This work presents an approach to this problem that blends three well-tested components. First: We apply Linear Genetic Programming (LGP) to those portions
   of the system that are not well understood -- for example, modelling data sets, such the control settings for industrial or chemical processes, geotechnical property
   prediction or UXO detection. LGP builds models inductively from known data about the physical system. The LGP approach we highlight is extremely fast and builds rapid to
   execute, high-precision models of a wide range of physical systems. Yet it requires few parameter adjustments and is very robust against overfitting. Second: We simulate
   those portions of the system -- for example, the cost model for the processes -- these are well understood with human built models. Finally: We optimise the resulting
   meta-model using Evolution Strategies (ES). ES is a fast, general-purpose optimiser that requires little pre-existing domain knowledge. We have developed this approach over
   a several years period and present results and examples that highlight where this approach can greatly improve the development and optimisation of complex physical systems.
%O FEA 2002
%8 20  April

%A Frank D. Francone
%A Larry M. Deschaine
%T Getting It Right at the Very Start -- Building Project Models where Data Is Expensive by Combining Human Expertise, Machine Learning and Information Theory
%B 2004 Business and Industry Symposium
%D 2004
%I 
%I Society for Modeling and Simulation
%C Washington, DC
%K genetic algorithms, genetic programming, Environmental Science, geophysics, information theory, underground anomaly detection, machine learning, expert systems
%U http://www.scs.org/docInfo.cfm?get=1720
%X Building models using machine learning techniques requires data. For some projects, gathering data is very expensive. In this type of project, there are two significant
   costs to using machine learning techniques in this type of project: (1) Machine learning models cannot even begin to make predictions until the project has already spent a
   lot of money gathering data; and (2) While the data is being gathered to train the machine learning system, unnecessary costs are incurred in making inefficient decisions.
   Engineers may address this type of problem efficiently when enough human expertise exists about the problem domain to be modelled. This work proposes an approach to
   combining human expertise, machine learning and information theory that makes efficient and effective decisions from the start of the project, while project data is being
   gathered.
%8 April

%A Frank D. Francone
%A Larry M. Deschaine
%A Tom Battenhouse
%A Jeffrey J. Warren
%T Discrimination of Unexploded Ordnance from Clutter Using Linear Genetic Programming
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.aimlearning.com/UXO.GECCO.2004.pdf
%X We used Linear Genetic Programming (LGP) to study the extent to which automated learning techniques may be used to improve Unexploded Ordinance (UXO) discrimination from
   Protem-47 and Geonics EM61 non-invasive electromagnetic sensors. We conclude that: (1) Even after geophysicists have analysed the EM61 signals and ranked anomalies in order
   of the likelihood that each comprises UXO, our LGP tool was able to substantially improve the discrimination of UXO from scrap-preexisting techniques require digging 62%
   more holes to locate all UXO on a range than do LGP derived models; (2) LGP can improve discrimination even though trained on a very small number of examples of UXO; and
   (3) LGP can improve UXO discrimination on data sets that contain a high-level of noise and little preprocessing.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp. See also \citefrancone:2005:GPTP.

%A Frank D. Francone
%A Larry M. Deschaine
%A Tom Battenhouse
%A Jeffrey J. Warren
%T Discrimination of Unexploded Ordnance from Clutter using Linear Genetic Programming
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 49--64
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Unexploded Ordnance, UXO Discrimination.
%X We used Linear Genetic Programming (LGP) to study the extent to which automated learning techniques may be used to improve Unexploded Ordinance (UXO) discrimination from
   Protem-47 and Geonics EM61 non-invasive electromagnetic sensors. We conclude that: (1) Even after geophysicists have analysed the EM61 signals and ranked anomalies in order
   of the likelihood that each comprises UXO, our LGP tool was able to substantially improve the discrimination of UXO from scrap preexisting techniques require digging
   62percent more holes to locate all UXO on a range than do LGP derived models; (2) LGP can improve discrimination even though trained on a very small number of examples of
   UXO; and (3) LGP can improve UXO discrimination on data sets that contain a high-level of noise and little preprocessing.
%O 4
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Frank D. Francone
%A Larry M. Deschaine
%A Jeffrey J. Warren
%T Discrimination of munitions and explosives of concern at F.E. Warren AFB using linear genetic programming
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1999--2006
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Real-World Applications, Discipulus, economics, EM61 MK2, geophysics, linear genetic programming, measurement, MEC, munitions and
   explosives of concern, unexploded ordnance, UXO, verification
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1999.pdf
%X Removing underground, unexploded bombs, mortars, cannon shells and other ordnance (MEC or UXO) from former military ranges is difficult and expensive. The principal
   difficulty is discriminating intact, underground ordnance from other metallic items such as fragments of exploded ordnance (Clutter), magnetic rocks, and historic items
   such as horseshoes, barbed-wire, and refrigerators. This study represents the first, large-scale, blind-test of MEC discrimination technology on production-grade,
   survey-mode data from the cleanup of a real impact site. The results reported here significantly advance the state-of-the-art in MEC discrimination over alternative forward
   modelling/ inversion approaches to performing MEC discrimination. We combined Linear Genetic Programming (LGP) and statistical analysis to process data from the cleanup of
   600 acres of the F.E.Warren Air Force Base. These data contained almost 30,000 targets of interest identified by geophysicists, including three-hundred thirty-two 75mm
   projectiles (75mm) and 37mm projectiles (37mm). A little under one-third of the ground truth was held back by the customer for blind-testing. Our task was to discriminate
   intact 37mm and 75mm from the clutter by ordering the targets from most-likely to be MEC to least-likely to be MEC in what is referred to as a prioritised dig list. We
   identified all 75mm by 28.2percent of the way through our prioritized dig-list and all 37mm by 64.2percent of the way through the prioritised dig list. Thus, depending on
   ordnance type, we reduced the number of targets that had to be excavated (false alarms) to clear the entire site by between 35percent and 72percent.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Steffen Frank
%A Stefan Klahold
%T Ein System zur Untersuchung der Moglichkeiten und Beschrankungen fur Genetisches Programmieren in JAVA Bytecode
%R M.S. Thesis
%D 1998
%I 
%I Dortmund University
%C Germany
%K genetic algorithms, genetic programming
%8 May
%Z http://ls11-www.cs.uni-dortmund.de/bb/review98-99/node66.html Joint project supervised by Robert Keller. See also \citeklahold:1998:eprGPJb

%A Toni Frankola
%A Marin Golub
%A Domagoj Jakobovic
%T Evolutionary algorithms for the resource constrained scheduling problem
%B 30th International Conference on Information Technology Interfaces, ITI 2008
%D 2008
%P 715--722
%I 
%K genetic algorithms, genetic programming, NP complete problems, evolutionary algorithms, optimal sequence finding, resource constrained project scheduling problem,
   constraint theory, project management, resource allocation, scheduling
%X This paper investigates the use of evolutionary algorithms for solving resource constrained scheduling problem which belongs to the class of NP complete problems. The
   problem involves finding optimal sequence of activities with given resource constraints. Evolutionary algorithms used in this paper are genetic algorithms and genetic
   programming, for which adequate scheduling mechanisms are defined. Presented solutions are compared with existing heuristics or optimal results.
%8 June
%Z p715 'With genetic programming we describe a methodology to evolve scheduling heuristics in the form of priority rules that can be used to find a solution of an acceptable
   quality in a small amount of time.' Also known as \cite4588499

%A A. F. Fraser
%T Animal welfare theory: The keyboard of the maintenance ethosystem
%J Applied Animal Behaviour Science
%V 22
%N 2
%D 1989
%P 177--190
%I 
%U http://www.sciencedirect.com/science/article/B6T48-49NRPH9-GK/2/ff144de289e78408a13991fc32da018c
%Z Not on GP

%A A. P. Fraser
%A J. R. Rush
%T Putting INK into a BIRo: A discussion of problem domain knowledge for evolutionary robotics
%B AISB Workshop on Evolutionary Computing
%E T. C. Fogarty
%D 1994
%I 
%I AISB
%C Leeds, UK
%K genetic algorithms, genetic programming
%8 11-13  April
%Z Proceedings of the Workshop on Artificial Intelligence and Simulation of Behaviour Workshop on Evolutionary Computing. Workshop in Leeds, UK, April 11-13, 1994 This paper
   does NOT appear in the proceedings published by Springer_Verlag

%A Colin Frayn
%T Genetic Programming in Finance
%B Proceedings of the 8th Joint Conference in Information Systems (JCIS 2005)
%E Heng-Da Cheng
%D 2005
%I 
%C Salt Lake City, USA
%K genetic algorithms, genetic programming
%8 21-25  July
%Z http://www.jcis.org/jcis_program/master_schedule.pdf

%A Stephen J. Freeland
%T The Darwinian Genetic Code: An Adaptation for Adapting?
%J Genetic Programming and Evolvable Machines
%V 3
%N 2
%D 2002
%P 113--127
%I 
%K error minimization, genetic code, evolution, adaptation
%X The genetic code is a ubiquitous interface between inert genetic information and living organisms, as such it plays a fundamental role in defining the process of evolution.
   There have been many attempts to identify features of the code that are themselves adaptations. So far, the strongest evidence for an adaptive code is that the assignments
   of amino acids (encoded objects) to codons (coding units) appear to be organized so as to minimize the change in amino acid hydrophobicity that results from random
   mutations. One possibility not previously discussed is that this feature of the code may in fact represent an adaptation to maximize the efficiency of adaptive evolution,
   particularly given the maximized connectedness of protein fitness landscapes afforded by the redundancy of the code.
%8 June
%Z Special issue on Gene Expression \citeKargupta:2002:GPEM Article ID: 408585

%A Stephen Freeland
%T Three Fundamentals of the Biological Genetic Algorithm
%B Genetic Programming Theory and Practice
%E Rick L. Riolo and Bill Worzel
%D 2003
%P 303--312
%I Kluwer
%K particulate genes, genetic code, phenotype, genotype, biology envy
%O 19
%Z Part of \citeRioloWorzel:2003

%A Jennifer J. Freeman
%T A Linear Representation for GP using Context Free Grammars
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 72--77
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, CFG/GP, PORS
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A M. Freischlad
%A M. Schnellenbach-Held
%T Multi-Objective Genetic Programming Based Design of Fuzzy Systems
%B Proceedings of the 2005 ASCE International Conference on Computing in Civil Engineering
%E Lucio Soibelman and Feniosky Pena-Mora
%D 2005
%I 
%C Cancun, Mexico
%K genetic algorithms, genetic programming
%X The Multi-Objective Domain Knowledge Augmented Genetic Fuzzy System (MODA-GFS) is a GP based fuzzy system for the data-driven generation of fuzzy rule based systems. The
   algorithm incorporates domain specific knowledge that is used by human knowledge engineers in the manual fuzzy system design process. The combination of characteristics of
   two individuals is most interesting if both individuals complement each other. In terms of fuzzy systems this means a potential crossover partner (parent B) has a lower
   approximation error in an area of the input space, where parent A has a higher error. Within MODA-GFS a method for the determination of feasible crossover mates is
   implemented. In addition MODA-GFS includes a method for the goal-oriented selection of parent rules that are handed down to the offspring. Especially in the domain of
   knowledge representation the quality of a fuzzy system is not only determined by its approximation capability but also by its transparency. In order to assure the automated
   generation of fuzzy systems that are both accurate and transparent multi-objective optimisation methods are implemented. Tests carried out on test functions as well as on
   real world data sets have shown that the incorporation of domain knowledge significantly speeds up the evolution process. Besides these test results the integration and
   application of the new methods for automated generation of fuzzy models within a learning expert system environment are described in this paper. Finally an outlook on the
   current and future work is given, i.e. the transfer of the presented findings to the evolutionary optimisation of large-scale structures.
%8 July  12-15
%Z c2005 ASCE

%A Alex A. Freitas
%T A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 96--101
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming, SQL
%U http://kar.kent.ac.uk/21483/2/A_Genetic_Programming_Framework_for_Two_Data_Mining_Tasks_Classification_and_Generalized_Rule_Induction.pdf
%X This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalised rule induction. The framework emphasises the
   integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database
   server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization. The paper also proposes some
   genetic operators tailored for the two above data mining tasks.
%8 13-16  July
%Z GP-97 Lazy learning, separation of query tree encodes Tuple-Set Descriptor (SQL), from goal attribute. Goal subject to three types of mutation

%A Alex A. Freitas
%T A Genetic Algorithm for Discovering Knowledge Nuggets
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Alex A. Freitas
%T Book Review: Data Mining Using Grammar-Based Genetic Programming and Applications
%J Genetic Programming and Evolvable Machines
%V 2
%N 2
%D 2001
%P 197--199
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%U http://ipsapp009.lwwonline.com/content/getfile/4723/5/7/fulltext.pdf
%8 June
%Z review of \citeManLeungWong:book Article ID: 335718

%A Alex Freitas
%T Data Mining and Knowledge Discovery with Evolutionary Algorithms
%D 2002
%I Springer-Verlag
%K genetic algorithms, genetic programming, data mining, classification, clustering
%U http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html
%@ 0-7923-8048-7

%A Alex Freitas
%T A review of evolutionary algorithms for e-commerce
%B E-Commerce and Intelligent Methods. Studies in Fuzziness and Soft Computing
%S Studies in Fuzziness and Soft Computing
%E J. Segovia and P. S. Szczepaniak and M. Niedzwiedzinski
%V 105
%D 2002
%P 159--179
%I Springer-Verlag
%K genetic algorithms, genetic programming, e-commerce
%U http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html
%O 10
%@ 3-7908-1499-7

%A Alex Alves Freitas
%T Evolutionary Computation
%B Handbook of Data Mining and Knowledge Discovery
%E W. Klosgen and J. Zytkow
%D 2002
%P 698--706
%I Oxford University Press
%K genetic algorithms, genetic programming, data mining, classification
%U http://citeseer.ist.psu.edu/460298.html
%X This chapter addresses the integration of knowledge discovery in databases (KDD) and evolutionary algorithms (EAs), particularly genetic algorithms and genetic programming.
   First we provide a brief overview of EAs. Then the remaining text is divided into three parts. Section 2 discusses the use of EAs for KDD. The emphasis is on the use of EAs
   in attribute selection and in the optimization of parameters for other kinds of KDD algorithms (such as decision trees and nearest neighbour algorithms). Section 3
   discusses three research problems in the design of an EA for KDD, namely: how to discover comprehensible rules with genetic programming, how to discover surprising
   (interesting) rules, and how to scale up EAs with parallel processing. Finally, section 4 discusses what the added value of KDD is for EAs. This section includes the remark
   that generalization performance on a separate test set (unseen during training, or EA run) is a basic principle for evaluating the quality of discovered knowledge, and then
   suggests that this principle should be followed in other EA applications.
%O 32

%A Alex Freitas
%T A survey of evolutionary algorithms for data mining and knowledge discovery
%B Advances in Evolutionary Computation
%E A. Ghosh and S. Tsutsui
%D 2002
%P 819--845
%I Springer-Verlag
%K genetic algorithms, genetic programming
%U http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html
%X This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery. We focus on the
   data mining task of classification. In addition, we discuss some preprocessing and postprocessing steps of the knowledge discovery process, focusing on attribute selection
   and pruning of an ensemble of classifiers. We show how the requirements of data mining and knowledge discovery influence the design of evolutionary algorithms. In
   particular, we discuss how individual representation, genetic operators and fitness functions have to be adapted for extracting high-level knowledge from data.
%O 33

%A Alex Alves Freitas
%A Gisele L. Pappa
%T Genetic Programming for Automatically Constructing Data Mining Algorithms
%B Encyclopedia of Data Warehousing and Mining
%E John Wang
%D 2009
%P 932--936
%I IGI Global
%K genetic algorithms, genetic programming
%U http://www.igi-global.com/bookstore/titledetails.aspx?titleid=346&detailstype=chapters
%O 144
%Z 4 Volumes.

%A R. L. B. French
%A R. I. Damper
%T Evolving a Nervous System of Spiking Neurons for a Behaving Robot
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 1099--1106
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, evolutionary robotics, spiking, neurons, emergent behaviours
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Clemens Frey
%A Gunter Leugering
%T Evolving Strategies for Global Optimization - A Finite State Machine Approach
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 27--33
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, finite state machines, optimizing controllers, dynamic systems, adapted spatial optimization strategies
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Clemens Frey
%T Co-Evolution of Finite State Machines for Optimization: Promotion of Devices Which Search Globally
%J International Journal of Computational Intelligence and Applications
%V 1
%N 2
%D 2002
%P 1--16
%I 
%K genetic algorithms, genetic programming
%U http://www.mathematik.tu-darmstadt.de/~frey/
%X In this work a co-evolutionary approach is used in conjunction with Genetic Programming operators in order to find certain transition rules for two-step discrete dynamical
   systems. This issue is similar to the well-known artificial-ant problem. We seek the dynamic system to produce a trajectory leading from given initial values to a maximum
   of a given spatial functional. This problem is recast into the framework of input-output relations for controllers, and the optimization is performed on program trees
   describing input filters and finite state machines incorporated by these controllers simultaneously. In the context of Genetic Programming there is always a set of test
   cases which has to be maintained for the evaluation of program trees. These test cases are subject to evolution here, too, so we employ a so-called host-parasitoid model in
   order to evolve optimizing dynamical systems. Reinterpreting these systems as algorithms for finding the maximum of a functional under constraints, we have derived a
   paradigm for the automatic generation of adapted optimization algorithms via optimal control. We provide numerical examples generated by the GP-system MathEvEco. These
   examples refer to key properties of the resulting strategies and they include statistical evidence showing that for this problem of system identification the
   co-evolutionary approach is superior to standard Genetic Programming.

%A Clemens Frey
%T Virtual Ecosystems - Evolutionary and Genetic Programming from the perspective of modern means of ecosystem-modelling
%R Ph.D. Thesis
%D 2002
%I 
%I Darmstadt University of Technology
%K genetic algorithms, genetic programming
%Z See Frey:2002

%A Clemens Frey
%T Virtual Ecosystems - Evolutionary and Genetic Programming from the perspective of modern means of ecosystem-modelling
%S Bayreuth Forum Ecology
%V 93
%D 2002
%I Institute for Terrestrial Ecosystems, Bayreuth
%C Bayreuth, Germany
%K genetic algorithms, genetic programming
%U http://www.bitoek.uni-bayreuth.de/bitoek/en/pub/pub/pub_detail.php?id_obj=7556
%X Genetische Algorithmen und verwandte Evolutions-algorithmen spielen in der angewandten Mathematik und in der Informatik eine wichtige Rolle als Werkzeuge, mit deren Hilfe
   komplizierte Optimierungsprobleme naherungsweise gelost werden konnen. Die Methoden basieren auf der Idee, Prinzipien nat\"urlicher Evolutionsablaufe algorithmisch zu
   formulieren und geeignet anzupassen, damit sie zur Problemlosung in nicht-biologischen Anwendungsfeldern eingesetzt werden konnen. Man verwendet zum Beispiel die sogenannte
   Genetische Programmierung zur automatischen, evolutionsbasierten Erzeugung von Computerprogrammen. Der Autor des vorliegenden Bandes geht von der Hypothese aus, dass
   nat\"urliche Evolution einen Verar-beitungsprozess genetischer Information darstellt. Es wird untersucht, ob Evolutionsalgorithmen in Umkehrung ihres bisherigen Profils
   auch als Modell f\"ur biologische Evolution verwendet werden konnen. An welchen Stellen muss die Methodik zu diesem Zweck verandert werden? - Zur Beant-wortung dieser
   Fragen wird eine mathematische Formulierung des Darwinschen Evolutionsprozesses im Rahmen hierarchischer, diskreter dynamischer Systeme vorgeschlagen. Auf diesem Fundament
   werden bestehende Methoden (Genetische Programmierung, Artificial Life) analysiert und ein neues, individuenbasiertes Evolutionsmodell realisiert. Dieses Modell wurde als
   Mathematica-Paket unter dem Namen MathEvEco implementiert; es wird in diesem Band ausf\"uhrlich dargestellt, ebenso wie die vielen durchgef\"uhrten Versuche zur
   automatischen Erzeugung von Suchprogrammen, sowie ihre Parameter und Ergebnisse. Der Leser gewinnt also nicht nur einen Einblick in den aktuellen Stand von
   Evolutionsalgorithmen und Ansatzen zur Simulation von Evolution in virtuellen \"Okosystemen, sondern wird schlie\sslich auch in der Lage sein, eigene Evolu-tionsexperimente
   durchzuf\"uhren.
%O (in German)
%Z Bayreuther Forum Okologie 93, 1-199 (2002)

%A Stephan Freyer
%A J{\"o}rg Graefe
%A Matthias Heinzel
%A Peter Marenbach
%T Evolutionary Generation and Refinement of Mathematical Process Models
%B Eufit '98, 6th European Congress on Intelligent Techniques and Soft Computing, ELITE - European Laboratory for Intelligent TechniquesEngineering
%E Hans-J\"urgen Zimmermann
%V III
%D 1998
%P 1471--1475
%I 
%C Aachen, Germany
%K genetic algorithms, genetic programming, SMOG, bioprocess, modelling
%U http://www.rt.e-technik.tu-darmstadt.de/LIT
%X Modelling of biotechnological processes is generally difficult and time consuming. In order to generate mathematical models of a studied reaction system in a short time
   period a new modelling technique for the optimisation of structures, based on the principles of evolution, was developed. This method generates transparent and
   comprehensible dynamic models in a data driven manner. In addition it is able to automatically refine sub-models or to verify model ideas. The transparent mathematical form
   of the generated models is a major advantage giving the user the opportunity to interpret the model and to influence the modelling process interactively. The article at
   hand presents two examples of biotechnological processes for which this new method was successfully applied.
%Z http://www.eufit.org/proceedings/98/volume3.htm BASF AG laboratories, high noise. Monod, SubLimTeissier, SubLimJost, SubInhAnstrews, SubInhWebb MATLAB/SIMULINK. Stresses
   importance of user understandable models, using prior knowledge, parsimony versus accuracy (trade off in fitness function). Batch fed fermentation.

%A Enrique Frias-Martinez
%A Fernand Gobet
%T Automatic Generation of Cognitive Theories using Genetic Programming
%J Minds and Machines
%V 17
%N 3
%D 2007
%P 287--309
%I Kluwer Academic Publishers
%C Hingham, MA, USA
%K genetic algorithms, genetic programming, Cognitive neuroscience, Computational neuroscience, Automatic generation of cognitive theories, Delayed-match-to-sample
%X Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive
   neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated
   by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic
   programming (GP). Our approach evolves from experimental data cognitive theories that explain the mental program that subjects use to solve a specific task. As an example,
   we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can
   use to develop better cognitive theories.
%8 October
%Z Artificial intelligence, cognitive memory. Fit both mean and standard deviation of experimental (people) results. Lisp. Data from Chao et al. 1999, pictures of animals and
   pictures of tools. WSTM. Table 3 putSTM ... "Write the input parameter in STM" Also known as \cite1298700

%A R. M. Friedberg
%T A learning machine: I
%J IBM Journal of Research and Development
%V 2
%N 1
%D 1958
%P 2--13
%I 
%K Machine Learning, intron, schema
%U http://www.research.ibm.com/journal/rd/021/ibmrd0201B.pdf
%X Machines would be more useful if they could learn to perform tasks for which they were not given precise methods. Difficulties that attend giving a machine this ability are
   discussed. It is proposed that the program of a stored-program computer be gradually improved by a learning procedure which tries many programs and chooses, from the
   intructions that may occupy a given location, the one most often associated with a successful result. An experimental test of this principle is described in detail.
%8 January
%Z Not a GP, for example there is no population and no genetic operations, nonetheless included for general GP interest. Includes discussion of introns and schema (but not
   under those names). IBM 704. 64 bits of memory. 64 (fixed) instructions. Conditional branch (ie IF), AND, MOVE and NOT. Loops halted after 64 time steps and given zero
   fitness. Hitch hikers. Herman and Sherman shows importance of details of representation (not fully understood). Cultural evolution, cf \citespector:1996:ctiGP. One bit
   identity (move a bit). Two bit sum (failed), high bit of sum, low bit of sum. One bit complement (not).

%A Anna Friedlander
%A Kourosh Neshatian
%A Mengjie Zhang
%T Meta-Learning and Feature Ranking Using Genetic Programming for Classification: Variable Terminal Weighting
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 940--947
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Classification, clustering, data analysis and data mining
%X We propose an online feature weighting method for classification by genetic programming (GP). GP's implicit feature selection was used to construct a feature weighting
   vector, based on the fitness of solutions in which the features were found and the frequency at which they were found. The vector was used to perform feature ranking and to
   perform meta-learning by biasing terminal selection in mutation. The proposed meta-learning mechanism significantly improved the quality of solutions in terms of
   classification accuracy on an unseen test set. The probability of success---the probability of finding the desired solution within a given number of generations (fitness
   evaluations)---was also higher than canonical GP. The ranking obtained by using the GP-provided feature weighting was very highly correlated with the ranking obtained by
   commonly-used feature ranking algorithms. Population information during evolution can help shape search behaviour (meta-learning) and obtain useful information about the
   problem domain such as the importance of input features with respect to each other.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Patri Friedman
%T Evolving a Program to Play Rock-Paper-Scissors
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 143--152
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Christoph M. Friedrich
%A Claudio Moraga
%T An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks
%B Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU '96)
%D 1996
%P 951--956
%I 
%C Granada, Spain
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/friedrich96evolutionary.html
%X This paper deals with the combination of Evolutionary Algorithms and Artificial Neural Networks (ANN). A new method is presented, to find good building-blocks for
   architectures of Artificial Neural Networks. The method is based on \em Cellular Encoding, a representation scheme by F. Gruau, and on Genetic Programming by J. Koza. First
   it will be shown that a modified Cellular Encoding technique is able to find good architectures even for non-boolean networks. With the help of a graph-database and a new
   graph-rewriting method, it is secondly possible to build architectures from modular structures. The information about building-blocks for architectures is obtained by
   statistically analyzing the data in the graph-database. Simulation results for two real-world problems are given.

%A Rodney Fry
%A Andy Tyrrell
%T Enhancing the Performance of GP Using an Ancestry-Based Mate Selection Scheme
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1804--1805
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, poster
%X The performance of genetic programming relies mostly on population-contained variation. If the population diversity is low then there will be a greater chance of the
   algorithm being unable to find the global optimum. We present a new method of approximating the genetic similarity between two individuals using ancestry information. We
   define a new diversity-preserving selection scheme, based on standard tournament selection, which encourages genetically dissimilar individuals to undergo genetic
   operation. The new method is illustrated by assessing its performance in a well-known problem domain: algebraic symbolic regression.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Rodney Fry
%A Stephen L. Smith
%A Andy M. Tyrrell
%T A Self-Adaptive Mate Selection Model for Genetic Programming
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2707--2714
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X extensions to the selection model in genetic programming, designed to be analogous to the more complex behaviour of selection in natural evolution. Specifically, a negative
   assortative mating scheme is presented in conjunction with a model of psychological evolution, allowing the mating strategy to change throughout the evolutionary process.
   Results show that self-adaptive mate selection accelerates evolution for several well known test problems.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. edit-distance, 4th and 7th order polynomials in one variable, exx+xpi, max problem 150 runs needed to find
   statistical effect.
%@ 0-7803-9363-5

%A Leeann L. Fu
%T The XCS Classifier System and Q-learning
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, Classifier Systems
%8 22-25  July
%Z GP-98LB

%A Weizhong Fu
%A Yuntao Zhang
%A Zhengjun Cheng
%T Improved gene expression programming and its application to QSAR
%B Sixth International Conference on Natural Computation (ICNC, 2010)
%V 8
%D 2010
%P 4057--4061
%I 
%K genetic algorithms, genetic programming, gene expression programming, 0-(2-phthalimidoethyl)-n-substituted thiocarbamates, HIV-1 inhibitors, QSAR, RDF descriptors, acquired
   immune deficiency syndrome, descriptor selection, feature selection, improved gene expression programming, quantitative structure-activity relationship model, radial
   distribution function descriptors, replacement method, ring-opened congeners, biocomputing, evolutionary computation, radial basis function networks
%X In the paper, the improved gene expression programming (IGEP) is proposed to develop a quantitative structure-activity relationship (QSAR) model of 70 compounds for
   O-(2-phthalimidoethyl)-N-substituted thiocarbamates and their ring-opened congeners as HIV-1 Inhibitors based on radial distribution function (RDF) descriptors for the
   first time. The replacement method (RM) is used as feature selection (descriptor selection). The five models (MLR, GEP, MC_GEP, IGEP, and SVM) are compared. The results
   show that IGEP has a good prediction ability.
%8 10-12  August

%A Wenlong Fu
%A Mark Johnston
%A Mengjie Zhang
%T Genetic Programming For Edge Detection: A Global Approach
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 254--261
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming
%X Edge detection is an important task in computer vision. This paper describes a global approach to edge detection using genetic programming (GP). Unlike most traditional
   edge detection methods which use local window filters, this approach directly uses an entire image as input and classifies pixels directly as edges or non-edges without
   preprocessing or postprocessing. Shifting operations and common standard operators are used to form the function set. Precision, recall and true negative rate are used to
   construct the fitness functions. This approach is examined and compared with the Laplacian and Sobel edge detectors on three sets of images providing edge detection
   problems of varying difficulty. The results suggest that the detectors evolved by GP outperform the Laplacian detector and compete with the Sobel detector in most cases.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Matthias Fuchs
%T Evolving Strategies Based on the Nearest Neighbor Rule and a Genetic Algorithm
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 485--490
%I MIT Press
%C Stanford University, CA, USA
%K Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 GA paper

%A Matthias Fuchs
%A Dirk Fuchs
%A Marc Fuchs
%T Solving Problems of Combinatory Logic with Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 102--110
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Fuchs_1997_spclGP.pdf
%8 13-16  July
%Z GP-97

%A Matthias Fuchs
%T Crossover versus Mutation: An Empirical and Theoretical Case Study
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 78--85
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Matthias Fuchs
%T A Data Mining Approach to Support the Creation of Loop Invariants Using Genetic Programming
%R Technical Report TR-ARP-09-98
%D 1999
%I 
%I Computer Science Laboaratory, Australian National University
%C Canberra, ACT 0200, Australia
%K genetic algorithms, genetic programming
%U http://arp.anu.edu.au/ftp/techreports/1998/TR-ARP-09-98.ps.gz
%X We describe a data-mining approach to creating central parts of loop invariants. The approach is based on producing a trace table by recording the values of program
   variables each time the condition of a loop is evaluated. From this trace table, functional dependencies between program variables can be extracted which may play a vital
   role in loop invariants. The extraction process is accomplished through the use of genetic programming which performs a symbolic regression on the data contained by the
   trace table. We illustrate our approach with examples.
%8 12  October

%A Marc Fuchs
%A Dirk Fuchs
%A Matthias Fuchs
%T Generating Lemmas for Tableau-based Proof Search Using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1027--1032
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-400.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Matthias Fuchs
%T Large Populations Are Not Always The Best Choice In Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1033--1038
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-410.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Matthias Fuchs
%T Evolving Gallery Layouts With Genetic Programming
%B Proceedings of The Third Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems
%E Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and Akira Namatame and Xin Yao and Mitsuo Gen
%D 1999
%I 
%C School of Computer Science Australian Defence Force Academy, Canberra, Australia
%K genetic algorithms, genetic programming
%8 22-25  November
%Z http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html The Australian National University

%A Matthias Fuchs
%T An Evolutionary Approach To Support Web Page Design
%R Technical Report TR-ARP-01-2000
%D 2000
%I 
%I Computer Science Laboaratory, Australian National University
%C Canberra, ACT 0200, Australia
%K Hill climbing
%U http://citeseer.ist.psu.edu/295439.html
%X Arranging pictures or photographs on a wall or a sheet of paper can be viewed as a layout problem that consists in placing a set of rectangles on a large rectangle so that
   there are no overlaps, and all edges are parallel to either the vertical or horizontal edge of the large rectangle. Automating this process is sensible in connection with
   web page design, in particular if frequent changes occur. For web pages, it is possible and it makes sense to scale pictures down so as to ensure that there is a solution
   to any such layout problem. The goal then is to find a layout that arranges the pictures in a way that is pleasing to the eye. We propose to use genetic programming to
   evolve layouts, using a representation similar to slicing tree structures, and a fitness measure that incorporates the idea of aesthetic appeal as minimising blank spaces.
%8 4  January
%Z Java "Preliminary experiments with GP using crossover did not reveal any benefits of the [our] crossover operator or a population-based search." p7. No details of GP.
   Binary tree representation. XO randomise leaf labels. Some experiments on effect of different fitness measures. Code produces HTML and LaTeX code See also
   \citefuchs:2000:AEASWD

%A Matthias Fuchs
%T An Evolutionary Approach to Support Web-Page Design
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 1312--1319
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K novel applications i
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644 See
   also \citefuchs:2000:AEASWDtr
%@ 0-7803-6375-2

%A Tim Fuhner
%A Christian Jacob
%T EvolVision - an Evolvica visualization tool
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 176
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster, EvolVision, Evolvica, visualization, Mathematica, Java, client/server application, plug-in architecture, pedigree diagrams
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Cory Fujiki
%A John Dickinson
%T Using the Genetic Algorithm to Generate Lisp Source Code to Solve the Prisoner's Dilemma
%B Genetic Algorithms and their Applications: Proceedings of the second international conference on Genetic Algorithms
%E John J. Grefenstette
%D 1987
%P 236--240
%I Lawrence Erlbaum Associates Hillsdale, NJ, USA
%I AAAI, Naval Research Laboratory, Bolt Beranek and Newman, Inc
%C MIT, Cambridge, MA, USA
%K genetic algorithms
%8 28-31  July
%Z Complete Lisp S-Expressions generated but are constrained to be a (variable length??) list of condition-action pairs, each of which is an s-expresion. These S-expressions
   are initially created at random and do _not_ evolve. Instead Mutation, Invert and crossover create new individuals using these existing components.

%A Alex S. Fukunaga
%A Andrew B. Kahng
%T Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evolution Function
%B 1995 IEEE Conference on Evolutionary Computation
%V 1
%D 1995
%P 182--187
%I IEEE Press Piscataway, NJ, USA
%C Perth, Australia
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/fukunaga95improving.html
%X Traditional evolutionary optimization algorithms assume a static environment in which solutions are evolved. Incremental evolution is an approach through which a dynamic
   evaluation function is scaled over time in order to improve the performance of evolutionary optimization. In this paper, we present empirical results that demonstrate the
   effectiveness of this approach for genetic programming. Using two domains, a two-agent pursuit-evasion game and the Tracker trail-following task, we demonstrate that
   incremental evolution is most successful when applied near the beginning of an evolutionary run. We also show that incremental evolution can be successful when the
   intermediate evaluation functions are more difficult than the target evaluation function, as well as they are easier than the target function.
%8 29  November  - 1  Decemeber
%Z ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva.

%A Alex Fukunaga
%A Andre Stechert
%A Darren Mutz
%T A Genome Compiler for High Performance Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 86--94
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/fukunaga98genome.html
%X Genetic Programming is very computationally expensive. For most applications, the vast majority of time is spent evaluating candidate solutions, so it is desirable to make
   individual evaluation as efficient as possible. We describe a genome compiler which compiles s-expressions to machine code, resulting in significant speedup of individual
   evaluations over standard GP systems. Based on performance results with symbolic regression, we show that the execution of the genome compiler system is...
%8 22-25  July
%Z GP-98 Thu, 25 Jun 1998 10:31:36 PDT We've recently developed a gp system based on lil-gp which evolves s-expressions and compiles it to machine code (specifically, Sparc
   machine code) to speed up evaluation. In our system, we've found that the overhead of compilation is negligible, since the vast majority of the time spent in execution in
   an s-expression interpreter (in our case, the lil-gp interpreter) is consumed by the recursive traversal of the tree. A full description, comparisons with previous
   GP-compiler systems and some experimental results with symbolic regression and image compression are described
%@ 1-55860-548-7

%A Alex Fukunaga
%A Andre Stechert
%T Evolving Nonlinear Predictive Models for Lossless Image Compression with Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 95--102
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/507773.html
%X We describe a genetic programming system which learns nonlinear predictive models for lossless image compression. Sexpressions which represent nonlinear predictive models
   are learned, and the error image is compressed using a Human encoder. We show that the proposed system is capable of achieving compression ratios superior to that of the
   best known lossless compression algorithms, although it is significantly slower than standard algorithms.
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Alex S. Fukunaga
%T Portfolios of Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 786
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-840.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Alex Fukunaga
%T Automated Discovery of Composite SAT Variable Selection Heuristics
%B Proceedings of the National Conference on Artificial Intelligence (AAAI)
%D 2002
%P 641--648
%I 
%K genetic algorithms, genetic programming, satisfiability, constraint satisfaction, local search
%U http://www.bol.ucla.edu/~fukunaga/AAAI02.pdf
%X Variants of GSAT and Walksat are among the most successful SAT local search algorithms. We show that several well-known SAT local search algorithms are the results of novel
   combinations of a set of variable selection primitives. We describe CLASS, an automated heuristic discovery system which generates new, effective variable selection
   heuristic functions using a simple composition operator. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty
   and R-Novelty . We also analyse the local search behaviour of the learned heuristics using the depth, mobility, and coverage metrics recently proposed by Schuurmans and
   Southey.

%A Alex Fukunaga
%T Efficient Implementations of SAT Local Search
%B The Seventh International Conference on Theory and Applications of Satisfiability Testing (SAT 2004)
%D 2004
%I 
%C Vancouver, BC, Canada
%K Poster
%U http://metahack.org/sat2004.pdf
%X Although most of the focus in SAT local search has been on search behavior (deciding which variable to flip next), the overall efficiency of an algorithm depends greatly on
   the efficiency of executing each variable flip and variable selection. This paper surveys, evaluates, and extends incremental data structures that have been used in SAT
   local search solvers (including the GSAT and Walksat families of solvers) to support efficient variable flips and selection.
%8 10-13  May
%Z cited by \citeFukunaga:2009:cec Does not mention GP. Does not appear to be in LNCS

%A Alex S. Fukunaga
%T Evolving Local Search Heuristics for SAT Using Genetic Programming
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 483--494
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030483.htm
%X Satisability testing (SAT) is a very active area of research today, with numerous real-world applications. We describe CLASS2.0, a genetic programming system for
   semi-automatically designing SAT local search heuristics. An empirical comparison shows that that the heuristics generated by our GP system outperform the state of the art
   human-designed local search algorithms, as well as previously proposed evolutionary approaches, with respect to both runtime as well as search efficiency (number of
   variable flips to solve a problem).
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Alex S. Fukunaga
%T Automated Discovery of Local Search Heuristics for Satisfiability Testing
%J Evolutionary Computation
%V 16
%N 1
%D 2008
%P 31--61
%I 
%K genetic algorithms, genetic programming, STGP, satisfiability, constraint satisfaction, SAT, hyper-heuristic, hybrid genetic-local search
%U http://metahack.org/ecj08-web-preprint.pdf
%X The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We
   investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as
   Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic
   programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be
   competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT
   instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyse the local search
   behaviour of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.
%8 Spring

%A Alex S. Fukunaga
%T A Parallel, Lisp-Based Genetic Programming System for Discovering Satisfiability Solvers
%B International Lisp Conference, ILC 2009
%E Guy L. Steele, Jr.
%D 2009
%P 137--148
%I 
%I ALU
%C Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
%K genetic algorithms, genetic programming
%8 March  22-25
%Z http://www.international-lisp-conference.org/2009/speakers

%A Alex S. Fukunaga
%T Massively Parallel Evolution of SAT Heuristics
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 1478--1485
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, STGP, hyperheuristics, MPI
%X Recent work has shown that it is possible to evolve heuristics for solving propositional satisfiability (SAT) problems which are competitive with the best hand-coded
   heuristics. However, previous work was limited by the computational resources required in order to evolve successful heuristics. In this paper, we describe a massively
   parallel genetic programming system for evolving SAT heuristics. Runs using up to 5.5 CPU core years of computation were executed, and resulted in new SAT heuristics which
   significantly outperform hand-coded heuristics.
%8 18-21  May
%Z up to a million fitness evaluations. pop=10000,100 gens. 8 percent reproduction. Sometimes used normal (Koza like) crossover and mutation, in place of own SAT composition
   operator. Complicated, stepped, fitness function. CLASS, PCLASS. Compiled common lisp includes S-expressions simplification and caching subexpressions. Master-slave
   parallelism 720 2.83GHz E5440 core Sun blade X6250 cluster (claims idle time approx 2 percent). SAT/UNSAT phase transition mkcnf SATLIB. CEC 2009 - A joint meeting of the
   IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Alex Fukunaga
%A Hideru Hiruma
%A Kazuki Komiya
%A Hitoshi Iba
%T Evolving controllers for high-level applications on a service robot: a case study with exhibition visitor flow control
%J Genetic Programming and Evolvable Machines
%V 13
%N 2
%D 2012
%P 239--263
%I 
%K genetic algorithms, genetic programming, Evolutionary robotics, Service robotics Applications
%X We investigate the application of simulation-based genetic programming to evolve controllers that perform high-level tasks on a service robot. As a case study, we
   synthesise a controller for a guide robot that manages the visitor traffic flow in an exhibition space in order to maximise the enjoyment of the visitors. We used genetic
   programming in a low-fidelity simulation to evolve a controller for this task, which was then transferred to a service robot. An experimental evaluation of the evolved
   controller in both simulation and on the actual service robot shows that it performs well compared to hand-coded heuristics, and performs comparably to a human operator.
%8 June

%A Yoshikazu Fukuyama
%A Shinichi Takayama
%A Yosuke Nakanishi
%A Hirotaka Yoshida
%T A Particle Swarm Optimization for Reactive Power and Voltage Control in Electric Power Systems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1523--1528
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-713.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Pablo Funes
%A Elizabeth Sklar
%A Hugues Juille
%A Jordan Pollack
%T The Internet as a Virtual Ecology: Coevolutionary Arms Races Between Human and Artificial Populations
%R Technical Report CS-97-197
%D 1997
%I 
%I Computer Science, Brandeis University
%C 415 South St., Waltham MA 02254 USA
%K genetic algorithms, genetic programming, autonomous agents, adaptive software, evolutionary robotics, game learning, coevolution, Tron, interactive evolution
%U http://www.demo.cs.brandeis.edu/papers/long.html#cs-97-197
%X we propose that learning complex behaviours can be achieved in a coevolutionary environment where one population consists of the human users of an interactive adaptive
   software tool and the "opposing" population is artificial, generated by a coevolutionary learning engine. We take advantage of the Internet, a connected community where
   people and software coexist. A new kind of adaptive agent can exploit its interactions with thousands of users-inside a virtual "niche"-to learn in a coevolutionary
   human-robot arms race. Our model is Tron, a simple dynamic game where introspective self-play quickly leads to collusive stagnation. We describe an application where
   thousands of small programs are sent to play with people through the Java interpreter running in their web browsers. The feedback provided by these agents is collected in
   our server and used to augment an ever improving fitness landscape for local robot-robot games. Speciation and fitness sharing provide diversity to challenge humans with a
   variety of differ ent strategies. In this way, we obtain an evolving environment where human as well as artificial adaptation are simultaneously taking place.
%Z See also \citefunes_sab98 and http://helen.cs-i.brandeis.edu/tron/html/about.html Two populations: computer play (1000), playing against people (100). Generation based.
   non-standard selection and migration strategies. Deterministic play. Limited knowledge of game arena. Java. Problem with "live and let live" or conclusion between (evolved)
   players. p10 People played 22494 games in two months. p11 Marginal improvement in computer players (28% -> 35%). Humans better than computer.

%A Pablo Funes
%A Jordan Pollack
%T Computer Evolution of Buildable Objects
%B Fourth European Conference on Artificial Life
%E P. Husbands and I. Harvey
%D 1997
%P 358--367
%I MIT Press
%K genetic algorithms, genetic programming, evolutionary design, evolutionary robotics, computer simulation
%U http://www.demo.cs.brandeis.edu/papers/ecal97.ps.gz
%X Creating artificial life forms through evolutionary robotics faces a "chicken and egg" problem: learning to control a complex body is dominated by inductive biases specific
   to its sensors and effectors, while building a body which is controllable is conditioned on the pre-existence of a brain. The idea of co-evolution of bodies and brains is
   becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in
   simulation has been constrained by the "reality gap" which implies that resultant objects are usually not buildable. The work we present takes a step in the problem of body
   evolution by applying evolutionary techniques to the design of structures assembled out of parts. Evolution takes place in a simulator we designed, which computes forces
   and stresses and predicts failure for 2-dimensional Lego structures. The final printout of our program is a schematic assembly, which can then be built physically. We
   demonstrate its functionality in several different evolved entities.
%Z An earlier revision of this paper is available in html: Brandeis University Computer Science Technical Report CS-97-191

%A Pablo Funes
%A Elizabeth Sklar
%A Hugues Juille
%A Jordan Pollack
%T Animal-Animat Coevolution: Using the Animal Population as Fitness Function
%B From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior
%E Rolf Pfeifer and Bruce Blumberg and Jean-Arcady Meyer and Stewart W. Wilson
%D 1998
%P 525--533
%I MIT Press
%C Zurich, Switzerland
%K genetic algorithms, genetic programming, adaptive agents, internet evolution, computer game playing
%U http://www.demo.cs.brandeis.edu/papers/tronsab98.ps
%X We show an artificial world where animals (humans) and animats (software agents) interact in a coevolutionary arms race. The two species each use adaptation schemes of
   their own. Learning through interaction with humans has been out of reach for evolutionary learning techniques because too many iterations are necessary. Our work
   demonstrates that the Internet is a new environment where this may be possible through an appropriate setup that creates mutualism, a relationship where human and animat
   species benefit from their interactions with each other.
%8 August  17-21
%Z http://www.isab.org.uk/confs/sab98.php section 3.4 "Hall of Fame" fig 11 http://www.demo.cs.brandeis.edu/papers/tronsab98.html
%@ 0-262-66144-6

%A Pablo J. Funes
%A Jordan B. Pollack
%T Componential Structural Simulator
%R Technical Report CS-98-198
%D 1998
%I 
%I Computer Science, Brandeis University
%C 415 South St., Waltham MA 02254 USA
%K genetic algorithms, genetic programming
%U http://www.demo.cs.brandeis.edu/papers/cs98-198.pdf
%X Our componential structural simulator procedure provides an approximate simulation that predicts resistance of structures made of modular components. The simulation focuses
   on torque strains and is able to predict stability of a structure whose breakage depends on torque stress. Structures that can be described in this fashion include those
   made out of building toy bricks such as Lego bricks, a well-known type of snap-on toy bricks, which we have used in our initial applications. The model could be applied to
   many other kinds of structures made out of modular components. It is a prediction tool that can be programmed in a computer and used to test the stability of a structure
   before proceeding to its construction.
%Z not about GP but about part of fitness function used in GP experiments, eg in \citefunes_alife

%A Pablo Funes
%A Jordan Pollack
%T Evolutionary Body Building: Adaptive Physical Designs for Robots
%J Artificial Life
%V 4
%N 4
%D 1998
%P 337--357
%I 
%K genetic algorithms, genetic programming, evolutionary robotics, body and brain coevolution, adaptive bodies, evolutionary design, lego, children's building blocks
%U http://mitpress.mit.edu/catalog/item/default.asp?sid=8F59C20B-F846-405E-9C5C-6F86770D37BB&ttype=6&tid=109
%X Creating artificial life forms through evolutionary robotics faces a "chicken and egg" problem: learning to control a complex body is dominated by problems specific to its
   sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain. The idea of co-evolution of bodies and brains is becoming popular,
   but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evo-lution of creatures in simulation has usually
   resulted in virtual entities which are not buildable, while embodied evolution in actual robotics is constrained by the slow pace of real time. The work we present takes a
   step in the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of elementary components which stick together. Evolution
   takes place in a simulator which computes forces and stresses and predicts stability of 3- dimensional brick structures. The final printout of our program is a schematic
   assembly, which is then built physically. We demonstrate the functionality of this approach to robot body building with many evolved artifacts.
%8 Fall
%Z ABS acrylonitrile butadiene styrene lego http://en.wikipedia.org/wiki/Acrylonitrile_butadiene_styrene Data in table 1 p341 appears to be wrong. Design respresented as lisp
   s-expression. \citekoza:book style crossover and mutation but with domain specific sanity checks. Details of tree pruning unclear. Unclear if repaired s-expression becomes
   geneotype or not. Only structually stable individuals allowed to become part of population (cf Tackett's \citeTackett:1995:grgsscp soft brood selection). Saftey margin only
   0.2 Cites \citefunes_cs98-198

%A Pablo J. Funes
%A Jordan B. Pollack
%T Computer Evolution of Buildable Objects
%B Evolutionary Design by Computers
%E Peter J. Bentley
%D 1999
%P 387--403
%I Morgan Kaufmann
%C San Francisco, USA
%K genetic algorithms, genetic programming, evolutionary design, evolutionary robotics, computer simulation
%U http://www.demo.cs.brandeis.edu/papers/edc98.ps.gz
%X evolution of buildable designs using miniature plastic bricks as modular components. Lego bricks are well known for their flexibility when it comes to creating low cost,
   handy designs of vehicles and structures. Their simple modular concept make toy bricks a good ground for doing evolution of computer simulated structures which can be built
   and deployed.
%O 17
%Z http://www.amazon.com/exec/obidos/ASIN/155860605X/qid=1114257064/sr=2-1/ref=pd_bbs_b_2_1/103-2923288-2944615
%@ 1-55860-605-X

%A Pablo Funes
%T Evolution of Complexity in Real-World Domains
%R Ph.D. Thesis
%D 2001
%I 
%I Computer Science, Brandeis University
%K genetic algorithms, genetic programming, AI
%U http://www.demo.cs.brandeis.edu/papers/funes_phd.html
%X Artificial Life research brings together methods from Artificial Intelligence (AI), philosophy and biology, studying the problem of evolution of complexity from what we
   might call a constructive point of view, trying to replicate adaptive phenomena using computers and robots. Here we wish to shed new light on the issue by showing how
   computer-simulated evolutionary learning methods are capable of discovering complex emergent properties in complex domains. Our stance is that in AI the most interesting
   results come from the interaction between learning algorithms and real domains, leading to discovery of emergent properties, rather than from the algorithms themselves. The
   theory of natural selection postulates that generate-test-regenerate dynamics, exemplified by life on earth, when coupled with the kinds of environments found in the
   natural world, have lead to the appearance of complex forms. But artificial evolution methods, based on this hypothesis, have only begun to be put in contact with
   real-world environments. In the present thesis we explore two aspects of real-world environments as they interact with an evolutionary algorithm. In our first experimental
   domain (chapter 2) we show how structures can be evolved under gravitational and geometrical constraints, employing simulated physics. Structures evolve that exploit
   features of the interaction between brick-based structures and the physics of gravitational forces. In a second experimental domain (chapter 3) we study how a virtual world
   gives rise to co-adaptation between human and agent species. In this case we look at the competitive interaction between two adaptive species. The purely reactive nature of
   artificial agents in this domain implies that the high level features observed cannot be explicit in the genotype but rather, they emerge from the interaction between
   genetic information and a changing domain. Emergent properties, not obvious from the lower level description, amount to what we humans call complexity, but the idea stands
   on concepts which resist formalisation -- such as difficulty or complicatedness. We show how simulated evolution, exploring reality, finds features of this kind which are
   preserved by selection, leading to complex forms and behaviours. But it does so without creating new levels of abstraction -- thus the question of evolution of modularity
   remains open.
%8 May

%A Pablo Jose Funes
%T Buildable Evolution
%J SIGEVOlution
%V 2
%N 3
%D 2007
%P 6--19
%I 
%K genetic algorithms, genetic programming, LEGO
%U http://www.sigevolution.org/issues/pdf/SIGEVOlution200703.pdf
%X The most interesting results in Artifical Life come about when some aspect of reality is captured. In the mid-1990s, Karl Sims energised the AL community with his
   ground-breaking work on evolved moving creatures [28, 29]. The life-like behaviour of Sims' creatures resulted from combining evolved morphology with a physics simulation
   based on Featherstone's earlier work [9]. The question that begged asking was: can a similar thing be done in the physical world? Can we make creatures that walk out of the
   computer screen and into the room? Two components were required: a language to evolve morphologies that have real-world counterparts, and a way to build them -- either in
   simulation or by automated building and testing. We set out to demonstrate that buildable evolution was possible using a readily available, cheap building system -- Lego
   bricks -- and an ad-hoc physics simulation that allowed us to study the interaction of the object with the physical world in silico; with respect to gravitational forces at
   least. The result [10, 14, 12, 13, 15, 16, 25, 23, 26, 24, 27] is a system that can evolve a variety of different shapes and is very easy to use, set up and replicate. Here
   I present an overview of the evolvable Lego structures project. Coinciding with the publication of this article, the source code is being released to the community
   (demo.cs.brandeis.edu/pr/buildable/source).
%8 Autumn
%Z Published April 2008. Grammar, 2D and 3D, mutation and crossover, development, bloat, test and prune, network torque propagation, NTP, EvoCAD, MNFPs, linear programming
   solver, crane, long bridge, table. homepage: http://www.icosystem.com DEMO lab Brandeis.

%A Marcus Furuholmen
%A Mats Hovin
%A Jim Torresen
%A Kyrre Glette
%T Continuous Adaptation in Robotic Systems by Indirect Online Evolution
%B ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008
%D 2008
%P 71--76
%I IEEE
%C Edinburgh
%K genetic algorithms, genetic programming, Gene Expression Programming, Automatic testing, Erbium, Gene expression, Informatics, Robot sensing systems, Robotics and
   automation, Sensor phenomena and characterisation, Sensor systems, System testing, US Department of Energy, adaptive systems, end effectors, vectors, continuous system
   identification, end effector, indirect online evolution, parameter optimisation, robotic arm, training vectors, Indirect Online Evolution, Machine Learning, Robotics
%X A conceptual framework for on line evolution in robotic systems called indirect online evolution (IDOE) is presented. A model specie automatically infers models of a hidden
   physical system by the use of gene expression programming (GEP). A parameter specie simultaneously optimises the parameters of the inferred models according to a specified
   target vector. Training vectors required for modelling are automatically provided online by the interplay between the two coevolving species and the physical system. At
   every generation, only the estimated fittest individual of the parameter specie is executed on the physical system. This approach thus limits both the evaluation time, the
   wear out and the potential hazards normally associated with direct online evolution (DOE) where every individual has to be evaluated on the physical system. Additionally,
   the approach enables continuous system identification and adaptation during normal operation. Features of IDOE are illustrated by inferring models of a simplified, robotic
   arm, and further optimising the parameters of the system according to a target position of the end effector. Simulated experiments indicate that the fitness of the IDOE
   approach is generally higher than the average fitness of DOE.
%8 6-8  August
%Z Also known as \cite4599430

%A Marcus Furuholmen
%A Kyrre Glette
%A Jim Torresen
%A Mats Hovin
%T Indirect Online Evolution - A Conceptual Framework for Adaptation in Industrial Robotic Systems
%B 8th International Conference on Evolvable Systems: From Biology to Hardware, ICES 2008
%S Lecture Notes in Computer Science
%E Gregory Hornby and Lukas Sekanina and Pauline C. Haddow
%V 5216
%D 2008
%P 165--176
%I Springer
%C Prague, Czech Republic
%K genetic algorithms, genetic programming
%X A conceptual framework for online evolution in robotic systems called Indirect Online Evolution (IDOE) is presented. A model specie automatically infers models of a
   physical system and a parameter specie simultaneously optimises the parameters of the inferred models according to a specified target behaviour. Training vectors required
   for modelling are automatically provided online by the interplay between the two coevolving species and the physical system. At every generation, only the estimated fittest
   individual of the parameter specie is executed on the physical system, hence limiting both the evaluation time, the wear out and the potential hazards normally associated
   with direct online evolution (DOE), where every candidate solution has to be evaluated on the physical system. Features of IDOE are demonstrated by inferring models of a
   simple hidden system containing geometric shapes that are further optimized according to a target value. Simulated experiments indicate that the fitness of the IDOE
   approach is generally higher than the average fitness of DOE.
%8 September  21-24
%Z ICES

%A Marcus Furuholmen
%A Kyrre Glette
%A Mats Hovin
%A Jim Torresen
%T Coevolving Heuristics for The Distributor's Pallet Packing Problem
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P -
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, gene expression programming
%X Efficient heuristics are required for on-line optimization problems where search-based methods are unfeasible due to frequent dynamics in the environment. This is
   especially apparent when operating on combinatorial NP-complete problems involving a large number of items. However, designing new heuristics for these problems may be a
   difficult and time consuming task even for domain experts. Therefore, automating this design process may benefit the industry when facing new and difficult optimization
   problems. The Distributor's Pallet Packing Problem (DPPP) is the problem of loading a pallet of non-homogenous items coming off a production line and is an instance of a
   range of resource-constrained, NP-complete, scheduling problems that are highly relevant for practical tasks in the industry. Common heuristics for the DPPP typically
   decompose the problem into two sub-problems; one of prescheduling all items on the production line and one of packing the items on the pallet. In this paper we concentrate
   on a two dimensional version of the DPPP and the more realistic scenario of having knowledge about only a limited set of the items on the production line. This paper aims
   at demonstrating that such an unknown heuristic may be evolved by Gene Expression Programming and Cooperative Coevolution. By taking advantage of the natural problem
   decomposition, two species evolve heuristics for pre-scheduling and packing respectively. We also argue that the evolved heuristics form part of a developmental stage in
   the construction of the finished phenotype, that is, the loaded pallet.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Marcus Furuholmen
%A Kyrre Harald Glette
%A Mats Erling Hovin
%A Jim Torresen
%T Scalability, generalization and coevolution -- experimental comparisons applied to automated facility layout planning
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 691--698
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Gene Expression Programming
%X Several practical problems in industry are difficult to optimize, both in terms of scalability and representation. Heuristics designed by domain experts are frequently
   applied to such problems. However, designing optimized heuristics can be a non-trivial task. One such difficult problem is the Facility Layout Problem (FLP) which is
   concerned with the allocation of activities to space. This paper is concerned with the block layout problem, where the activities require a fixed size and shape (modules).
   This problem is commonly divided into two sub problems; one of creating an initial feasible layout and one of improving the layout by interchanging the location of
   activities. We investigate how to extract novel heuristics for the FLP by applying an approach called Cooperative Coevolutionary Gene Expression Programming (CCGEP). By
   taking advantage of the natural problem decomposition, one species evolves heuristics for pre-scheduling, and another for allocating the activities onto the plant. An
   experimental, comparative approach investigates various features of the CCGEP approach. The results show that the evolved heuristics converge to suboptimal solutions as the
   problem size grows. However, coevolution has a positive effect on optimization of single problem instances. Expensive fitness evaluations may be limited by evolving
   generalized heuristics applicable to unseen fitness cases of arbitrary sizes.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Marcus Furuholmen
%A Kyrre Glette
%A Mats Hovin
%A Jim Torressen
%T An Indirect Approach to the Three-dimensional Multi-pipe Routing Problem
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 86--97
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X This paper explores an indirect approach to the Three-dimensional Multi-pipe Routing problem. Variable length pipelines are built by letting a virtual robot called a turtle
   navigate through space, leaving pipe segments along its route. The turtle senses its environment and acts in accordance with commands received from heuristics currently
   under evaluation. The heuristics are evolved by a Gene Expression Programming based Learning Classifier System. The suggested approach is compared to earlier studies using
   a direct encoding, where command lines were evolved directly by genetic algorithms. Heuristics generating higher quality pipelines are evolved by fewer generations compared
   to the direct approach, however the evaluation time is longer and the search space is more complex. The best evolved heuristic is short and simple, builds modular
   solutions, exhibits some degree of generalization and demonstrates good scalability on test cases similar to the training case.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Marcus Furuholmen
%A Kyrre Glette
%A Mats H{\o}vin
%A Jim Torresen
%T Evolutionary Approaches to the Three-dimensional Multi-pipe Routing Problem: A Comparative Study Using Direct Encodings
%B Evolutionary Computation in Combinatorial Optimization, 10th European Conference, EvoCOP 2010, Istanbul, Turkey, April 7-9, 2010. Proceedings
%S Lecture Notes in Computer Science
%E Peter I. Cowling and Peter Merz
%V 6022
%D 2010
%P 71--82
%I Springer
%K genetic algorithms
%Z 'not using GP, but rather GA - however, the results are compared with GP in another paper which is in GP bib.'

%A Marcus Furuholmen
%A Kyrre Glette
%A Mats Hovin
%A Jim Torresen
%T A Coevolutionary, Hyper Heuristic approach to the optimization of Three-dimensional Process Plant Layouts -A comparative study
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X A Coevolutionary, Hyper Heuristic approach to the optimisation of Three-dimensional Process Plant Layouts (3DPPLs) is explored. By taking advantage of the natural problem
   decomposition, one population of layout heuristics, and another population of scheduling heuristics are coevolved. Generalised heuristics are evolved by training on
   multiple small problem instances, so that training time is reduced. The best generalized heuristic builds arbitrary sized 3DPPLs which reduce the cost by 18percent when
   compared to a handmade heuristic. Specialised heuristics are evolved by optimising each problem instance and outperforms the generalized heuristics after a fixed number of
   generations. Compared to a direct-encoded Genetic Algorithm, the benefit of specialized heuristics increases with the size of the problem, and costs are reduced by
   30percent when compared to the handmade heuristic.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586329

%A Hiroshi Furutani
%T Analytical Solutions for Infinite Population Genetic Algorithms on Multiplicative Landscape
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 204--211
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%X eigen values, eigenvectors, walsh functions
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Colin Fyfe
%A John Paul Marney
%A Heather F. E. Tarbert
%T Technical analysis versus market efficiency - a genetic programming approach
%J Applied Financial Economics
%V 9
%N 2
%D 1999
%P 183--191
%I 
%K genetic algorithms, genetic programming
%U http://alidoro.catchword.com/vl=8080356/cl=18/nw=1/fm=docpdf/rpsv/catchword/routledg/09603107/v9n2/s7/p183
%X In the paper the authors maintain that the prevalence of technical analysis in professional investment argues that such techniques should perhaps be taken more seriously by
   academics. The new technique of genetic programming is used to investigate a long time series of price data for a quoted property investment company, to discern whether
   there are any patterns in the data which could be used for technical trading purposes. A successful buy rule is found which generates returns in excess of what would be
   expected from the best-fitting null time-series model. Nevertheless, this turns out to be a more sophisticated variant of the buy and hold rule, which the authors term
   timing specific buy and hold. Although the rule does outperform simple buy and hold, it really does not provide sufficient grounds for the rejection of the efficient market
   hypothesis, though it does suggest that further investigation of the specific conditions of applicability of the EMH may be appropriate.
%8 April

%A Moncef Gabbouj
%T Multidimensional particle swarm optimization and applications in data clustering and image retrieval
%B Image Processing Theory Tools and Applications (IPTA), 2010 2nd International Conference on
%D 2010
%P 5
%I 
%X Particle swarm optimization (PSO) was introduced by Kennedy and Eberhart in 1995 as a population based stochastic search and optim
%8 July
%Z Also known as \cite5586831

%A Mark Gabel
%A Zhendong Su
%T A Study of the Uniqueness of Source Code
%B Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
%D 2010
%P 147--156
%I ACM
%C Santa Fe, New Mexico, USA
%K genetic algorithms, genetic programming, large scale study, software uniqueness, source code
%U http://www.cs.ucdavis.edu/~su/publications/fse10.pdf
%X This paper presents the results of the first study of the uniqueness of source code. We define the uniqueness of a unit of source code with respect to the entire body of
   written software, which we approximate with a corpus of 420 million lines of source code. Our high-level methodology consists of examining a collection of 6,000 software
   projects and measuring the degree to which each project can be `assembled' solely from portions of this corpus, thus providing a precise measure of `uniqueness' that we
   call syntactic redundancy. We parametrised our study over a variety of variables, the most important of which being the level of granularity at which we view source code.
   Our suite of experiments together consumed approximately four months of CPU time, providing quantitative answers to the following questions: at what levels of granularity
   is software unique, and at a given level of granularity, how unique is software? While we believe these questions to be of intrinsic interest, we discuss possible
   applications to genetic programming and developer productivity tools.
%8 7-11  November
%Z Brief mention of GP and how their results apply to GP. C,C++,Java. n-grams. p147 'Singularity in software engineering's future'. p149 'syntactically redundant' p152
   'striking similarity' between 30 current sourceforge projects. p155 Almost all small code fragments have been written many times (Small means 'approximately one to seven
   lines of source code'). Cites Jiang and Zu ISSTA 2009, \citekoza:book and \citeWeimer:2009:ICES. FSE '10, Gabel:2010:SUS:1882291.1882315

%A Christian Gagn{\'e}
%A Marc Parizeau
%T Open BEAGLE: A New C++ Evolutionary Computation Framework
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 888
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, poster paper, artificial intelligence, evolutionary computation framework, object oriented genetic programming, software
   engineering, software tools
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Christian Gagn{\'e}
%A Marc Parizeau
%T Open BEAGLE: A New Versatile C++ Framework for Evolutionary Computation
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 161--168
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%U http://vision.gel.ulaval.ca/en/publications/Id_43/PublDetails.php
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp C++ STL GPL

%A Christian Gagne
%A Marc Parizeau
%A Marc Dubreuil
%T Distributed BEAGLE: An Environment for Parallel and Distributed Evolutionary Computations
%B Procceedings of the 17th Annual International Symposium on High Performance Computing Systems and Applications (HPCS) 2003
%D 2003
%I 
%C Sherbrooke, Quebec, Canada
%K genetic algorithms, genetic programming
%U http://vision.gel.ulaval.ca/en/publications/Id_439/PublDetails.php
%X Evolutionary computation is a promising artificial intelligence field involving the simulation of natural evolution to solve problems. Given its implicit parallelism and
   high computational requirements, evolutionary computation is the perfect candidate for high performance parallel computers. This paper presents Distributed BEAGLE, a new
   master-slave architecture for parallel and distributed evolutionary computations. It is designed as a robust, adaptive, and scalable system targeted for local networks of
   workstations and Beowulf clusters. Results obtained with a plausible deployment scenario demonstrate that system performance degrades gracefully when failures occurred,
   while still achieving near linear speedup in the ideal case.
%8 May  11-14

%A Christian Gagne
%A Marc Parizeau
%A Marc Dubreuil
%T The Master-Slave Architecture for Evolutionary Computations Revisited
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1578--1579
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, poster
%U http://vision.gel.ulaval.ca/en/publications/Id_440/PublDetails.php
%X The recent availability of cheap Beowulf clusters has generated much interest for Parallel and Distributed Evolutionary Computations (PDEC). Another often neglected source
   of CPU power for PDEC are networks of PCs, in many case very powerful workstations, that run idle each day for long periods of time. To exploit efficiently both Beowulfs
   and networks of heterogeneous workstations we argue that the classic master-slave distribution model is superior to the currently more popular island-model. Results
   obtained with a plausible deployment scenario demonstrate that system performance degrades gracefully when failures occurred, while still achieving near linear speedup in
   the ideal case.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eights Annual Genetic Programming Conference (GP-2003) BEAGLE
%@ 3-540-40603-4

%A Christian Gagne
%A Marc Parizeau
%A Marc Dubreuil
%T A Robust Master-Slave Distribution Architecture for Evolutionary Computations
%B Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Bart Rylander
%D 2003
%P 80--87
%I 
%C Chicago, USA
%K genetic algorithms, genetic programming
%U http://vision.gel.ulaval.ca/en/publications/Id_456/PublDetails.php
%X This paper presents a new robust master-slave distribution architecture for multiple populations Evolutionary Computations (EC). It discusses the main advantages and
   drawbacks of master-slave models over island models for parallel and distributed EC. It also formulates a mathematical model of the master-slave distribution policies in
   order to show that, contrary to what is implied by current mainstream developments in island models, a well designed master-slave approach can be both robust and scalable
   (up to a certain point). Finally, it introduces some of the details of a new C++ framework named Distributed BEAGLE, which implements this architecture over the Open BEAGLE
   EC framework.
%8 12--16  July
%Z GECCO-2003LB

%A Christian Gagne
%T Algorithmes evolutionnaires appliques a la reconnaissance des formes et a la conception optique
%R Ph.D. Thesis
%D 2005
%I 
%I Laval University
%C Quebec (QC), Canada
%K genetic algorithms, genetic programming
%U http://www.theses.ulaval.ca/2005/22701/22701.pdf
%X Open BEAGLE est organis\'e en trois couches logicielles principales, avec \`a la base les fondations orient\'ees objet, sur lesquelles s'ajoute une framework g\'en \'erique
   comprenant les m\'ecanismes g\'en\'eraux de l'outil, ainsi que plusieurs frameworks sp\'ecialis\'ees qui implantent diff\'erentes saveurs d'AE. L'outil comporte \'egalement
   deux extensions servant \`a distribuer des calculs sur plusieurs ordinateurs et \`a visualiser des r\'esultats. Ensuite, trois applications illustrent diff\'erentes
   approches d'utilisation des AE dans un contexte de reconnaissance des formes. Premi\`erement, on optimise des classifieurs bas\'es sur la r\`egle du plus proche voisin avec
   la s\'election de prototypes par un algorithme g\'en\'etique, simultan\'ement \`a la construction de mesures de voisinage par programmation g\'en\'etique (PG). \`A cette
   co-\'evolution coop\'erative \`a deux esp\`eces, on ajoute la co-\'evolution comp\'etitive d'une troisi\`eme esp\`ece pour la s\'election de donn\'ees de test, afin
   d'am\'eliorer la capacit\'e de g\'en\'eralisation des solutions. La deuxi\`eme application consiste en l'ing\'enierie de repr\'esentations par PG pour la reconnaissance de
   caract\`eres manuscrits. Cette ing\'enierie \'evolutionnaire s'effectue par un positionnement automatique de r\'egions dans la fen\^etre d'attention jumel\'e \`a la
   s\'election d'ensembles flous pour l'extraction de caract\'eristiques. Cette application permet d'automatiser la recherche de repr\'esentations de caract\`eres, op\'eration
   g\'en\'eralement effectu\'ee par des experts humains suite \`a un processus d'essais et erreurs. Pour la troisi\`eme application en reconnaissance des formes, on propose un
   syst\`eme extensible pour la combinaison hi\'erarchique de classifieurs dans un arbre de d\'ecision flou. Dans ce syst\`eme, la topologie des arbres est \'evolu\'ee par PG
   alors que les param\`etres num\'eriques des unit\'es de classement sont d\'etermin \'es par des techniques d'apprentissage sp\'ecialis\'ees. Le syst\`eme est test\'e avec
   trois types simples d'unit\'es de classement. Pour toutes ces applications en reconnaissance des formes, on utilise une mesure d'ad\'equation \`a deux objectifs afin de
   minimiser les erreurs de classement et la complexit\'e des solutions. Une derni\`ere application d\'emontre l'efficacit\'e des AE pour la conception de syst` emes de
   lentilles. On utilise des strat\'egies d'\'evolution auto-adaptatives hybrid\'ees avec une technique d'optimisation locale sp\'ecialis\'ee pour la r\'esolution de deux
   probl\`emes complexes de conception optique. Dans les deux cas, on d\'emontre que les AE hybrides sont capables de g\'en\'erer des r\'esultats comparables ou sup\'erieurs
   \`a ceux produits par des experts humains. Ces r\'esultats sont prometteurs dans la perspective d'une automatisation plus pouss\'ee de la conception optique. On pr\'esente
   \'egalement une exp\'erience suppl\'ementaire avec une mesure \`a deux objectifs servant \`a maximiser la qualit\'e de l'image et \`a minimiser le co\^ut du syst\`eme de
   lentilles.;
%8 May
%Z Cf posting to GP-list Tue, 11 Oct 2005 09:50:18 +0200 Entirely written in French

%A Christian Gagn{\'e}
%A Marc Schoenauer
%A Marc Parizeau
%A Marco Tomassini
%T Genetic Programming, Validation Sets, and Parsimony Pressure
%R ARTCOLLOQUE inria-00000996
%D 2006
%I HAL - CCSd - CNRS
%I l'Equipe TAO INRIA Futurs
%C LRI Bat. 490, Universite Paris Sud, 91405 Orsay CEDEX, France
%K genetic algorithms, genetic programming, Computer Science/Learning
%U http://arxiv.org/abs/cs/0601044
%X Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a
   limited number of samples. This paper is an investigation on two methods to improve generalization in GP-based learning: 1) the selection of the best-of-run individuals
   using a three data sets methodology, and 2) the application of parsimony pressure in order to reduce the complexity of the solutions. Results using GP in a binary
   classification setup show that while the accuracy on the test sets is preserved, with less variances compared to baseline results, the mean tree size obtained with the
   tested methods is significantly reduced.
%O Christian Gagn\'e <christian.gagne@lri.fr>
%8 January ~09

%A Christian Gagn\'e
%A Marc Schoenauer
%A Marc Parizeau
%A Marco Tomassini
%T Genetic Programming, Validation Sets, and Parsimony Pressure
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 109--120
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050109.pdf
%X Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a
   limited number of samples. This paper is an investigation on two methods to improve generalisation in GP-based learning: 1) the selection of the best-of-run individuals
   using a three data sets methodology, and 2) the application of parsimony pressure in order to reduce the complexity of the solutions. Results using GP in a binary
   classification setup show that while the accuracy on the test sets is preserved, with less variances compared to baseline results, the mean tree size obtained with the
   tested methods is significantly reduced.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006 Also known as \citeoai:hal.ccsd.cnrs.fr:inria-00000996_v1 overfitting,
   regularisation, V-C dimension, MDL, UCI, fit the noise.
%@ 3-540-33143-3

%A Christian Gagn\'e
%A Marc Parizeau
%T Genericity in Evolutionary Computation Software Tools: Principles and Case Study
%J International Journal on Artificial Intelligence Tools
%V 15
%N 2
%D 2006
%P 173--194
%I 
%K genetic algorithms, genetic programming, Evolutionary computation, genetic algorithms, software engineering, object oriented programming
%U http://vision.gel.ulaval.ca/~parizeau/Publications/IJAIT06.pdf
%X This paper deals with the need for generic software development tools in evolutionary computations (EC). These tools will be essential for the next generation of
   evolutionary algorithms where application designers and researchers will need to mix different combinations of traditional EC (e.g. genetic algorithms, genetic programming,
   evolutionary strategies, etc.), or to create new variations of these EC, in order to solve complex real world problems. Six basic principles are proposed to guide the
   development of such tools. These principles are then used to evaluate six freely available, widely used EC software tools. Finally, the design of Open BEAGLE, the framework
   developed by the authors, is presented in more detail.
%O 22 pages
%8 April
%Z Laboratoire de Vision et Systemes Numeriques (LVSN), Departement de Genie electrique et de Genie Informatique, Universite Laval, Quebec (QC), Canada, G1K 7P4, Canada

%A Christian Gagn\'e
%A Marc Parizeau
%T Open BEAGLE A C++ Framework for your Favorite Evolutionary Algorithm
%J SIGEvolution
%V 1
%N 1
%D 2006
%P 12--15
%I 
%K genetic algorithms, genetic programming, CMA-ES, NSGA-II, NSGA2, coevolution, onemax
%U http://www.sigevolution.org/2006/01/issue.pdf
%8 April

%A Christian Gagne
%A Marc Schoenauer
%A Michele Sebag
%A Marco Tomassini
%T Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection
%B Parallel Problem Solving from Nature - PPSN IX
%S LNCS
%E Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao
%V 4193
%D 2006
%P 1008--1017
%I Springer-Verlag Berlin
%C Reykjavik, Iceland
%K genetic algorithms, genetic programming, hyperheuristic, DSS, coevolution, open beagle
%U http://arxiv.org/abs/cs/0611135
%X Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalised as a well-posed
   optimisation problem; ii) nonlinear learning can be brought into linear learning thanks to the kernel trick and the mapping of the initial search space onto a high
   dimensional feature space. The kernel is designed by the ML expert and it governs the efficiency of the SVM approach. In this paper, a new approach for the automatic design
   of kernels by Genetic Programming, called the Evolutionary Kernel Machine (EKM), is presented. EKM combines a well-founded fitness function inspired from the margin
   criterion, and a co-evolution framework ensuring the computational scalability of the approach. Empirical validation on standard ML benchmark demonstrates that EKM is
   competitive using state-of-the-art SVMs with tuned hyper-parameters.
%8 9-13  September
%Z PPSN-IX evolved Kernels are forced to be symmetric functions. Mercer's condition not enforced, but evolved. 3 co-evolving populations. runtime < 1 hour. Size based
   parsimony pressure. Comparison with k-nn nearest neighbours and SVM, GK-SVM (both with somewhat optimised parameters). 6 undemanding UCI benchmarks.
%@ 3-540-38990-3

%A Christian Gagne
%A Marc Parizeau
%T Genetic Engineering of Hierarchical Fuzzy Regional Representations for Handwritten Character Recognition
%J International Journal on Document Analysis and Recognition
%V 8
%N 4
%D 2006
%P 223--231
%I 
%K genetic algorithms, genetic programming
%U http://vision.gel.ulaval.ca/fr/publications/Id_607/PublDetails.php
%X This paper presents a genetic programming based approach for optimising the feature extraction step of a handwritten character recogniser. This recognizer uses a simple
   multilayer perceptron as a classifier and operates on a hierarchical feature space of orientation, curvature, and centre of mass primitives. The nodes of the hierarchy
   represent rectangular sub-regions of their parent node, the tree root corresponding to the character's bounding box. Within each sub-region, a variable number of fuzzy
   features are extracted. Genetic programming is used to simultaneously learn the best hierarchy and the best combination of fuzzy features. Moreover, the fuzzy features are
   not predetermined, they are inferred from the evolution process which runs a two-objective selection operator. The first objective maximises the recognition rate, and the
   second minimises the feature space size. Results on Unipen data show that, using this approach, robust representations could be obtained that out-performed comparable
   human-designed hierarchical fuzzy regional representations.
%8 September

%A Christian Gagn\'e
%A Marc Parizeau
%T Co-evolution of Nearest Neighbor Classifiers
%J International Journal of Pattern Recognition and Artificial Intelligence
%V 21
%N 5
%D 2007
%P 921--946
%I 
%K genetic algorithms, genetic programming
%U http://vision.gel.ulaval.ca/en/publications/Id_692/PublDetails.php
%X This paper presents experiments of Nearest Neighbour (NN) classifier design using different evolutionary computation methods. Through multi-objective and co-evolution
   techniques, it combines genetic algorithms and genetic programming to both select NN prototypes and design a neighbourhood proximity measure, in order to produce a more
   efficient and robust classifier. The proposed approach is compared with the standard NN classifier, with and without the use of classic prototype selection methods, and
   classic data normalisation. Results on both synthetic and real data sets show that the proposed methodology performs as well or better than other methods on all tested data
   sets.
%8 August

%A Alexei A. Gaivoronski
%T Modeling of Complex Economic Systems with Agent Nets
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1265--1272
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-041.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Pawel Gajda
%A Krzysztof Krawiec
%T Evolving a Vision-Driven Robot Controller for Real-World Indoor Navigation
%B Proceedings of EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops
%S Lecture Notes in Computer Science
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Rolf Drechsler and Anik\'o Ek\'art and Anna Esparcia-Alc\'azar and Muddassar Farooq and
   Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang
%V 4974
%D 2008
%P 184--193
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%X In this paper, we use genetic programming (GP) to evolve a vision-driven robot controller capable of navigating in a real-world environment. To this aim, we extract visual
   primitives from the video stream provided by a camera mounted on the robot and let them to be interpreted by a GP individual. The response of GP expressions is then used to
   control robot's servos. Thanks to the primitive-based approach, evolutionary process is less constrained in the process of synthesising image features. Experiments
   concerning navigation in indoor environment indicate that the evolved controller performs quite well despite very limited human intervention in the design phase.
%8 26-28  March

%A Zbysek Gajda
%A Lukas Sekanina
%T Gate-Level Optimization of Polymorphic Circuits Using Cartesian Genetic Programming
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 1599--1604
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, cartesian genetic programming
%X Polymorphic digital circuits contain ordinary and polymorphic gates. In the past, Cartesian Genetic Programming (CGP) has been applied to synthesize polymorphic circuits at
   the gate level. However, this approach is not scalable. Experimental results presented in this paper indicate that larger and more efficient polymorphic circuits can be
   designed by a combination of conventional design methods (such as BDD, Espresso or ABC System) and evolutionary optimization (conducted by CGP). Proposed methods are
   evaluated on two benchmark circuits - Multiplier/Sorter and Parity/Majority circuits of variable input size.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Zbysek Gajda
%A Lukas Sekanina
%T When does Cartesian genetic programming minimize the phenotype size implicitly?
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 983--984
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, Cartesian genetic programming, Poster
%X A new method is proposed to minimize the number of gates in combinational circuits using Cartesian Genetic Programming (CGP). We show that when the selection of the parent
   individual is performed on basis of its functionality solely (neglecting thus the phenotype size) smaller circuits can be evolved even if the number of gates is not
   considered by a fitness function. This phenomenon is confirmed on the evolutionary design of combinational multipliers.
%8 7-11  July
%Z Also known as \cite1830661 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Zbysek Gajda
%A Lukas Sekanina
%T An Efficient Selection Strategy for Digital Circuit Evolution
%B Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010
%S Lecture Notes in Computer Science
%E Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller
%V 6274
%D 2010
%P 13--24
%I Springer
%C York
%K genetic algorithms, genetic programming, cartesian genetic programming
%X In this paper, we propose a new modification of Cartesian Genetic Programming (CGP) that enables to optimise's digital circuits more significantly than standard CGP. We
   argue that considering fully functional but not necessarily smallest-discovered individual as the parent for new population can decrease the number of harmful mutations and
   so improve the search space exploration. This phenomenon was confirmed on common benchmarks such as combinational multipliers and the LGSynth91 circuits.
%8 September  6-8

%A G. Galeano
%A F. Fernandez
%A M. Tomassini
%A L. Vanneschi
%T Studying the influence of Synchronous and Asynchronous parallel GP on Programs' Length Evolution
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 1727--1732
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%X We present a study of parallel and distributed genetic programming models and their relationships with the bloat phenomenon. The experiments that we have performed have
   also allowed us to find an interesting link between the number of processes, subpopulations and the model we should use when applying parallelism to GP. We study the
   synchronous and asynchronous version of the island-model in GP domain.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A John C. Gallagher
%A Randall D. Beer
%T Evolution and Analysis of Dynamical Neural Networks for Agents Integrating Vision, Locomotion, and Short-Term Memory
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1273--1280
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-005.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Marcus Gallagher
%A Marcus Frean
%A Tom Downs
%T Real-valued Evolutionary Optimization using a Flexible Probability Density Estimator
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 840--846
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%U http://www.itee.uq.edu.au/~marcusg/papers/gallagher_gecco99.ps.gz
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Peter Galos
%A Peter Nordin
%A Joel Ols{\'e}n
%A Kristofer Sund{\'e}n Ringn{\'e}r
%T A General Approach to Automatic Programming Using Occam's Razor, Compression, and Self-Inspection
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1806--1807
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, poster
%X general method for automatic programming which can be seen as a generalization of techniques such as genetic programming and ADATE. The approach builds on the assumption
   that data compression can be used as a metaphor for cognition and intelligence. The proof-of-concept system is evaluated on sequence prediction problems. As a starting
   point, the process of inferring a general law from a data set is viewed as an attempt to compress the observed data. From an artificial intelligence point of view,
   compression is a useful way of measuring how deeply the observed data is understood. If the sequence contains redundancy it exists a shorter description i.e. the sequence
   can be compressed.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A L. B. Gamage
%A C. W. {de Silva}
%T A System Framework With Online Monitoring and Evaluation for Design Evolution of Engineering Systems
%J Journal of Computing and Information Science in Engineering
%V 10
%N 3
%D 2010
%I 
%K genetic algorithms, genetic programming
%X This paper presents a methodology for the design evolution of engineering systems, with a mechatronic emphasis. The developed approach specifically integrates machine
   health monitoring and an expert system and carries out the design evolution of a multidomain dynamic system using bond graph modelling and genetic programming. The
   evolution of a bond graph model of a mechatronic system through genetic programming enables the exploration of the design space, thereby generating a global optimum design
   solution in an automated manner. Domain knowledge and expertise are used to control the design exploration and to restrict it only to a meaningful design space. As an
   illustrative example, the developed methodology is applied to redesign the electrohydraulic manipulator of an existing industrial fish processing machine
%O Technical Briefs
%8 September
%Z Industrial Automation Laboratory, Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada American Society of Mechanical
   Engineers

%A Zhaohui Gan
%A Tao Shang
%A Gang Shi
%A Min Jiang
%T Evolutionary Design of Combinational Logic Circuits Using an Improved Gene Expression-Based Clonal Selection Algorithm
%B Fifth International Conference on Natural Computation, ICNC '09
%V 4
%D 2009
%P 37--41
%I 
%K genetic algorithms, genetic programming, gene expression programming, GEP encoding, gene expression-based clonal selection algorithm, multioutput combinational logic
   circuit, biomolecular electronics, combinational circuits, genetics, molecular biophysics
%X In this paper, an improved gene expression-based clonal selection algorithm (IGE-CSA) is proposed, which is aimed at solving synthesis problems of combinational logic
   circuits. The encoding of gene expression programming (GEP) is improved. Compared with GEP encoding, the proposed encoding is more compact and fits to represent
   multi-output combinational logic circuit. Clonal selection algorithm (CSA) is applied as search engine of the proposed approach. The proposed method is applied into
   combinational logic circuit design successfully. Two kinds of combinational logic circuits are synthesised to verify the effectiveness of the proposed approach. The
   experimental results show that the proposed approach can automatically generate combinational logic circuits efficiently and effectively. Compared with other method, the
   obtained circuits by the proposed method are optimal.
%8 August
%Z Also known as \cite5365151

%A Zhaohui Gan
%A Tommy W. S. Chow
%A W. N. Chau
%T Clone selection programming and its application to symbolic regression
%J Expert Systems with Applications
%V 36
%N 2, Part 2
%D 2009
%P 3996--4005
%I 
%K genetic algorithms, genetic programming, gene expression programming, Clone selection, Programming, Immune system, Gene expression
%U http://www.sciencedirect.com/science/article/B6V03-4S02048-9/2/d5a34ad92d4cf0f6f5e33f4407a2776f
%X A new idea [`]clone selection programming (CSP)' is introduced in this paper. The proposed methodology is used for deriving new algorithms in the area of evolutionary
   computing aimed at solving a wide range of problems. In CSP, antibodies represent candidate solutions, which are encoded according to the structure of antibody. The
   antibodies are able to keep syntax correct even they are changed with iterations. Also, the clone selection principle is developed as a search strategy. The proposed
   strategies have been thoroughly evaluated by intensive simulations. The results demonstrate the effectiveness and excellent convergent qualities of the CSP based search
   strategy. In our study, the convergence rate with respect to population size and other parameters is studied. A thorough comparative study between our proposed CSP based
   method with the gene expression programming (GEP), and immune programming (IP) are included. The comparative results show that the CSP based method can significantly
   improve the program performance. The experimental results indicate that the proposed method is very robust under all the investigated cases.

%A Zhaohui Gan
%A Zhenkun Yang
%A Tao Shang
%A Tianyou Yu
%A Min Jiang
%T Automated synthesis of passive analog filters using graph representation
%J Expert Systems with Applications
%V 37
%N 3
%D 2010
%P 1887--1898
%I 
%K genetic algorithms, genetic programming, Analog passive filter synthesis, Automatic design, Clone selection algorithm, Graph-based encoding scheme
%U http://www.sciencedirect.com/science/article/B6V03-4WXHBSP-5/2/32ead9142a06172b08c290d1ce58b362
%X In this paper, a novel method based on graph encoding scheme and clone selection algorithm is proposed for synthesising passive analog filters. Graph is the most natural
   and convenient data structure to represent analog electronic circuit. The proposed graph-based encoding scheme can represent any topologies of passive analog circuit and
   their component values. Combined with the efficient analog circuit encoding scheme, clone selection algorithm is employed as a search engine for automatic design of passive
   analog filters. The proposed method can synthesise both topology and sizing (component parameters) of circuit simultaneously. Three filter design tasks are experimented to
   evaluate the proposed method. The experimental results demonstrate that passive analog filters can be generated effectively with modest computation time. Taking more
   practical conditions into account, the proposed method can be applied into automatic design of passive analog filters for engineering application without the guidance of
   experienced engineers.

%A A. H. Gandomi
%A A. H. Alavi
%A S. S. Sadat Hosseini
%T A Discussion on "Genetic programming for retrieving missing information in wave records along the west coast of India" [Applied Ocean Research 2007; 29 (3): 99-111]
%J Applied Ocean Research
%V 30
%N 4
%D 2008
%P 338--339
%I 
%K genetic algorithms, genetic programming, Linear structure, Wave height
%U http://www.sciencedirect.com/science/article/B6V1V-4VXJVY5-1/2/f5aca485c623afab39556b3979e70bff
%X The discussers appreciate the work conducted by the authors for examining the potential of the application of genetic programming (GP) for filling up the missing
   significant wave height values at a given location based on the same being collected at the nearby stations. The proposed approach has been implemented using two different
   softwares, Discipulus and Kernel software. A comparison of the GP-based predictions with those of artificial neural networks (ANNs) was performed in the aforementioned
   study. The discussers would like to present the following important viewpoints, which the authors and potential researchers need to consider. The discussion will focus on
   main points that are not considered in the study.
%Z Discussion of \citeKalra200799. See also reply \citeDeo2008340

%A A. H. Gandomi
%A A. H. Alavi
%A S. Kazemi
%A M. M. Alinia
%T Behavior appraisal of steel semi-rigid joints using Linear Genetic Programming
%J Journal of Constructional Steel Research
%V 65
%N 8-9
%D 2009
%P 1738--1750
%I 
%K genetic algorithms, genetic programming, Semi-rigid joints, Steel structures
%U http://www.sciencedirect.com/science/article/B6V3T-4W8KHNW-4/2/4833ff184048303a27710677ee1f047f
%X This paper proposes an alternative approach for predicting the flexural resistance and initial rotational stiffness of semi-rigid joints in steel structures using Linear
   Genetic Programming (LGP). Three types of steel beam-column joints i.e. end plates, welded, and end bolted joints with angles are investigated. Models are constructed by
   using test results available in the literature. The accuracy of the proposed models is verified by comparing the outcomes to the experimental results. LGP models are
   further compared to the corresponding design code (Eurocode 3), reference values and several existing models. The results demonstrate that the LGP based models in most
   cases provide superior performance than other models.

%A Amir Hossein Gandomi
%A Amir Hossein Alavi
%A Mohammad Ghasem Sahab
%A Parvin Arjmandi
%T Formulation of elastic modulus of concrete using linear genetic programming
%J Journal of Mechanical Science and Technology
%V 24
%N 6
%D 2010
%P 1273--1278
%I 
%K genetic algorithms, genetic programming, Tangent elastic modulus, Linear genetic programming, Compressive strength, Normal and high strength concrete, Formulation
%U http://www.springerlink.com/content/h0m3414774224425/
%X This paper proposes a novel approach for the formulation of elastic modulus of both normal-strength concrete (NSC) and high-strength concrete (HSC) using a variant of
   genetic programming (GP), namely linear genetic programming (LGP). LGP-based models relate the modulus of elasticity of NSC and HSC to the compressive strength, as
   similarly presented in several codes of practice. The models are developed based on experimental results collected from the literature. A subsequent parametric analysis is
   further carried out to evaluate the sensitivity of the elastic modulus to the compressive strength variations. The results demonstrate that the proposed formulae can
   predict the elastic modulus with an acceptable degree of accuracy. The LGP results are found to be more accurate than those obtained using the buildings codes and various
   solutions reported in the literature. The LGP-based formulas are quite simple and straightforward and can be used reliably for routine design practice.
%8 June
%Z 1Structural Health Monitoring Research Group, College of Civil Engineering, Tafresh University, Tafresh, Iran

%A Amir Hossein Gandomi
%A Amir Hossein Alavi
%A Mohammad Ghasem Sahab
%T New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming
%J Materials and Structures
%V 43
%N 7
%D 2010
%P 963--983
%I 
%K CFRP confinement, Linear genetic programming, Formulation, Concrete compressive strength
%X This paper proposes a new approach for the formulation of compressive strength of carbon fibre reinforced plastic (CFRP) confined concrete cylinders using a promising
   variant of genetic programming (GP) namely, linear genetic programming (LGP). The LGP-based models are constructed using two different sets of input data. The first set of
   inputs comprises diameter of concrete cylinder, unconfined concrete strength, tensile strength of CFRP laminate and total thickness of CFRP layers. The second set includes
   unconfined concrete strength and ultimate confinement pressure which are the most widely used parameters in the CFRP confinement existing models. The models are developed
   based on experimental results collected from the available literature. The results demonstrate that the LGP-based formulae are able to predict the ultimate compressive
   strength of concrete cylinders with an acceptable level of accuracy. The LGP results are also compared with several CFRP confinement models presented in the literature and
   found to be more accurate in nearly all of the cases. Moreover, the formulas evolved by LGP are quite short and simple and seem to be practical for use. A subsequent
   parametric study is also carried out and the trends of the results have been confirmed via some previous laboratory studies.
%8 August
%Z College of Civil Engineering, Tafresh University, Tafresh, Iran

%A Amir Hossein Gandomi
%A Amir Hossein Alavi
%A Mohammad Reza Mirzahosseini
%A Fereidoon Moghadas Nejad
%T Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures
%J ASCE Journal of Materials in Civil Engineering
%V 23
%N 3
%D 2011
%P 248--263
%I 
%K genetic algorithms, genetic programming, gene expression programming, Marshall mix design, Formulation
%U http://ascelibrary.org/mto/resource/1/jmcee7/v23/i3/p248_s1
%X Rutting has been considered as the most serious distresses in flexible pavements for many years. Flow number is an explanatory index for the evaluation of rutting potential
   of asphalt mixtures. In this study, a promising variant of genetic programming, namely gene expression programming (GEP) is used to predict the flow number of dense
   asphalt-aggregate mixtures. The proposed constitutive models relate the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of air
   voids, percentage of voids in mineral aggregate, Marshall stability and flow. Different correlations were developed using different combinations of the influencing
   parameters. The comprehensive experimental database used for the development of the correlations was established upon a series of uniaxial dynamic creep tests conducted in
   this study. Relative importance values of various predictor variables were calculated to determine their contributions to the flow number prediction. A multiple least
   squares regression (MLSR) analysis was performed using the same variables and data sets to benchmark the GEP models. For more verification, a subsequent parametric study
   was carried out and the trends of the results were confirmed with the results of previous studies. The results indicate that the proposed correlations are effectively
   capable of evaluating the flow number of asphalt mixtures. The GEP-based formulae are simple, straightforward and particularly valuable for providing an analysis tool
   accessible to practising engineers.
%8 March
%Z 1Research Assistant, National Elites Foundation, Tehran, Iran & College of Civil Engineering, Tafresh University, Tafresh, Iran. 2PhD Student, School of Architecture, Civil
   and Environmental Engineering, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland. 3Assistant Professor, College of Civil Engineering, Iran University
   of Science & Technology, Tehran, Iran. 4Assistant Professor, College of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.

%A Amir Hossein Gandomi
%A Seyed Morteza Tabatabaei
%A Mohammad Hossein Moradian
%A Ata Radfar
%A Amir Hossein Alavi
%T A new prediction model for the load capacity of castellated steel beams
%J Journal of Constructional Steel Research
%V 67
%N 7
%D 2011
%P 1096--1105
%I 
%K genetic algorithms, genetic programming, Castellated beam, Failure load, Gene expression programming
%U http://www.sciencedirect.com/science/article/B6V3T-52BVR2R-1/2/9f40e5717143288037afed5176f8d52e
%X In this study, a robust variant of genetic programming, namely gene expression programming (GEP), is used to build a prediction model for the load capacity of castellated
   steel beams. The proposed model relates the load capacity to the geometrical and mechanical properties of the castellated beams. The model is developed based on a reliable
   database obtained from the literature. To verify the applicability of the derived model, it is employed to estimate the load capacity of parts of the test results that were
   not included in the modelling process. The external validation of the model was further verified using several statistical criteria recommended by researchers. A multiple
   least squares regression analysis is performed to benchmark the GEP-based model. A sensitivity analysis is further carried out to determine the contributions of the
   parameters affecting the load capacity. The results indicate that the proposed model is effectively capable of evaluating the failure load of the castellated beams. The
   GEP-based design equation is remarkably straightforward and useful for pre-design applications.

%A Amir Hossein Gandomi
%A Amir Hossein Alavi
%A Mehdi Mousavi
%A Seyed Morteza Tabatabaei
%T A hybrid computational approach to derive new ground-motion prediction equations
%J Engineering Applications of Artificial Intelligence
%V 24
%N 4
%D 2011
%P 717--732
%I 
%K genetic algorithms, genetic programming, Time-domain ground-motion parameters, Prediction equations, Orthogonal least squares, Nonlinear modelling
%U http://www.sciencedirect.com/science/article/B6V2M-52C83TR-1/2/0e8d2ec5097e6a0e7eef643a7e26d527
%X A novel hybrid method coupling genetic programming and orthogonal least squares, called GP/OLS, was employed to derive new ground-motion prediction equations (GMPEs). The
   principal ground-motion parameters formulated were peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The proposed GMPEs relate
   PGA, PGV and PGD to different seismic parameters including earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms.
   The equations were established based on an extensive database of strong ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER). For more
   validity verification, the developed equations were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. A sensitivity analysis was carried
   out to determine the contributions of the parameters affecting PGA, PGV and PGD. The sensitivity of the models to the variations of the influencing parameters was further
   evaluated through a parametric analysis. The obtained GMPEs are effectively capable of estimating the site ground-motion parameters. The equations provide a prediction
   performance better than or comparable with the attenuation relationships found in the literature. The derived GMPEs are remarkably simple and straightforward and can
   reliably be used for the pre-design purposes.

%A Wang Gang
%A Terence Soule
%T How to Choose Appropriate Function Sets for GP
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 198--207
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=198
%X The choice of functions in a genetic program can have a significant effect on the GP's performance, but there have been no systematic studies of how to select functions to
   optimise performance. We investigate how to choose appropriate function sets for general genetic programming problems. For each problem multiple functions sets are tested.
   The results show that functions can be classified into function groups of equivalent functions. The most appropriate function set for a problem is one that is optimally
   diverse; a set that includes one function from each function group.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A HongLei Gao
%A WenZhong Guo
%A GuoLong Chen
%A YanHua Liu
%A Mei Gao
%T Cyberspace Situation Prediction Based on Gene Expression Programming
%B Fifth International Conference on Natural Computation, 2009. ICNC '09
%E Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun
%D 2009
%P 191--195
%I IEEE Computer Society
%C Tianjian, China
%K genetic algorithms, genetic programming, gene expression programming
%X The accurate prediction of cyberspace situation is fundamental to intrusion prevention in large scale networks. After analysing the cyberspace situation, a cyberspace
   situation prediction model based on gene expression programming (GEP-CSP) is proposed, to predict the time series of cyberspace situation. Besides, since its own intrinsic
   characteristics, GEP-CSP solves the problem that the traditional time series methods can't make an accurate prediction without the pre-knowledge. By employing GEP-CSP, the
   experiments on Abilene network flow data reached the expectation and made a precise prediction.
%8 14-16  August

%A Li Gao
%A Norman W. Loney
%T Evolutionary polymorphic neural network in chemical process modeling
%J Computers \& Chemical Engineering
%V 25
%N 11-12
%D 2001
%P 1403--1410
%I 
%K genetic algorithms, genetic programming, Evolutionary polymorphic neural network (EPNN), Neural network, Process modeling
%U http://www.sciencedirect.com/science/article/B6TFT-449TFB0-2/2/b9c50f18933d4b739a9d8a2843b45548
%X Evolutionary polymorphic neural network (EPNN) is a novel approach to modelling dynamic process systems. This approach has its basis in artificial neural networks and
   evolutionary computing. As demonstrated in the studied dynamic CSTR system, EPNN produces less error than a traditional recurrent neural network with a less number of
   neurons. Furthermore, EPNN performs networked symbolic regressions for input-output data, while it performs multiple step ahead prediction through adaptable feedback
   structures formed during evolution. In addition, the extracted symbolic formulae from EPNN can be used for further theoretical analysis and process optimisation.

%A Li Gao
%T Evolutionary Polymorphic Neural Networks in Chemical Engineering Modeling
%R Ph.D. Thesis
%D 2001
%I 
%I Department of Chemical Engineering, New Jersey Institute of Technology
%K genetic algorithms, genetic programming, Evolutionary Polymorphic Neural Network (EPNN), Artificial intelligence, Evolutionary computing
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/LiGao_thesis.pdf
%X Evolutionary Polymorphic Neural Network (EPNN) is a novel approach to modeling chemical, biochemical and physical processes. This approach has its basis in modern
   artificial intelligence, especially neural networks and evolutionary computing. EPNN can perform networked symbolic regressions for input-output data, while providing
   information about both the structure and complexity of a process during its own evolution. In this work three different processes are modeled: 1. A dynamic neutralisation
   process. 2. An aqueous two-phase system. 3. Reduction of a biodegradation model. In all three cases, EPNN shows better or at least equal performances over published data
   than traditional thermodynamics /transport or neural network models. Furthermore, in those cases where traditional modeling parameters are difficult to determine, EPNN can
   be used as an auxiliary tool to produce equivalent empirical formulae for the target process. Feedback links in EPNN network can be formed through training (evolution) to
   perform multiple steps ahead predictions for dynamic nonlinear systems. Unlike existing applications combining neural networks and genetic algorithms, symbolic formulae can
   be extracted from EPNN modeling results for further theoretical analysis and process optimisation. EPNN system can also be used for data prediction tuning. In which case,
   only a minimum number of initial system conditions need to be adjusted. Therefore, the network structure of EPNN is more flexible and adaptable than traditional neural
   networks. Due to the polymorphic and evolutionary nature of the EPNN system, the initially randomised values of constants in EPNN networks will converge to the same or
   similar forms of functions in separate runs until the training process ends. The EPNN system is not sensitive to differences in initial values of the EPNN population.
   However, if there exists significant larger noise in one or more data sets in the whole data composition, the EPNN system will probably fail to converge to a satisfactory
   level of prediction on these data sets. EPNN networks with a relatively small number of neurons can achieve similar or better performance than both traditional
   thermodynamic and neural network models. The developed EPNN approach provides alternative methods for efficiently modeling complex, dynamic or steady-state chemical
   processes. EPNN is capable of producing symbolic empirical formulae for chemical processes, regardless of whether or not traditional thermodynamic models are available or
   can be applied. The EPNN approach does overcome some of the limitations of traditional thermodynamic /transport models and traditional neural network models.
%8 August
%Z Advisory Committee: Loney, Norman W. Baltzis, Basil C. Barat, Robert B. Knox, Dana E. Blackmore, Denis Wasser, Daniel J.

%A Xueshan Gao
%A Koki Kikuchi
%A Xiaobing Wu
%A Katsuya Kanai
%A Keisuke Somiya
%T Study on the Symmetry of Evolutionary Robotic System
%B 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems
%D 2006
%P 1638--1643
%I IEEE
%C Beijing
%K genetic algorithms, genetic programming
%X This paper deals with the concept that effective robotic function emerges from intelligence and the balance between morphology and intelligence, the morphology and
   intelligence of the robot are represented respectively. Both them are automatically generated and evolved by genetic programming for a task of maintaining a certain
   distance between the robot and an object. And then evolutionary simulation and two experiments are performed. Furthermore, the symmetry properties which have two phases and
   emerge are discussed
%8 October
%Z Sch. of Mechatronic Eng., Beijing Inst. of Technol.
%@ 1-4244-0259-X

%A Jaime Garces-Perez
%A Dale A. Schoenefeld
%A Roger L. Wainwright
%T Solving Facility Layout Problems Using Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 182--190
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X This research applies techniques and tools from Genetic Programming GP to the facility layout problem The facility layout problem FLP is an NP-complete combinatorial
   optimisation problem that has applications to efficient facility design for manufacturing and service industries. A facility layout is represented as a collection of
   rectangular blocks using a slicing tree structure (STS) We use a multiple purpose genetic programming kernel to generate slicing trees that are converted into candidate
   solutions for an FLP The utility of our techniques is established using eight previously published benchmark problems Our genetic programming techniques that evolve STSs
   are more natural and more flexible than all of the previously published genetic algorithm and simulated annealing techniques Previous genetic algorithm techniques use a
   twophase optimisation strategy The first phase uses clustering techniques to determine a near optimal fixed tree structure that is represented as a chromosome in a genetic
   algo rithm Within the constraints implied by the fixed tree structure genetic algorithm techniques are applied during the second phase to optimise the placement of
   facilities in relation to each other Our genetic programming technique is a single phase global optimization strategy using an un constrained tree structure This yields
   superior results
%8 28--31  July
%Z GP-96

%A Beatriz Garcia
%A Ricardo Aler
%A Agapito Ledezma
%A Araceli Sanchis
%T Protein-protein functional association prediction using genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 347--348
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, bioinformatics, classifier systems, control bloat, data integration, evolutionary computation, machine learning, protein
   interaction prediction, computational biology: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p347.pdf
%X Determining if a group of proteins are functionally associated among themselves is an open problem in molecular biology. Within our long term goal of applying Genetic
   Programming (GP) to this domain, this paper evaluates the feasibility of GP to predict if a given pair of proteins interacts. GP has been chosen because of its potential
   flexibility in many aspects, such as the definition of operations. In this paper, the if-unknown operation is defined, which semantically is the most appropriate in this
   domain for handling missing values. We have also used the Tarpeian bloat control method to decrease the computational time and the solution size. Our results show that GP
   is feasible for this domain and that the Tarpeian method can obtain large improvements in search efficiency and interpretability of solutions.
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389156 E.coli. Training on subsets 5000 positive and 5000 negatives. Missing values if_? if(unknown) then else. p348 GP about
   as accruate as other machine learning. ADTree KODE KStar MLP PART Simple Logistic SMO. Tarpeian bloat control \citepoli03 effective.

%A Beatriz Garcia
%A Ricardo Aler
%A Agapito Ledezma
%A Araceli Sanchis
%T Genetic Programming for Predicting Protein Networks
%B Proceedings of the 11th Ibero-American Conference on AI, IBERAMIA 2008
%S Lecture Notes in Computer Science
%E Hector Geffner and Rui Prada and Isabel Machado Alexandre and Nuno David
%V 5290
%D 2008
%P 432--441
%I Springer
%C Lisbon, Portugal
%K genetic algorithms, genetic programming, Protein interaction prediction, data integration, bioinformatics, evolutionary computation, machine learning, classification,
   control bloat
%U http://www.caos.inf.uc3m.es/~beatriz/papers/garcia_et.al._iberamia08-paper_InPress.pdf
%X One of the definitely unsolved main problems in molecular biology is the protein-protein functional association prediction problem. Genetic Programming (GP) is applied to
   this domain. GP evolves an expression, equivalent to a binary classifier, which predicts if a given pair of proteins interacts. We take advantages of GP flexibility,
   particularly, the possibility of defining new operations. In this paper, the missing values problem benefits from the definition of if-unknown, a new operation which is
   more appropriate to the domain data semantics. Besides, in order to improve the solution size and the computational time, we use the Tarpeian method which controls the
   bloat effect of GP. According to the obtained results, we have verified the feasibility of using GP in this domain, and the enhancement in the search efficiency and
   interpretability of solutions due to the Tarpeian method.
%O Advances in Artificial Intelligence
%8 October  14-17
%Z lilgp. BIND, DIP, Butland, IntAct, EcoCyc, KEGG, iHoP. P436 Training 'instances is reduced to 264,752' Actually 10000 used for training. Function set: +, -, * and protected
   division, if, if_?. FS 'closed always returning the unknown value ? if any of their input values is ?'. Comparison with WEKA.

%A Guillermo Garcia
%T Estimation of Multiple Fundamental Frequencies in Audio Signals using a Genetic Algorithm
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 153--159
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Ricardo A. Garcia
%T Towards the Automatic Generation of Sound Synthesis Techniques: Preparatory Steps
%B AES 109th Convention
%D 2000
%I 
%I Audio Engineering Society
%C Los Angeles
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/454347.html
%X An overview of an algorithm that searches through the space of the sound synthesis techniques is presented. A modular approach to construct sound synthesis techniques is
   introduced. The preparatory steps needed to use genetic programming as a search tool for this space are explained, focusing in the manipulation and evaluation of the
   modular descriptions of the topologies.
%O The Pennsylvania State University CiteSeer Archives
%8 22-25 Sepetember
%Z http://www.aes.org/events/109/

%A Ricardo A. Garcia
%T Automating The Design Of Sound Synthesis Techniques Using Evolutionary Methods
%B Proceedings of the COST G-6 Conference on Digital Audio Effects (DAFX-01)
%E Mikael Fernstrom
%D 2001
%I 
%C Limerick, Ireland
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/569030.html
%X Digital sound synthesizers, ubiquitous today in sound cards, software and dedicated hardware, use algorithms (Sound Synthesis Techniques, SSTs) capable of generating sounds
   similar to those of acoustic instruments and even totally novel sounds. The design of SSTs is a very hard problem. It is usually assumed that it requires human ingenuity to
   design an algorithm suitable for synthesizing a sound with certain characteristics. Many of the SSTs commonly used are the fruit of experimentation and a long refinement
   processes. A SST is determined by its functional form and internal parameters. Design of SSTs is usually done by selecting a fixed functional form from a handful of
   commonly used SSTs, and performing a parameter estimation technique to find a set of internal parameters that will best emulate the target sound. A new approach for
   automating the design of SSTs is proposed. It uses a set of examples of the desired behavior of the SST in the form of inputs + target sound. The approach is capable of
   suggesting novel functional forms and their internal parameters, suited to follow closely the given examples. Design of a SST is stated as a search problem in the SST space
   (the space spanned by all the possible valid functional forms and internal parameters, within certain limits to make it practical). This search is done using evolutionary
   methods; specifically, Genetic Programming (GP).
%O The Pennsylvania State University CiteSeer Archives
%8 Decemeber  6-8
%Z http://www.csis.ul.ie/dafx01/programme.html

%A Alma Lilia Garcia-Almanza
%A Edward P. K. Tsang
%T Forecasting stock prices using Genetic Programming and Chance Discovery
%B 12th International Conference On Computing In Economics And Finance
%D 2006
%P number 489
%I 
%I Society for Computational Economics
%K genetic algorithms, genetic programming
%U http://ideas.repec.org/p/sce/scecfa/489.html
%X In recent years the computers have shown to be a powerful tool in financial forecasting. Many machine learning techniques have been used to predict movements in financial
   markets. Machine learning classifiers involve extending the past experiences into the future. However the rareness of some events makes difficult to create a model that
   detect them. For example bubbles burst and crashes are rare cases, however their detection is crucial since they have a significant impact on the investment. One of the
   main problems for any machine learning classifier is to deal with unbalanced classes. Specifically Genetic Programming has limitation to deal with unbalanced environments.
   In a previous work we described the Repository Method, it is a technique that analyses decision trees produced by Genetic Programming to discover classification rules. The
   aim of that work was to forecast future opportunities in financial stock markets on situations where positive instances are rare. The objective is to extract and collect
   different rules that classify the positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future.
   The objective of the present work is to find out the factors that work in favour of Repository Method, for that purpose a series of experiments was performed.
%8 July
%Z CEF 2006

%A Alma Lilia Garcia-Almanza
%A Edward P. K. Tsang
%T Simplifying Decision Trees Learned by Genetic Programming
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%P 7906--7912
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%U http://privatewww.essex.ac.uk/~algarc/Publications/WCCI2006.pdf
%X This work is motivated by financial forecasting using Genetic Programming. This paper presents a method to post-process decision trees. The processing procedure is based on
   the analysis and evaluation of the components of each tree, followed by pruning. The idea behind this approach is to identify and eliminate rules that cause
   misclassification. As a result we expect to keep and generate rules that enhance the classification. This method was tested on decision trees generated by a genetic program
   whose aim was to discover classification rules in financial stock markets. From experimental results we can conclude that our method is able to improve the accuracy and
   precision of the classification.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-9487-9

%A Alma L Garcia-Almanza
%A Edward P. K. Tsang
%T The Repository Method for Chance Discovery in Financial Forecasting
%B KES 2006, Proceedings of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems
%S Lecture Notes in Computer Science
%E Bogdan Gabrys and Robert J. Howlett and Lakhmi C. Jain
%V 4253
%D 2006
%P 30--37
%I Springer-Verlag
%C Bournemouth, UK
%K genetic algorithms, genetic programming
%X The aim of this work is to forecast future opportunities in financial stock markets, in particular, we focus our attention on situations where positive instances are rare,
   which falls into the domain of Chance Discovery. Machine learning classifiers extend the past experiences into the future. However the imbalance between positive and
   negative cases poses a serious challenge to machine learning techniques. Because it favours negative classifications, which has a high chance of being correct due to the
   nature of the data. Genetic Algorithms have the ability to create multiple solutions for a single problem. To exploit this feature we propose to analyse the decision trees
   created by Genetic Programming. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different
   ways, positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. To illustrate our approach,
   it was applied to predict investment opportunities with very high returns. From experiment results we showed that the Repository Method can consistently improve both the
   recall and the precision.
%O Part III
%8 October  9-11
%@ 3-540-46542-1

%A Alma Lilia Garcia-Almanza
%A Edward P. K. Tsang
%T Repository Method to Suit Different Investment Strategies
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 790--797
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X This work is motivated by the interest in finding significant movements in financial stock prices. The detection of such movements is important because these could
   represent good opportunities for invest. However, when the number of profitable opportunities is very small the prediction of these cases is very difficult.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Alma Lilia Garcia-Almanza
%A Edward P. K. Tsang
%T Detection of stock price movements using chance discovery and genetic programming
%J International Journal of Knowledge-Based and Intelligent Engineering Systems
%V 11
%N 5
%D 2007
%P 329--344
%I IOS
%K genetic algorithms, genetic programming
%U http://iospress.metapress.com/content/k30kgl00u6r42812/
%X The aim of this work is to detect important movements in financial stock prices that may indicate future opportunities or risks. The occurrence of such movements is scarce,
   thus this problem falls into the domain of Chance Discovery, a new research area whose objective is to identify rare events that may represent potential opportunities and
   risks. In this work we propose to capture patterns of the rare instances in different ways in order to increase the probability of identifying similar cases in the future.
   To generate more variety of solutions we evolve a genetic program, which is an evolutionary technique that is able to create multiple solutions for a single problem. The
   idea is to mine the knowledge acquired by the evolutionary process to extract and collect different rules that model the positive cases in several and novel ways. Once an
   important movement in financial markets has been discovered, human interaction is needed to analyze the markets conditions and determine if that movement could be a good
   opportunity to invest or could be the principle of a bubble or another critical event that represents a risk. Standard decision trees methods capture patterns from training
   data sets. However, when the chances are scare, some of the patters captured by the best rules may not repeat themselves in unseen cases. In this work we propose Repository
   Method which comprises multiple rules to form a more reliable classifier in rare cases. To illustrate our approach, it was applied to discover important movements in stock
   prices. From experimental results we showed that our approach can consistently detect rare cases in extreme imbalanced data sets.

%A Alma Lilia Garcia-Almanza
%A Edward P. K. Tsang
%T Evolving Decision Rules to Predict Investment Opportunities
%J International Journal of Automation and Computing
%V 5
%N 1
%D 2008
%P 22--31
%I Institute of Automation, Chinese Academy of Sciences, co-published with Springer-Verlag GmbH
%K genetic algorithms, genetic programming, Machine learning, classification, imbalanced classes, evolution of rules
%X This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the
   prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules (EDR) to detect financial opportunities. The objective of EDR is to
   classify the minority class (positive cases) in imbalanced environments. EDR provides a range of classifications to find the best balance between not making mistakes and
   not missing opportunities. The goals of this paper are: 1) to show that EDR produces a range of solutions to suit the investor's preferences and 2) to analyse the factors
   that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour,
   another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to
   show how a bigger collection of rules is able to classify more positive cases in imbalanced data sets. Experimental results show that: 1) EDR offers a range of solutions to
   fit the risk guidelines of different types of investors, and 2) a bigger collection of rules is able to classify more positive cases in imbalanced environments.
%8 January

%A Alma Lilia {Garcia Almanza}
%T New Classification Methods for Gathering Patterns in the Context of Genetic Programming
%R Ph.D. Thesis
%D 2008
%I 
%I Department of Computing and Electronic Systems, University of Essex
%C Colchester, UK
%K genetic algorithms, genetic programming
%U http://www.bracil.net/finance/papers/Garcia-PhD2008.pdf
%X Machine learning techniques extend the past experiences into the future. However, when the number of examples in the minority class (positive cases) is very small in
   comparison with the remaining classes, it poses a serious challenge to the machine learning [63],[119],[5],[81]. In this kind of problems, the prediction of the majority
   class is favoured because it has a high chance of being correct. This characteristic is present in many real-world problems, whose objective is to classify the minority
   class in imbalanced data sets. However, a prediction that detects more positive cases may be paid for with more false alarms. It is important to determine a balance between
   the detection of positive cases and false alarms. A range of classifications would give users the option to choose the best tradeoff between detecting positive cases and
   false alarms according to their requirements. On the other hand, we consider it is important to provide a comprehensive solution, which shows the real variables and
   conditions in the prediction. Thus, the users could combine their knowledge in order to make a more informed decision. In this thesis, we present three novel approaches:
   Repository Method (RM), Evolving Decision Rules (EDR) and Scenario Method (SM). We use Genetic Programming (GP) and supervised learning to build the methods proposed in
   this thesis. The main objectives of RM and EDR are: to predict the minority class in imbalanced environments, to generate a range of solutions to suit different users'
   preferences and to provide an comprehensible solution for the user. On the other hand, SM has been designed to improve the precision and accuracy of the prediction.
   However, such improvement is paid for with a decrease in the recall. But, the users have to make the decision of which of these parameters is more adequate to satisfy their
   needs. This work is illustrated predicting future opportunities in financial stock markets. Experiments of our methods were carried out, and these showed promising results
   for achieving our goals. RM and EDR were compared to a standard Genetic Programming, EDDIE-Arb and C5.0. The methods presented in this thesis can also be used in other
   fields of knowledge, these should not be limited to financial forecasting problems.
%8 July

%A Alma Lilia Garcia-Almanza
%A Biliana Alexandrova-Kabadjova
%A Serafin Martinez-Jaramillo
%T Understanding Bank Failure: A Close Examination of Rules Created by Genetic Programming
%B Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2010
%D 2010
%P 34--39
%I 
%K genetic algorithms, genetic programming, bank failure detection, bankruptcy prediction, data set, evolving decision rules, financial ratio, receiving operating
   characteristic space, banking, sensitivity analysis
%X This paper presents a novel method to predict bankruptcy, using a Genetic Programming (GP) based approach called Evolving Decision Rules (EDR). In order to obtain the
   optimum parameters of the classifying mechanism, we use a data set, obtained from the US Federal Deposit Insurance Corporation (FDIC). The set consists of limited financial
   institutions' data, presented as variables widely used to detect bank failure. The outcome is a set of comprehensible decision rules, which allows to identify cases of
   bankruptcy. Further, the reliability of those rules is measured in terms of the true and false positive rate, calculated over the whole data set and plot over the Receiving
   Operating Characteristic (ROC) space. In order to test the accuracy performance of the mechanism, we elaborate two experiments: the first, aimed to test the degree of the
   variables' usefulness, provides a quantitative and a qualitative analysis. The second experiment completed over 1000 different re-sampled cases is used to measure the
   performance of the approach. To our knowledge this is the first computational technique in this field able to give useful insights of the method's predictive structure. The
   main contributions of this work are three: first, we want to bring to the arena of bankruptcy prediction a competitive novel method which in pure performance terms is
   comparable to state of the art methods recently proposed in similar works, second, this method provides the additional advantage of transparency as the generated rules are
   fully interpretable in terms of simple financial ratios, third and final, the proposed method includes cutting edge techniques to handle highly unbalanced samples,
   something that is very common in bankruptcy applications.
%8 28  September -  October  1
%Z Also known as \cite5692308

%A Alma Lilia {Garcia Almanza}
%A Edward Tsang
%T Evolutionary Applications for Financial Prediction: Classification Methods to Gather Patterns Using Genetic Programming
%D 2011
%I VDM Verlag Dr. Muller
%C Saarbrucken, Germany
%K genetic algorithms, genetic programming
%U http://www.amazon.com/Evolutionary-Applications-Financial-Prediction-Classification/dp/3639307674/ref=sr_1_1?ie=UTF8&qid=1305383401&sr=8-1
%X This book presents three applications, based on Machine Learning and Genetic Programming, which are devoted to find useful patterns to predict future events. The objective
   is to train the algorithms by using past data to produce a classifier that identifies the positive cases and discriminates the false alarms. This work uses examples for
   predicting future opportunities in financial stock markets in cases where the number of profitable opportunities is scarce. However, when the number of positive examples is
   small in comparison with the number of total cases, the identification of useful patterns becomes a serious challenge. Nevertheless, the objective of many real world
   problems, is precisely to identify the minority class as the fraud detection problem, or medical diagnosis and many other examples. The techniques of this book are suitable
   to deal with imbalanced data sets, provide comprehensible results that allow users to understand the factors that are involved in the decision, as well as to generate a
   range of solutions that let the user choose the best trade off according to their risk preferences.
%@ 3639307674

%A Alma Lilia {Garcia Almanza}
%A Serafin {Martinez Jaramillo}
%A Biliana Alexandrova-Kabadjova
%A Edward Tsang
%T Using Genetic Programming Systems as Early Warning to Prevent Bank Failure
%B Information Systems for Global Financial Markets: Emerging Developments and Effects
%E Alexander Y. Yap
%D 2011
%P 369--382
%I IGI global
%K genetic algorithms, genetic programming
%U http://www.amazon.com/Information-Systems-Global-Financial-Markets/dp/1613501625
%X Corporate bankruptcy has been always an active area of financial research. Furthermore, after the Lehman Brothers' default and its consequences on the global financial
   system, this topic has attracted even more attention from regulators and researchers. This event has brought an imperious urge to change the regulatory framework regardless
   of whether this is good or bad. Consequently, the need for timely signals for supervisory actions and the development of tools that help to determine which financial
   information is more relevant to predict distress is very important. During crisis periods the bankruptcy of a bank or a group of banks can make things far worse if
   contagion effects are transmitted first to other participants of the financial system and then to the real economy. In a previous work, developed by Garcia et al. (2010),
   an evolutionary technique named Evolving Decision Rules (EDR) was used to identify patterns in data from the Federal Deposit Insurance Corporation (FDIC) for generating a
   set of comprehensible rules, which were able to predict bank bankruptcy. The major contribution of that work was to show a series of decision rules constituted by simple
   financial ratios, despite that the method is not restricted to the use of such type of information. The main advantage of creating understandable rules is that users are
   able to interpret and identify the events that may trigger bankruptcy. By using the method that we propose in this work, it is possible to identify when certain financial
   indicators are getting close to specific thresholds, something that can turn into an undesirable situation. This is particularly relevant if the companies we are referring
   to are banks. The contribution of this chapter is to improve the prediction by means of a multi-population approach. The experimental results were evaluated using the
   Receiver Operating Characteristic (ROC) described in Fawcett and Provost (1997). We show that our approach could improve the Area Under the ROC Curve in 5percent with
   respect to the same method proposed in Garcia et al. (2010). Additionally, a series of experiments were performed in order to find out the reasons of success of the EDR
%O 14
%8 November
%@ 1-61350-162-5

%A M. Garcia-Arnau
%A D. Manrique
%A J. Rios
%A A. Rodriguez-Paton
%T Initialization method for grammar-guided genetic programming
%J Knowledge-Based Systems
%V 20
%N 2
%D 2007
%P 127--133
%I 
%K genetic algorithms, genetic programming, Grammar-guided genetic programming, Initialisation method, Tree-generation algorithm, Breast cancer prognosis, GGGP
%X This paper proposes a new tree-generation algorithm for grammar-guided genetic programming that includes a parameter to control the maximum size of the trees to be
   generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the
   search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two
   different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer
   prognosis. In both problems, comparisons have been made to another five important initialisation methods.
%O AI 2006, The 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
%8 March

%A P. Garcia-Sanchez
%A J. J. Merelo
%A J. P. Sevilla
%A J. L. J. Laredo
%A A. M. Mora
%A P. A. Castillo
%T Automatic generation of XSLT stylesheets using evolutionary algorithms
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1701--1702
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, evolutionary computation techniques, style sheets, XML, XSLT, Real-World application: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1701.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389417

%A Pablo Garcia-Sanchez
%A Juan J. Merelo
%A Juan L. J. Laredo
%A Antonio Mora
%A Pedro A. Castillo
%T Evolving XSLT Stylesheets for Document Transformation
%B Parallel Problem Solving from Nature - PPSN X
%S LNCS
%E Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume
%V 5199
%D 2008
%P 1021--1030
%I Springer
%C Dortmund
%K genetic algorithms, genetic programming
%X This paper presents a new version of an evolutionary algorithm that creates XSLT programs from its intended input and output. XSLT is a general purpose, document-oriented
   functional language, generally used to transform XML documents (or, in general, solve any problem that can be coded as an XML document). Previously, a solution that solved
   the problem efficiently was proposed. In this paper, we improve on those results by testing different fitness functions, adding a new operator and changing the type of
   desired output document that can be obtained. The experiments show that the best results are obtained without considering the XSLT length and including this new operator.
%8 13-17  September
%Z PPSN X
%@ 3-540-87699-5

%A Marc-Andre Gardner
%A Christian Gagne
%A Marc Parizeau
%T Bloat control in genetic programming with a histogram-based accept-reject method
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 187--188
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming: Poster
%X Recent bloat control methods such as dynamic depth limit (DynLimit) and Dynamic Operator Equalisation (DynOpEq) aim at modifying the tree size distribution in a population
   of genetic programs. Although they are quite efficient for that purpose, these techniques have the disadvantage of evaluating the fitness of many bloated Genetic
   Programming (GP) trees, and then rejecting most of them, leading to an important waste of computational resources. We are proposing a method that makes a histogram-based
   model of current GP tree size distribution, and uses the so-called accept-reject method for generating a population with the desired target size distribution, in order to
   make a stochastic control of bloat in the course of the evolution. Experimental results show that the method is able to control bloat as well as other state-of-the-art
   methods, with minimal additional computational efforts compared to standard tree-based GP.
%8 12-16  July
%Z symbolic regression, Santa Fe Ant, 6 parity. Like operator equalisation?? but does not need to evaluate fitness before deciding if child fits into desired distribution of
   program sizes. Cut off wrong word. Above target allow size histogram falls exponentially. Does not seem to limit small programs. Seem to be missing point about distribution
   of sizes actually generated by crossover. HARM-GP deap.googlecode.com Also known as \cite2001963 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Michael L. Gargano
%A William Edelson
%A Olga Koval
%T A Genetic Algorithm With Feasible Search Space For Minimal Spanning Trees With Time-Dependent Edge Costs
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 495
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Ivan Garibay
%A Annie S. Wu
%A Ozlem Garibay
%T Emergence of genomic self-similarity in location independent representations Favoring positive correlation between the form and quality of candidate solutions
%J Genetic Programming and Evolvable Machines
%V 7
%N 1
%D 2006
%P 55--80
%I 
%K genetic algorithms, Representation, Proportional genetic algorithm, Self-organisation, Genomic self-similarity, Emergence
%X A key property for predicting the effectiveness of stochastic search techniques, including evolutionary algorithms, is the existence of a positive correlation between the
   form and the quality of candidate solutions. In this paper we show that when the ordering of genomic symbols in a genetic algorithm is completely independent of the fitness
   function and therefore free to evolve along with the candidate solutions it encodes, the resulting genomes self-organise into self-similar structures that favour this key
   stochastic search property.
%8 March
%Z white noise

%A Ivan Garibay
%T Dario Floreano and Claudio Mattiussi (eds): Bio-inspired artificial intelligence: theories, methods, and technologies
%J Genetic Programming and Evolvable Machines
%V 11
%N 3/4
%D 2010
%P 441--443
%I 
%K genetic algorithms, genetic programming
%O Book review
%8 September
%Z See Erratum \citeGaribay:2011:GPEM

%A Ivan Garibay
%T Erratum to: Dario Floreano and Claudio Mattiussi: Bio-inspired artificial intelligence: theories, methods, and technologies
%J Genetic Programming and Evolvable Machines
%V 12
%N 1
%D 2011
%P 89--89
%I 
%K Computer Science
%X The publisher regrets that the following book review incorrectly listed the authors Dario Floreano and Claudio Mattiussi as editors of their book, Bio-Inspired Artificial
   Intelligence: Theories, Methods, and Technologies. Dario Floreano and Claudio Mattiussi are the sole authors of this volume.
%8 March
%Z Correction to \citeGaribay:2010:GPEM

%A A. Beatriz Garmendia-Doval
%A Chilukuri K. Mohan
%A Mohit K. Prasad
%T Evolving Tree Representations of Stack Filters
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 103--108
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/494447.html
%X Evolutionary algorithms are applied to the design of a class of nonlinear discrete-time filters: the positive Boolean function defining a stack filter is derived from its
   properties specified in terms of `selection probabilities'. For window size 9, with search space of at least 2 126 , best results were obtained using a tree representation
   for each positive Boolean function.
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A A. Beatriz Garmendia-Doval
%A S. David Morley
%A Szilveszter Juhos
%T Post Docking Filtering Using Cartesian Genetic Programming
%B Evolution Artificielle, 6th International Conference
%S Lecture Notes in Computer Science
%E Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer
%V 2936
%D 2003
%P 189--200
%I Springer
%C Marseilles, France
%K genetic algorithms, genetic programming, Artificial Evolution, Cartesian Genetic Programming
%X Structure-based virtual screening is a technology increasingly used in drug discovery. Although successful at estimating binding modes for input ligands, these technologies
   are less successful at ranking true hits correctly by binding free energy. We present initial attempts to automate the removal of false positives from virtual hit sets, by
   evolving a post docking filter using Cartesian Genetic Programming.
%O Revised Selected Papers
%8 27-30  October
%Z EA'03 HSP90 data. RiboTargets Ltd, Granta Park, Cambridge, England, CB1 6GB
%@ 3-540-21523-9

%A A. Beatriz Garmendia-Doval
%A Julian Miller
%A S. David Morley
%T Post Docking Filtering Using Cartesian Genetic Programming
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 225--244
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, cartesian genetic programming, molecular docking prediction, virtual screening, machine learning, evolutionary algorithms, neutral
   evolution
%X Structure-based virtual screening is a technology increasingly used in drug discovery. Although successful at estimating binding modes for input ligands, these technologies
   are less successful at ranking true hits correctly by binding free energy. This chapter presents the automated removal of false positives from virtual hit sets, by evolving
   a post docking filter using Cartesian Genetic Programming(CGP). We also investigate characteristics of CGP for this problem and confirm the absence of bloat and the
   usefulness of neutral drift.
%O 14
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A M. Garzon
%A P. Neathery
%A R. Deaton
%A R. C. Murphy
%A D. R. Franschetti
%A S. E. {Stevens Jr.}
%T A New Metric for DNA Computing
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 472--478
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K DNA Computing
%8 13-16  July
%Z GP-97

%A Max H. Garzon
%A Russell J. Deaton
%A Ken Barnes
%T On Self-Assembling Graphs in vitro
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1805--1809
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K dna and molecular computing
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Max Garzon
%A Rusell Deaton
%A Luis F. Nino
%A Ed Stevens
%A Michal Wittner
%T Encoding Genomes for DNA Computing
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 684--690
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K DNA Computing
%U http://www.csce.uark.edu/~rdeaton/dna/papers/gp98c-2.pdf
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Max H. Garzon
%T Biomolecular Machines and Artificial Evolution
%J Genetic Programming and Evolvable Machines
%V 4
%N 2
%D 2003
%P 107--109
%I 
%K DNA computing
%8 June
%Z Special Issue on Biomolecular Machines and Artificial Evolution Article ID: 5122739

%A Max Garzon
%A Derrel Blain
%A Kiran Bobba
%A Andrew Neel
%A Michael West
%T Self-Assembly of DNA-like Structures In Silico
%J Genetic Programming and Evolvable Machines
%V 4
%N 2
%D 2003
%P 185--200
%I 
%K Hamiltonian path problem, online genetic algorithms, DNA-based associative memories, efficiency of DNA computing, reaction kinetics in DNA-based computational protocols
%X Through evolution, biomolecules have resolved fundamental problems as a highly interactive parallel and distributed system that we are just beginning to decipher.
   Biomolecular Computing (BMC) protocols, however, are unreliable, inefficient and unscalable when compared to computational algorithms run in silico. An alternative approach
   is explored to exploiting these properties by building biomolecular analogs (eDNA) and virtual test tubes in electronics that would capture the best of both worlds. A
   distributed implementation is described of a virtual tube, Edna, on a cluster of PCs that does capture the massive asynchronous parallel interactions typical of BMC.
   Results are reported from over 1000 experiments that calibrate and benchmark Edna's performance, reproduce and extend Adleman's solution to the Hamiltonian Path problem for
   larger families of graphs than has been possible on a single processor or has been actually carried out in wet labs, and benchmark the feasibility and performance of
   DNA-based associative memories. The results required a million-fold less molecules and are at least as reliable as in vitro experiments, and so provide strong evidence that
   the paradigm of molecular computing can be implemented much more efficiently (in terms of time, cost, and probability of success) in silico than the corresponding wet
   experiments, at least in the range where Edna can be practically run. This approach also demonstrates intrinsic advantages in using electronic analogs of DNA as genomes for
   genetic algorithms and evolutionary computation.
%8 June
%Z Special Issue on Biomolecular Machines and Artificial Evolution Article ID: 5122745

%A Chris Gathercole
%A Peter Ross
%T Some Training Subset Selection Methods for Supervised Learning in Genetic Programming
%D 1994
%I 
%K genetic algorithms, genetic programming, LEF, DSS
%U http://citeseer.ist.psu.edu/gathercole94some.html
%X When using the Genetic Programming (GP) Algorithm on a difficult problem with a large set of training cases, a large population size is needed and a very large number of
   function-tree evaluations must be carried out. This paper describes how to reduce the number of such evaluations by selecting a small subset of the training data set on
   which to actually carry out the GP algorithm. Three subset selection methods described in the paper are: Dynamic Subset Selection (DSS), using the current...
%O Presented at ECAI'94 Workshop on Applied Genetic and other Evolutionary Algorithms

%A Chris Gathercole
%A Peter Ross
%T Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
%B Parallel Problem Solving from Nature III
%S LNCS
%E Yuval Davidor and Hans-Paul Schwefel and Reinhard M\"anner
%V 866
%D 1994
%P 312--321
%I Springer-Verlag Berlin, Germany
%C Jerusalem
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/94-006.ps.gz
%X When using the Genetic Programming (GP) Algorithm on a difficult problem with a large set of training cases, a large population size is needed and a very large number of
   function-tree evaluations must be carried out. This paper describes how to reduce the number of such evaluations by selecting a small subset of the training data set on
   which to actually carry out the GP algorithm. Three subset selection methods described in the paper are: Dynamic Subset Selection (DSS), using the current GP run to select
   difficult and/or disused cases, Historical Subset Selection (HSS), using previous GP runs, Random Subset Selection (RSS). Various runs have shown that GP+DSS can produce
   better results in less than 20percent of the time taken by GP. GP+HSS can nearly match the results of GP, and, perhaps surprisingly, GP+RSS can occasionally approach the
   results of GP. GP+DSS also produced better, more general results than those reported in a paper for a variety of Neural Networks when used on a substantial problem, known
   as the Thyroid problem.
%8 9-14  October
%Z PPSN3 Describes how to reduce the number of fitness case evaluations in difficult GP problems by selecting a small subset of the training data. Dynamic Subset Selection can
   produce better results than GP in less than 20percent of the time. Population size of 5,000 and 10,000.
%@ 3-540-58484-6

%A Chris Gathercole
%A Peter Ross
%T The MAX Problem for Genetic Programming - Highlighting an Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth
%R Technical Report
%D 1995
%I 
%I Department of Artificial Intelligence, University of Edinburgh
%C 80 South Bridge, Edinburgh, EH1 1HN, UK
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/gathercole95max.html
%X The Crossover operator is common to most implementations of Genetic Programming (GP). Another, usually unavoidable, factor is some form of restriction on the size of trees
   in the GP population. This paper concentrates on the interaction between the Crossover operator and a restriction on tree depth demonstrated by the MAX problem, which
   involves returning the largest possible value for given function and terminal sets. Some characteristics and inadequacies of Crossover in `normal' use are...
%Z p.s. On a related theme, and only blowing my own trumpet a little bit, I have recently written a paper [Gathercole] (soon to be submitted to GP96) which looks at an
   unfortunate interaction in GP between the Crossover operator and restrictions on tree size. Its more or less finished Published as \citeGathercole:1996:aicrtd

%A Chris Gathercole
%A Peter Ross
%T An Adverse Interaction between Crossover and Restricted Tree Depth in Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 291--296
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96, Update of Gathercole. Slides at http://www.dai.ed.ac.uk/students/chrisg/gp96_slides.html "penalising large trees appears to work well, especially when it is used
   only to discriminate between trees that would otherwise have the same fitness." p296 URLs broken 2006

%A Chris Gathercole
%A Peter Ross
%T Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 111--118
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/189252.html
%8 13-16  July
%Z GP-97 slides at http://www.dai.ed.ac.uk/students/chrisg/gp97/small_pops/slides.html tictactoe, noughts and crosses, uci thyroid

%A Chris Gathercole
%A Peter Ross
%T Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 119--127
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/79389.html
%8 13-16  July
%Z GP-97 slides at http://www.dai.ed.ac.uk/students/chrisg/gp97/lef/slides.html

%A Chris Gathercole
%T An Investigation of Supervised Learning in Genetic Programming
%R Ph.D. Thesis
%D 1998
%I 
%I University of Edinburgh
%K genetic algorithms, genetic programming
%U http://hdl.handle.net/1842/533
%X This thesis is an investigation into Supervised Learning (SL) in Genetic Programming (GP). With its flexible tree-structured representation, GP is a type of Genetic
   Algorithm, using the Darwinian idea of natural selection and genetic recombination, evolving populations of solutions over many generations to solve problems. SL is a
   common approach in Machine Learning where the problem is presented as a set of examples. A good or fit solution is one which can successfully deal with all of the examples.
   In common with most Machine Learning approaches, GP has been used to solve many trivial problems. When applied to larger and more complex problems, however, several
   difficulties become apparent. When focusing on the basic features of GP, this thesis highlights the immense size of the GP search space, and describes an approach to
   measure this space. A stupendously flexible but frustratingly useless representation, Anarchically Automatically Defined Functions, is described. Some difficulties
   associated with the normal use of the GP operator Crossover (perhaps the most common method of combining GP trees to produce new trees) are demonstrated in the simple MAX
   problem. Crossover can lead to irreversible sub-optimal GP performance when used in combination with a restriction on tree size. There is a brief study of tournament
   selection which is a common method of selecting fit individuals from a GP population to act as parents in the construction of the next generation. The main contributions of
   this thesis however are two approaches for avoiding the fitness evaluation bottleneck resulting from the use of SL in GP. To establish the capability of a GP individual
   using SL, it must be tested or evaluated against each example in the set of training examples. Given that there can be a large set of training examples, a large population
   of individuals, and a large number of generations, before good solutions emerge, a very large number of evaluations must be carried out, often many tens of millions. This
   is by far the most time-consuming stage of the GP algorithm. Limited Error Fitness (LEF) and Dynamic Subset Selection (DSS) both reduce the number of evaluations needed by
   GP to successfully produce good solutions, adaptively using the capabilities of the current generation of individuals to guide the evaluation of the next generation. LEF
   curtails the fitness evaluation of an individual after it exceeds an error limit, whereas DSS picks out a subset of examples from the training set for each generation.
   Whilst LEF allows GP to solve the comparatively small but difficult Boolean Even N parity problem for large N without the use of a more powerful representation such as
   Automatically Defined Functions, DSS in particular has been successful in improving the performance of GP across two large classification problems, allowing the use of
   smaller population sizes, many fewer and faster evaluations, and has more reliably produced as good or better solutions than GP on its own. The thesis ends with an
   assertion that smaller populations evolving over many generations can perform more consistently and produce better results than the `established' approach of using large
   populations over few generations.

%A L. L. Gatlin
%T Triplet frequencies in DNA and the genetic program
%J Journal of Theoretical Biology
%V 5
%N 3
%D 1963
%P 360--371
%I 
%U http://www.sciencedirect.com/science/article/B6WMD-4F1J81C-T5/2/12c96a984135797062556122da338822
%Z Not on GP

%A Surabhi Gaur
%A M. C. Deo
%T Real-time wave forecasting using genetic programming
%J Ocean Engineering
%V 35
%N 11-12
%D 2008
%P 1166--1172
%I 
%K genetic algorithms, genetic programming, Wave forecasts, Wave heights, Real-time forecasting
%U http://www.sciencedirect.com/science/article/B6V4F-4SD6SSR-1/2/619ec0df2657e8e39b38b7d533d37ec4
%X The forecasting of ocean waves on real-time or online basis is necessary while carrying out any operational activity in the ocean. In order to obtain forecasts that are
   station-specific a time-series-based approach like stochastic modeling or artificial neural network was attempted by some investigators in the past. This paper presents an
   application of a relatively new soft computing tool called genetic programming for this purpose. Genetic programming is an extension of genetic algorithm and it is suited
   to explore dependency between input and output data sets. The wave rider buoy measurements available at two locations in the Gulf of Mexico are analyzed. The forecasts of
   significant wave heights are made over lead times of 3, 6, 12 and 24h. The sample size belonged to a period of 15 years and it included an extensive testing period of 5
   years. The forecasts made by the approach of genetic programming indicated that it can be regarded as a promising tool for future applications to ocean predictions.

%A Dimitris Gavrilis
%A Ioannis G. Tsoulos
%A Evangelos Dermatas
%T Selecting and constructing features using grammatical evolution
%J Pattern Recognition Letters
%V 29
%N 9
%D 2008
%P 1358--1365
%I 
%K genetic algorithms, genetic programming, Grammatical evolution, Artificial neural networks, Feature selection, Feature construction
%U http://www.sciencedirect.com/science/article/B6V15-4S01WDH-4/2/aaff3c40c5eca125dfacb426d88fa177
%X A novel method for feature selection and construction is introduced. The method improves the classification accuracy, using the well-established technique of grammatical
   evolution by creating non-linear mappings of the original features to artificial ones in order to improve the effectiveness of artificial intelligence tools such as
   multi-layer perceptron (MLP), Radial-basis-function (RBF) neural networks and nearest neighbor (KNN) classifier. The proposed method has been applied on a series of
   classification and regression problems and an experimental comparison is carried out against the accuracy obtained on the original features as well as on features created
   by the PCA method.

%A Chris Gearhart
%T Genetic Programming as Policy Search in Markov Decision Processes
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 61--67
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/Gearhart.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A R. {Geetha Ramani}
%A R. Subramanian
%A P. Viswanath
%T Genetic Programming Method of Evolving the Robotic Soccer Player Strategies with Ant Intelligence
%J International Journal of Advanced Robotic Systems
%V 6
%N 2
%D 2009
%P 79--90
%I 
%K genetic algorithms, genetic programming, Robotic Soccer, Social Insect Behaviors, Ant intelligence, Learning methods, E CJ simulator, Teambots.
%U http://intechweb.org/Genetic_Programming_Method_of_Evolving_the_Robotic_Soccer_Player_Strategies_with_Ant_Intelligence.pdf
%X This paper presents the evolved soccer player strategies with ant-intelligence through genetic programming. To evolve the code for players we used the Evolutionary
   Computation tool (ECJ simulator- Evolutionary Computation in Java). We tested the evolved player strategies with already existing teams in soccerbots of teambots. This
   paper presents brief information regarding learning methods and ant behaviors. Experimental results depicts the performance of the evolved player strategies.
%Z football Dept. Of CSE & IT, Pondicherry Engineering College http://intechweb.org/journal.php?id=3

%A Erol Gelenbe
%T Genetic Algorithms with Analytical Solution
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 437--443
%I MIT Press
%C Stanford University, CA, USA
%K Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 GA paper

%A Sylvain Gelly
%A Olivier Teytaud
%A Nicolas Bredeche
%A Marc Schoenauer
%T A statistical learning theory approach of bloat
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1783--1784
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Poster, code bloat, code growth, reliability, statistical learning theory, theory
%U http://www.lri.fr/~teytaud/eabloat/eabloat.html
%X Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the
   framework of symbolic regression in GP, from the viewpoint of Statistical Learning Theory, a well grounded mathematical toolbox for Machine Learning. Two kinds of bloat
   must be distinguished in that context, depending whether the target function lies in the search space or not. Then, important mathematical results are proved using
   classical results from Statistical Learning. Namely, the Vapnik-Chervonenkis dimension of programs is computed, and further results from Statistical Learning allow to prove
   that a parsimonious fitness ensures Universal Consistency (the solution minimising the empirical error does converge to the best possible error when the number of examples
   goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of examples might still result in
   programs of infinitely increasing size with their accuracy; a more complicated modification of the fitness is proposed that theoretically avoids unnecessary bloat while
   nevertheless preserving the Universal Consistency. Full paper available at http://www.lri.fr/~teytaud/longBloat.pdf \citegelly:2005:longBloat
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052 eabloat.pdf is substantially more complete than poster in GECCO proceedings
%@ 1-59593-010-8

%A Sylvain Gelly
%A Olivier Teytaud
%A Nicolas Bredeche
%A Marc Schoenauer
%T A Statistical Learning Theory Approach of Bloat
%D 2005
%I 
%K genetic algorithms, genetic programming, Vapnik-Chervonenkis, VC dimension, bloat
%U http://www.lri.fr/~gelly/paper/antibloatGecco2005_long_version.pdf
%X Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the
   framework of symbolic regression in GP, from the viewpoint of Statistical Learning Theory, a well grounded mathematical toolbox for Machine Learning. Two kinds of bloat
   must be distinguished in that context, depending whether the target function lies in the search space or not. Then, important mathematical results are proved using
   classical results from Statistical Learning. Namely, the Vapnik-Chervonenkis dimension of programs is computed, and further results from Statistical Learning allow to prove
   that a parsimonious fitness ensures Universal Consistency (the solution minimising the empirical error does converge to the best possible error when the number of examples
   goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of examples might still result in
   programs of infinitely increasing size with their accuracy; a more complicated modification of the fitness is proposed that theoretically avoids unnecessary bloat while
   nevertheless preserving the Universal Consistency.
%O www
%Z cited by \cite1068309 Replaced by \citeDBLP:conf/cfap/GellyTBS05 Equipe TAO - INRIA Futurs LRI, Bat. 490, University Paris-Sud 91405 Orsay Cedex. France

%A Sylvain Gelly
%A Olivier Teytaud
%A Nicolas Bredeche
%A Marc Schoenauer
%T Apprentissage statistique et programmation g\'en\'etique: la croissance du code est-elle in\'evitable?
%B Actes de CAP 05, Conf\'erence francophone sur l'apprentissage automatique
%E Fran\ccois Denis
%D 2005
%P 163--178
%I PUG
%C Nice, France
%K genetic algorithms, genetic programming, VC, Bloat
%U http://www.lri.fr/~gelly/paper/bloatCap2005.pdf
%X Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the
   framework of symbolic regression in GP, from the viewpoint of Statistical Learning Theory, a well grounded mathematical toolbox for Machine Learning. Two kinds of bloat
   must be distinguished in that context, depending whether the target function lies in the search space or not. Then, important mathematical results are proved using
   classical results from Statistical Learning. Namely, the Vapnik-Cervonenkis dimension of programs is computed, and further results from Statistical Learning allow to prove
   that a parsimonious fitness ensures Universal Consistency (the solution minimising the empirical error does converge to the best possible error when the number of samples
   goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of samples might still result in
   programs of infinitely increasing size with their accuracy ; a more complicated modification of the fitness is proposed that theoretically avoids unnecessary bloat while
   nevertheless preserving the Universal Consistency.
%O A Statistical Learning Theory Approach of Bloat
%8 31  May -3  June
%Z CAP 2005 http://www.lif.univ-mrs.fr/~fdenis/cap05/ In english. an improved version of \citegelly:2005:longBloat Part of DBLP:conf/cfap/2005

%A Sylvain Gelly
%A Olivier Teytaud
%A Nicolas Bredeche
%A Marc Schoenauer
%T Universal Consistency and Bloat in GP
%J Revue d'Intelligence Artificielle
%V 20
%N 6
%D 2006
%P 805--827
%I HAL - CCSd - CNRS
%K genetic algorithms, genetic programming, Computer Science/Learning; Mathematics/Optimization and Control
%U http://ria.revuesonline.com/article.jsp?articleId=8936
%X In this paper, we provide an analysis of Genetic Programming (GP) from the Statistical Learning Theory viewpoint in the scope of symbolic regression. Firstly, we are
   interested in Universal Consistency, i.e. the fact that the solution minimising the empirical error does converge to the best possible error when the number of examples
   goes to infinity, and secondly, we focus our attention on the uncontrolled growth of program length (i.e. bloat), which is a well-known problem in GP. Results show that (1)
   several kinds of code bloats may be identified and that (2) Universal consistency can be obtained as well as avoiding bloat under some conditions. We conclude by describing
   an ad hoc method that makes it possible simultaneously to avoid bloat and to ensure universal consistency.
%O Sylvain Gelly <sylvain.gelly@m4x.org>
%O Issue on New Methods in Machine Learning. Theory and Applications
%Z in english

%A Sylvain Gelly
%T A contribution to Reinforcement Learning: Application to Computer-Go
%R Ph.D. Thesis
%D 2007
%I 
%I Universite, Paris-Sud
%C 91405 Orsay, Cedex, France
%K genetic algorithms, Monte-Carlo Random Trees, UCT, MoGo, OpenDP, SVM, CMA-ES
%U http://bibliographie.jeudego.org/these_sylvain-gelly.pdf
%8 25  September
%Z Informatique, Number 8754. Written in english. TAO, LRI.FR OpenBeagle implementation used.

%A Nur Merve Amil
%A Nicolas Bredeche
%A Christian Gagn{\'e}
%A Sylvain Gelly
%A Marc Schoenauer
%A Olivier Teytaud
%T A Statistical Learning Perspective of Genetic Programming
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 327--338
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming, poster
%X This paper proposes a theoretical analysis of Genetic Programming (GP) from the perspective of statistical learning theory, a well grounded mathematical toolbox for machine
   learning. By computing the Vapnik-Chervonenkis dimension of the family of programs that can be inferred by a specific setting of GP, it is proved that a parsimonious
   fitness ensures universal consistency. This means that the empirical error minimization allows convergence to the best possible error when the number of test cases goes to
   infinity. However, it is also proved that the standard method consisting in putting a hard limit on the program size still results in programs of infinitely increasing size
   in function of their accuracy. It is also shown that cross-validation or hold-out for choosing the complexity level that optimizes the error rate in generalization also
   leads to bloat. So a more complicated modification of the fitness is proposed in order to avoid unnecessary bloat while nevertheless preserving universal consistency.
%8 April  15-17
%Z Also known as \citeDBLP:conf/eurogp/AmilBGGST09 Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Ajish D. George
%A Scott A. Tenenbaum
%T Informatic Resources for Identifying and Annotating Structural RNA Motifs
%J Molecular Biotechnology
%V 41
%N 2
%D 2009
%P 180--193
%I 
%U http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2770092/pdf/nihms152441.pdf
%X Post-transcriptional regulation of genes and transcripts is a vital aspect of cellular processes, and unlike transcriptional regulation, remains a largely unexplored
   domain. One of the most obvious and most important questions to explore is the discovery of functional RNA elements. Many RNA elements have been characterized to date
   ranging from cis-regulatory motifs within mRNAs to large families of non-coding RNAs. Like protein coding genes, the functional motifs of these RNA elements are highly
   conserved, but unlike protein coding genes, it is most often structure and not sequence that conserved. Proper characterization of these structural RNA motifs is both the
   key and the limiting step to understanding the post-transcriptional aspects of the genomic world. Here we focus on the task of structural motif discovery and provide a
   survey of the informatics resources geared towards this task.
%8 February
%Z Refers briefly to \citeYuh-JyhHu:2003:NAR Gen*NY*Sis Center for Excellence in Cancer Genomics, University at Albany-SUNY, Department of Biomedical Sciences, School of
   Public Health, 1 Discovery Drive, Room 220, Rensselaer, NY 12144 Gen*NY*Sis Center for Excellence in Cancer Genomics, University at Albany-SUNY, Department of Biomedical
   Sciences, School of Public Health, 1 Discovery Drive, Room 220, Rensselaer, NY 12144, Phone (518) 591-7157; FAX (518) 591-7201 PMCID: PMC2770092

%A Ashley George
%A Malcolm I. Heywood
%T Improving GP classifier generalization using a cluster separation metric
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 939--940
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming: Poster, classification, clustering, evaluation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p939.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Loukas Georgiou
%A William J. Teahan
%T jGE - A Java implementation of Grammatical Evolution
%B 10th WSEAS International Conference on Systems
%D 2006
%P 534--869
%I 
%C Athens, Greece
%K genetic algorithms, genetic programming, grammatical evolution, genetic algorithms, evolutionary computation,agents, jGE, libGE, GP, GE
%X Grammatical Evolution (GE) is a novel evolutionary algorithm which uses an arbitrary variable-length binary string to govern which production rule of a Backus Naur Form
   grammar will be used in a genotype-to-phenotype mapping process. This paper introduces the Java GE project (jGE), which is an implementation of GE in the Java language. The
   main difference between jGE and libGE, a public domain implementation of GE in C++, is that jGE incorporates the functionality of libGE as a component and provides
   implementation of the Search Engine as well as the Evaluator. The main idea behind the jGE Library it can be downloaded at is to create a framework for evolutionary
   algorithms which can be extended to any specific implementation such as Genetic Algorithms, Genetic Programming and Grammatical Evolution.
%8 July  10-15

%A Loukas Georgiou
%A William J. Teahan
%T Implication of Prior Knowledge and Population Thinking in Grammatical Evolution: Toward a Knowledge Sharing Architecture
%J WSEAS Transactions on Systems
%V 5
%N 10
%D 2006
%P 2338--2345
%I 
%K genetic algorithms, genetic programming, grammatical evolution, genetic algorithms, evolutionary computation,agents, jGE, libGE, GP, GE
%X Grammatical Evolution (GE) is a novel evolutionary algorithm which uses an arbitrary variable-length binary string to govern which production rule of a Backus Naur Form
   grammar will be used in a genotype-to-phenotype mapping process. This paper introduces the Java GE project (jGE), which is an implementation of GE in the Java language, and
   presents the results of the first experiments which have been conducted towards a knowledge sharing approach using families of populations. The initial results show that
   the application of Prior Knowledge and Population Thinking in jGE is promising and this drives us toward a further investigation of the family-based approach whose main
   characteristics are the incorporation of genetic/phenotypic diversity in the population and the sharing of knowledge between individuals of the same groups (families).
%8 October
%Z Check title

%A Loukas Georgiou
%A William J. Teahan
%T Experiments with Grammatical Evolution in Java
%B Knowledge-Driven Computing: Knowledge Engineering and Intelligent Computations
%S Studies in Computational Intelligence
%E C. Cotta and S. Reich and R. Schaefer and A. Ligeza
%V 102
%D 2008
%P 45--62
%I Springer
%K genetic algorithms, genetic programming, Grammatical Evolution, Evolutionary Computation, jGE, libGE, GP
%X Grammatical Evolution (GE) is a novel evolutionary algorithm that uses a genotype-to-phenotype mapping process where variable-length binary strings govern which production
   rules of a Backus Naur Form grammar are used to generate programs. This paper describes the Java GE project (jGE), which is an implementation of GE in the Java language, as
   well as some proof-of-concept experiments. The main idea behind the jGE Library is to create a framework for evolutionary algorithms which can be extended to any specific
   implementation such as Genetic Algorithms, Genetic Programming and Grammatical Evolution.
%O 4
%Z Online version of book available http://www.springer.com/engineering/book/978-3-540-77474-7?detailsPage=toc

%A Loukas Georgiou
%A William J. Teahan
%T Grammatical Evolution and the Santa Fe Trail Problem
%B Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)
%E Agostinho Rosa
%D 2010
%P 10--19
%I 
%C Valencia, Spain
%K genetic algorithms, genetic programming, Grammatical Evolution, Artificial Ant Problem, Santa Fe Trail Problem, Genetic Programming, Genetic Algorithms, jGE, jGE NetLogo,
   Java, NetLogo
%X In this paper we present the results of a series of experiments which explore the effectiveness of Grammatical Evolution for the Santa Fe Trail problem. The experiments
   which are presented support the claim of other published work that the comparison mentioned in the Grammatical Evolution literature between Grammatical Evolution (GE) and
   Genetic Programming (GP) regarding the Santa Fe Trail problem is not a fair one. Namely, GE literature claims that GE outperforms GP in the Santa Fe Trail problem, but we
   show that this happens only because the GE experiments described in the literature use a different and narrower search space. In order to perform the experiments, a series
   of tools and models have been developed and are presented: a) jGE, a Java implementation of the Grammatical Evolution system; b) jGE NetLogo, an extension of jGE for the
   NetLogo modelling environment; c) the Santa Fe Trail model, a simulation of the problem in NetLogo; and d) a NetLogo model for the execution of the experiments. Finally, we
   show that Grammatical Evolution is capable of finding solutions in the Santa Fe Trail problem that require fewer steps than the solutions mentioned in the GP and GE
   literature.
%8 24-26  October
%Z http://www.icec.ijcci.org/ICEC2010/home.asp http://www.ecta.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm

%A Loukas Georgiou
%A William J. Teahan
%T Constituent Grammatical Evolution
%B Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence
%E Toby Walsh
%D 2011
%P 1261--1268
%I AAAI Press Menlo Park, California, USA
%I International Joint Conferences on Artificial Intelligence
%C Barcelona, Spain
%K genetic algorithms, genetic programming, Grammatical Evolution
%U http://ijcai.org/papers11/Papers/IJCAI11-214.pdf
%X We present Constituent Grammatical Evolution (CGE), a new evolutionary automatic programming algorithm that extends the standard Grammatical Evolution algorithm by
   incorporating the concepts of constituent genes and conditional behaviour-switching. CGE builds from elementary and more complex building blocks a control program which
   dictates the behaviour of an agent and it is applicable to the class of problems where the subject of search is the behaviour of an agent in a given environment. It takes
   advantage of the powerful Grammatical Evolution feature of using a BNF grammar definition as a plug-in component to describe the output language to be produced by the
   system. The main benchmark problem in which CGE is evaluated is the Santa Fe Trail problem using a BNF grammar definition which defines a search space semantically
   equivalent with that of the original definition of the problem by Koza. Furthermore, CGE is evaluated on two additional problems, the Los Altos Hills and the Hampton Court
   Maze. The experimental results demonstrate that Constituent Grammatical Evolution outperforms the standard Grammatical Evolution algorithm in these problems, in terms of
   both efficiency (percent of solutions found) and effectiveness (number of required steps of solutions found).
%8 16-22  July
%Z Santa Fe Ant, Lost Altos Hills, Hampton Court Maze, jGE http://ijcai.org/papers11/contents.php

%A Efstratios F. Georgopoulos
%A George P. Zarogiannis
%A Adam V. Adamopoulos
%A Anastasios P. Vassilopoulos
%A Spiridon D. Likothanassis
%T A Genetic Programming Environment for System Modeling
%B 5th Hellenic Conference on AI, SETN 2008
%S Lecture Notes in Computer Science
%E John Darzentas and George A. Vouros and Spyros Vosinakis and Argyris Arnellos
%V 5138
%D 2008
%P 85--96
%I Springer
%C Syros, Greece
%K genetic algorithms, genetic programming, Evolutionary Algorithms, System Modeling, MEG modeling, fatigue modeling
%X In the current paper we present an integrated genetic programming environment with a graphical user interface (GUI), called jGPModeling. The jGPModeling environment was
   developed using the JAVA programming language, and is an implementation of the steady-state genetic programming algorithm. That algorithm evolves tree based structures that
   represent models of input-output relation of a system. During the design and implementation of the application, we focused on the execution time optimization and tried to
   limit the bloat effect. In order to evaluate the performance of the jGPModeling environment, two different real world system modeling tasks were used.
%8 October  2-4
%Z SQUID, MEG

%A George Georgoulas
%A Dimitris Gavrilis
%A Ioannis G. Tsoulos
%A Chrysostomos Stylios
%A Joao Bernardes
%A Peter P. Groumpos
%T Novel approach for fetal heart rate classification introducing grammatical evolution
%J Biomedical Signal Processing and Control
%V 2
%N 2
%D 2007
%P 69--79
%I 
%K genetic algorithms, genetic programming, grammatical evolution, Fetal heart rate, Multilayer perceptron, Feature construction, Classification
%U http://www.sciencedirect.com/science/article/B7XMN-4P9K9C1-1/2/26899c02af37c6edf88c6baa6282a061
%X Fetal heart rate (FHR) variations reflect the level of oxygenation and blood pressure of the fetus. Electronic Fetal Monitoring (EFM), the continuous monitoring of the FHR,
   was introduced into clinical practice in the late 1960s and since then it has been considered as an indispensable tool for fetal surveillance. However, EFM evaluation and
   its merit is still an open field of controversy, mainly because it is not consistently reproducible and effective. In this work, we present a novel method based on
   grammatical evolution to discriminate acidemic from normal fetuses, using features extracted from the FHR signal during the minutes immediately preceding delivery. The
   proposed method identifies linear and nonlinear correlations among the originally extracted features and creates/constructs a set of new ones, which, in turn, feed a
   nonlinear classifier. The classifier, which also uses a hybrid method for training, along with the constructed features was tested using a set of real data achieving an
   overall performance of 90percent (specificity=sensitivity=90percent).

%A Adrian Gepp
%A Phil Stocks
%T A review of procedures to evolve quantum algorithms
%J Genetic Programming and Evolvable Machines
%V 10
%N 2
%D 2009
%P 181--228
%I 
%K genetic algorithms, genetic programming, Evolving quantum algorithms, Quantum computing, Evolutionary algorithms, Quantum algorithms
%X There exist quantum algorithms that are more efficient than their classical counterparts; such algorithms were invented by Shor in 1994 and then Grover in 1996. A lack of
   invention since Grover's algorithm has been commonly attributed to the non-intuitive nature of quantum algorithms to the classically trained person. Thus, the idea of using
   computers to automatically generate quantum algorithms based on an evolutionary model emerged. A limitation of this approach is that quantum computers do not yet exist and
   quantum simulation on a classical machine has an exponential order overhead. Nevertheless, early research into evolving quantum algorithms has shown promise. This paper
   provides an introduction into quantum and evolutionary algorithms for the computer scientist not familiar with these fields. The exciting field of using evolutionary
   algorithms to evolve quantum algorithms is then reviewed.
%8 June

%A Marcos Gestal
%A Juan R. Rabu{\~n}al
%A Julian Dorado
%A Javier {Pereira Loureiro}
%T Description of RANNs and their generalisation capabilities by means of rule extraction by genetic programming
%B Artificial Intelligence and Soft Computing
%E Angel P. Del Pobil
%D 2006
%P 323--328
%I IASTED/ACTA Press
%C Palma de Mallorca, Spain
%K genetic algorithms, genetic programming, Recurrent Artificial Neural Networks, Rule Extraction, Algorithm of Example Generation, Generalisation Capabilities, Series
   Prediction
%U http://sabia.tic.udc.es/sabia/secciones/publications/?id=311
%X Artificial Neural Networks have achieved satisfactory results in different fields such as example classification or image identification. Real-world processes usually have
   a temporal evolution, and they are the type of processes where Recurrent Networks have special success. Nevertheless they are still reluctantly used, mainly due to the fact
   that they do not adequately justify their response. But, if ANNs offer good results, why giving them up? Suffice it to find a method that might search an explanation to the
   outputs that the ANN provides. This work presents a technique, totally independent from ANN architecture and the learning algorithm used, which makes possible the
   justification of the ANN outputs by means of expression trees.
%8 August  28-30
%@ 0-88986-612-0

%A A. Geyer
%A Andreas Geyer-Schulz
%A A. Taudes
%T A Fuzzy Times Series Analyzer
%B Progress in Fuzzy Sets and Systems
%S Series D: Systems Theory, Knowledge Engineering and Problem Solving
%E Wolfgang H. Janko and Marc Roubens and H.-J. Zimmermann
%V 5
%D 1990
%P 63--74
%I Kluwer Academic Publishers
%C The Netherlands
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Geyer_1990_pfss.pdf

%T $14^th$ Linz Seminar on Fuzzy Set Theory: Non-Classical Logics and their Applications
%B $14^th$ Linz Seminar on Fuzzy Set Theory: Non-Classical Logics and their Applications
%E Ulrich H\"ohle and Peter Klement
%D 1992
%I Johannes Kepler Universit\"at Linz
%C Linz
%K genetic algorithms, genetic programming

%T Fuzzy Logic: State of the Art
%B Fuzzy Logic: State of the Art
%S Series D: System Theory, Knowledge Engineering and Problem Solving
%E Robert Lowen and Marc Roubens
%D 1993
%I Kluwer Academic Publishers
%I IFSA
%C Dordrecht

%T Modelling Uncertain Data
%B Modelling Uncertain Data
%S Mathematical Research
%E Hans Bandemer
%V 68
%D 1993
%I Akademie Verlag
%I GAMM
%C Berlin
%K genetic algorithms, genetic programming
%U http://books.google.co.uk/books?id=FzjvAAAAMAAJ
%@ 3-05-501578-9

%T Informationswirtschaft, Aktuelle Entwicklungen und Perspektiven : Symposion
%B Informationswirtschaft
%E Walter Frisch and Alfred Taudes
%D 1993
%I Physica-Verlag Heidelberg
%C Vienna
%K genetic algorithms, genetic programming
%U http://books.google.co.uk/books?id=PAXYPQAACAAJ
%8 29-30  September
%@ 3-7908-0727-3

%A Andreas Geyer-Schulz
%T Speeding Up Genetic Machine Learning -- A case for Fuzzy Rule Languages
%B First European Congress on Fuzzy and Intelligent Technologies, EUFIT'93
%V 2
%D 1993
%P 1083--1089
%I Elite-Foundation D-52076 Aachen
%C Aachen, Germany
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Geyer-Schulz_1993_EUFIT.pdf
%8 7-10  September
%Z Boston Consulting Group rule language grammar

%A Andreas Geyer--Schulz
%T Fuzzy Rule-Based Expert Systems and Genetic Machine Learning
%S Studies in Fuzziness
%V 3
%D 1995
%I Physica-Verlag
%C Heidelberg
%K genetic algorithms, genetic programming
%U http://www.amazon.com/Rule-Based-Systems-Learning-Fuzziness-Computing/dp/3790809640
%Z reviewed by Dick Bowman, Dogon Research http://www.apl.demon.co.uk/aplandj/fuzzy.htm
%@ 3-7908-0830-X

%A Andreas Geyer--Schulz
%T Genetic Machine Learning
%R Technical Report
%D 1995
%I 
%I ACM SIGAPL
%C New York, N.Y.
%K genetic algorithms, genetic programming
%O Tutorial held at APL'95 at San Antonio, Texas

%T Genetic Algorithms and Soft Computing
%B Genetic Algorithms and Soft Computing
%S Studies in Fuzziness and Soft Computing
%E F. Herrera and J. L. Verdegay
%V 8
%D 1996
%I Physica-Verlag
%I Physica-Verlag
%C Heidelberg
%K genetic algorithms, genetic programming
%U http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=379080956X
%8 September
%@ 3-7908-0956-X

%A Andreas Geyer--Schulz
%T Fuzzy Rule-Based Expert Systems and Genetic Machine Learning
%S Studies in Fuzziness and Soft Computing
%V 3
%D 1996
%I Physica-Verlag
%C Heidelberg
%K genetic algorithms, genetic programming
%U http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=3790809640

%T Betriebliche Anwendungen von Fuzzy Technologien
%B Betriebliche Anwendungen von Fuzzy Technologien
%E J. Biethahn and A. H\"ohnerloh and J. Kuhl and V. Nissen
%D 1996
%I Georg-August Universit\"at G\"ottingen, Institut f\"ur Wirtschaftsinformatik
%I AFN -- Arbeitsgemeinschaft Fuzzy Logik und Softcomputing Norddeutschland
%C G\"ottingen
%K genetic algorithms, genetic programming
%U http://www.amazon.de/Betriebliche-Anwendungen-von-Fuzzy-Technologien-Softcomputing/dp/B003E8W9ZE

%A Andreas Geyer--Schulz
%T Fuzzy Genetic Programming and Dynamic Decision Making
%J Proc. ICSE'96
%D 1996
%P 686--691
%I 
%K genetic algorithms, genetic programming
%8 June

%A Andreas Geyer--Schulz
%T Compound Derivations in Fuzzy Genetic Programming
%B 1996 Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS
%D 1996
%P 510--514
%I 
%K genetic algorithms, genetic programming, a priori knowledge, compound derivations, context-free language, equivalence transformations, fuzzy genetic programming, genetic
   algorithms, grammar, k-bounded context-free languages, lambda abstraction, machine-learning method, nonlinear transformations, speedup theorems, context-free languages,
   fuzzy logic, genetic algorithms, grammars, heuristic programming, learning (artificial intelligence)
%X We introduce the concept of compound derivations in fuzzy genetic programming as an alternative to lambda abstraction. We show that in fuzzy genetic programming based on
   simple genetic algorithms over k-bounded context-free languages compound derivations provide a powerful tool for generating automatically equivalence transformations on the
   grammar of a context-free language. Although such transformations do not change the language generated by the grammar, the probability of generating words can be
   transformed almost at will. We apply this property to: nonlinear transformations of the probability of generating words for initialising a population,; incorporating a
   priori knowledge; the new genetic operator compound which provides an alternative to lambda abstraction; and proving speedup theorems
%8 July

%A Andreas Geyer--Schulz
%T Learning Strategies for Managing New and Innovative Products
%B Classification and Knowledge Organization Proceedings of the 20th Annual Conference of the Gesellschaft fuer Klassifikation e.V., GfKl'96
%S Studies in Classification, Data Analysis, and Knowledge Organization
%E Ruediger Klar and Otto Opitz
%V XX
%D 1996
%P 262--269
%I Springer
%C University of Freiburg, Germany
%K genetic algorithms, genetic programming
%U http://www.springer.com/economics/book/978-3-540-62981-8?cm_mmc=Google-_-Book%20Search-_-Springer-_-0
%8 6-8  March
%Z published 1997 (2012 Currently out of stock)

%A Andreas Geyer--Schulz
%T Fuzzy Genetic Algorithms
%J Handbook of Fuzzy Systems
%D 1996
%I 
%K genetic algorithms, genetic programming
%O Work in progress
%8 April

%A Andreas Geyer-Schulz
%T The Next 700 Programming Languages for Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 128--136
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Geyer-Schulz_1997_700.pdf
%8 13-16  July
%Z GP-97

%A R. Ghanea-Hercock
%A A. P. Fraser
%T Evolution of autonomous robot control architectures
%B Evolutionary Computing, AISB workshop
%E T. C. Fogarty
%D 1994
%I 
%I AISB
%C Leeds, UK
%K genetic algorithms, genetic programming
%8 11-13  April
%Z Does NOT appear in the published proceedings LNCS 865 DOI: 10.1007/3-540-58483-8 Proceedings of the Workshop on Artificial Intelligence and Simulation of Behaviour Workshop
   on Evolutionary Computing. Workshop in Leeds, UK, April 11-13, 1994

%A Robert Ghanea-Hercock
%A Divine T. Ndumu
%A Jaron Collis
%T Distributed Genetic Programming with Mobile Agents
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1441
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, artificial life, adaptive behavior and agents, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-004.ps
%X java based mobil agents, MATS
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Kamran Ghani
%A John A. Clark
%T Widening the Goal Posts: Program Stretching to Aid Search Based Software Testing
%B Proceedings of the 1st International Symposium on Search Based Software Engineering (SSBSE'09)
%D 2009
%I IEEE
%C Cumberland Lodge, Windsor, UK
%K genetic algorithms, genetic programming, SBSE
%X Search based software testing has emerged in recent years as an important research area within automated software test data generation. The general approach of couching the
   satisfaction of test goals as numerical optimisation problems has been applied to a variety of problems such as satisfying structural coverage criteria, specification
   falsification, exception generation, breaking unit pre-conditions and software hazard discovery. However, some test goals may be hard to satisfy. For example, a program
   branch may be difficult to reach via a search based technique, because the domain of the data that causes it to be taken is exceedingly small or the non-linearity of the
   fitness landscape precludes the provision of effective guidance to the search for test data. In this paper we propose to stretch relevant conditions within a program to
   make them easier to satisfy. We find test data that satisfies the corresponding test goal of the stretched program. We then seek to transform the stretched program by
   stages back to the original, simultaneously migrating the obtained test data to produce test data that satisfies the goal for the original program. The stretching device is
   remarkably simple and shows significant promise for obtaining hard-to-find test data and also gives efficiency improvements over standard search based testing approaches.
%8 13-15  May

%A Kamran Ghani
%A John A. Clark
%T Automatic Test Data Generation for Multiple Condition and MCDC Coverage
%B Fourth International Conference on Software Engineering Advances, ICSEA'09
%D 2009
%P 152--157
%I 
%K genetic algorithms, genetic programming, SBSE, MCDC coverage, automatic test data generation, search based software engineering, search based test data generation, search
   based testing, software engineering community, software functional property, software nonfunctional property, structural testing, automatic testing, program testing,
   software engineering
%X Recently search based software engineering (SBSE) has evolved as a major research field in the software engineering community. SBSE has been applied successfully to many
   software engineering activities ranging from requirement engineering to software maintenance and quality assessment. One area where SBSE has seen much application is test
   data generation. Search based test data generation techniques have been applied to automatically generate data for testing functional and non-functional properties of
   softwares. For structural testing, most of the time, the criterion used, is branch coverage. However, this is not enough. For the wider acceptance of search based test data
   generation techniques, much stronger criteria are needed. we propose an automatic framework that extend search based testing techniques to more stronger criteria such as
   multiple condition and MCDC coverage.
%O Winner of top paper prize
%8 September
%Z Also known as \cite5298463

%A Adam Ghozeil
%A David B. Fogel
%T Discovering Patterns in Spatial Data using Evolutionary Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 521--527
%I MIT Press
%C Stanford University, CA, USA
%K Evolutionary Programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 EP paper

%A Mario Giacobini
%A Marco Tomassini
%A Leonardo Vanneschi
%T How Statistics Can Help In Limiting The Number Of Fitness Cases In Genetic Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 889
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, poster paper, entropy, fitness Cases, statistics
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Mario Giacobini
%A Marco Tomassini
%A Leonardo Vanneschi
%T Limiting the Number Fitness Cases in Genetic Programming Using Statistics
%B Parallel Problem Solving from Nature - PPSN VII
%S Lecture Notes in Computer Science, LNCS
%E Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel
%N 2439
%D 2002
%P 371--380
%I Springer-Verlag
%C Granada, Spain
%K genetic algorithms, genetic programming, Parameter tuning, Fitness Evaluation, Theory of evolutionary computing
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=371
%X Fitness evaluation is often a time consuming activity in genetic programming applications and it is thus of interest to find criteria that can help in reducing the time
   without compromising the quality of the results. We use well-known results in statistics and information theory to limit the number of fitness cases that are needed for
   reliable function reconstruction in genetic programming. By using two numerical examples, we show that the results agree with our theoretical predictions. Since our
   approach is problem-independent, it can be used together with techniques for choosing an efficient set of fitness cases.
%O Available from http://link.springer.de/link/service/series/0558/papers/2439/243900371.pdf
%8 7-11  September
%@ 3-540-44139-5

%A Alexandros Giagkos
%A Myra S. Wilson
%T A Cross-layer Design for Bee-Inspired Routing Protocols in MANETs
%B TAROS 2009 Towards Autonomous Robotic Systems
%S Intelligent Systems Research Centre Technical Report Series
%E Theocharis Kyriacou and Ulrich Nehmzow and Chris Melhuish and Mark Witkowski
%D 2009
%P 25--32
%I 
%C University of Ulster, Londonderry, United Kingdom
%K wireless, mobile, ad hoc, bee-inspired, crosslayering, routing
%U http://isrc.ulster.ac.uk/images/stories/publications/report-series/TAROS_2009.pdf
%X The field of robotics relies heavily on various technologies such as mechanical and electronic engineering, computing systems, and wireless communication. The latter plays
   a significant role in the area of mobile robotics by supporting remote interactions. An effective, fast, and reliable communication among homogeneous or heterogeneous
   robots, as well as the ability to adapt to the rapidly changing environmental conditions predicates the robots success and completion of their tasks. In this paper we
   present our research position in the area of adaptive nature-inspired routing protocols for mobile ad hoc networks (MANETs). Our approach is based on the honeybee foraging
   behaviour and ability to find and exchange information about productive sources of food in a rapidly changing environment. We describe the research problem, present a brief
   review of the relative literature, and illustrate our future plan.
%8 August  31 -  September  2
%Z Inspired by GP? http://www.infm.ulst.ac.uk/~ulrich/Taros09/

%A Antonella Giani
%T A Study of Parallel Cooperative Classifier Systems
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://www.ai.mit.edu/people/unamay/phd-ws-abstracts/gianni.ps broken
%8 22-25  July
%Z GP-98LB, GP-98PhD Student Workshop see http://www.di.unipi.it/phd/tesi/tesi_1999.html

%A Jonathan Gibbs
%T Easy Inverse Kinematics using Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 422
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A W. Wayt Gibbs
%T Programming with Primordial Ooze
%J Scientific American
%V 275
%N 4
%D 1996
%P 30--31
%I 
%K genetic algorithms, genetic programming
%U http://www.sciamdigital.com/index.cfm?fa=Products.ViewIssuePreview&ISSUEID_CHAR=999FE651-ED4C-44FB-A474-45B350186E9&ARTICLEID_CHAR=F6E35349-F7F4-4AA4-8027-C720B177667
%X Computer programmers ascended the economic food chain by inventing clever algorithms to make manufacturing and service laborers redundant. But some programmers may one day
   find themselves automated out of a job. In university labs, scientists are teaching computers how to write their own programs. Borrowing from the principles of natural
   selection, the researchers have built artificial ecosystems that, for a few problems at least, can evolve solutions better than any yet devised by humans. Someday such
   systems may even be able to design new kinds of computers. The idea of evolving rather than inducing algorithms is not new. John H. Holland of the University of Michigan
   worked out the method 21 years ago. But Hollandżs strategy, based on a rigorous analogy to chromosomes, is limited to problems whose solutions can be expressed as
   mathematical formulas. It works well only if a human programmer figures out how many numbers the computer should plug into the formula.
%8 October
%Z Summary Report on GP96. Notes on papers by Jamie J. Fernandez, Conor Ryan, Brian Howley, Lee Spector and Adrian Thompson

%A W. Wayt Gibbs
%T Cybernetic Cells
%J Scientific American
%V 265
%N 2
%D 2001
%P 42--47
%I 
%K genetic algorithms, genetic programming
%U http://www.sciamdigital.com/index.cfm?fa=Products.ViewIssuePreview&ARTICLEID_CHAR=56B6AD77-E68F-4CD9-86A3-97B9BAD6FD6
%Z favourable mention of Koza's psb 2001 work \citekoza:2001:PSB PMID: 11478002 [PubMed - indexed for MEDLINE]

%A Kevin A. Gibbs
%T Implementation and Evaluation of a Novel ``Branch'' Construct for Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 93--101
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.205
%X This paper describes a technique for implementing a novel type of "branch " operator within a genetic programming system. This branch construct is a new operator type that
   allows arbitrary branching from one location in an individual's execution tree to another. The branch can be understood as alternatively allowing arbitrary code reuse or
   approximating access to a potentially infinite number of automatically defined functions. This paper describes the proposed design of this branch operator. This proposed
   design is then implemented in a real world system, and the performance effects of the branch operator are evaluated in two well known genetic programming problems: the
   artificial ant problem and the lawnmower problem. [1,2] The branch is found to provide some performance benefits in both of these problems, and areas for further
   investigation are outlined. Introduction and Overview In the day-to-day programming done by humans, most all control structures in code originate from a high level. Whether
   programming in a low-level language like C or a higher-level language like LISP, we are accustomed to using high-level control constructs like functions, loops, if
   statements, and recursion to control the path of execution and maximize code reuse. The thought of using a branch, or goto or jump
%8 June
%Z part of \citekoza:2002:gagp Artificial ant. Lawn Mower. "allowing arbitrary code reuse" or "potentially infinite number of ADFs". Goto. "branch" function with "random"
   destination p95. Limits on total number of instructions and number of branch instructions, defaults given if limits reached. lilgp. Branch destinations stored as relative
   offsets into the array of instructions.

%A C. Gielen
%T Genetic programming: J.R. Koza. The MIT Press, Cambridge, MA. ISBN 0-262-11170-5. 819 pp., \$ 74,25
%J Neurocomputing
%V 6
%N 1
%D 1994
%P 120--122
%I 
%U http://www.sciencedirect.com/science/article/B6V10-48TCT75-5M/2/118608d812226c1e01a24920532a2702
%O Backpropagation, Part III
%Z \citekoza:book

%A Philippe Gigure
%A David E. Goldberg
%T Population Sizing for Optimum Sampling with Genetic Algorithms: A Case Study of the Onemax Problem
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 496--503
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Labiba Gilani
%A Asifullah Khan
%A Anwar M. Mirza
%T Distortion Estimation in Digital Image Watermarking using Genetic Programming
%J International Journal of Applied Science, Engineering and Technology
%V 15
%N 20
%D 2006
%P 103--108
%I 
%K genetic algorithms, genetic programming
%U http://www.waset.org/ijaset/v15/v15-20.pdf
%X This paper introduces a technique of distortion estimation in image watermarking using Genetic Programming (GP). The distortion is estimated by considering the problem of
   obtaining a distorted watermarked signal from the original watermarked signal as a function regression problem. This function regression problem is solved using GP, where
   the original watermarked signal is considered as an independent variable. GP-based distortion estimation scheme is checked for Gaussian attack and Jpeg compression attack.
   We have used Gaussian attacks of different strengths by changing the standard deviation. JPEG compression attack is also varied by adding various distortions. Experimental
   results demonstrate that the proposed technique is able to detect the watermark even in the case of strong distortions and is more robust against attacks.
%Z http://www.waset.org/ijaset/ Lena

%A Richard J. Gilbert
%A Royston Goodacre
%A Beverly Shann
%A Douglas B. Kell
%A Janet Taylor
%A Jem J. Rowland
%T Genetic Programming-Based Variable Selection for High-Dimensional Data
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 109--115
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Richard J. Gilbert
%A Royston Goodacre
%A Andrew M. Woodward
%A Douglas B. Kell
%T Genetic programming: A novel method for the quantitative analysis of pyrolysis mass spectral data
%J ANALYTICAL CHEMISTRY
%V 69
%N 21
%D 1997
%P 4381--4389
%I 
%K genetic algorithms, genetic programming
%U http://pubs.acs.org/journals/ancham/article.cgi/ancham/1997/69/i21/pdf/ac970460j.pdf
%X A technique for the analysis of multivariate data by genetic programming (GP) is described, with particular reference to the quantitative analysis of orange juice
   adulteration data collected by pyrolysis mass spectrometry (PyMS). The dimensionality of the input space was reduced by ranking variables according to product moment
   correlation or mutual information with the outputs. The GP technique as described gives predictive errors equivalent to, if not better than, more widespread methods such as
   partial least squares and artificial neural networks but additionally can provide a means for easing the interpretation of the correlation between input and output
   variables. The described application demonstrates that by using the GP method for analyzing PyMS data the adulteration of orange juice with 10% sucrose solution can be
   quantified reliably over a 0-20% range with an RMS error in the estimate of ? 1%.

%A Richard J. Gilbert
%A Helen E. Johnson
%A Michael K. Winson
%A Jem J. Rowland
%A Royston Goodacre
%A Aileen R. Smith
%A Michael A. Hall
%A Douglas B. Kell
%T Genetic Programming as an Analytical Tool for Metabolome Data
%B Late-Breaking Papers of EuroGP-99
%E W. B. Langdon and Riccardo Poli and Peter Nordin and Terry Fogarty
%D 1999
%P 23--33
%I 
%I EvoGP
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.ps.Z
%X Genetic programming, in conjunction with advanced analytical instruments, is a novel tool for the investigation of complex biological systems at the whole-tissue level. In
   this study, samples from tomato fruit grown hydroponically under both high- and low-salt conditions were analysed using Fourier-transform infrared spectroscopy (FTIR), with
   the aim of identifying spectral and biochemical features linked to salinity in the growth environment. FTIR spectra are not amenable to direct visual analysis, so
   supervised machine learning was used to generate models capable of classifying the samples based on their spectral characteristics. The genetic programming (GP) method was
   chosen, since it has previously been shown to perform with the same accuracy as conventional data modelling methods, but in a readily-interpretable form. Examination of the
   GP-derived models showed that there was a small number of spectral regions that were consistently being used. In particular, the spectral region containing absorbances
   potentially due to a cyanide/nitrile functional group was identified as discriminatory. The explanatory power of the GP models enabled a chemical interpretation of the
   biochemical differences to be proposed. The combination of FTIR and GP is therefore a powerful and novel analytical tool which, in this study, improves our understanding of
   the biochemistry of salt tolerance in tomato plants.
%8 26-27  May
%Z EuroGP'99LB part of \citelangdon:1999:egplb

%A Richard Gilbert
%A Kris Birchall
%A William Bains
%T Classification of Cytochrome P450 3A4 Ligands Using Genetic Programming
%D 2002
%I 
%K genetic algorithms, genetic programming
%U http://www.amedis-pharma.com/Docs/3A4_ligand_poster.ppt
%X The cytochrome P450 [CYP] family is a set of haem-containing oxidoreductase enzymes which are involved in the first-pass metabolism of xenobiotic compounds such as drug
   molecules. CYP 3A4 is the most abundant of these enzymes in humans, and is capable of metabolising approximately 80percent of drugs to some extent. As CYP3A4 has a limited
   capacity, both competing substrates and inhibitors can affect the rate at which CYP3A4 metabolises drugs, and hence the amount of the compound that reaches systemic
   circulation. Identifying whether a compound is metabolised by CYPs in general, and CYP3A4 in particular, is important for judging its potential as a drug. We describe an
   approach to the computational identification of CYP3A4 ligands (substrates and inhibitors) that is based on a type of evolutionary computing called genetic programming. The
   method is a supervised learning system, i.e. it is guided by past examples, in this case actual measured biological data on CYP ligand status. The GP system creates
   predictive models by Darwinian operations of mutation, crossover and fitness selection, operating on a population of potential solutions. Parent solutions are chosen
   according to their ability to explain the training data. New models are generated by mutation or crossover, and may replace less-fit individuals already in the population.
   After sufficient iterations, the population comprises models able to explain the observations much more effectively than the initial random population. Applying this to
   publicly available CYP3A4 data, we show that we can predict the ligand status of a diverse set of known drugs to approximately 90percent accuracy, and to predict whether a
   ligand will be a substrate or an inhibitor to approximately 85percent accuracy. The GP method also identifies structural characteristics of the molecule which it is using
   to build the decision algorithms, and these are consistent with more exhaustive, quantum mechanical predictions of substrate status. The evolutionary nature of GPs allows
   generation of multiple solutions, which allow statistical validation of the results.
%Z Amedis Pharmaceuticals Limited, Upton House, Baldock Street, Royston, Herts SG8 5AY, UK

%A Andrew Gildfind
%A Michael A. Gigante
%A Ghassan Al-Qaimari
%T Evolving performance control systems for digital puppetry
%J Journal of Visualization and Computer Animation
%V 11
%N 4
%D 2000
%P 169--183
%I John Wiley & Sons, Ltd.
%K genetic algorithms, genetic programming, performance animation, motion capture, performance control systems, puppetry, adaptive user interfaces
%U http://citeseer.ist.psu.edu/438189.html
%X We describe a new approach for creating performance control systems for digital puppetry. Genetic programming with fitness values specified directly by the puppeteer is
   used. A generic device and model representation combined with the inherent domain independence of the genetic programming paradigm allows this approach to create control
   systems for arbitrary combinations of input devices and models. In addition, a number of unique interaction techniques have been developed to support the user-directed
   search. In this paper we introduce the approach, describe the implementation and user interface and present the results from a comprehensive evaluation with expert users.
   We show that a search-based approach can provide an effective alternative to manually designing performance control systems and an elegant mechanism for unifying low-level
   input devices with a broad range of model control modes.
%8 3  October

%A Jaysen Gillespie
%T A Genetic Algorithm Solution to the Project Selection Problem Using Static and Dynamic Fitness Functions
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 76--85
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A S. D. Gillmor
%A Q. Liu
%A L. Wang
%A C. E. Jordan
%A A. G. Frutos
%A A. J. Theil
%A T. C. Stother
%A A. E. Condon
%A R. M. Corn
%A L. M. Smith
%A M. G. Lagally
%T Addressed-Array Approach to DNA Computation Readout through UV Photopatterning
%B 1998 March Meeting of the American Physical Society
%D 1998
%P 2403-+
%I 
%I APS
%C Los Angeles
%K genetic algorithms, genetic programming
%U http://flux.aps.org/meetings/YR98/BAPSMAR98/abs/S4160003.html
%X Surfaced-based DNA computation allows for the efficient manipulation of operations on DNA strands. The readout operation determines the DNA strand sequence that encodes the
   solution of a combinatorial problem of interest; to perform it, densely addressed arrays are a necessity. In our surfaced-based approach, we photopattern self-assembled
   monolayers (SAMs) attached to a gold surface creating specific regions of hydrophilic islands in a hydrophobic background, and we characterise the chemically modified
   surface through reflection FTIR and fluorometry. Subsequently, the DNA strands, short 31 base-pair oligonucleotides that encode 4-8 bits of data, attach to the hydrophilic
   islands and form addressed arrays with feature sizes in the submillimeter range. With simple addressed arrays, we can perform the readout operation for a combinatorial
   problem. Expanding this simple technique, possibly with ink jet printer technology, readout can be modified to solve complex combinatorial problems employing arrays of 16
   by 16 or larger with features sizes on the micrometer scale.
%8 16-20  March
%Z See \citegillmor:1998:aaaDNAcrUVp

%A S. D. Gillmor
%A Q. Liu
%A L. Wang
%A C. E. Jordan
%A A. G. Frutos
%A A. J. Theil
%A T. C. Stother
%A A. E. Condon
%A R. M. Corn
%A L. M. Smith
%A M. G. Lagally
%T Addressed-Array Approach to DNA Computation Readout through UV Photopatterning
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB See \cite1998APS..MAR.U2403G

%A Ilaria Giordani
%T Relational clustering for knowledge discovery in life sciences
%R Ph.D. Thesis
%D 2009
%I 
%I Universita degli Studi di Milano-Bicocca
%C Italy
%K genetic algorithms, genetic programming, Relational Clustering, Feature Selection, Knowledge integration, Mixed data types
%U http://boa.unimib.it/bitstream/10281/7830/1/phd_unimib_032791.pdf
%X provide an overview of traditional clustering methods with some important distance measures and then we analyse three particular challenges that we try to overcome with
   different proposed methods: 'feature selection' to reduce high dimensional input space and remove noise from data; 'mixed data types' to handle in clustering procedure both
   numeric and categorical values, typically of life science applications; finally, 'knowledge integration' in order to improve the semantic value of clustering incorporating
   the background knowledge. Regarding the first challenge we propose a novel approach based on using of genetic programming, an evolutionary algorithm-based methodology, in
   order to automatically perform feature selection. Different clustering algorithms are been investigated regarding the second challenge. A modify version of a particular
   algorithm is proposed and applied to clinical data. Particularly attention is given to the final challenge, the most important objective of this Thesis: the development of
   a new relational clustering framework in order to improve the semantic value of clustering taking into account in the clustering algorithm relationships learnt from
   background knowledge. We investigate and classify existing clustering methods into two principal categories: - Structure driven approaches: that are bound to data
   structure. The data clustering problem is tackled from several dimensions: clustering concurrently columns and rows of a given dataset, like biclustering algorithm or
   vertical 3-D clustering. - Knowledge driven approaches: where domain information is used to drive the clustering process and interpret its results: semi-supervised
   clustering, that using both labelled and unlabeled data, has attracted significant attention. This kind of clustering algorithms represents the first step to implement the
   proposed general framework that it is classified into this category. In particular the thesis focuses on the development of a general framework for relational clustering
   instantiating it for three different life science applications: the first one with the aim of finding groups of gene with similar behaviour respect to their expression and
   regulatory profile. The second one is a pharmacogenomics application, in which the relational clustering framework is applied on a benchmark dataset (NCI60) to identify a
   drug treatment to a given cell line based both on drug activity pattern and gene expression profile. Finally, the proposed framework is applied on clinical data: a
   particular dataset containing different information about patients in anticoagulant therapy has been analyzed to find group of patients with similar behaviour and responses
   to the therapy.
%8 October
%Z NCI60, Saccharomyces Genome Database, Oral anticoagulation therapy Also known as \cite10281_7830

%A Romain Giot
%A Baptiste Hemery
%A Christophe Rosenberger
%T Low Cost and Usable Multimodal Biometric System Based on Keystroke Dynamics and 2D Face Recognition
%B 20th International Conference on Pattern Recognition (ICPR 2010)
%D 2010
%P 1128--1131
%I 
%K genetic algorithms, genetic programming, 2D face recognition, chimeric database, fusion methods, keystroke dynamics, multimodal biometric system, privacy, biometrics
   (access control), data privacy, face recognition, keyboards
%X We propose in this paper a low cost multimodal biometric system combining keystroke dynamics and 2D face recognition. The objective of the proposed system is to be used
   while keeping in mind: good performances, acceptability, and aspect of privacy. Different fusion methods have been used (min, max, mul, svm, weighted sum configured with
   genetic algorithms, and, genetic programming) on the scores of three keystroke dynamics algorithms and two 2D face recognition ones. This multimodal biometric system
   improves the recognition rate in comparison with each individual method. On a chimeric database composed of 100 individuals, the best keystroke dynamics method obtains an
   EER of 8.77percent, the best face recognition one has an EER of 6.38percent, while the best proposed fusion system provides an EER of 2.22percent.
%8 23-26  August
%Z GREYC Lab., Univ. of CAEN, Caen, France Also known as \cite5595872

%A Ra{\'u}l Gir{\'a}ldez
%A Roberto Ruiz
%T Applying Genetic Programming to obtain Separation Surfaces
%B WSEAS NNA-FSFS-EC 2001
%D 2001
%P paper ID number 644
%I 
%I The World Scientific and Engineering Academy and Society (WSEAS)
%C Puerto De La Cruz, Tenerife, Spain
%K genetic algorithms, genetic programming, Classification, Dynamical systems
%8 February ~11-15
%Z www.wseas.com/2001.xls

%A Gilson A. Giraldi
%A Renato Portugal
%A Ricardo N. Thess
%T Genetic Algorithms and Quantum Computation
%R Technical Report 0403003
%D 2004
%I 
%I National Laboratory for Scientific Computing, Petropolis, RJ, Brazil
%K genetic algorithms, genetic programming, Quantum Computing, Evolutionary Strategies
%U http://arxiv.org/pdf/cs.NE/0403003
%X Recently, researchers have applied genetic algorithms (GAs) to address some problems in quantum computation. Also, there has been some works in the designing of genetic
   algorithms based on quantum theoretical concepts and techniques. The so called Quantum Evolutionary Programming has two major sub-areas: Quantum Inspired Genetic Algorithms
   (QIGAs) and Quantum Genetic Algorithms (QGAs). The former adopts qubit chromosomes as representations and employs quantum gates for the search of the best solution. The
   later tries to solve a key question in this field: what GAs will look like as an implementation on quantum hardware? As we shall see, there is not a complete answer for
   this question. An important point for QGAs is to build a quantum algorithm that takes advantage of both the GA and quantum computing parallelism as well as true randomness
   provided by quantum computers. In the first part of this paper we present a survey of the main works in GAs plus quantum computing including also our works in this area.
   Henceforth, we review some basic concepts in quantum computation and GAs and emphasise their inherent parallelism. Next, we review the application of GAs for learning
   quantum operators and circuit design. Then, quantum evolutionary programming is considered. Finally, we present our current research in this field and some perspectives.

%A Marta Girdea
%A Liviu Ciortuz
%T A Hybrid Genetic Programming and Boosting Technique for Learning Kernel Functions from Training Data
%B Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2007
%E Viorel Negru and Tudor Jebelean and Dana Petcu and Daniela Zaharie
%D 2007
%P 395--402
%I IEEE Computer Society
%C Timisoara, Romania
%K genetic algorithms, genetic programming, InfoBoost procedure, RBF kernel function learning, boosting technique, nonlinear SVM classification, training data, learning
   (artificial intelligence), pattern classification, radial basis function networks, support vector machines
%X This paper proposes a technique for learning kernel functions that can be used in non-linear SVM classification. The technique uses genetic programming to evolve kernel
   functions as additive or multiplicative combinations of linear, polynomial and RBF kernels, while a procedure inspired from InfoBoost helps the evolved kernels concentrate
   on the most difficult objects to classify. The kernels obtained at each boosting round participate in the training of non-linear SVMs which are combined, along with their
   confidence coefficients, into a final classifier. We compared on several data sets the performance of the kernels obtained in this manner with the performance of classic
   RBF kernels and of kernels evolved using a pure GP method, and we concluded that the boosted GP kernels are generally better.
%8 September  26-29
%Z 'Alexandru loan Cuza' Univ. of Iasi, Iasi

%A Sertan Girgin
%A Philippe Preux
%T Feature Discovery in Reinforcement Learning Using Genetic Programming
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 218--229
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z See also http://hal.inria.fr/inria-00187997/en/ Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Orazio Giustolisi
%T Using genetic programming to determine Chezy resistance coefficient in corrugated channels
%J Journal of Hydroinformatics
%V 6
%N 3
%D 2004
%P 157--173
%I 
%K genetic algorithms, genetic programming, evolutionary strategies, data mining, corrugated pipes
%U http://www.iwaponline.com/jh/006/0157/0060157.pdf
%X Genetic Programming has been used to determine Chezy resistance coefficient for full circular corrugated channels. Three corrugated plastic pipes have been experimentally
   studied in order to generate data. The tests aim at measuring hydraulic parameters of the open-channel flow for some slopes, from 3.49-17.37percent (2-10), in order to
   discover the dependence of the channel resistance coefficient when wake-interference flow occurs. The monomial formula for the Chezy resistance coefficient performs well on
   experimental data, both from measurement errors and from a technical point of view. In this paper, we present some very parsimonious formulae that have been created by
   Genetic Programming with few constants and which fit the data better than the monomial formula. Moreover, two of the Genetic Programming formulae, after 'physical
   post-refinement', seem to better explain the role of the roughness in the Chezy resistance coefficient for corrugated channels with respect to its traditional expression
   for rough channels. This fact suggests that at least the structure of those formulae can be extrapolated to other types of corrugated channels. Finally, the work stresses
   the fact that the Genetic Programming hypothesis can be easily manipulated by means of 'human' physical insight. Therefore, Genetic Programming should be considered more
   than a simple data-driven technique, especially when it is used to perform scientific discovery.
%8 July
%Z Morris' wake interference. Cadim

%A Orazio Giustolisi
%A Dragan A. Savic
%T A symbolic data-driven technique based on evolutionary polynomial regression
%J Journal of Hydroinformatics
%V 8
%N 3
%D 2006
%P 207--222
%I 
%K genetic algorithms, genetic programming, EPR, Chezy resistance coefficient, Colebrook-White formula, data-driven modelling, evolutionary computing, regression
%U http://www.iwaponline.com/jh/008/0207/0080207.pdf
%X This paper describes a new hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming symbolic
   regression technique. The key idea is to employ an evolutionary computing methodology to search for a model of the system/process being modelled and to employ parameter
   estimation to obtain constants using least squares. The new technique, termed Evolutionary Polynomial Regression (EPR) overcomes shortcomings in the GP process, such as
   computational performance; number of evolutionary parameters to tune and complexity of the symbolic models. Similarly, it alleviates issues arising from numerical
   regression, including difficulties in using physical insight and over-fitting problems. This paper demonstrates that EPR is good, both in interpolating data and in
   scientific knowledge discovery. As an illustration, EPR is used to identify polynomial formulae with progressively increasing levels of noise, to interpolate the
   Colebrook-White formula for a pipe resistance coefficient and to discover a formula for a resistance coefficient from experimental data.

%A O. Giustolisi
%A D. A. Savic
%T Advances in data-driven analyses and modelling using EPR-MOGA
%J Journal of Hydroinformatics
%V 11
%N 3
%D 2009
%P 225--236
%I 
%K genetic algorithms, genetic programming, data-driven modelling, evolutionary computing, groundwater resources, multiobjective optimization, symbolic regression
%U http://www.iwaponline.com/jh/011/0225/0110225.pdf
%X Evolutionary Polynomial Regression (EPR) is a recently developed hybrid regression method that combines the best features of conventional numerical regression techniques
   with the genetic programming/symbolic regression technique. The original version of EPR works with formulae based on true or pseudo-polynomial expressions using a
   single-objective genetic algorithm. Therefore, to obtain a set of formulae with a variable number of pseudo-polynomial coefficients, the sequential search is performed in
   the formulae space. This article presents an improved EPR strategy that uses a multi-objective genetic algorithm instead. We demonstrate that multi-objective approach is a
   more feasible instrument for data analysis and model selection. Moreover, we show that EPR can also allow for simple uncertainty analysis (since it returns polynomial
   structures that are linear with respect to the estimated coefficients). The methodology is tested and the results are reported in a case study relating groundwater level
   predictions to total monthly rainfall.
%Z Brindisi, multi objective, ANN

%A D. Gladwin
%A Paul Stewart
%A Jill Stewart
%T A novel genetic programming approach to the design of engine control systems for the voltage stabilisation of hybrid electric vehicle generator outputs
%J Proceedings of the Institute of Mechanical Engineers Part D - Automobile Engineering
%R PeerReviewed
%V 225
%N 10
%D 2011
%P 1334--1346
%I Institute of Mechanical Engineers
%K genetic algorithms, genetic programming, electronic and electrical engineering
%U http://eprints.lincoln.ac.uk/4352/
%X This paper describes a Genetic Programming based automatic design methodology applied to the maintenance of a stable generated electrical output from a series-hybrid
   vehicle generator set. The generator set comprises a 3-phase AC generator whose output is subsequently rectified to DC.The engine/generator combination receives its control
   input via an electronically actuated throttle, whose control integration is made more complex due to the significant system time delay. This time delay problem is usually
   addressed by model predictive design methods, which add computational complexity and rely as a necessity on accurate system and delay models. In order to eliminate this
   reliance, and achieve stable operation with disturbance rejection, a controller is designed via a Genetic Programming framework implemented directly in Matlab, and
   particularly, Simulink. the principal objective is to obtain a relatively simple controller for the time-delay system which doesn't rely on computationally expensive
   structures, yet retains inherent disturbance rejection properties. A methodology is presented to automatically design control systems directly upon the block libraries
   available in Simulink to automatically evolve robust control structures.
%8 October
%Z http://www.uk.sagepub.com/journals/Journal202018

%A Dan Gladwin
%A Paul Stewart
%A Jill Stewart
%T Internal combustion engine control for series hybrid electric vehicles by parallel and distributed genetic programming/multiobjective genetic algorithms
%J International Journal of Systems Science
%V 42
%N 2
%D 2011
%P 249--261
%I 
%K genetic algorithms, genetic programming, automotive, model-reference control, time-delay, hybrid vehicles, parallel and distributed evolutionary computation, mechanical
   systems, PID control, distrubed evolutionary
%U http://eprints.lincoln.ac.uk/3986/
%X This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid
   prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent
   development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with
   significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on
   the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller,
   and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC
   engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy
   storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external
   load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation,
   in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple
   controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is
   presented to use the myriad of existing control blocks in the Simulink libraries to automatically evolve optimal control structures.
%O Computational Intelligence for Modelling and Control of Advanced Automotive Drivetrains

%A William Edward Glaholt
%T GP-Lab: The Genetic Programming Laboratory
%R M.S. Thesis Masters of Science
%D 2004
%I 
%I Computer Science, California State University, Sacramento
%K genetic algorithms, genetic programming
%U http://www.theglaholts.net/gplab/GPLab-ThesisDoc%20Final.pdf
%X Evolutionary Programming, also known as Genetic Programming ("GP"), is an Artificial Intelligence paradigm in which an algorithm is synthesised in the style of Charles
   Darwin's theory of Evolution. Algorithms are generated through 'reverse-engineering,' the concept in which a desired solution is known, as are the tools, functions, and
   objects used to generate the solution, but the algorithm that solves the solution is unknown. GP creates a random population of 'individuals', evaluates those individuals
   for fitness (a term used to judge how 'close' the solution is to a targeted solution), then iteratively creates new generations by 'cross-breeding' genes of the more fit
   individuals, evaluating, crossbreeding, and so on until the 'best' solution is found. Current tools in the discipline are generally targeted towards solving one explicit
   problem, or require actual source code modification of the software packages1 in order to effect such a generation. In addition, the solutions generated by existing
   software tools are not normally immediately usable, are obscure, or are in 'LISP-style' function format, which may be difficult to translate to the average programmer.
   GP-Lab is based upon, and is an extension of the tool created in a previous Master's thesis by Michael Kramer ("GAPS - Genetic Algorithm Programming System", 1996) [1], as
   well as several other current tools, e.g. 'lil-gp' and 'GARAGE'. GP-Lab adds many user-flexible features, including graphic outputs, direct-to-C compile-ready code solution
   translation, and a full, extensible procedural programming language with user-created functions. As such, GP-Lab is a tool targeted toward the average programmer who has a
   known desired solution, a set of tools upon which the solution may be based, and wishes to know the algorithm used to solve that solution.
%Z Approved by: Dr. Du Zhang, Advisor and Committee Chair W. Scott Gordon, Associate Professor

%A William E. Glaholt
%A Du Zhang
%T GP-Lab: the Genetic Programming Laboratory
%B 16th IEEE International Conference on Tools with Artificial Intelligence, 2004. ICTAI 2004
%D 2004
%P 388--395
%I IEEE
%C Boca Raton, FL, USA
%K genetic algorithms, genetic programming
%X Currently, tools in the field of genetic programming are either geared towards solving certain type of problems, or are not easy to use (e.g., requiring actual source code
   modification of the software packages in order to generate a genetic programming environment). In addition, the solutions generated by existing tools are usually not ready
   for deployment in applications. We describe a genetic programming tool called GP-Lab. GP-Lab is based upon, and an extension to an earlier tool reported in [Kramer, MD et
   al. (1996) \citeKramer:mastersthesis, (2000); Zhang, D et al. (2003)] GP-Lab supports a full and extensible programming language, and allows solutions to be automatically
   generated in C+ + source code format ready to be compiled for deployment. It is a general tool and has many user-flexible features, including contextually aware genetic
   operations and graphic outputs.
%8 15-17  November
%@ 0-7695-2236-X

%A Sean Gleason
%T Tuning and Creation of Discrete Differentiators using Genetic Algorithms and Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 160--169
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Matthew Glickman
%A Katia Sycara
%T Evolutionary Algorithms: Exploring the Dynamics of Self-Adaptation
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 762--769
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K evolutionary programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Matthew R. Glickman
%A Katia Sycara
%T Evolution of Goal-Directed Behavior from Limited Information in a Complex Environment
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1281--1288
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-015.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Al Globus
%A John Lawton
%A Todd Wipke
%T Automatic molecular design using evolutionary techniques
%B The Sixth Foresight Conference on Molecular Nanotechnology
%E Al Globus and Deepak Srivastava
%D 1998
%I 
%I Foresight Institute
%C Westin Hotel in Santa Clara, CA, USA
%K genetic algorithms, genetic programming, ring crossover, graphs, drugs
%U http://www.nas.nasa.gov/Research/Reports/Techreports/1999/nas-99-005.html
%X Molecular nanotechnology is the precise, three-dimensional control of materials and devices at the atomic scale. An important part of nanotechnology is the design of
   molecules for specific purposes. This paper describes early results using genetic software techniques to automatically design molecules under the control of a fitness
   function. The fitness function must be capable of determining which of two arbitrary molecules is better for a specific task. The software begins by generating a population
   of random molecules. The population is then evolved towards greater fitness by randomly combining parts of the better individuals to create new molecules. These new
   molecules then replace some of the worst molecules in the population. The unique aspect of our approach is that we apply genetic crossover to molecules represented by
   graphs, i.e., sets of atoms and the bonds that connect them. We present evidence suggesting that crossover alone, operating on graphs, can evolve any possible molecule
   given an appropriate fitness function and a population containing both rings and chains. Prior work evolved strings or trees that were subsequently processed to generate
   molecular graphs. In principle, genetic graph software should be able to evolve other graph representable systems such as circuits, transportation networks, metabolic
   pathways, computer networks, etc.
%8 November  12-15 1998
%Z http://www.foresight.org/Conferences/MNT6/index.html

%A Al Globus
%A John Lawton
%A Todd Wipke
%T Automatic molecular design using evolutionary techniques
%J Nanotechnology
%V 10
%N 3
%D 1999
%P 290--299
%I 
%K genetic algorithms, genetic programming
%U http://people.nas.nasa.gov/~globus/home.html
%X Molecular nanotechnology is the precise, three-dimensional control of materials and devices at the atomic scale. An important part of nanotechnology is the design of
   molecules for specific purposes. This paper describes early results using genetic software techniques to automatically design molecules under the control of a fitness
   function. The fitness function must be capable of determining which of two arbitrary molecules is better for a specific task. The software begins by generating a population
   of random molecules. The individual molecules in a population are then evolved towards greater fitness by randomly combining parts of the better existing molecules to
   create new molecules. These new molecules then replace some of the less fit molecules in the population. We apply a unique genetic crossover operator to molecules
   represented by graphs, i.e., sets of atoms and the bonds that connect them. We present evidence suggesting that crossover alone, operating on graphs, can evolve any
   possible molecule given an appropriate fitness function and a population containing both rings and chains. Most prior work evolved strings or trees that were subsequently
   processed to generate molecular graphs. In principle, genetic graph software should be able to evolve other graph-representable systems such as circuits, transportation
   networks, metabolic pathways, and computer networks.
%8 September

%A Al Globus
%A Eric Langhirt
%A Miron Livny
%A Ravishankar Ramamurthy
%A Marvin Solomon
%A Steve Traugott
%T JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms
%B Java Grande 2000, sponsored by ACM SIGPLAN
%D 2000
%I 
%C San Francisco, California
%K genetic algorithms, genetic programming
%U http://people.nas.nasa.gov/~globus/papers/JavaGrande2000/JavaGrandePaper.html
%X A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor
   cycle-scavenging batch system managing 100-170 desktop, desk-side, and rack-mounted SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to
   problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java
   was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with
   ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer
   arithmetic, and array-bounds index checking reduces the frequency of these bugs, substantially reducing development time. While a run-time performance penalty must be paid,
   the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of
   custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware
   version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly
   optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic
   algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology [Globus, et al. 1999], and
   another paper in preparation.
%8 3-4  June

%A Al Globus
%A John Lawton
%A Todd Wipke
%T Graph Crossover
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 761
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, Poster, graphs, crossover, molecules, drug, design
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d04.pdf
%8 7-11  July
%Z A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO see \citeglobus:2001:GECCOtr
%@ 1-55860-774-9

%A Al Globus
%A Sean Atsatt
%A John Lawton
%A Todd Wipke
%T Graph Crossover
%D 2000
%I 
%K genetic algorithms, genetic programming
%U http://people.nas.nasa.gov/~globus/papers/JavaGenes2/JavaGenesPaper.html
%X Most genetic algorithms use string or tree representations. To apply genetic algorithms to graphs, a good crossover operator is necessary. We have developed a
   general-purpose, novel crossover operator for directed and undirected graphs, and used this operator to evolve molecules and circuits. Unlike strings or trees, a single
   point in the representation cannot divide every possible graph into two parts, because graphs may contain cycles. Thus, the crossover operator is non-trivial. A
   steady-state, tournament selection genetic algorithm code (JavaGenes) was used test the graph crossover operator. JavaGenes has successfully evolved pharmaceutical drug
   molecules and simple digital circuits. For example, morphine, cholesterol, and diazepam were successfully evolved by 30-60% of runs within 10,000 generations using a
   population of 1000 molecules. Since representation strongly affects genetic algorithm performance, adding graphs to the evolutionary programmer's bag-of-tricks should be
   beneficial. Also, since graph evolution operates directly on the phenotype, genotype to phenotype decoding is eliminated.
%O www
%8 5  May
%Z see \citeglobus:2001:GECCO

%A Al Globus
%T Towards 100,000 CPU Cycle-Scavenging by Genetic Algorithms
%R Technical Report NAS-0-011
%D 2001
%I 
%I CSC at NASA Ames Research Center
%K genetic algorithms
%U http://people.nas.nasa.gov/~globus/papers/Cycle-ScavengingGA/paper.html
%X Cycle scavenging systems offer 100s to 100,000s of otherwise-idle CPUs for embarrassingly parallel computations such as genetic algorithms. While genetic algorithms are
   generally easy to parallelize, cycle scavenged resources come and go at random so some sophistication in necessary, particularly when hundreds of thousands of CPUs are
   available. In this paper we propose a master-slave architecture for multi-objective genetic algorithms on cycle scavengers. The architecture consists of slave computations
   running on computational nodes with relatively small populations, and a central master managing a large pareto front in a disc-based relational data base . Each slave runs
   an individual genetic algorithm and different slaves use different techniques, evolution-parameters and/or a subset of the objective functions as determined by the master.
   The slaves accept immigrants from the master and, after evolution, the best individuals emigrate back to the master. Slaves may then get new immigrants and/or evolution
   policy to be applied to the existing population. Allowing slaves to use different evolution techniques and parameters can, when many CPUs are available, avoid committing to
   a single evolution concept for a given problem. A sophisticated master can treat the running slaves as a population of evolutionary techniques and parameters that can be
   evolved. We examine a web-centric design using standard tools such as web servers, web browsers, PHP, and mySQL. We also consider the applicability of Information Power
   Grid tools such as the Globus (no relation to the author) Toolkit. We intend to implement this architecture with JavaGenes running on at least two cycle-scavengers: Condor
   and United Devices. JavaGenes, a genetic algorithm code written in Java, will be used to evolve multi-species reactive molecular force field parameters.
%8 October

%A Al Globus
%A Charles Bauschlicher
%A Sandra Johan
%A Deepak Srivastava
%T JavaGenes: Evolving Molecular Force Field Parameters
%B Ninth Foresight Conference on Molecular Nanotechnology
%D 2001
%I 
%C Santa Clara, California
%K genetic algorithms
%U http://people.nas.nasa.gov/~globus/home.html
%8 9-11  November

%A Al Globus
%A Madhu Menon
%A Deepak Srivastava
%T Enabling Computational Nanotechnology through JavaGenes in a Cycle Scavenging Environment
%D 2002
%I 
%K genetic algorithms, Condor, Java, distributed
%U http://people.nas.nasa.gov/~globus/papers/JavaGenesSupercomputing2002/finalVersion.pdf
%O www
%8 July
%Z Available in MS Word, pdf and html; the pdf and html versions have problems caused by bugs in the MS conversion software..

%A Al Globus
%A Madhu Menon
%A Deepak Srivastava
%T JavaGenes: Evolving Molecular Force Field Parameters with Genetic Algorithm
%J Computer Modeling in Engineering and Science
%V 3
%N 5
%D 2002
%P 557--574
%I 
%K genetic algorithms
%U http://people.nas.nasa.gov/~globus/home.html

%A Al Globus
%A James Crawford
%A Jason Lohn
%A Robert Morris
%T Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach
%B Proceedings of the 3rd International NASA Workshop on Planning and Scheduling for Space
%D 2002
%I 
%C Houston, Texas
%K genetic algorithms
%U http://people.nas.nasa.gov/~globus/home.html
%8 October  27-29

%A Al Globus
%A Ecleamus Ricks
%A Madhu Menon
%A Deepak Srivastava
%T Evolving Molecular Force Field Parameters for Si and Ge
%B Proceedings of the 2003 Nanotechnology Conference and Trade Show
%D 2003
%I 
%C San Francisco, California, U.S.A.
%K genetic algorithms
%U http://people.nas.nasa.gov/~globus/home.html
%8 February  23-27

%A Al Globus
%A James Crawford
%A Jason Lohn
%A Anna Pryor
%T Scheduling Earth Observing Satellites with Evolutionary Algorithms
%B International Conference on Space Mission Challenges for Information Technology (SMC-IT)
%D 2003
%I 
%C Pasadena, CA, USA
%K genetic algorithms
%U http://people.nas.nasa.gov/~globus/home.html
%8 July

%A Fernand Gobet
%A Amanda Parker
%T Evolving structure-function mappings in cognitive neuroscience using genetic programming
%J Swiss Journal of Psychology
%V 64
%N 4
%D 2005
%P 231--239
%I 
%K genetic algorithms, genetic programming
%X A challenging goal of psychology and neuroscience is to map cognitive functions onto neuroanatomical structures. This paper shows how computational methods based upon
   evolutionary algorithms can facilitate the search for satisfactory mappings by efficiently combining constraints from neuroanatomy and physiology (the structures) with
   constraints from behavioural experiments (the functions). This methodology involves creation of a database coding for known neuroanatomical and physiological constraints,
   for mental programs made of primitive cognitive functions, and for typical experiments with their behavioural results. The evolutionary algorithms evolve theories mapping
   structures to functions in order to optimize the fit with the actual data. These theories lead to new, empirically testable predictions. The role of the prefrontal cortex
   in humans is discussed as an example. This methodology can be applied to the study of structures or functions alone, and can also be used to study other complex systems.
   (PsycINFO Database Record (c) 2008 APA, all rights reserved)
%8 Decemeber

%A Nicole Gockel
%A Martin Keim
%A Rolf Drechsler
%A Bernd Becker
%T A Genetic Algorithm for Sequential Circuit Test Generation based on Symbolic Fault Simulation
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 363--369
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Genetic Algorithms
%8 13-16  July
%Z GP-97

%A Nicole Gockel
%A Rolf Drechsler
%A Bernd Becker
%T Learning Heuristics by Evolutionary Algorithms with Variable Size Representation
%D 1997
%I 
%C East Lansing, MI, USA
%K genetic algorithms, Evolvable Hardware, variable size representation
%O Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97
%8 20  July

%A Ben Goertzel
%A Cassio Pennachin
%A Lucio {de Souza Coelho}
%A Mauricio Mudado
%T Identifying Complex Biological Interactions based on Categorical Gene Expression Data
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 5583--5590
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, poster
%U http://www.biomind.com/docs/WCCI_EC_feb06_06_fixed_v2.pdf
%X A novel method, MUTIC ( Clustering), is described for identifying complex interactions between genes or gene-categories based on gene expression data. The method deals with
   binary categorical data, which consists of a set of gene expression profiles divided into two biologically meaningful categories. It does not require data from multiple
   time points. Gene expression profiles are represented by feature vectors whose component features are either gene expression values, or averaged expression values
   corresponding to Gene Ontology or Protein Information Resource categories. A supervised learning algorithm (genetic programming) is used to learn an ensemble of
   classification models distinguishing the two categories based on the feature vectors corresponding to their members. Each feature is associated with a model usage vector,
   which has an entry for each high-quality classification model found, indicating whether or not the feature was used in that model. These usage vectors are then clustered
   using a variant of hierarchical clustering called Omniclust. The result is a set of model-usage-based clusters, in which features are gathered together if they are often
   considered together by classification models which may be because they are co-expressed, or may be for subtler reasons involving multi-gene interactions. The MUTIC method
   is illustrated via applying it to a dataset regarding gene expression in human brains of various ages. Compared to traditional expression-based clustering, MUTIC yields
   clusters that have higher mathematical quality (in the sense of homogeneity and separation) and also yield novel insights into the underlying biological processes.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Benjamin N Goertzel
%A Cassio Pennachin
%A Lucio {de Souza Coelho}
%A Elizabeth M Maloney
%A James F Jones
%A Brian Gurbaxani
%T Allostatic load is associated with symptoms in chronic fatigue syndrome patients
%J Pharmacogenomics
%V 7
%N 3
%D 2006
%P 485--494
%I 
%K genetic algorithms, genetic programming
%U http://www.futuremedicine.com/doi/abs/10.2217/14622416.7.3.485
%X Objectives: To further explore the relationship between chronic fatigue syndrome (CFS) and allostatic load (AL), we conducted a computational analysis involving 43 patients
   with CFS and 60 nonfatigued, healthy controls (NF) enrolled in a population-based case-control study in Wichita (KS, USA). We used traditional biostatistical methods to
   measure the association of high AL to standardized measures of physical and mental functioning, disability, fatigue and general symptom severity. We also used nonlinear
   regression technology embedded in machine learning algorithms to learn equations predicting various CFS symptoms based on the individual components of the allostatic load
   index (ALI). Methods: An ALI was computed for all study participants using available laboratory and clinical data on metabolic, cardiovascular and
   hypothalamic-pituitary-adrenal (HPA) axis factors. Physical and mental functioning/impairment was measured using the Medical Outcomes Study 36-item Short Form Health Survey
   (SF-36); current fatigue was measured using the 20-item multidimensional fatigue inventory (MFI); frequency and intensity of symptoms was measured using the 19-item symptom
   inventory (SI). Genetic programming, a nonlinear regression technique, was used to learn an ensemble of different predictive equations rather just than a single one.
   Statistical analysis was based on the calculation of the percentage of equations in the ensemble that used each input variable, producing a measure of the 'utility' of the
   variable for the predictive problem at hand. Traditional biostatistics methods include the median and Wilcoxon tests for comparing the median levels of subscale scores
   obtained on the SF-36, the MFI and the SI summary score. Results: Among CFS patients, but not controls, a high level of AL was significantly associated with lower median
   values (indicating worse health) of bodily pain, physical functioning and general symptom frequency/intensity. Using genetic programming, the ALI was determined to be a
   better predictor of these three health measures than any subcombination of ALI components among cases, but not controls.

%A Gerard Kian-Meng Goh
%A James A. Foster
%T Evolving Molecules for Drug Design Using Genetic Algorithms via Molecular Trees
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 27--33
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GA141.pdf
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A C. Goh
%A Y. Li
%T GA Automated Design and Synthesis of Analog Circuits with Practical Constrains
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 170--177
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, CAD, Circuit Synthesis, preferred value components, PSpice
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = Fixed length
   chromosome but inclusion of "null" makes it effectively variable length but bounded.
%@ 0-7803-6658-1

%A David E. Goldberg
%A Una-May O'Reilly
%T Where does the Good Stuff Go, and Why? How contextual semantics influence program structure in simple genetic programming
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 16--36
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/96596.html
%X Using deliberately designed primitive sets, we investigate the relationship between context-based expression mechanisms and the size, height and density of genetic program
   trees during the evolutionary process. We show that contextual semantics influence the composition, location and flows of operative code in a program. In detail we analyze
   these dynamics and discuss the impact of our findings on micro-level descriptions of genetic programming.
%8 14-15  April
%Z EuroGP'98 Also presented at the Canadian AI-98 Workshop on Evolutionary Computation Schedule, 17 June 1998 Simon Fraser University Harbour Center, Canada
%@ 3-540-64360-5

%A David E. Goldberg
%A Siegfried Voessner
%T Optimizing Global-Local Search Hybrids
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 220--228
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-882.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A David E. Goldberg
%T Using Time Efficiently: Genetic-Evolutionary Algorithms and the Continuation Problem
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 212--219
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-881.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Andrew Goldfish
%T Noisy Wall-Following and Maze Navigation through Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 423
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Brian W. Goldman
%A Daniel R. Tauritz
%T Self-configuring crossover
%B GECCO 2011 1st workshop on evolutionary computation for designing generic algorithms
%E Gisele L. Pappa and Alex A. Freitas and Jerry Swan and John Woodward
%D 2011
%P 575--582
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X Crossover is a core genetic operator in many evolutionary algorithms (EAs). The performance of such EAs on a given problem is dependent on properly configuring crossover. A
   small set of common crossover operators is used in the vast majority of EAs, typically fixed for the entire evolutionary run. Selecting which crossover operator to use and
   tuning its associated parameters to obtain acceptable performance on a specific problem often is a time consuming manual process. Even then a custom crossover operator may
   be required to achieve optimal performance. Finally, the best crossover configuration may be dependent on the state of the evolutionary run. This paper introduces the
   Self-Configuring Crossover operator encoded with linear genetic programming which addresses these shortcomings while relieving the user from the burden of crossover
   configuration. To demonstrate its general applicability, the novel crossover operator was applied without any problem specific tuning. Results are presented showing it to
   outperform the traditional crossover operators arithmetic crossover, uniform crossover, and n-point crossover on the Rosenbrock, Rastrigin, Offset Rastrigin, DTrap, and NK
   Landscapes benchmark problems.
%8 12-16  July
%Z Also known as \cite2002051 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A T. Golonek
%A D. Grzechca
%A J. Rutkowski
%T Application of genetic programming to edge detector design
%B Proceedings of the IEEE International Symposium on Circuits and Systems, ISCAS 2006
%D 2006
%I IEEE
%K genetic algorithms, genetic programming
%X The new approach to edge detection is presented in this paper. The proposed method uses genetic programming (GP) to search for digital transfer function of image edge
   detector. The found function can be easily implemented to any programmable logic device (PLD) that allows to build a fast system of image processing.
%O 4 pp, CD-ROM
%8 21-24  May
%Z Inst. of Electron., Silesian Univ. of Technol., Gliwice, Poland
%@ 0-7803-9389-9

%A Tomasz Golonek
%A Jerzy Rutkowski
%T Application of Genetic Programming to Analog Fault Decoder Design
%B The 16th European Conference on Circuits Theory and Design, ECCTD'03
%D 2003
%I 
%I ECS, IEEE
%C Electrical Engineering, AGH University of Science and Technology, Krakow, Poland
%K genetic algorithms, genetic programming
%U http://platforma.polsl.pl/rau3/mod/resource/Appl.of_GP_to_AFD-ECCTD03.pdf
%X Genetic Programming (GP) is an evolutionary, heuristic technique of optimisation, which allows to solve many difficult problems. A new method using GP to analog testing is
   proposed. After a brief introduction to the GP technique, the use of this technique to fault decoder construction is explained. The experimental results are presented and
   they seem to be very promising. In the last section, some conclusions are presented.
%8 1-4  September
%Z http://ecctd03.zet.agh.edu.pl/docs/program.html

%A Igor E. Golovkin
%A Roberto C. Mancini
%A Sushil J. Louis
%T Plasma X-ray Spectra Analysis Using Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1529--1534
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-734b.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A F. Golshan
%A K. Mohamadi
%T An intelligent watermarking algorithm based on Genetic Programming
%B 10th International Conference on Information Sciences Signal Processing and their Applications (ISSPA 2010)
%D 2010
%P 97--100
%I 
%K genetic algorithms, genetic programming, DCT domain, JPEG compression, digital image attacks, digital image watermarking, intelligent perceptual shaping function,
   intelligent watermarking algorithm, low pass filtering, median filtering, low-pass filters, median filters, watermarking
%X In this paper we propose an algorithm to develop an intelligent perceptual shaping function based on Genetic Programming (GP) in DCT domain. In digital image watermarking,
   robustness and imperceptibility compete with each other. In this paper we applied GP to make a trade off between these two characteristics. Here, the original image is
   divided into 8 #x00D7;8 non-overlapping blocks and the DCT coefficients in each block are sorted by means of zigzag. One AC coefficient in each block is changed according
   to a perceptual shaping function. This perceptual shaping function is obtained from the GP core and is dependent on average of all block coefficients and the related AC
   coefficient. The experimental results show that this proposed algorithm is robust against some digital image attacks such as low pass filtering, median filtering and JPEG
   compression. In addition the improvement in watermarked image quality also is achieved.
%8 May
%Z Fac. of Electr. Eng., Karaj Islamic Azad Univ., Rajaeeshahr, Iran Also known as \cite5605497

%A Wolfgang Golubski
%A Thomas Feuring
%T Evolving Neural Network Structures by Means of Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 211--220
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=211
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP
%@ 3-540-65899-8

%A Wolfgang Golubski
%T New Results on Fuzzy Regression by Using Genetic Programming
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 308--315
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%X In this paper we continue the work on symbolic fuzzy regression problems. That means that we are interesting in finding a fuzzy function <i>f</i> with best matches given
   data pairs <i>(x<sub>i</sub>,y<sub>i</sub>)</i> <i>1<= i <= k</i> of fuzzy numbers. We use a genetic programming approach for finding a suitable fuzzy function and will
   present test results about linear, quadratic and cubic fuzzy functions.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Wolfgang Golubski
%T Distributed Genetic Programming for Regression Analysis
%B WSEAS IMCCAS-ISA-SOSM and MEM-MCP
%D 2002
%I 
%C Cancun, Mexico
%K genetic algorithms, genetic programming, Distributed Genetic Programming, Symbolic Regression, Master-Worker
%8 May ~12-16

%A Wolfgang Golubski
%T Regression Analysis on Uncertain Data
%B WSEAS IMCCAS-ISA-SOSM and MEM-MCP
%D 2002
%I 
%C Cancun, Mexico
%K genetic algorithms, genetic programming, Regression Analysis, Genetic Programming, Fuzzy Numbers, Evolutionary Algorithm, Fuzzy Application
%8 May ~12-16

%A Wolfgang Golubski
%T Genetic Programming: A Parallel Approach
%B Soft-Ware 2002: Computing in an Imperfect World : First International Conference
%S Lecture Notes in Computer Science
%E D. Bustard and W. Liu and R. Sterritt
%V 2311
%D 2002
%P 166--173
%I Springer
%C Belfast, Northern Ireland
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2311/23110166.pdf", acknowledgement = ack-nhfb
%X In this paper we introduce a parallel master-worker model for genetic programming where the master and each worker have their own equal-sized populations. The workers
   execute in parallel starting with the same population and are synchronized after a given interval where all worker populations are replaced by a new one. The proposed model
   will be applied to symbolic regression problems. Test results on two test series are presented.
%8 8-10  April

%A Juan A. Gomez-Pulido
%A Miguel A. Vega-Rodriguez
%A Juan M. Sanchez-Perez
%A Silvio Priem-Mendes
%A Vitor Carreira
%T Accelerating floating-point fitness functions in evolutionary algorithms: a FPGA-CPU-GPU performance comparison
%J Genetic Programming and Evolvable Machines
%V 12
%N 4
%D 2012
%P 403--427
%I 
%K genetic algorithms, evolvable hardware, EHW, Evolutionary algorithms, Fitness, Reconfigurable circuits, GPU, Floating-Point, Performance, Parallelism
%X Many large combinatorial optimisation problems tackled with evolutionary algorithms often require very high computational times, usually due to the fitness evaluation. This
   fact forces programmers to use clusters of computers, a computational solution very useful for running applications of intensive calculus but having a high acquisition
   price and operation cost, mainly due to the Central Processing Unit (CPU) power consumption and refrigeration devices. A low-cost and high-performance alternative comes
   from reconfigurable computing, a hardware technology based on Field Programmable Gate Array devices (FPGAs). The main objective of the work presented in this paper is to
   compare implementations on FPGAs and CPUs of different fitness functions in evolutionary algorithms in order to study the performance of the floating-point arithmetic in
   FPGAs and CPUs that is often present in the optimization problems tackled by these algorithms. We have taken advantage of the parallelism at chip-level of FPGAs pursuing
   the acceleration of the fitness functions (and consequently, of the evolutionary algorithms) and showing the parallel scalability to reach low cost, low power and high
   performance computational solutions based on FPGA. Finally, the recent popularity of GPUs as computational units has moved us to introduce these devices in our performance
   comparisons. We analyse performance in terms of computation times and economic cost.
%8 Decemeber
%Z Nvidia Quadro FX 580, OpenCL C++ bindings. Vertex5 FPGAs (Virtex2Pro and Spartan3) ECC, Radio Network design problem (RND). Malaga, X-ray (XRAY) pseudo-Voigt. Xilinx ISE
   9.2i ModelSim 6, VHDL, Handel-C. Custom designed HPs. FPGA power less than one watt. 50 times.

%A Takashi Gomi
%T Book Review: Evolutionary Robotics: the Biology, Intelligence, and Technology of Self-Organizing Machines
%J Genetic Programming and Evolvable Machines
%V 4
%N 1
%D 2003
%P 95--98
%I 
%K genetic algorithms, genetic programming, evolvable hardware, robot
%X Review of ISBN:0-262-14070-5 MIT press Authors: Stefano Nolfi and Dario Floreano
%8 March
%Z Article ID: 5113075

%A Gabriel Silva Goncalves
%A Moises G. {de Carvalho}
%A Alberto H. F. Laender
%A Marcos Andre Goncalves
%T Automatic Selection of Training Examples for a Record Deduplication Method Based on Genetic Programming
%J Journal of Information and Data Management
%V 1
%N 2
%D 2010
%P 213--228
%I 
%K genetic algorithms, genetic programming, replica identification, artificial intelligence
%U http://seer.lcc.ufmg.br/index.php/jidm/article/view/59
%X Recently, machine learning techniques have been used to solve the record deduplication problem. However, these techniques require examples, manually generated in most
   cases, for training purposes. This hinders the use of such techniques because of the cost required to create the set of examples. In this article, we propose an approach
   based on a deterministic technique to automatically suggest training examples for a deduplication method based on genetic programming. Our experiments with synthetic
   datasets show that, by using only 15percent of the examples suggested by our approach, it is possible to achieve results in terms of F1 that are equivalent to those
   obtained when using all the examples, leading to savings in training time of up to 85percent
%8 June
%Z An official publication of the Brazilian Computer Society Special Interest Group on Databases

%A Ivo Goncalves
%A Sara Silva
%A Joana B. Melo
%A Joao M. B. Carreiras
%T Random Sampling Technique for Overfitting Control in Genetic Programming
%B Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012
%S LNCS
%E Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta
%V 7244
%D 2012
%P 218--229
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Over fitting, Generalisation
%X One of the areas of Genetic Programming (GP) that, in comparison to other Machine Learning methods, has seen fewer research efforts is that of generalization.
   Generalisation is the ability of a solution to perform well on unseen cases. It is one of the most important goals of any Machine Learning method, although in GP only
   recently has this issue started to receive more attention. In this work we perform a comparative analysis of a particularly interesting configuration of the Random Sampling
   Technique (RST) against the Standard GP approach. Experiments are conducted on three multidimensional symbolic regression real world datasets, the first two on the
   pharmacokinetics domain and the third one on the forestry domain. The results show that the RST decreases over fitting on all datasets. This technique also improves testing
   fitness on two of the three datasets. Furthermore, it does so while producing considerably smaller and less complex solutions. We discuss the possible reasons for the good
   performance of the RST, as well as its possible limitations.
%8 11-13  April
%Z Part of \citeMoraglio:2012:GP EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012

%A Eloy Gonzales
%A Karla Taboada
%A Kaoru Shimada
%A Shingo Mabu
%A Kotaro Hirasawa
%A Jinglu Hu
%T Class Association Rule Mining for Large and Dense Databases with Parallel Processing of Genetic Network Programming
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 4615--4622
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Among several methods of extracting association rules that have been reported, a new evolutionary computation method named Genetic Network Programming (GNP) has also shown
   its effectiveness for small datasets that have a relatively small number of attributes.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Eloy Gonzales
%A Shingo Mabu
%A Karla Taboada
%A Kotaro Hirasawa
%A Kaoru Shimada
%T Pruning association rules using statistics and genetic relation algoritm
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 419--420
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, Evolution strategies and evolutionary programming, Poster
%X Most of the classification methods proposed produces too many rules for humans to read over, that is, the number of generated rules is thousands or millions which means
   complex and hardly understandable for the users. In this paper, a new post-processing pruning method for class association rules is proposed by a combination of statistics
   and an evolutionary method named Genetic Relation Algorithm (GRA). The algorithm is carried out in two phases. In the first phase the rules are pruned depending on their
   matching degree and in the second phase GRA selects the most interesting rules using the distance between them and their strength.
%8 7-11  July
%Z Also known as \cite1830562 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Daniel Lombrana Gonzalez
%A Francisco {Fernandez de Vega}
%T On the Intrinsic Fault-Tolerance Nature of Parallel Genetic Programming
%B 15th Euromicro Conference on Parallel, Distributed and Network-based Processing
%E Pasqua D'Ambra and Mario R. Guarracino
%D 2007
%P 450--458
%I IEEE
%C Naples
%K genetic algorithms, genetic programming, fault tolerance, parallel genetic programming
%X In this paper we show how Parallel Genetic Programming can run on a distributed system with volatile resources without any lack of efficiency. By means of a series of
   experiments, we test whether Parallel GP -and consistently Evolutionary Algorithms- are intrinsically fault-tolerant. The interest of this result is crucial for researchers
   dealing with real-life problems in which parallel and distributed systems are required for obtaining results on a reasonable time. In that case, parallel GP tools will not
   require the inclusion of fault-tolerant computing techniques or libraries when running on Meta-systems undergoing volatility, such us Desktop Grids offering Public Resource
   Computing. We test the performance of the algorithm by studying the quality of solutions when running over distributed resources undergoing processors failures, when
   compared with a fault-free environment. This new feature, which shows its advantages, improves the dependability of the Parallel Genetic Programming Algorithm.
%8 7-9  February
%Z PDP 2007 http://www.na.icar.cnr.it/~pdp2007
%@ 0-7695-2784-1

%A Daniel Lombrana Gonzalez
%A Francisco {Fernandez de Vega}
%T Dynamic populations and length evolution: key factors for analyzing fault tolerance on parallel genetic programming
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1752--1752
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming: Poster, management, measurement, parallel and distributed evolutionary algorithm, reliability, size evolution, bloat
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1752.pdf
%X This paper presents an experimental research on the size of individuals when fixed and dynamic size populations are employed with Genetic Programming (GP). We propose an
   improvement to the Plague operator (PO), that we have called Random Plague (RPO). Then by further studies based on the RPO results we analysed the Fault Tolerance on
   Parallel Genetic Programming.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071 'Each removed individual [ie selected against] is a lost/error computer in a simulation of a fine grained parallel GP.' cites
   \citeDBLP:conf/pdp/GonzalezV07

%A Daniel Lombrana Gonzalez
%A Francisco {Fernandez de Vega}
%T Analyzing Fault Tolerance on Parallel Genetic Programming by Means of Dynamic-Size Populations
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 4392--4398
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X This paper presents an experimental research on the size of individuals when dynamic size populations are employed with Genetic Programming (GP). By analysing the
   individual's size evolution, some ideas are presented for reducing the length of the best individual while also improving the quality. This research has been performed
   studying both individual's size and quality of solutions, considering the fixed-size populations and also dynamic size by means of the plague operator. We propose an
   improvement to the Plague operator, that we have called Random Plague, that positively affects the quality of solutions and also influences the individuals' size. The
   results are then considered from a quite different point of view, the presence of processors failures when parallel execution over distributed computing environments are
   employed. We show that results strongly encourage the use of Parallel GP on non fault-tolerant computing resources: experiments shows the fault tolerant nature of Parallel
   GP.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Daniel Lombrana Gonzalez
%A Francisco {Fernandez de Vega}
%A L. Trujillo
%A G. Olague
%A M. Cardenas
%A L. Araujo
%A P. Castillo
%A K. Sharman
%A A. Silva
%T Interpreted Applications within BOINC Infrastructure
%B IBERGRID 2nd Iberian Grid Infrastructure Conference Proceedings
%E Fernando Silva and Gaspar Barreira and Ligia Ribeiro
%D 2008
%P 261--272
%I netbiblo.com Oleiros (La Coruna), Spain
%C Porto, Portugal
%K genetic algorithms, genetic programming
%8 12-14  May
%Z IBERGRID, ECJ 42 runs 1 week. 41 PCs. R script (required extension via virtualisation of BOINC framework). IAP. Java.

%A Daniel Lombrana Gonzalez
%A Francisco {Fernandez de Vega}
%A Leonardo Trujillo
%A Gustavo Olague
%A Lourdes Araujo
%A Pedro Castillo
%A Juan Julian Merelo
%A Ken Sharman
%T Increasing GP Computing Power for Free via Desktop GRID Computing and Virtualization
%B 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing
%D 2009
%P 419--423
%I 
%C Weimar, Germany
%K genetic algorithms, genetic programming, BOINC framework, GP source code, desktop grid computing, evolutionary algorithms, genetic programming computing power, volunteer
   computing, grid computing, software engineering
%X This paper presents how it is possible to increase the genetic programming (GP) computing power (CP) for free, via volunteer computing (VC), using the well known framework
   BOINC plus a new ``virtualization'' layer which adds all the benefits from the virtualization paradigm. Two different experiments, employing a standard GP tool and a
   complex GP system, are performed with distributed PCs over several cities to show the free achieved CP by means of VC, without the necessity of modifying or adapting the
   original GP source code. The methodology can be easily extended to evolutionary algorithms (EAs).
%8 18-20  February
%Z Also known as \cite4912963

%A Daniel Lombrana Gonzalez
%A Francisco {Fernandez de Vega}
%A Henri Casanova
%T Characterizing fault tolerance in genetic programming
%B BADS '09: Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems
%E Gianluigi Folino and Natalio Krasnogor and Carlo Mastroianni and Franco Zambonelli
%D 2009
%P 1--10
%I ACM New York, NY, USA
%C Barcelona, Spain
%K genetic algorithms, genetic programming, Fault-tolerance, parallel genetic programming, desktop grids
%U http://navet.ics.hawaii.edu/~casanova/homepage/papers/lombrana_bads2007.pdf
%X Evolutionary Algorithms (EAs), and particularly Genetic Programming (GP), are techniques frequently employed to solve difficult real-life problems, which can require up to
   days or months of computation. One approach to reduce the time to solution is to use parallel computing on distributed platforms. Distributed platforms are prone to
   failures, and when these platforms are large and/or low-cost, failures are expected events rather than catastrophic exceptions. Therefore, fault tolerance and recovery
   techniques often become necessary. It turns out that Parallel GP (PGP) applications have an inherent ability to tolerate failures. This ability is quantified via simulation
   experiments performed using failure traces from real-world distributed platforms, namely, desktop grids (DGs), for two well-known GP problems. A simple technique is then
   proposed by which PGP applications can better tolerate the different, and often high, failures rates seen in different platforms.
%8 June  15-19
%Z Also known as \cite1555286 even-5-parity, 11-multiplexor

%A D. Daniel {Lombrana Gonzalez}
%T Programacion genetica tolerante a fallos: despliegue de programacion genetica sobre computacion grid de sobremesa
%R Ph.D. Thesis
%D 2010
%I 
%I Universidad de Extremadura
%C Spain
%K genetic algorithms, genetic programming
%U http://dialnet.unirioja.es/servlet/tesis?codigo=21131
%X En esta tesis se presenta un estudio sobre la tolerancia a fallos de programacion genetica en entornos desktop grid, En la primera parte de la tesis se analizan las
   caracteristicas principales de los sistemas destkop grid, explicando por que son una buena plataforma para ejecutar algoritmos evolutivos, en general, y programacion
   genetica paralela en particular. Ademas, se proponen dos mejoras para estos sistemas (una herramienta de gestion de recursos y un sistema de entornos de ejecucion a medida)
   con el objetivo de acercar estos sistemas a los investigadores de algoritmos evolutivos. En la segunda parte de la tesis se analizan las caracteristicas de la programacion
   genetica paralela desde el punto de vista de la tolerancia a fallos y se estudia la posibilidad de ejecutar estas aplicaciones en entornos desktop grid sin la utilizacion
   de tecnicas de tolerancia a fallos. El estudio se realiza utilizando datos de tres sistemas desktop grid reales, llegando a la conclusion de que la programacion genetica
   paralela es tolerante a fallos por naturaleza.
%Z In english Running Parallel Evolutionary Algorithms in Desktop Grid Systems. Evolutionary Algorithms. Parallel and Distributed Systems. Desktop Grid Computing. Improving
   BOINC Based Desktop Grid Systems. Studying the fault-tolerance nature of Parallel Genetic Programming. within Desktop Grid Systems. Fault Tolerance. Computer Failures and
   Genetic Programming Bloat. Plague Operator and Computer Failures. Resumen en Espanol

%A Daniel {Lombrana Gonzalez}
%A Francisco {Fernandez de Vega}
%A Henri Casanova
%T Characterizing fault tolerance in genetic programming
%J Future Generation Computer Systems
%V 26
%N 6
%D 2010
%P 847--856
%I 
%K genetic algorithms, genetic programming, Fault tolerance, Parallel genetic programming, Desktop grids
%U http://www.sciencedirect.com/science/article/B6V06-4YDT3S4-2/2/0a9075d8d9c6905e388ad608f0c81e79
%X Evolutionary algorithms, including genetic programming (GP), are frequently employed to solve difficult real-life problems, which can require up to days or months of
   computation. An approach for reducing the time-to-solution is to use parallel computing on distributed platforms. Large platforms such as these are prone to failures, which
   can even be commonplace events rather than rare occurrences. Thus, fault tolerance and recovery techniques are typically necessary. The aim of this article is to show the
   inherent ability of parallel GP to tolerate failures in distributed platforms without using any fault-tolerant technique. This ability is quantified via simulation
   experiments performed using failure traces from real-world distributed platforms, namely, desktop grids, for two well-known problems.
%8 June
%Z 5.1.1. Even parity 5 5.1.2. 11-bit multiplexer

%A Fabio A. Gonzalez
%A Dipankar Dasgupta
%T Anomaly Detection Using Real-Valued Negative Selection
%J Genetic Programming and Evolvable Machines
%V 4
%N 4
%D 2003
%P 383--403
%I 
%K artificial immune systems, anomaly detection, negative selection, matching rule, self-organizing maps
%X a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal)
   samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training
   phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against
   an anomaly detection technique that uses self-organising maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results
   are reported.
%8 Decemeber
%Z Special issue on artificial immune systems Article ID: 5144849

%A Gerardo Gonzalez
%A Dean F. Hougen
%T Elitism, fitness, and growth
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1851--1852
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Poster
%X Bloat may occur when evolution allows chromosome growth. Recently it has been shown that elitism can inhibit bloat. Here we study interactions between growth, elitism, and
   fitness landscapes. Our results show that in some cases elitism neither constrains growth nor increases the rate of fitness accumulation, and when elitism does constrain
   growth it may stall the search completely.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A David {Gonzalez Munoz}
%A Oscar Gustafsson
%A Lars Wanhammar
%T Evolution of filter order equations for linear-phase FIR filters using gene expression programming
%B RVK 2005 RadioVetenskap och Kommunikation
%D 2005
%P 679--682
%I 
%I FOI
%C Linkoping, Sweden
%K genetic algorithms, genetic programming, gene expression programming
%U http://www.es.isy.liu.se/publications/papers_and_reports/2005/RVK05_oscarg_FIRorder.pdf
%X Estimation of the minimum filter order for linear-phase FIR filters is commonly performed during the design of DSP systems. In this work gene expression programming is used
   to discover new equations for the linear-phase FIR filter order. The results are shown to be as least as accurate as previously proposed estimates.
%8 14-16  June
%Z http://www.rvk05.foi.se/ P18T http://www.rvk05.foi.se/Sessions_final.html Linkoping University, Linkoping, Sweden

%A Omar Alfrego Gonzalez Padilla
%A Felix Francisco Ramos Corchado
%A Jean-Paul Bartes
%T Genetic Programming for Task Selection in Dialogue Systems
%B Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2010
%D 2010
%P 180--184
%I 
%K genetic algorithms, genetic programming, automatic generation, dialogue systems, multi-agent system, natural language interfaces, ripple down rules, task selection
   mechanism, user interaction, interactive systems, multi-agent systems, natural language interfaces
%X Natural language is too complex and ambiguous to be understood by a computer using currently known methods. However, in some cases natural language interfaces are possible
   because interaction is limited by the set of tasks the system can perform. In this context, when a user starts a dialog, the system tries to identify the intended task,
   which determines the course of the dialog. Modelling tasks in order to allow selecting one is labour intensive and may cause conflicts if the system performs many tasks. We
   propose using ripple down rules as a task selection mechanism, and genetic programming for automatic generation of such rules. Advantages of this approach are ease of
   generation and possibility to learn from user interaction. We tested the approach in a multi-agent system named OMAS, where agents interact with users using natural
   language.
%8 28  September -1  October
%Z Also known as \cite5692333

%A Antonio Gonzalez-Pardo
%A David Camacho
%T Analysis of Grammatical Evolution Approaches to Regular Expression Induction
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 632--639
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, grammatical evolution, Data mining
%X Regular expressions, or regexes, have been used traditionally as a pattern matching tool to search for structures in a set of objects, like files, text documents or
   folders. Pattern matching can be used to look for files whose name contains a given string, to search files that contain a specific pattern within them, or simply to
   extract text in a set of documents. It is very popular to apply regexes to detect and extract patterns that represent phone numbers, URLs, email addresses, etc. These kind
   of information can be characterised because it has a well defined structure. Nevertheless, regexes are not very frequently used because its high complexity in both, syntax
   and grammatical rules, makes regexes difficult to understand. For this reason, the development of programs able to automatically generate, and evaluate, regexes has become
   a valuable task. This work analyses the performance of different grammatical evolutionary approaches in the generation of regexes able to extract URL patterns. Four
   different types of grammars have been evaluated: a context-free grammar, a context-free grammar with a penalised fitness function, an extensible context-free grammar, and a
   Christiansen grammar. For the considered problem, the experimental results show that the best performance of the system, measured as cumulative success rate, is achieved
   using Christiansen grammars.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A R. Goodacre
%A B. Shann
%A R. J. Gilbert
%A E. M. Timmins
%A A. C. McGovern
%A B. K. Alsberg
%A N. A. Logan
%A D. B. Kell
%T The characterisation of Bacillus species from PyMS and FT IR data
%B Proceedings of the 1997 ERDEC Scientific Conference on Chemical and Biological Defense Research
%N ERDEC-SP-063
%D 1997
%I 
%C Aberdeen Proving Ground
%K genetic algorithms, genetic programming

%A R. Goodacre
%A R. J. Gilbert
%T The detection of caffeine in a variety of beverages using Curie-point pyrolysis mass spectrometry and genetic programming
%J The Analyst
%V 124
%D 1999
%P 1069--1074
%I 
%K genetic algorithms, genetic programming
%U http://www.rsc.org/CFCart/displayarticlefree.cfm?article=8%2D9%223%24%5D%5EQB%218%27%5D%5CY%28%3C%5C%23R5%3DX4PPL%3D29%23%3C%0A
%X Freeze dried coffee, filter coffee, tea and cola were analysed by Curie-point pyrolysis mass spectrometry (PyMS). Cluster analysis showed, perhaps not surprisingly, that
   the discrimination between coffee, tea and cola was very easy. However, cluster analysis also indicated that there was a secondary difference between these beverages which
   could be attributed to whether they were caffeine- containing or decaffeinated. Artificial neural networks (ANNs) could be trained, with the pyrolysis mass spectra from
   some of the freeze dried coffees, to classify correctly the caffeine status of the unseen spectra of freeze dried coffee, filter coffee, tea and cola in an independent test
   set. However, the information in terms of which masses in the mass spectrum are important was not available, which is why ANNs are often perceived as a 'black box' approach
   to modelling spectra. By contrast, genetic programs (GPs) could also be used to classify correctly the caffeine status of the beverages, but which evolved function trees
   (or mathematical rules) enabling the deconvolution of the spectra and which highlighted that m/z 67, 109 and 165 were the most significant massed for this classification.
   Moreover, the chemical structure of these mass ions could be assigned to the reproducible pyrolytic degradation products from caffeine.

%A Royston Goodacre
%A Beverley Shann
%A Richard J. Gilbert
%A Eadaoin M. Timmins
%A Aoife C. McGovern
%A Bjorn K. Alsberg
%A Douglas B. Kell
%A Niall A. Logan
%T The detection of the dipicolinic acid biomarker in Bacillus spores using Curie-point pyrolysis mass spectrometry and Fourier-transform infrared spectroscopy
%J Analytical Chemistry
%V 72
%N 1
%D 2000
%P 119--127
%I American Chamical Society
%K genetic algorithms, genetic programming
%U http://pubs.acs.org/cgi-bin/article.cgi/ancham/2000/72/i01/html/ac990661i.html
%X Thirty-six strains of aerobic endospore-forming bacteria confirmed by polyphasic taxonomic methods to belong to Bacillus amyloliquefaciens, Bacillus cereus, Bacillus
   licheniformis, Bacillus megaterium, Bacillus subtilis (including Bacillus niger and Bacillus globigii), Bacillus sphaericus, and Brevi laterosporus were grown axenically on
   nutrient agar, and vegetative and sporulated biomasses were analyzed by Curie-point pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier-transform
   infrared spectroscopy (FT-IR). Chemometric methods based on rule induction and genetic programming were used to determine the physiological state (vegetative cells or
   spores) correctly, and these methods produced mathematical rules which could be simply interpreted in biochemical terms. For PyMS it was found that m/z 105 was
   characteristic and is a pyridine ketonium ion (C6H3ON+) obtained from the pyrolysis of dipicolinic acid (pyridine-2,6-dicarboxylic acid; DPA), a substance found in spores
   but not in vegetative cells; this was confirmed using pyrolysis-gas chromatography/mass spectrometry. In addition, a pyridine ring vibration at 1447-1439 cm-1 from DPA was
   found to be highly characteristic of spores in FT-IR analysis. Thus, although the original data sets recorded hundreds of spectral variables from whole cells
   simultaneously, a simple biomarker can be used for the rapid and unequivocal detection of spores of these organisms.
%8 1  January
%Z PMID: 10655643

%A R. Goodacre
%A D. B. Kell
%T Evolutionary Computation for the Interpretation of Metabolomic Data
%B Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis
%E George G. Harrigan and Royston Goodacre
%D 2003
%I Kluwer Academic Publishers
%C Boston, USA
%K genetic algorithms, genetic programming
%O 13
%8 January
%Z http://www.wkap.nl/prod/b/1-4020-7370-4 Pharmacia Corporation, Chesterfield, MO, USA University of Manchester Institute of Science and Technology (UMIST), UK
%@ 1-4020-7370-4

%A Royston Goodacre
%A Emma V. York
%A James K. Heald
%A Ian M. Scott
%T Chemometric discrimination of unfractionated plant extracts analyzed by electrospray mass spectrometry
%J Phytochemistry
%V 62
%N 6
%D 2003
%P 859--863
%I 
%K genetic algorithms, genetic programming, Pharbitis nil, Convolvulaceae, Japanese Morning Glory, Electrospray ionization mass spectrometry, Neural networks, Metabolic
   fingerprinting
%U http://www.sciencedirect.com/science/article/B6TH7-47WBXD4-7/2/91ff09f988be54824c55a1cb596f7839
%X Metabolic fingerprints were obtained from unfractionated Pharbitis nil leaf sap samples by direct infusion into an electrospray ionization mass spectrometer. Analyses took
   less than 30 s per sample and yielded complex mass spectra. Various chemometric methods, including discriminant function analysis and the machine-learning methods of
   artificial neural networks and genetic programming, could discriminate the metabolic fingerprints of plants subjected to different photoperiod treatments. This rapid
   automated analytical procedure could find use in a variety of phytochemical applications requiring high sample throughput.
%8 March
%Z GMax-Bio, Plant Metabolomics

%A Royston Goodacre
%T Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules
%J Vibrational Spectroscopy
%V 32
%N 1
%D 2003
%P 33--45
%I 
%K genetic algorithms, genetic programming, Artificial neural networks, ANN, FT-IR
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.8811
%X Whole organism or tissue profiling by vibrational spectroscopy produces vast amounts of seemingly unintelligible data. However, the characterisation of the biological
   system under scrutiny is generally possible only in combination with modern supervised machine learning techniques, such as artificial neural networks (ANNs). Nevertheless,
   the interpretation of the calibration models from ANNs is often very difficult, and the information in terms of which vibrational modes in the infrared or Raman spectra are
   important is not readily available. ANNs are often perceived as 'black box' approaches to modelling spectra, and to allow the deconvolution of complex hyperspectral data it
   is necessary to develop a system that itself produces 'rules' that are readily comprehensible. Evolutionary computation, and in particular genetic programming (GP), is an
   ideal method to achieve this. An example of how GP can be used for Fourier transform infrared (FT-IR) image analysis is presented, and is compared with images produced by
   principal components analysis (PCA), discriminant function analysis (DFA) and partial least squares (PLS) regression.
%O A collection of Papers Presented at Shedding New Light on Disease: Optical Diagnostics for the New Millennium (SPEC 2002) Reims, France 23-27 June 2002
%8 5  August

%A Royston Goodacre
%A Seetharaman Vaidyanathan
%A Warwick B. Dunn
%A George G. Harrigan
%A Douglas B. Kell
%T Metabolomics by numbers: acquiring and understanding global metabolite data
%J Trends in Biotechnology
%V 22
%N 5
%D 2004
%P 245--252
%I 
%K genetic algorithms, genetic programming, ILP
%U http://dbkgroup.org/Papers/trends%20in%20biotechnology_22_(245).pdf
%X In this postgenomic era, there is a specific need to assign function to orphan genes in order to validate potential targets for drug therapy and to discover new biomarkers
   of disease. Metabolomics is an emerging field that is complementary to the other 'omics and proving to have unique advantages. As in transcriptomics or proteomics, a
   typical metabolic fingerprint or metabolomic experiment is likely to generate thousands of data points, of which only a handful might be needed to describe the problem
   adequately. Extracting the most meaningful elements of these data is thus key to generating useful new knowledge with mechanistic or explanatory power.
%8 1  May
%Z many topics covered not just GP

%A Royston Goodacre
%A David Broadhurst
%A Age K. Smilde
%A Bruce S. Kristal
%A J. David Baker
%A Richard Beger
%A Conrad Bessant
%A Susan Connor
%A Giorgio Capuani
%A Andrew Craig
%A Tim Ebbels
%A Douglas B. Kell
%A Cesare Manetti
%A Jack Newton
%A Giovanni Paternostro
%A Ray Somorjai
%A Michael Sjostrom
%A Johan Trygg
%A Florian Wulfert
%T Proposed minimum reporting standards for data analysis in metabolomics
%J Metabolomics
%V 3
%D 2007
%P 231--241
%I 
%K genetic algorithms, genetic programming, Chemometrics, Multivariate, Megavariate Unsupervised learning, Supervised learning, Informatics Bioinformatics, Statistics,
   Biostatistics
%U http://dbkgroup.org/Papers/goodacre_MSIdataanalysis07.pdf
%X The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning
   etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete
   metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data
   acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier
   detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to
   the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on
   whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual
   representations and hypotheses obtained from the data analyses.
%Z R. Goodacre D. Broadhurst (&) D. B. Kell School of Chemistry and Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7ND,
   UK A. K. Smilde Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Nieuwe Achtergracht 166, Amsterdam 1018 WV, Netherlands A. K.
   Smilde TNO Quality of Life, Utrechtseweg 48, P.O. Box 360, Zeist 3700 AJ, Netherlands B. S. Kristal Department of Neurosurgery, Brigham and Women\u2019s Hospital, 221
   Longwood Ave, Boston, MA 02115, USA J. D. Baker Pfizer, Inc, Ann Arbor, MI, USA R. Beger Division of Systems Toxicology, National Center for Toxicological Research, 3900
   NCTR Road, Jefferson, AR 72079, USA C. Bessant C. Manetti Cranfield University, Silsoe, Bedfordshire MK45 4DT, UK S. Connor Safety Assessment, GlaxoSmithKline, Park Road,
   Ware, Herts SG12 0DP, UKG. Capuani Dipartimento di Chimica, Universita` degli Studi di Roma Piazzale Aldo Moro 5, Rome 00185, Italy A. Craig BlueGnome Ltd, Breaks House,
   Mill Court, Great Shelford, Cambridge CB2 5LD, UK T. Ebbels Department of Biomolecular Medicine, Imperial College London, London SW7 2AZ, UK J. Newton Chenomx Inc, Suite
   800, 10050 112 St, T5K 2J1 Edmonton, AB, Canada G. Paternostro Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA R. Somorjai
   Institute for Biodiagnostics, NRCC, 435 Ellice Ave, R3B 1Y6 Winnipeg, MB, Canada M. Sjostrom J. Trygg Research Group for Chemometrics, Organic Chemistry, Department of
   Chemistry, Umea University, Umea 901 87, Sweden F. Wulfert Division of Food Sciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK

%T Late Breaking Papers at the 2001 Genetic and Evolutionary Computation Conference
%E Erik Goodman
%D 2001
%I 
%C San Francisco, California, USA
%8 7-11  July

%A Erik D. Goodman
%A Kisung Seo
%A Ronald C. Rosenberg
%A Zhun Fan
%A Jianjun Hu
%A Baihai Zhang
%T Automated Design Methodology for Mechatronic Systems Using Bond Graphs and Genetic Programming
%B Proceedings 2002 NSF Design, Service and Manufacturing Grantees and Research Conference
%D 2002
%P 206--221
%I National Science Foundation
%I National Science Foundation
%C San Juan, Puerto Rico
%K genetic algorithms, genetic programming
%U http://garage.cse.msu.edu/papers/GARAGe02-01-01.pdf
%X We suggest an automated design methodology for synthesising designs for multi-domain systems, such as mechatronic systems. The domain of mechatronic systems includes
   mixtures of, for example, electrical, mechanical, hydraulic, pneumatic, and thermal components, making it difficult to design a system to meet specified performance goals
   with a single design tool. The multi-domain design approach is not only efficient for mixed domain problems, but is also useful for addressing separate single-domain design
   problems with a single tool. Bond graphs are domain independent, allow free composition, and are efficient for classification and analysis of models, allowing rapid
   determination of various types of acceptability or feasibility of candidate designs. This can sharply reduce the time needed for analysis of designs that are infeasible or
   otherwise unattractive. Genetic programming is well recognised as a powerful tool for open-ended search. The combination of these two powerful methods is therefore an
   appropriate target for a better system for synthesis of complex multi-domain systems. The approach described here will evolve new designs (represented as bond graphs) with
   ever-improving performance, in an iterative loop of synthesis, analysis, and feedback to the synthesis process. The suggested design methodology has been applied here to
   two design examples. One is domain independent, an eigenvalues-placement design problem which is tested for some sample target sets of eigenvalues. The other is in the
   electrical domain -- namely, design of analog filters to achieve specified performance over a given frequency range.
%8 January

%A Erik D. Goodman
%T A Word from the Chair of ISGEC
%J Genetic Programming and Evolvable Machines
%V 5
%N 1
%D 2004
%P 9
%I 
%8 March
%Z Chair of the Executive Board, International Society for Genetic and Evolutionary Computation Article ID: 5264732

%A Kasthurirangan Gopalakrishnan
%A Halil Ceylan
%A Sunghwan Kim
%A Siddhartha K. Khaitan
%T Natural Selection of Asphalt Mix Stiffness Predictive Models with Genetic Programming
%B ANNIE 2010, Intelligent Engineering Systems through Artificial Neural Networks
%E Cihan H. Dagli
%V 20
%D 2010
%P paper 48
%I ASME
%I Smart Engineering Systems Laboratory, Systems Engineering Graduate Programs, Missouri University of Science and Technology, 600 W. 14th St., Rolla, MO 65409 USA
%C St. Louis, Mo, USA
%K genetic algorithms, genetic programming
%X Genetic Programming (GP) is a systematic, domain-independent evolutionary computation technique that stochastically evolves populations of computer programs to perform a
   user-defined task. Similar to Genetic Algorithms (GA) which evolves a population of individuals to better ones, GP iteratively transforms a population of computer programs
   into a new generation of programs by applying biologically inspired operations such as crossover, mutation, etc. In this paper, a population of Hot-Mix Asphalt (HMA)
   dynamic modulus stiffness prediction models is genetically evolved to better ones by applying the principles of genetic programming. The HMA dynamic modulus (|E*|), one of
   the stiffness measures, is the primary HMA material property input in the new Mechanistic Empirical Pavement Design Guide (MEPDG) developed under National Cooperative
   Highway Research Program (NCHRP) 1-37A (2004) for the American State Highway and Transportation Officials (AASHTO). It is shown that the evolved HMA model through GP is
   reasonably compact and contains both linear terms and low-order non-linear transformations of input variables for simplification.
%8 November  1-3
%Z http://annie.mst.edu/conference_schedule/ConferenceSchedule.html ASME Order Number: 859599

%A F. Gordillo
%A A. Bernal
%T Optimal Control of an Inverted Pendulum Using Genetic Programming: Practical Aspects
%B Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97
%E George D. Smith and Nigel C. Steele and Rudolf F. Albrecht
%D 1997
%I Springer-Verlag
%C University of East Anglia, Norwich, UK
%K genetic algorithms, genetic programming
%O published in 1998
%Z http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html
%@ 3-211-83087-1

%A Francisco Gordillo
%A Ismael Alcala
%A Javier Aracil
%T A Tool for Solving Differential Games with Co-evolutionary Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1535--1542
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-775.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Benjamin M. Gordon
%T Exploring the Underlying Structure of Natural Images Through Genetic Programming
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 49--56
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming, MSE, pixels
%8 Decemeber
%Z This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-187263-3

%A Michael Gordon
%A Weiguo (Patrick) Fan
%A Praveen Pathak
%T Adaptive Web Search: Evolving a Program That Finds Information
%J IEEE Intelligent Systems
%V 21
%N 5
%D 2006
%P 72--77
%I 
%K genetic algorithms, genetic programming, Internet, information needs, relevance feedback, search engines, Web pages, adaptive Web search, document relevance feedback,
   genetic programming, retrieval algorithms, retrieval technique, search engines, user judgement feedback, user persistent information needs
%X Anyone who's used a computer to find information on the Web knows that the experience can be frustrating. Search engines are incorporating new techniques (such as examining
   document link structures) to increase effectiveness. However, searchers all too often face one of two outcomes: reviewing many more Web pages than they'd prefer or failing
   to find as much useful information as they really want. We introduce a new retrieval technique that exploits users' persistent information needs. These users might include
   business analysts specialising in genetic technologies, stockbrokers keeping abreast of wireless communications, and legislators needing to understand computer privacy and
   security developments. To help such searchers, we evolve effective search programs by using feedback based on users' judgments about the relevance of the documents they've
   retrieved. This approach uses genetic programming to automatically evolve new retrieval algorithms based on a user's evaluation of previously viewed documents
%8 September -  October
%Z IR, cosine nearness measure, keyword weighting. Log. Pop=200. TREC 80000 documents. Large number (500) papers returned to user. GP way better in comparison with SMART
   (Singhal, 1996) and ANN.

%A V. Scott Gordon
%A Rebecca Pirie
%A Adam Wachter
%A Scottie Sharp
%T Terrain-Based Genetic Algorithm (TBGA): Modeling Parameter Space as Terrain
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 229--235
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://ecs.csus.edu/~gordonvs/papers/tbga.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Timothy G. W. Gordon
%T Book Review: Hardware evolution: automatic design of electronic circuits in reconfigurable hardware by artificial evolution
%J Genetic Programming and Evolvable Machines
%V 2
%N 4
%D 2001
%P 409--411
%I 
%K genetic algorithms, evolvable hardware
%8 Decemeber
%Z Book review of ISBN: 3-540-76253-1 Author: Adrian Thompson Publisher: Springer-Verlag London Ltd. 1998. Article ID: 386364

%A Timothy G. W. Gordon
%A Peter J. Bentley
%T Development Brings Scalability to Hardware Evolution
%B Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
%E Jason Lohn and David Gwaltney and Gregory Hornby and Ricardo Zebulum and Didier Keymeulen and Adrian Stoica
%D 2005
%P 272--279
%I IEEE Press IEEE Service Center 445 Hoes Lane Asia P.O. Box 1331 Piscataway, NJ 08855-1331
%I NASA, DoD
%C Washington, DC, USA
%K genetic algorithms, genetic programming, EHW
%U http://www.cs.ucl.ac.uk/staff/t.gordon/gordont_scalability.pdf
%X The scalability problem is a major impediment to the use of hardware evolution for real-world circuit design problems. A potential solution is to model the map between
   genotype and phenotype on biological development. Although development has been shown to improve scalability for a few toy problems, it has not been demonstrated for any
   circuit design problems. This paper presents such a demonstration for two problems, the n-bit adder with carry and even n-bit parity problems, and shows that development
   imposes, and benefits from, fewer constraints on evolutionary innovation than other approaches to scalability.
%8 29  June -1  July
%Z EH2005 IEEE Computer Society Order Number P2399
%@ 0-7695-2399-4

%A Timothy Glennie Wilson Gordon
%T Exploiting Development to Enhance the Scalability of Hardware Evolution
%R Ph.D. Thesis
%D 2005
%I 
%I University College, London
%K genetic algorithms, genetic programming, EHW
%U http://www.bcs.org/upload/pdf/tgordon.pdf
%X Evolutionary algorithms do not scale well to the large, complex circuit design problems typical of the real world. Although techniques based on traditional design
   decomposition have been proposed to enhance hardware evolution's scalability, they often rely on traditional domain knowledge that may not be appropriate for evolutionary
   search and might limit evolution's opportunity to innovate. It has been proposed that reliance on such knowledge can be avoided by introducing a model of biological
   development to the evolutionary algorithm, but this approach has not yet achieved its potential. Prior demonstrations of how development can enhance scalability used toy
   problems that are not indicative of evolving hardware. Prior attempts to apply development to hardware evolution have rarely been successful and have never explored its
   effect on scalability in detail. This thesis demonstrates that development can enhance scalability in hardware evolution, primarily through a statistical comparison of
   hardware evolution's performance with and without development using circuit design problems of various sizes. This is reinforced by proposing and demonstrating three key
   mechanisms that development uses to enhance scalability: the creation of modules, the reuse of modules, and the discovery of design abstractions. The thesis includes
   several minor contributions: hardware is evolved using a common reconfigurable architecture at a lower level of abstraction than reported elsewhere. It is shown that this
   can allow evolution to exploit the architecture more efficiently and perhaps search more effectively. Also the benefits of several features of developmental models are
   explored through the biases they impose on the evolutionary search. Features that are explored include the type of environmental context development uses and the
   constraints on symmetry and information transmission they impose, genetic operators that may improve the robustness of gene networks, and how development is mapped to
   hardware. Also performance is compared against contemporary developmental models.
%8 July
%Z Evolvable hardware rather than GP Runner up 2006 Distinguished Dissertations http://www.bcs.org/server.php?show=conWebDoc.10343 Exploiting Development to Enhance the
   Scalability of Hardware Evolution Tim Gordon University College London Supervised by Peter Rounce Timothy Gordon received the B.Sc. in Chemistry, the M.Sc. in Information
   Technology and the Ph.D. in Computer Science from University College London in 1994, 1995 and 2005 respectively. His Ph.D. research focussed on the application of
   evolutionary algorithms and computational development to hardware design. His recent interests include the use of evolutionary algorithms in finance. He currently works for
   a London hedge fund.

%A Martina Gorges-Schleuter
%T An Analysis of Local Selection in Evolution Strategies
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 847--854
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Timothy Gosling
%T Moshe Sipper: Evolved to Win
%J Genetic Programming and Evolvable Machines
%V 13
%N 2
%D 2012
%P 269--270
%I 
%K genetic algorithms, genetic programming
%O Book review
%8 June

%A Stanley Phillips Gotshall
%A Terence Soule
%T Stochastic training of a biologically plausible spino-neuromuscular system model
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 1
%D 2007
%P 253--260
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware, breeding swarm optimisers, genetic algorithms,
   neural networks, particle swarm optimiser, spiking networks, spinal cord
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p253.pdf
%X A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control
   systems that process sensory information in a way similar to humans. Artificial neural networks have been an increasingly popular option for this because they consist of
   processing units that approximate the synaptic activity of biological signal processing units, i.e. neurons. In this paper we train a nonlinear recurrent
   spino-neuromuscular system (SNMS) model and compare the performance of genetic algorithms (GA)s, particle swarm optimisers (PSO)s, and GA/PSO hybrids. Several key features
   of the SNMS model have previously been modelled individually but have not been combined into a single model as is done here. The results show that each algorithm produces
   fit solutions and generates fundamental biological behaviours, such as tonic tension behaviors and triceps activation patterns, that are not explicitly trained.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Stanley Gotshall
%A Kathy Browder
%A Jessica Sampson
%A Terence Soule
%A Richard Wells
%T Stochastic optimization of a biologically plausible spino-neuromuscular system model
%J Genetic Programming and Evolvable Machines
%V 8
%N 4
%D 2007
%P 355--380
%I 
%K genetic algorithms, Biological neural networks, Particle swarm optimisers, PSO, Breeding swarm optimisers
%X Simulations and modelling techniques are becoming increasingly important in understanding the behaviour of biological systems. Detailed models help researchers answer
   questions in diverse areas such as the behavior of bacteria and viruses and aiding in the diagnosis and treatment of injuries and diseases. However, to yield meaningful
   biological behaviour, biological simulations often include hundreds of parameters that correspond to biological components and characteristics. This paper demonstrates the
   effectiveness of genetic algorithms (GA) and particle swarm optimizer (PSO) based techniques in training biologically plausible behaviour in a neuromuscular simulation of a
   biceps/triceps pair. The results are compared to human subjects during flexion/extension movements to show that these algorithms are effective in training biologically
   plausible behaviours on both neural and gross anatomical levels. Specific behaviors of interest that emerge include tonic tensions in both muscles during resting periods,
   biceps/triceps coactivation patterns, and recruitment-like behaviours. These are all fundamental characteristics of biological motor control and emerge without direct
   selection for these behaviours. This is the first time that all of these characteristic behaviours emerge in a model of this detail without direct selective pressure.
%O special issue on medical applications of Genetic and Evolutionary Computation
%8 Decemeber
%Z See Erratum \citeGotshall:2011:GPEM

%A Stanley Gotshall
%A Kathy Browder
%A Jessica Sampson
%A Terence Soule
%A Richard Wells
%T Erratum to: Stochastic optimization of a biologically plausible spino-neuromuscular system model A comparison with human subjects
%J Genetic Programming and Evolvable Machines
%V 12
%N 1
%D 2011
%P 87--88
%I 
%X The on line version of the original article can be found under doi:10.1007/s10710-007-9044-8.
%8 March
%Z Fig 11, Equation 5 and Sec 4.4 in \citeGotshall:2007:GPEM

%A Jens Gottlieb
%T Evolutionary Algorithms for Multidimensional Knapsack Problems: the Relevance of the Boundary f the Feasible Region
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 787
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Alexander Gounares
%A Prakash Sikchi
%T Adaptive problem solving method and apparatus utilizing evolutionary computation techniques
%D 2001
%I 
%K genetic algorithms, genetic programming
%X A system for adaptively solving sequential problems in a target system using evolutionary computation techniques and in particular genetic algorithms and modified genetic
   algorithms. Stimuli to a target system such as a software system are represented as actions. A single sequence of actions is a chromosome. Chromosomes are generated by a
   goal-seeking algorithm that uses a hint database and recursion to intelligently and efficiently generate a robust chromosome population. The chromosomes are applied to the
   target system one action at a time and the change in properties of the target system is measured after each action is applied. A fitness rating is calculated for each
   chromosome based on the property changes produced in the target system by the chromosome. The fitness rating calculation is defined so that successive generations of
   chromosomes will converge upon desired characteristics. For example, desired characteristics for a software testing application are defect discovery and code coverage.
   Chromosomes with high fitness ratings are selected as parent chromosomes and various techniques are used to mate the parent chromosomes to produce children chromosomes.
   Children chromosomes with high fitness ratings are entered into the chromosome population. Defects in a target software system are minimised by evolving ever-shorter
   chromosomes that produce the same defect. Defect discovery rate, or any other desired characteristic, is thereby maximised.
%O U.S. Patent
%8 28  August
%Z 6,282,527 Assignee: Microsoft Corporation (Redmond, WA)

%A Cristopher T. M. Graae
%A Peter Nordin
%A Mats Nordahl
%T Stereoscopic Vision for a Humanoid Robot Using Genetic Programming
%B Real-World Applications of Evolutionary Computing
%S LNCS
%E Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter
   and Terence C. Fogarty
%V 1803
%D 2000
%P 12--21
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=12
%8 17  April
%Z EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings
   http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61
%@ 3-540-67353-9

%A Jeanine Graf
%A Wolfgang Banzhaf
%T Interactive Evolution for Simulated Natural Evolution
%B Artificial Evolution
%S LNCS
%E Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald and Marc Schoenauer and Dominique Snyers
%V 1063
%D 1996
%P 259--272
%I Springer Verlag
%K genetic algorithms, genetic programming, Growth, Paleontology, Evolutionary Algorithms, Simulation of Natural Evolution
%X Evolutionary algorithms of selection and variation by recombination and/or mutation have been used to simulate biological evolution. This paper demonstrates how interactive
   evolution can be used to study the evolution of simulated natural evolution. Since interactive evolution allows the user to direct the development of models of natural
   systems, it can be used to direct the evolution of models of animals and plants. We show that interactivity of artificial evolution can serve as a useful tool in the
   ontogenesis and phylogenesis of simulated models. This may help paleontologists solve problems in identifying likely missing links and provides a technique to generate
   constrained conjectures regarding gaps in evolutionary data.
%Z Selected papers from two conferences: Evolution Artificielle 94 and Evolution Artificielle 95 http://www.cmap.polytechnique.fr/www.eark/ea95.html cf also ICEC 1995

%A Mario Graff
%A Riccardo Poli
%T Practical Model of Genetic Programming's Performance on Rational Symbolic Regression Problems
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 122--133
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Also known as \citeconf/eurogp/GraffP08 Least Angle Regression (LAR). p151 "Angle between GP systems". System used by Koza \citekoza:book and TinyGP \citepoli08:fieldguide.
   neato. Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Mario Graff
%A Riccardo Poli
%T Automatic Creation of Taxonomies of Genetic Programming Systems
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 145--158
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%X A few attempts to create taxonomies in evolutionary computation have been made. These either group algorithms or group problems on the basis of their similarities.
   Similarity is typically evaluated by manually analysing algorithms/problems to identify key characteristics that are then used as a basis to form the groups of a taxonomy.
   This task is not only very tedious but it is also rather subjective. As a consequence the resulting taxonomies lack universality and are sometimes even questionable. In
   this paper we present a new and powerful approach to the construction of taxonomies and we apply it to Genetic Programming (GP). Only one manually constructed taxonomy of
   problems has been proposed in GP before, while no GP algorithm taxonomy has ever been suggested. Our approach is entirely automated and objective. We apply it to the
   problem of grouping GP systems with their associated parameter settings. We do this on the basis of performance signatures which represent the behaviour of each system
   across a class of problems. These signatures are obtained thorough a process which involves the instantiation of models of GP's performance.We test the method on a large
   class of Boolean induction problems.
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Mario Graff
%A Riccardo Poli
%T Practical performance models of algorithms in evolutionary program induction and other domains
%J Artificial Intelligence
%V 174
%N 15
%D 2010
%P 1254--1276
%I 
%K genetic algorithms, genetic programming, Evolution algorithms, Program induction, Performance prediction, Algorithm taxonomies, Algorithm selection problem
%U http://www.sciencedirect.com/science/article/B6TYF-50KWG15-1/2/3fb87252c46b990fe9a47f5dbd261a82
%X Evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of
   both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems
   or algorithms with unrealistic parameters. In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program
   induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider
   two important classes of problems -- symbolic regression and Boolean function induction -- and we model different versions of genetic programming, gene expression
   programming and stochastic iterated hill climbing in program space. We illustrate the generality of our technique by also accurately modelling the performance of a training
   algorithm for artificial neural networks and two heuristics for the off-line bin packing problem. We show that our models, besides performing accurate predictions, can help
   in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy
   for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance point of view,
   which are not detected by ordinary experimentation.

%A Mario Graff
%A Riccardo Poli
%T Performance Models for Evolutionary Program Induction Algorithms based on Problem Difficulty Indicators
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 118--129
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming, cartesian genetic programming
%X Most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. In this paper, two models of evolutionary program-induction algorithms
   (EPAs) are proposed which overcome this limitation. We test our approach with two important classes of problems --- symbolic regression and Boolean function induction ---
   and a variety of EPAs including: different versions of genetic programming, gene expression programing, stochastic iterated hill climbing in program space and one version
   of cartesian genetic programming. We compare the proposed models against a practical model of EPAs we previously developed and find that in most cases the new models are
   simpler and produce better predictions. A great deal can also be learnt about an EPA via a simple inspection of our new models. E.g., it is possible to infer which
   characteristics make a problem difficult or easy for the EPA.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Jonathan M. Graham
%T Optimal Placement of Distributed Iterrelated Data Components using Genetic Algorithms
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%P 52--58
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, OX, EM
%8 22-25  July
%Z GP-98LB

%A Lee Graham
%A Rob Cattral
%A Franz Oppacher
%T Beneficial Preadaptation in the Evolution of a 2D Agent Control System with Genetic Programming
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 303--314
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming, poster
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Duncan Graham-Rowe
%T Elvis Lives
%J New Scientist
%D 1999
%I 
%K genetic algorithms, genetic programming
%U http://www.newscientist.com/ns/19990821/newsstory4.html
%X Description of Peter Nordin humanoid robot Elvis
%8 21  August

%A Duncan Graham-Rowe
%T Evolve or die
%J New Scientist
%D 2001
%I 
%K genetic algorithms, genetic programming, enzyme genetic programming
%U http://www.newscientist.com/hottopics/tech/article.jsp?id=23142200&sub=Computing
%X ENZYMES, amino acids and genes are not normally in the computer geek's vernacular. But that could all change with the start of the next revolution in computer hardware and
   software which some scientists say could be a biological one.
%8 27  October
%Z Michael A Lones

%A Duncan Graham-Rowe
%T Radio emerges from the electronic soup
%J New Scientist
%D 2002
%I 
%K genetic algorithms, evolvable hardware
%U http://www.newscientist.com/news/news.jsp?id=ns99992732
%X A self-organising electronic circuit has stunned engineers by turning itself into a radio receiver.
%8 13  August
%Z Paul Layzell and Jon Bird at the University of Sussex

%A Duncan Graham-Rowe
%T Google's search for meaning
%J New Scientist
%V 2484
%D 2005
%P 21
%I 
%K genetic algorithms, genetic programming, complearn
%U http://www.newscientist.com/channel/info-tech/mg18524846.100
%X COMPUTERS can learn the meaning of words simply by plugging into Google. The finding could bring forward the day that true artificial intelligence is developed.
%8 29  January
%Z Paul Vitanyi and Rudi Cilibrasi at the www.CWI.nl \citecs.CL/0412098 see http://homepages.cwi.nl/~paulv/lectures/google-lecture.pdf

%T Late breaking papers at Genetic and Evolutionary Computation Conference (GECCO'2006)
%E J\"orn Grahl
%D 2006
%I 
%C Seattle, WA, USA
%K genetic algorithms, genetic programming, MOO, PSO, NN, LCS
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2006

%A Stephen Grand
%A Dave Cliff
%A Anil Malhotra
%T Creatures: Artificial Life Autonmous Software Agents for Home Entertainment
%B The First International Conference on Autonomous Agents (Agents '97)
%E W. Lewis Johnson
%D 1997
%P 22--29
%I ACM Press 1515 Broadway, New York, NY 10036, USA
%I ACM SIGART
%C Marina del Rey, California, USA
%K Arificial Life
%8 February  5-8
%Z http://www.isi.edu/isd/AA97/info.html
%@ 0-89791-877-0

%A Michael S. Grant
%T An Investigation into Genetic Programming
%R M.S. Thesis
%D 1996
%I 
%I Department of Computer Science and Applied Mathematics, Aston University
%C Birmingham, UK
%K genetic algorithms, genetic programming
%U http://www.michael-grant.me.uk/msc.zip broken
%X An investigation was undertaken of the field of Genetic Programming, an offshoot of Genetic Algorithms. The GP system was implemented in Emacs Lisp. Study was undertaken of
   three alternative methods of GP - the original method, the Stack system and the Pygmy Algorithm. The implementation of the Stack system was shown to suffer from premature
   convergence; that of the Pygmy Algorithm was shown under certain conditions to be superior to the original method. A novel problem, that of generating mazes, was
   implemented and shown to be capable of solution by the GP system and by the Pygmy Algorithm.
%8 September

%A Michael Sean Grant
%T An Investigation into the Suitability of Genetic Programming for Computing Visibility Areas for Sensor Planning
%R Ph.D. Thesis
%D 2000
%I 
%I Department of Computing and Electrical Engineering, Heriot-Watt University
%C Riccarton, Edinburgh EH14 4AS, United Kingdom
%K genetic algorithms, genetic programming
%U http://www.michael-grant.me.uk/phd.zip broken
%X This thesis considers the application of Genetic Programming to visibility space calculation, for Sensor Planning in Machine Vision. This is a problem considerably more
   complex than most for which GP has been used; no closed-form algorithm for it yet exists in the most general case. The main contributions and results are the application of
   GP to a new field, and the conclusion that GP is better suited to solve this complex problem by a generate-and-test approach than an analytic one. Three systems were
   implemented to evolve programs for calculating visibility spaces. The first used untyped GP and low-level operations, for maximum flexibility in evolution, but could solve
   the problem only for trivial cases. The second used high-level geometric operations and typed GP, but tended to get trapped in local optima. Approaches used,
   unsuccessfully, to obviate this included altering the fitness cases and function set both statically and dynamically, parameter tuning, seeding the population, using
   program templates, and using a simpler system for modelling evolution. The third system, which used a generate-and-test approach, evolved useful solutions. When seeded with
   hand-crafted partial solutions, it was able to improve them considerably. The work shows the potential of GP to evolve or refine a region-growing generate-and-test
   algorithm for calculating visibility spaces, a problem not hitherto approached by the GP community.
%8 May

%A G. J. Gray
%A Yun Li
%A D. J. Murray-Smith
%A K. C. Sharman
%T Structural System Identification Using Genetic Programming and a Block Diagram Oriented Simulation Tool
%R Technical Report CSC-96003
%D 1996
%I 
%I Department of Electronics and Electrical Engineering, University of Glasgow
%C Glasgow, G12 8QQ, U.K.
%K genetic algorithms, genetic programming, system identification, nonlinear mathematical modelling, SIMULINK
%U http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc96003.ps
%X Genetic programming can be used for structural optimisation. Combined with a hybrid simplex/simulated annealing algorithm, it is applied to the identification of nonlinear
   dynamic models from simulated experimental data. Nonlinear models similar to the original test model of the system are identified yielding both correct structures and
   accurate parameters
%O Submitted to: Electronics Letters
%8 13  June
%Z See \citegray:1996:ssi2

%A Gary J. Gray
%A Yun Li
%A D. J. Murray-Smith
%A K. C. Sharman
%T Structural system identification using genetic programming and a block diagram oriented simulation tool
%J Electronics Letters
%V 32
%N 15
%D 1996
%P 1422--1424
%I 
%K genetic algorithms, genetic programming, structural system identification, block diagram, simulation tool, structural optimisation, hybrid simplex/simulated annealing
   algorithm, nonlinear dynamic model, identification, simulation, simulated annealing, nonlinear dynamical systems
%U http://ieeexplore.ieee.org/iel1/2220/11173/00511160.pdf?isNumber=11173
%X Genetic programming can be used for structural optimisation. Combined with a hybrid simplex/simulated annealing algorithm, it is applied to the identification of nonlinear
   dynamic models from simulated experimental data. Nonlinear models similar to the original test model of the system are identified, yielding both correct structures and
   accurate parameters.
%8 18  July
%Z See also \citegray:1996:ssi SIMULINK, MATLAB. Numerical parameters optimised using combination of Nelder simplex minimisation and simulated annealing. A.P.Fraser's gpc++.

%A Gary J. Gray
%A David J. Murray-Smith
%A Yun Li
%A Ken C. Sharman
%T Nonlinear Model Structure Identification Using Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 32--37
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/60878.html
%X Genetic programming can be used to evolve an algebraic expression as part of an equation representing measured inputoutput response data. Parts of the nonlinear
   differential equations describing a dynamic system are identified along with their numerical parameters using genetic programming. The results of several such optimisations
   are analysed to produce a nonlinear physical representation of the dynamic system. This method is applied to the identification of fluid flow through pipes in a coupled
   water tank system. A representative nonlinear model is identified.
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 See also FACULTY OF ENGINEERING,
   GLASGOW G12 8QQ, U.K. TECHNICAL REPORT: CSC-96xxx
%@ 0-18-201031-7

%A Gary J. Gray
%A David J. Murray-Smith
%A Yun Li
%A Ken C. Sharman
%T Nonlinear Structural System Identification Using Genetic Programming
%B Proceedings of Second International Symposium on Mathematical modelling
%S ARGESIM Report Series
%E Inge Troch and Felix Breitenecker
%N 11
%D 1997
%P 301--306
%I 
%I IMACS/IFAC
%C Technical University Vienna, Austria
%K genetic algorithms, genetic programming
%8 5-7  February
%Z http://web.iti.upv.es/~ken/kenpubs.html http://polaris.dit.upm.es/~jpuente/ifac/newsletter497/mathmod.html
%@ 3-901608-11-7

%A G. J. Gray
%A T. Weinbrenner
%A D. J. Murray-Smith
%A Y. Li
%A K. C. Sharman
%T Issues in Nonlinear Model Structure Identification Using Genetic Programming
%B Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA
%E Ali Zalzala
%D 1997
%P 308--313
%I Institution of Electrical Engineers Savoy Place, London WC2R 0BL, UK
%C University of Strathclyde, Glasgow, UK
%K genetic algorithms, genetic programming
%U http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019970CP446000308000001&idtype=cvips&prog=normal
%X Genetic programming (GP) is a powerful nonlinear optimisation tool which can be applied to the identification of the nonlinear structure of dynamic systems. Several issues
   must be considered. The model format must be defined and a simulation routine integrated with the GP optimisation code to evaluate each candidate model. Numerical
   parameters of the model must be identified and the model's "goodness-of-fit" must be quantified. The GP algorithm must be configured for model identification and optimised
   for computation time. Finally, general nonlinear modelling issues such as experimental design and model validation must be considered. All these issues are addressed in
   this paper.
%8 1-4  September
%Z GALESIA'97
%@ 0-85296-693-8

%A Gary J. Gray
%A David J. Murray-Smith
%A Yun Li
%A Ken C. Sharman
%A Thomas Weinbrenner
%T Nonlinear model structure identification using genetic programming
%J Control Engineering Practice
%V 6
%N 11
%D 1998
%P 1341--1352
%I 
%K genetic algorithms, genetic programming, nonlinear models, system identification, helicopter dynamics, Nonlinear control systems, Identification (control systems),
   Mathematical programming, Differential equations, Error analysis, Mathematical models, Computer simulation, Water tanks, Helicopter rotors, Speed control, Control system
   analysis
%U http://www.sciencedirect.com/science/article/B6V2H-3W1GPR8-4/1/047d9c74e28a6a1a117a3ed9a6d6c409
%X Genetic Programming is an optimisation procedure which may be applied to the identification of the nonlinear structure of a dynamic model from experimental data. In such
   applications, the model structure may be described either by differential equations or by a block diagram and the algorithm is configured to minimise the sum of the squares
   of the error between the recorded experimental response from the real system and the corresponding simulation model output. The technique has been applied successfully to
   the modelling of a laboratory scale process involving a coupled water tank system and to the identification of a helicopter rotor speed controller and engine from flight
   test data. The resulting models provide useful physical insight.

%A H. F. Gray
%A R. J. Maxwell
%A I. Martinez-Perez
%A C. Arus
%A S. Cerdan
%T Genetic Programming for Classification of Brain Tumours from Nuclear Magnetic Resonance Biopsy Spectra
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 424
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A H. F. Gray
%A R. J. Maxwell
%T Genetic Programming for Multi-class Classification of Magnetic Resonance Spectroscopy Data
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 137
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Gray_1997_GPmcMRS.pdf
%8 13-16  July
%Z GP-97

%A Helen Gray
%T Genetic Programming for Classification of Medical Data
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 291
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Helen F. Gray
%A Ross J. Maxwell
%A Irene Martinez-Perez
%A Carles Arus
%A Sebastian Cerdan
%T Genetic programming for classification and feature selection: analysis of 1H nuclear magnetic resonance spectra from human brain tumour biopsies
%J NMR Biomedicine
%V 11
%N 4-5
%D 1998
%P 217--224
%I 
%K genetic algorithms, genetic programming, brain tumour, artificial intelligence, classification, feature selection
%X Genetic programming (GP) is used to classify tumours based on 1H nuclear magnetic resonance (NMR) spectra of biopsy extracts. Analysis of such data would ideally give not
   only a classification result but also indicate which parts of the spectra are driving the classification (i.e. feature selection). Experiments on a database of variables
   derived from 1H NMR spectra from human brain tumour extracts (n = 75) are reported, showing GP's classification abilities and comparing them with that of a neural network.
   GP successfully classified the data into meningioma and non-meningioma classes. The advantage over the neural network method was that it made use of simple combinations of
   a small group of metabolites, in particular glutamine, glutamate and alanine. This may help in the choice of the most informative NMR spectroscopy methods for future
   non-invasive studies in patients.
%8 June -  August
%Z PMID: 9719576, UI: 98384081 Computer Science Department, Arhus University, Denmark.

%A Helen Frances Gray
%A Ross James Maxwell
%T Genetic Programming Optimisation of Nuclear Magnetic Resonance Pulse Shapes
%B Medical Data Analysis: First International Symposium, ISMDA 2000, Proceedings
%S Lecture Notes in Computer Science
%E R. W. Brause and E. Hanisch
%V 1933
%D 2000
%P 242--??
%I Springer-Verlag Heidelberg
%C Frankfurt, Germany
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/1933/19330242.pdf
%X Genetic Programming is used to generate pulse sequence elements for a Nuclear Magnetic Resonance system and evaluate them directly on that system without human
   intervention. The method is used to optimise pulse shapes for a series of solvent suppression problems. The method proves to be successful, with results showing an
   improvement in fitness of up to two orders of magnitude. The method is capable of producing both simple and novel solutions.
%8 September

%A D. J. Greeff
%A C. Aldrich
%T Evolution of Empirical Models for Metallurgical Process Systems
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 138
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Greeff_1997_eemmps.pdf
%8 13-16  July
%Z GP-97

%A D. J Greeff
%A C. Aldrich
%T Empirical modelling of chemical process systems with evolutionary programming
%J Computers \& Chemical Engineering
%V 22
%N 7-8
%D 1998
%P 995--1005
%I 
%K genetic algorithms, genetic programming, empirical modelling
%U http://www.sciencedirect.com/science/article/B6TFT-3TKV02R-F/2/30657596f48ca16571ac48098a948833
%X Through the use of evolutionary computation, empirical models for chemical processes can be evolved that are more cost-effective than models determined by means of
   classical statistical techniques. These strategies do not require explicit specification of a model structure, but explore candidate models assembled from sets of
   variables, parameters and simple mathematical operators. The application of the proposed strategies is illustrated by means of three examples, two of which are based on
   data pertaining to leaching experiments. Since the evolved models were derived from terminal sets containing only the most basic operators, their structures tended to be
   complicated, making for less easy interpretation, similar to neural networks and other non-parametric models. Nonetheless, the evolved models were either of comparable
   accuracy or significantly more accurate than those which were previously developed by means of standard least-squares methods.

%A Buster Greene
%T A Deterministic Analysis of Stationary Diploid/Dominance
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 770--776
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K evolutionary programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Casey S. Greene
%A Bill C. White
%A Jason H. Moore
%T Using expert knowledge in initialization for genome-wide analysis of epistasis using genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 351--352
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, expert knowledge, genetic analysis, Initialisation, Bioinformatics, computational biology: Poster, TuRF, Relief, SNP, MDR, SDA
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p351.pdf
%X In human genetics it is now possible to measure large numbers of DNA sequence variations across the human genome. Given current knowledge about biological networks and
   disease processes it seems likely that disease risk can best be modelled by interactions between biological components, which may be examined as interacting DNA sequence
   variations. The machine learning challenge is to effectively explore interactions in these datasets to identify combinations of variations which are predictive of common
   human diseases. Genetic programming is a promising approach to this problem. The goal of this study is to examine the role that an expert knowledge aware initialiser can
   play in the framework of genetic programming. We show that this expert knowledge aware initializer outperforms both a random initializer and an enumerative initialiser.
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389158 Comparison of three ways of loading problem inputs (10000+) into initial population to predict clinical end point
   (death). Artificial datasets.

%A Casey S. Greene
%A Jason H. Moore
%T Human Genetics Using GP
%J SIGEVOlution
%V 3
%N 2
%D 2008
%I 
%K genetic algorithms, genetic programming
%U http://www.sigevolution.org/issues/pdf/SIGEVOlution200802.pdf
%8 Summer

%A Casey S. Greene
%A Jeff Kiralis
%A Jason H. Moore
%T Nature-Inspired Algorithms for the Genetic Analysis of Epistasis in Common Human Diseases: Theoretical Assessment of Wrapper vs. Filter Approaches
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 800--807
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X In human genetics, new technological methods allow researchers to collect a wealth of information about genetic variation among individuals quickly and relatively
   inexpensively. Studies examining more than one half of a million points of genetic variation are the new standard. Quickly analyzing these data to discover single gene
   effects is both feasible and often done. Unfortunately as our understanding of common human disease grows, we now believe it is likely that an individual's risk of these
   common diseases is not determined by simple single gene effects. Instead it seems likely that risk will be determined by nonlinear gene-gene interactions, also known as
   epistasis. Unfortunately searching for these nonlinear effects requires either effective search strategies or exhaustive search. Previously we have employed both filter and
   nature-inspired probabilistic search wrapper approaches such as genetic programming (GP) and ant colony optimization (ACO) to this problem. We have discovered that for this
   problem, expert knowledge is critical if we are to discover these interactions. Here we theoretically analyze both an expert knowledge filter and a simple
   expert-knowledge-aware wrapper. We show that under certain assumptions, the filter strategy leads to the highest power. Finally we discuss the implications of this work for
   this type of problem, and discuss how probabilistic search strategies which outperform a filtering approach may be designed.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Casey S. Greene
%A Bill C. White
%A Jason H. Moore
%T Sensible Initialization Using Expert Knowledge for Genome-Wide Analysis of Epistasis Using Genetic Programming
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 1289--1296
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X For biomedical researchers it is now possible to measure large numbers of DNA sequence variations across the human genome. Measuring hundreds of thousands of variations is
   now routine, but single variations which consistently predict an individual's risk of common human disease have proven elusive. Instead of single variants determining the
   risk of common human diseases, it seems more likely that disease risk is best modeled by interactions between biological components. The evolutionary computing challenge
   now is to effectively explore interactions in these large datasets and identify combinations of variations which are robust predictors of common human diseases such as
   bladder cancer. One promising approach to this problem is genetic programming (GP). A GP approach for this problem will use Darwinian inspired evolution to evolve programs
   which find and model attribute interactions which predict an individual's risk of common human diseases. The goal of this study is to develop and evaluate two initializers
   for this domain. We develop a probabilistic initializer which uses expert knowledge to select attributes and an enumerative initializer which maximizes attribute diversity
   in the generated population.We compare these initializers to a random initializer which displays no preference for attributes. We show that the expert-knowledge-aware
   probabilistic initializer significantly outperforms both the random initializer and the enumerative initializer.We discuss implications of these results for the design of
   GP strategies which are able to detect and characterize predictors of common human diseases.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Francis Manwell Greene
%T Genetic Synthesis of Signal Processing Networks Utilizing Diploid/Dominance
%R Ph.D. Thesis
%D 1997
%I 
%I Department of Electrical Engineering. University of Washington
%C Seattle, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/fgDissertation.pdf
%X Dissertation Proposal (July 29, 2001) Introduction This proposal is a result of research over the past two years, and whose purpose was to develop a design methodology for
   low cost ultrasonic blood flow and tissue quantification using signal processing. My original desire was to improve feature extraction techniques for use in statistical
   pattern recognition, but was almost immediately redirected along the lines of efficient genetic search of network solution spaces. Over ten years of experience with Doppler
   flow measurement suggests that dynamic processing of the clinical signals involved can be done with interconnected functional elements such as delays, filters, and
   thresholds. Some details of the processing issues and reasons for using genetic search will follow. The point of this dissertation is to study and develop a specific method
   for synthesising processing networks that aid in the use, interpretation, and diagnostic power of low-cost medical technology. Conclusions Results of synthesising a signal
   processing network that correctly recognises fiducial points in a simulated two-heart cycle, spectrally represented, wave form suggests the ability to handle similar
   applications with real clinical Doppler data. The solution described in the previous section made use of a delay element that matches the heart-cycle period and is
   otherwise sensible. Search difficulty was increased by including in the function set a number of function/operators not actually needed to solve the problem. This was done
   purposely to eliminate the necessity of defining a problem dependent function set as may be necessary for medical data. A multiple trial, multi-modal, partially deceptive
   test problem provide further evidence that the Max(f1,f2) diploid/dominance implementation can provide better than or equal processing efficiency, compared to haploid. This
   conclusion is supported by a similar, though less thorough, comparison using the R-wave network synthesis problem. The Max(f1,f2) approach has been observed to do about the
   same as haploid with either very simple (e.g., unimodal) or very difficult or poorly formulated problems. Diploid/dominance as implemented here can be used in conjunction
   with other improvements (e.g., more refined crossover, inversion, species formation, etc.) to the standard GA. The experiments with alternating fitness environments show
   that multiploid populations are capable of storing and rapidly recalling as many global optima as there are homologues in each individual chromosome and shows that
   diploid/dominance retains recessive alleles and schema. The diploid approach could immediately make use of a two-processor system, since the algorithm used involves two
   function evaluations per generations.
%8 6  March
%Z Supervisior Dr. Alistair Holden. fgDissertation.pdf is Dissertation Proposal (July 29, 2001)

%A William A. Greene
%T A Non-Linear Schema Theorem for Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 189--194
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GA068.pdf
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A William A. Greene
%T Non-Linear Bit Arrangements in Genetic Algorithms
%B 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Erik D. Goodman
%D 2001
%P 138--144
%I 
%C San Francisco, California, USA
%K genetic algorithms, poster
%U http://www.cs.uno.edu/People/Faculty/bill/NonLinBits-GECCO-2001-lateBreakPaper.pdf
%X In earlier research we laid out a theoretical basis for the supposition that genetic algorithms can succeed even if bits are arranged in ways other than as a linear
   sequence. In the present paper we report on certain experiments that show such success can occur in practice. Our experiments consider cases in which bits are arranged in
   two-dimensional grids, in three-dimensional cubes, and as the nodes of a complete binary tree. Moreover, our experiments consider several ways of cutting parental genetic
   material when performing mating with crossover, and also consider several notions of fitness. Our problems are not particularly difficult, but clearly show the convergence
   we seek, under these much liberalised ways of arranging bits.
%8 9-11  July
%Z GECCO-2001LB. Two dimensional grid chromosome, three_D cubes, complete binary tree. Follows up \citeGreene:2000:GECCO 576bit onemax. eight queens problem (also 20 queens).
   Three target binary trees (all 9 levels, full, each node labelled with 0 or 1). Twins, Palindrome trees. Extension of \citegreene:2001:GECCO.

%A William A. Greene
%T Schema Disruption in Chromosomes That Are Structured as Binary Trees
%B Genetic and Evolutionary Computation -- GECCO-2004, Part I
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3102
%D 2004
%P 1197--1207
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3102/31021197.htm
%X We are interested in schema disruption behaviour when chromosomes are structured as binary trees. We give the definition of the disruption probability dp(H) of a schema H,
   and also the relative diameter rel?(H) of H. We show that in the general case that dp(H) can far exceed rel?(H), but when the chromosome is a complete binary tree then the
   inequality dp(H) = rel?(H) holds almost always. Thus the more compactly the tree chromosome is structured, the better is the behavior to be expected from geneticism.
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22344-4

%A Casey S. Greene
%A Bill C. White
%A Jason H. Moore
%T An Expert Knowledge-Guided Mutation Operator for Genome-Wide Genetic Analysis Using Genetic Programming
%B Proceedings of the second IAPR International Workshop Pattern Recognition in Bioinformatics, PRIB 2007
%S Lecture Notes in Computer Science
%E Jagath C. Rajapakse and Bertil Schmidt and L. Gwenn Volkert
%V 4774
%D 2007
%P 30--40
%I Springer
%C Singapore
%K genetic algorithms, genetic programming, TuRF
%X Human genetics is undergoing a data explosion. Methods are available to measure DNA sequence variation throughout the human genome. Given current knowledge it seems likely
   that common human diseases are best predicted by interactions between biological components, which can be examined as interacting DNA sequence variations. The challenge is
   thus to examine these high-dimensional datasets to identify combinations of variations likely to predict common diseases. The goal of this paper was to develop and evaluate
   a genetic programming (GP) mutator suited to this task by exploiting expert knowledge in the form of Tuned ReliefF (TuRF) scores during mutation. We show that using expert
   knowledge guided mutation performs similarly to expert knowledge guided selection. This study demonstrates that in the context of an expert knowledge aware GP, mutation may
   be an appropriate component of the GP used to search for interacting predictors in this domain.
%8 October  1-2

%A Casey S. Greene
%A Douglas P. Hill
%A Jason H. Moore
%T Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics
%B Genetic Programming Theory and Practice VII
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Una-May O'Reilly and Trent McConaghy
%D 2009
%P 19--36
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Genetic Epidemiology, Symbolic Discriminant Analysis, Epistasis
%O 2
%8 14-16  May
%Z part of \citeRiolo:2009:GPTP

%A Aaron Greenfield
%T Evolution of Communication Among Prey in a Hostile Environment
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 170--179
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Simon M. Greenwold
%T AGENCY GP: Genetic programming for architectural design
%B Graduate Student Workshop
%E Conor Ryan and Una-May O'Reilly and William B. Langdon
%D 2000
%P 273--276
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%8 8  July
%Z GECCO-2000WKS Part of \citewu:2000:GECCOWKS

%A Garrison W. Greenwood
%T Experimental Observation of Chaos in Evolution Strategies
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 439--444
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K evolutionary programming and evolution strategies
%8 13-16  July
%Z GP-97

%A Garrison W. Greenwood
%T Book Review: Bio-Inspired Computing Machines: Towards Novel Computational Architectures
%J Genetic Programming and Evolvable Machines
%V 2
%N 1
%D 2001
%P 75--78
%I 
%K genetic algorithms, genetic programming, evolutionary programming, evolution strategies, evolvable hardware, FPGA, L-Systems
%8 March
%Z review of \citemange:1998:bicm Article ID: 319814

%A John Grefenstette
%A Kenneth {De Jong}
%A Connie Ramsey
%A Annie Wu
%T The Virtual Virus Project
%D 1997
%I 
%C East Lansing, MI, USA
%K genetic algorithms, variable size representation
%O Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97
%8 20  July

%A Michael Gregory
%T Genetic Algorithm Optimisation of Distributed Database Queries
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%P 271--276
%I IEEE Press
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming
%X Distributed relational database query optimisation is a combinatorial optimisation problem. This paper reports on an initial investigation into the potential for a genetic
   algorithm (GA) to optimise distributed queries. A genetic algorithm is developed and its performance compared with alternative stochastic optimisation techniques: random
   search, multistart, and simulated annealing. The problem of fully reducing all tables in a tree query is used to compare the techniques. For this problem, evaluating the
   fitness function is an expensive operation. The proposed GA uses a tree-structured data model with tailored crossover and mutation operators that avoid the need to fully
   re-evaluate the fitness function for new solutions. Query optimisation is a task that must be performed in real-time. A technique is required that performs well at the
   start of a search, but avoids the problem of premature convergence. The proposed GA uses a local search phase to deliver the required real-time performance. Experiments
   show that the proposed GA can perform better than the alternative techniques tested. The potential for a GA to deliver valuable distributed query processing cost reductions
   is demonstrated.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
%@ 0-7803-4869-9

%A A. R. Griffioen
%A S. K. Smit
%A A. E. Eiben
%T Learning Benefits Evolution if Sex Gives Pleasure
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%U http://www.cs.vu.nl/~gusz/papers/2008-CEC-Griffioen-Smit-Eiben.pdf
%X In this paper the effects of individual learning on an evolving population of situated agents are investigated. We work with a novel type of system where agents can decide
   autonomously (by their controllers) if/when they reproduce and the bias in the agent controllers for the mating action is adaptable by individual learning. Our experiments
   show that in such a system reinforcement learning with the straightforward rewards system based on energy makes the agents lose their interest in mating. In other words, we
   see that learning frustrates evolution, killing the whole population on the long run. This effect can be counteracted by introducing a specially designated positive mating
   reward, pretty much like an orgasm in Nature.With this twist individual learning becomes a positive force. It can make the otherwise disappearing population viable by
   keeping agents alive that did not yet learn the task at hand. This hiding effect proves positive for it provides a smooth road for the population to adapt and learn the
   task with a lower risk of extinction.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A J. B. Grimbleby
%T An automatic Analogue Network Synthesis using Genetic Algorithms
%B First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA
%E A. M. S. Zalzala
%V 414
%D 1995
%P 53--58
%I IEE London, UK
%C Sheffield, UK
%K genetic algorithms, genetic programming, analogue network synthesis, frequency-domain, linear networks, time-domain, analogue circuits, circuit CAD, circuit optimisation,
   linear network synthesis
%X Genetic algorithms provide a basis for automatic synthesis of analogue electronic networks. Passive linear networks have been generated to meet both frequency-domain and
   time-domain specifications. The networks generated are both novel and effective. It should be possible to extend the technique to deal with active networks
%8 12-14  September
%Z 12--14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm Evolves passive analogue circuits using a
   fixed length GA which allows non-ops to specify network connectivity and components forming links. 'Even a small amount of cross-over provides considerable efficiency
   benefits' [page 55]
%@ 0-85296-650-4

%A C. A. Grimes
%T Application of Genetic Techniques to the Planning of Railway Track Maintenance Work
%B First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA
%E A. M. S. Zalzala
%V 414
%D 1995
%P 467--472
%I IEE London, UK
%C Sheffield, UK
%K genetic algorithms, genetic programming, scheduling, maintenance, PC-MARPAS
%U http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019950CP414000467000001&idtype=cvips&prog=normal
%X Track maintenance work was planned using GA and GP, with profit as the optimisation criteria. The results where compared with an existing determinstic technique. It was
   found the GP method gave the best results, with the GA method giving good results for a short section (10 miles) and poor results for a long section (50 miles).
%8 12-14  September
%Z 12--14 September 1995, Halifax Hall, University of Sheffield, UK see also http://www.iee.org.uk/LSboard/Conf/program/galprog.htm
%@ 0-85296-650-4

%A Alexandre Grings
%T Regress\~ao simb\'olica via programa\c c\~ao gen\'etica: um estudo de caso com modelagem geof\'isica
%R Ph.D. Thesis Tese ou Dissertacao Eletronica
%D 2006
%I 
%I Biblioteca Digital da Universidade Federal de Uberl\^andia
%C Brazil
%K genetic algorithms, genetic programming, Symbolic regression, Gene expression programming, Geophisical modeling, Regressao simbolica, Programacao genetica, Programacao da
   expressao genica, Modelagem geofisica, CIENCIA DA COMPUTACAO, Programacao genetica Computacao
%U http://www.bdtd.ufu.br//tde_busca/arquivo.php?codArquivo=550.pdf
%X A regress\~ao simb\'olica, que consiste na manipula\c c\~ao de express\~oes matem\'aticas para descobertade fun\c c\~oes que descrevam um conjunto de dados, foi uma tarefa
   exclusivamente humanaat\'e pouco tempo atr\'as. Recentemente, foram desenvolvidas v\'arias t\'ecnicas computacionais paraautomatizar a regress\~ao simb\'olica. Uma dessas
   t\'ecnicas \'e a programa\c c\~ao gen\'etica, uma sub\'areada computa\c c\~ao evolutiva que usa analogia \`a teoria da evolu\c c\~ao de Darwin e id\'eias do campoda
   Gen\'etica para desenvolver um grupo de programas de computador na busca por solu\c c\~oes atarefas computacionais. O presente trabalho visa a testar as capacidades de
   regress\~ao simb\'olicada programa\c c\~ao gen\'etica com objetivo de verificar sua viabilidade como ferramenta paraa pesquisa de um problema geof\'isico. Esse problema diz
   respeito a fen\^omenos que ocorremna ionosfera, a regi\~ao da atmosfera ionizada pela a\c c\~ao dos raios solares, que desempenham umpapel fundamental para as
   telecomunica\c c\~oes. No intercurso dessa tentativa, faz-se o uso deduas implementa\c c\~oes tradicionais de programa\c c\~ao gen\'etica e de uma variante, chamada
   programa\c c\~aoda express\~ao g\^enica. Problemas como o sistema estudado demandam muito tempode processamento e mem\'oria, desse modo, o trabalho culmina com uma
   implementa\c c\~ao distribu\'idade programa\c c\~ao gen\'etica com o intuito de acelerar o processamento da modelagem.; Symbolic regression, which is in principal the
   handling of mathematical expressions for finding a function that describes a data set, was until recently carried out exclusively by humans. But now, several computational
   techniques of symbolic regression automatisation have appeared.One of these techniques is genetic programming, a subarea of evolutive computing that uses an analogy to
   Darwin's evolutionary theory and some ideas from the Genetics field to develop group of computer programs in a search for solutions to computational tasks. This work aims
   to test the symbolic regression capabilities of genetic programming with the objective of verifying its viability as a tool for a specific geophysical research. This
   research concerns phenomena that occurs in the ionosphere, the region of earth's atmosphere ionised by the action of solar rays,that play a fundamental role in
   telecommunications. In the course of this trial, we used two implementations of traditional genetic programming and one implementation of a variant, named gene expression
   programming. Problems like the one under study demand a lot of processor time and are memory consuming, therefore, the work culminates with a distributed implementation of
   genetic programming with the objective of accelerating the modelling process.
%8 24  February
%Z in Portuguese

%A L. Gritz
%A J. K. Hahn
%T Genetic Programming for Articulated Figure Motion
%J Journal of Visualization and Computer Animation
%V 6
%N 3
%D 1995
%P 129--142
%I 
%K genetic algorithms, genetic programming
%U http://www.icg.seas.gwu.edu/Publications/gpafm.ps
%X Three dimensional computer animation has become increasingly popular over the past decade. Computer animation now has an important role in entertainment, education, and
   simulation. For computer animation of characters, the role of the animator has unfortunately stayed similar to that of a stop motion animator, rather than like a film
   director. Research in computer animation has tried to address this by giving higher levels of control to the animator, but these methods often result in lack of fine
   control over the animated characters. This is inadequate because fine control is essential to both aesthetics and the ability of the animator to direct a meaningful
   narrative. This dissertation presents methods of articulated figure motion control which attempt to bridge the gap between high level direction and low level control of
   subtle motion. These methods define motion in terms of goals and ratings. The agents are dynamically-controlled robots whose behavior is determined by robotic controller
   programs. The controller programs for the robots are evaluated at each time step to yield torque values which drive the dynamic simulation of the motion. We use the AI
   technique of Genetic Programming (GP) to automatically derive control programs for the agents which achieve the goals. This type of motion specification is an alternative
   to key framing which allows a highly automated, learning-based approach to generation of motion. This method of motion control is very general (it can be applied to any
   type of motion), yet it allows for specifications of the types of specific motion which are desired for a high quality animation. We show that complex, specific, physically
   plausible, and aesthetically appealing motion can be generated using these methods. Both skill-based and action-based motion can be specified in this manner. We also
   introduce the new paradigm of key marks, a generalization of key framing which is not subject to many of the limitations of key framing.
%Z Larry Gritz is at Pixar: http://www.seas.gwu.edu/~graphics/papers/gritzdissert.html http://www.seas.gwu.edu/student/gritz/index.html James Hahn is at the George Washington
   University: http://www.seas.gwu.edu/facu lty/hahn/

%A Larry Gritz
%A James K. Hahn
%T Genetic Programming Evolution of Controllers for 3-D Character Animation
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 139--146
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.icg.seas.gwu.edu/Publications/gpec-gp97.ps
%X The dominant paradigm for 3-D character animation requires an animator to specify the values for all degrees of freedom of an articulated figure at key frames. Specifying
   motion that is physically believable and biologically plausible is a tedious practice requiring great skill. We use evolutionary techniques (specifically Genetic
   Programming) as a means of controller synthesis for character animation. Controllers which drive a dynamic simulation of the character are evolved using the goals of the
   animation as an objective function, resulting in physically plausible motion. We discuss the development of objective functions used to guide the controller evolution,
   making reusable skill controllers, and comparisons of the convergence rates for different parameters of the evolutionary runs.
%8 13-16  July
%Z GP-97 S-expression per degree of freedom in each joint in the character. Joints controlled by proportional derivative (PD) controllers. Aninmated desk lamp Luxo, Jr. L*xo 4
   links and 3 internally controllable degrees of freedom. Robust = reusable. Randomly generated test cases

%A Larry Israel Gritz
%T Evolutionary Controller Synthesis for 3-D Character Animation
%R Ph.D. Thesis
%D 1999
%I 
%I The George Washington University
%C Washington, DC, USA
%K genetic algorithms, genetic programming, computer animation
%U http://www.seas.gwu.edu/~graphics/papers/gritzdissert.html
%X Three dimensional computer animation has become increasingly popular over the past decade. Computer animation now has an important role in entertainment, education, and
   simulation. For computer animation of characters, the role of the animator has unfortunately stayed similar to that of a stop motion animator, rather than like a film
   director. Research in computer animation has tried to address this by giving higher levels of control to the animator, but these methods often result in lack of fine
   control over the animated characters. This is inadequate because fine control is essential to both aesthetics and the ability of the animator to direct a meaningful
   narrative. This dissertation presents methods of articulated figure motion control which attempt to bridge the gap between high level direction and low level control of
   subtle motion. These methods define motion in terms of goals and ratings. The agents are dynamically-controlled robots whose behavior is determined by robotic controller
   programs. The controller programs for the robots are evaluated at each time step to yield torque values which drive the dynamic simulation of the motion. We use the AI
   technique of Genetic Programming (GP) to automatically derive control programs for the agents which achieve the goals. This type of motion specification is an alternative
   to key framing which allows a highly automated, learning-based approach to generation of motion. This method of motion control is very general (it can be applied to any
   type of motion), yet it allows for specifications of the types of specific motion which are desired for a high quality animation. We show that complex, specific, physically
   plausible, and aesthetically appealing motion can be generated using these methods. Both skill-based and action-based motion can be specified in this manner. We also
   introduce the new paradigm of key marks, a generalization of key framing which is not subject to many of the limitations of key framing.
%8 15  May

%A Andre Gronemeier
%T Approximating Boolean Functions by OBDDs
%B 29th Symposium on Mathematical Foundations of Computer Science MFCS 2004
%S Lecture Notes in Computer Science
%E Jir\'\i Fiala and V\'aclav Koubek and Jan Kratochv\'\il
%V 3153
%D 2004
%P 251--262
%I Springer
%C Prague, Czech Republic
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3153&spage=251
%X In learning theory and genetic programming, OBDDs are used to represent approximations of Boolean functions. This motivates the investigation of the OBDD complexity of
   approximating Boolean functions with respect to given distributions on the inputs. We present a new type of reduction for one?round communication problems that is suitable
   for approximations. Using this new type of reduction, we prove the following results on OBDD approximations of Boolean functions: 1. We show that OBDDs approximating the
   well-known hidden weighted bit function for uniformly distributed inputs with constant 1/4 error have size 2?(n ) , improving a previously known result. 2. We prove that
   for every variable order ? the approximation of some output bits of integer multiplication with constant error requires ?-OBDDs of exponential size.
%8 August  22-27
%Z replaced by \citeGronemeier:2007:DAM
%@ 3-540-22823-3

%A Andre Gronemeier
%T Approximating Boolean functions by OBDD
%J Discrete Applied Mathematics
%V 155
%N 2
%D 2007
%P 194--209
%I 
%K genetic algorithms, genetic programming, OBDD, Communication complexity, Approximation
%X In learning theory and genetic programming, OBDDs are used to represent approximations of Boolean functions. This motivates the investigation of the OBDD complexity of
   approximating Boolean functions with respect to given distributions on the inputs. We present a new type of reduction for one-round communication problems that is suitable
   for approximations. Using this new type of reduction, we improve a known lower bound on the size of OBDD approximations of the hidden weighted bit function for uniformly
   distributed inputs to an asymptotically tight bound and prove new results about OBDD approximations of integer multiplication and squaring for uniformly distributed inputs.
%O 29th Symposium on Mathematical Foundations of Computer Science MFCS 2004
%8 15  January
%Z replaces \citeDBLP:conf/mfcs/Gronemeier04

%A Marko Gronroos
%T A Comparison of Some Methods for Evolving Neural Networks
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1442
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-006.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Charles-Henri Gros
%T Genetic Evolution of Neural Networks
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 68--74
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/Gros.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Crina Grosan
%A Ajith Abraham
%A Sang-Yong Han
%T MEPIDS: Multi-Expression Programming for Intrusion Detection System
%B Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial
   Computation, IWINAC 2005, Proceedings, Part II
%S Lecture Notes in Computer Science
%E Jose Mira and Jose R. Alvarez
%V 3562
%D 2005
%P 163--172
%I Springer Berlin / Heidelberg
%C Las Palmas, Canary Islands, Spain
%K genetic algorithms, genetic programming
%U http://www.cs.ubbcluj.ro/~cgrosan/iwinac05.pdf
%X An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been
   misused. An IDS does not eliminate the use of preventive mechanism but it works as the last defensive mechanism in securing the system. This paper evaluates the
   performances of Multi-Expression Programming (MEP) to detect intrusions in a network. Results are then compared with Linear Genetic Programming (LGP) approach. Empirical
   results clearly show that genetic programming could play an important role in designing light weight, real time intrusion detection systems.
%8 June  15-18
%@ 3-540-26319-5

%A Crina Grosan
%A Ajith Abraham
%A Vitorino Ramos
%A Sang Yong Han
%T Stock Market Prediction Using Multi Expression Programming
%B ALEA-05, Workshop on Artificial Life and Evolutionary Algorithms at EPIA'05 - Proc. of the 12th Portuguese Conference on Artificial Intelligence
%E C. Bento and A. Cardoso and G. Dias
%D 2005
%P 73--78
%I 
%C Covilha, Portugal
%K genetic algorithms, genetic programming, Stock Market Prediction, Multi Expression Programming, Nasdaq-100, CNX NIFTY stock index
%U http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-EPIA05.pdf
%X The use of intelligent systems for stock market predictions has been widely established. In this paper, we introduce a genetic programming technique (called
   Multi-Expression Programming) for the prediction of two stock indices. The performance is then compared with an Artificial Neural Network trained using Levenberg-Marquardt
   algorithm, Support Vector Machine, Takagi-Sugeno Neuro-Fuzzy model and Difference Boosting Neural Network. We considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S
   and P CNX NIFTY stock index as test data.
%Z ALEA Workshop held in conjunction with Progress in Artificial Intelligence, 12th Portuguese Conference on Artificial Intelligence, EPIA 2005, Covilha, Portugal, December
   5-8, 2005, Proceedings, Springer, LNCS 3808, isbn 3-540-30737-0.

%A C. Grosan
%A A. Abraham
%T Ensemble of genetic programming models for designing reactive power controllers
%B Fifth International Conference on Hybrid Intelligent Systems, HIS-05
%D 2005
%I 
%K genetic algorithms, genetic programming
%X In this paper, we present an ensemble combination of two genetic programming models namely linear genetic programming (LGP) and multi expression programming (MEP). The
   proposed model is designed to assist the conventional power control systems with added intelligence. For on-line control, voltage and current are fed into the network after
   preprocessing and standardisation. The model was trained with a 24-hour load demand pattern and performance of the proposed method is evaluated by comparing the test
   results with the actual expected values. For performance comparison purposes, we also used an artificial neural network trained by a backpropagation algorithm. Test results
   reveal that the proposed ensemble method performed better than the individual GP approaches and artificial neural network in terms of accuracy and computational
   requirements.
%8 6-9  November

%A Crina Grosan
%A Ajith Abraham
%T Stock Market Modeling Using Genetic Programming Ensembles
%B Genetic Systems Programming: Theory and Experiences
%S Studies in Computational Intelligence
%E Nadia Nedjah and Ajith Abraham and Luiza de Macedo Mourelle
%V 13
%D 2006
%P 133--148
%I Springer
%C Germany
%K genetic algorithms, genetic programming
%U http://www.cs.ubbcluj.ro/~cgrosan/stock-chapter.pdf
%X The use of intelligent systems for stock market predictions has been widely established. This chapter introduces two Genetic Programming (GP) techniques: Multi-Expression
   Programming (MEP) and Linear Genetic Programming (LGP) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained
   using Levenberg-Marquardt algorithm and Takagi-Sugeno neuro-fuzzy model. We considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index as test
   data. Empirical results reveal that Genetic Programming techniques are promising methods for stock prediction. Finally formulate an ensemble of these two techniques using a
   multiobjective evolutionary algorithm. Results obtained by ensemble are better than the results obtained by each GP technique individually.
%O Forthcoming
%Z http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html
%@ 3-540-29849-5

%A Benjamin Grosman
%A Daniel R. Lewin
%T MPC using nonlinear models generated by genetic programming
%B European Symposium on Computer Aided Process Engineering - 11, 34th European Symposium of the Working Party on Computer Aided Process Engineering
%S Computer Aided Chemical Engineering
%E Rafiqul Gani and Sten Bay Jorgensen
%V 9
%D 2001
%P 663--668
%I Elsevier
%C Kolding, Denmark
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B8G5G-4P40D5J-3R/2/96212e409c54e5c4c1781f7f1780816e
%8 May  27-30
%Z ESCAPE-11

%A Benyamin Grosman
%A Daniel R. Lewin
%T Automated nonlinear model predictive control using genetic programming
%J Computers \& Chemical Engineering
%V 26
%N 4-5
%D 2002
%P 631--640
%I 
%K genetic algorithms, genetic programming, Empirical process modeling, Nonlinear model predictive control
%U http://www.sciencedirect.com/science/article/B6TFT-44YWM6B-B/2/b0dbb5bfa3d6c3d92f1904e01e559d3f
%X This paper describes the use of genetic programming (GP) to generate an empirical dynamic model of a process, and its use in a nonlinear, model predictive control (NMPC)
   strategy. GP derives both a model structure and its parameter values in such a way that the process trajectory is predicted accurately. Consequently, the performance of the
   NMPC strategy is expected to improve on the performance obtained using linear models. The GP approach and the nonlinear MPC strategy are described, and demonstrated by
   simulation on two multivariable process: a mixing tank, which involves only moderate nonlinearities, and the more complex Karr liquid-liquid extraction column.

%A Benyamin Grosman
%A Daniel R. Lewin
%T Adaptive genetic programming for steady-state process modeling
%J Computers \& Chemical Engineering
%V 28
%N 12
%D 2004
%P 2779--2790
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6TFT-4DMW22F-1/2/3e0d065d49ca47901dac832951154da0
%X Genetic programming is one of the computer algorithms in the family of evolutionary-computational methods, which have been shown to provide reliable solutions to complex
   optimisation problems. The genetic programming under discussion in this work relies on tree-like building blocks, and thus supports process modelling with varying
   structure. This paper, which describes an improved GP to facilitate the generation of steady-state nonlinear empirical models for process analysis and optimization, is an
   evolution of several works in the field. The key feature of the method is its ability to adjust the complexity of the required model to accurately predict the true process
   behaviour. The improved GP code incorporates a novel fitness calculation, the optimal creation of new generations, and parameter allocation. The advantages of these
   modifications are tested against the more commonly used approaches.
%8 15  November

%A Benyamin Grosman
%A Sivan Lachman-Shalem
%A Raaya Swissa
%A D. R. Lewin
%T Yield enhancement in photolithography through model-based process control: average mode control
%J IEEE Transactions on Semiconductor Manufacturing
%V 18
%N 1
%D 2005
%P 86--93
%I 
%K genetic algorithms, genetic programming, integrated circuit manufacture, multivariable control systems, nonlinear control systems, photolithography, predictive control,
   process control, scanning electron microscopy, semiconductor process modelling KLA-Tencor-FINLE PROLITH package, average mode control, fabrication facility implementation,
   genetic programming, model based process control, multivariable feedback regulatory strategy, multivariable nonlinear model predictive controller, nonlinear empirical
   models, optimal parameters, optimal structure, scanning electron microscopy, setpoint values, simulated photolithography, stepper inputs, yield enhancement
%X This work describes the fabrication facility (FAB) implementation of a multivariable nonlinear model predictive controller (NMPC) for the regulation of critical dimensions
   (CD) in photolithography. The controller is based on nonlinear empirical models relating the stepper inputs, exposure dose and focus on the isolated and dense CDs measured
   by scanning electron microscopy. Since the adjustments are made on the basis of the average value of five measured points in each wafer, this is referred to as average mode
   control. The optimal structure and parameters of these empirical models were determined by genetic programming, to closely match FAB data. The tuning and testing of the
   NMPC regulator were facilitated by the use of a simulated photolithography track, using the KLA-Tencor-FINLE PROLITH package, suitably calibrated to match FAB conditions.
   On implementation in the FAB, the NMPC has been demonstrated to consistently maintain the CDs close to their setpoint values, despite unmeasured disturbances such as shifts
   in uncontrolled inputs. It was also shown that adopting the multivariable feedback regulatory strategy to regulate the CDs results in significant improvements in the die
   yield.
%8 February

%A B. Grosman
%A D. R. Lewin
%T Lyapunov-based Stability Analysis Automated by Genetic Programming
%B IEEE International Symposium on Computer-Aided Control Systems Design, 2006
%D 2006
%P 766--771
%I IEEE
%C Munich, Germany
%K genetic algorithms, genetic programming
%X This contribution describes an automatic technique for detecting maximal domains of attraction for nonlinear systems using genetic programming (GP). The theoretical basis
   for the work is Lyapunov's direct method, which provides sufficient conditions for the existence of a region of attraction of a stable focus. In work presented here, our GP
   approach for defining Lyapunov functions that accurately predict the maximum region of attraction has been extended by defining a target function accounting for level sets.
   We demonstrate the approach on the analysis of two dynamic systems: (a) van der Pol's equation, which features both a stable and unstable limit cycle; and (b) a model of an
   exothermic, continuous stirred tank reactor (CSTR), whose stable trajectories tend to move away from the origin before converging
%8 4-6  October
%Z Dept. of Chem. Eng., Technion-Israel Inst. of Technol., Haifa
%@ 0-7803-9797-5

%A Benyamin Grosman
%T Stability Analysis of Nonlinear Control Systems Using Genetic Programming
%R Ph.D. Thesis
%D 2008
%I 
%I Department of Chemical Engineering, Technion
%K genetic algorithms, genetic programming
%U http://www.graduate.technion.ac.il/Theses/Abstracts.asp?Id=24203
%X This thesis describes the use of genetic programming in stability analysis and control synthesis for nonlinear autonomous dynamic systems. The main ideas are associated
   with the Lyapunov direct method and optimal control synthesis driven by the solution of the Hamilton-Jacobi-Bellman (HJB) equation. A novel genetic programming code was
   written for the purpose of disclosing non-trivial Lyapunov functions. These functions were used initially for stability analysis, and subsequently for the synthesis of
   nonlinear optimal controllers. The work required the transformation of abstract mathematical concepts into a computer language format. This included satisfying the general
   Lyapunov conditions for stability, the identification of connected sets, the detection of their boundaries and other related topics. In addition it was necessary to address
   optimal control issues, through the near-solution of the Hamilton-Jacobi-Bellman (HJB) equation. The GP has the capacity to discover non-trivial Lyapunov functions that
   achieve good approximations to the domains of attraction for a variety of nonlinear dynamic systems. Moreover, the task of finding an approximation to the solution of the
   HJB equation around a working point was demonstrated on a number of autonomous control systems. In cases where the results included non-polynomial terms that are difficult
   to solve analytically, this obstacle was overcome by using high-order Taylor series expansions. These expansions were shown to be proper Lyapunov functions, which were
   analysed using a positivity test for multivariable polynomials. Numerous case-studies were examined, including a comparison of the method with the well-known work of
   Vennelli and Vidyasagar on detecting domains of attraction. Moreover, the control synthesis was compared with well-established control techniques such as feedback
   linearisation as well as other related works on optimal control. The methodology demonstrated in this work represents a viable attractive alternative analysis method for
   the investigation of nonlinear dynamic systems, both in open and closed loop, which can be harnessed in numerous fields of research where a guideline for disclosing unknown
   Lyapunov functions is lacking.
%Z Supervisor: Prof. Lewin Daniel

%A Benyamin Grosman
%A Daniel R. Lewin
%T Lyapunov-based stability analysis automated by genetic programming
%J Automatica
%V 45
%N 1
%D 2009
%P 252--256
%I 
%K genetic algorithms, genetic programming, Lyapunov stability
%U http://www.sciencedirect.com/science/article/B6V21-4V402MR-3/2/500948c7466e5824a72a3930c046e8aa
%X This contribution describes an automatic technique to detect suitable Lyapunov functions for nonlinear systems. The theoretical basis for the work is Lyapunov's Direct
   Method, which provides sufficient conditions for stability of equilibrium points. In our proposed approach, genetic programming (GP) is used to search for suitable Lyapunov
   functions, that is, those that best predict the true domain of attraction. In the work presented here, our GP approach has been extended by defining a target function
   accounting for the Lyapunov function level sets.

%A R. Gross
%A K. Albrecht
%A W. Kantschik
%A W. Banzhaf
%T Evolving Chess Playing Programs
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 740--747
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, chess, distributed computing, evolution strategies
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A F. Gruau
%T Cellular encoding of Genetic Neural Networks
%R Technical report 92-21
%D 1992
%I 
%I Laboratoire de l'Informatique du Parallilisme. Ecole Normale Supirieure de Lyon
%C France
%K genetic algorithms, genetic programming

%A Frederic Gruau
%T Genetic Synthesis of Boolean Neural Networks with a Cell Rewriting Developmental Process
%B Proceedings of the Workshop on Combinations of Genetic Algorithms and Neural Networks (COGANN92)
%E J. D. Schaffer and D. Whitley
%D 1992
%P 55--74
%I The IEEE Computer Society Press
%K genetic algorithms, connectionism, neural networks, 40 inputs symmetry function, 50 inputs parity function, Boolean functions, Boolean neural networks, cell rewriting
   developmental process, cell rewriting grammar, genetic synthesis, scalability property, Boolean functions, encoding, grammars, neural nets, rewriting systems
%X Genetic algorithms (GAS) are used to generate neural networks that implement Boolean functions. Neural networks both involve an architecture that is a graph of connections,
   and a set of weights. The algorithm that is put forward yields both the architecture and the weights by using chromosomes that encode an algorithmic description based upon
   a cell rewriting grammar. The developmental process interprets the grammar for l cycles and develops a neural net parametrised by l. The encoding along with the
   developmental process have been designed in order to improve the existing approaches. They implement the following key-properties. The representation on the chromosome is
   abstract and compact. Any chromosome develops a valid phenotype. The developmental process gives modular and interpretable architectures with a powerful scalability
   property. The GA finds a neural net for the 50 inputs parity function, and for the 40 inputs symmetry function

%A Frederic Gruau
%T Cellular encoding as a graph grammar
%J IEE Colloquium on Grammatical Inference: Theory, Applications and Alternatives
%E Simon Lucas
%V (Digest No.092)
%D 1993
%P 17/1--10
%I IEE
%C London
%K genetic algorithm connectionism neural networks cogann
%X ABSTRACT Cellular encoding is a method for encoding a family of neural networks into a set of labeled trees. Such sets of trees can be evolved by the genetic algorithm so
   as to find a particular set of trees that encodes a family of Boolean neural networks for computing a family of Boolean functions. Cellular encoding is presented as a graph
   grammar. A method is proposed for translating a cellular encoding into a set of graph grammar rewriting rules of the kind used in the Berlin algebraic approach to graph
   rewriting. The genetic search of neural networks via cellular encoding appears as a grammatical inference process where the language to parse is implicitly specified,
   instead of explicitly by positive and negative examples. Experimental results shows that the genetic algorithm can infer grammars that derive neural networks for the
   parity, symmetry and decoder Boolean function of arbitrary large size.
%8 22-23  April

%A Frederic Gruau
%T Genetic Synthesis of Modular Neural Networks
%B Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93
%E Stephanie Forrest
%D 1993
%P 318--325
%I Morgan Kaufmann
%C University of Illinois at Urbana-Champaign
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga93_gruau.pdf
%8 17-21  July

%A F. Gruau
%T Neural Network Synthesis using Cellular Encoding and the Genetic Algorithm.
%R Ph.D. Thesis
%D 1994
%I 
%I Laboratoire de l'Informatique du Parallilisme, Ecole Normale Supirieure de Lyon
%C France
%K genetic algorithms, genetic programming
%U ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/PhD/PhD1994/PhD1994-01-F.ps.Z
%X Artificial neural networks used to be considered only as a machine that learns using small modifications of internal parameters. Now this is changing. Such learning method
   do not allow to generate big neural networks for solving real world problems. This thesis defends the following three points: (1) The key word to go out of that dead-end is
   "modularity". (2) The tool that can generate modular neural networks is cellular encoding. (3) The optimization algorithm adapted to the search of cellular codes is the
   genetic algorithm. The first point is now a common idea. A modular neural network means a neural network that is made of several sub-networks, arranged in a hierarchical
   way. For example, the same sub-network can be repeated. This thesis encompasses two parts. The first part demonstrates the second point. Cellular encoding is presented as a
   machine language for neural networks, with a theoretical basis (it is a parallel graph grammar that checks a number of properties) and a compiler of high level language.
   The second part of the thesis shows the third point. Application of genetic algorithm to the synthesis of neural networks using cellular encoding is a new technology. This
   technology can solve problems that were still unsolved with neural networks. It can automatically and dynamically decompose a problem into a hierarchy of sub-problems, and
   generate a neural network solution to the problem. The structure of this network is a hierarchy of sub-networks that reflects the structure of the problem. The technology
   allows to experience new scientific domains like the interaction between learning and evolution, or the set up of learning algorithms that suit the GA.

%A Frederic Gruau
%T Genetic micro programming of Neural Networks
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 495--518
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%O 24

%A F. Gruau
%A D. Whitley
%T The cellular development of neural networks: The interaction of learning and evolution
%R Technical report 93-04
%D 1993
%I 
%I Laboratoire de l'Informatique du Parallilisme, Ecole Normale Supirieure de Lyon
%C France
%K genetic algorithms, genetic programming

%A F. Gruau
%A D. Whitley
%T Adding learning to the cellular development process: a comparative study
%J Evolutionary Computation
%V 1
%N 3
%D 1993
%P 213--233
%I 
%K genetic algorithms, genetic programming
%X A grammar tree is used to encode a cellular developmental process that can generate whole families of Boolean neural networks for computing parity and symmetry. The
   development process resembles biological cell division. A genetic algorithm is used to find a grammar tree that yields both architecture and weights specifying a particular
   neural network for solving specific Boolean functions. The current study particularly focuses on the addition of learning to the development process and the evolution of
   grammar trees. Three ways of adding learning to the development process are explored. Two of these exploit the Baldwin effect by changing the fitness landscape without
   using Lamarckian evolution. The third strategy is Lamarckian in nature. Results for these three modes of combining learning with genetic search are compared against genetic
   search without learning. Our results suggest that merely using learning to change the fitness landscape can be as effective as Lamarckian strategies at improving search.

%A Frederic Gruau
%A Darrell Whitley
%T A Programming Language for Artificial Development
%B Evolutionary Programming IV Proceedings of the Fourth Annual Conference on Evolutionary Programming
%E John Robert McDonnell and Robert G. Reynolds and David B. Fogel
%D 1995
%P 415--434
%I MIT Press
%C San Diego, CA, USA
%K genetic algorithms, Neural Networks, parellel architectures
%8 1-3  March
%Z EP-95, Extension of cellular encoding. Says can build neural network that can emulate any functional language (eg SISAL).
%@ 0-262-13317-2

%A Frederic Gruau
%T Automatic Definition of Modular Neural Networks
%J Adaptive Behaviour
%V 3
%N 2
%D 1995
%P 151--183
%I 
%K genetic algorithms, genetic programming, animats, cellular encoding, modularity, locomotion, automatic definition of neural subnetworks
%U http://citeseer.ist.psu.edu/gruau95automatic.html
%X This article illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex artificial neural networks
   (ANNs). The artificial developmental system can develop a graph grammar into a modular ANN made of a combination of simpler subnetworks. A genetic algorithm is used to
   evolve coded grammars that generate ANNs for controlling six-legged robot locomotion. A mechanism for the automatic definition of neural subnetworks is incorporated. Using
   this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this
   subANN to build a higher-level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic
   definition of subnetworks is suppressed. We support our argument with pictures that describe the steps of development, how ANN structures are evolved, and how the ANNs
   compute.
%Z ANN for controlling six legged robot locomotion, http://www.isab.org/journal/adap3_2.php

%A Frederic Gruau
%T On Using Syntactic Constraints with Genetic Programming
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 377--394
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/394
%O 19
%@ 0-262-01158-1

%A Frederic Gruau
%A Darrell Whitley
%A Larry Pyeatt
%T A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 81--89
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X This paper compares the efficiency of two encoding schemes for Artificial Neural Networks optimised by evolutionary algorithms. Direct Encoding encodes the weights for an a
   priori fixed neural network architecture. Cellular Encoding encodes both weights and the architecture of the neural network. In previous studies, Direct Encoding and
   Cellular Encoding have been used to create neural networks for balancing 1 and 2 poles attached to a cart on a fixed track. The poles are balanced...
%8 28--31  July
%Z See also \citeHeidrichMeisner2009152. GP-96

%A Frederic Gruau
%A Kameel Quatramaran
%T Cellular Encoding for Interactive Evolutionary Robotics
%R Cognitive Science Research Paper 425
%D 1996
%I 
%I School of Cognitive and Computing Sciences, University of Sussex
%C Falmer, Brighton, Sussex, UK
%K genetic algorithms, genetic programming
%U http://www.cogs.susx.ac.uk/cgi-bin/htmlcogsreps?csrp425
%X This work reports experiments in interactive evolutionary robotics. The goal is to evolve an Artificial Neural Network (ANN) to control the locomotion of an 8-legged robot.
   The ANNs are encoded using a cellular developmental process called cellular encoding. In a previous work similar experiments have been carried on successfully on a
   simulated robot. They took however around 1 million different ANN evaluations. In this work the fitness is determined on a real robot, and no more than a few hundreds
   evaluations can be performed. Various ideas were implemented so as to decrease the required number of evaluations from 1 million to 200. First we used cell cloning and link
   typing. Second we did as many things as possible interactively: interactive problem decomposition, interactive syntactic constraints, interactive fitness. More precisely:
   1- A modular design was chosen where a controller for an individual leg, with a precise neuronal interface was developed. 2- Syntactic constraints were used to promote
   useful building blocs and impose an 8-fold symmetry. 3- We determine the fitness interactively by hand. We can reward features that would otherwise be very difficult to
   locate automatically. Interactive evolutionary robotics turns out to be quite successful, in the first bug-free run a global locomotion controller that is faster than a
   programmed controller could be evolved.
%8 July

%A Frederic Gruau
%T Modular Genetic Neural Networks for Six-Legged Locomotion
%B Artificial Evolution
%S LNCS
%E Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald and Marc Schoenauer and Dominique Snyers
%V 1063
%D 1996
%P 201--219
%I Springer Verlag
%K genetic algorithms, genetic programming
%X This paper illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Networks
   (ANN). Artificial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnetworks. Genetic programming is used to
   evolve coded grammars that generates ANNs for controlling a six-legged robot locomotion. A mechanism for the automatic definition of sub-neural networks is incorporated.
%Z Selected papers from two conferences: Evolution Artificielle 94 and Evolution Artificielle 95 http://www.cmap.polytechnique.fr/www.eark/ea95.html

%A Frederic Gruau
%A Yves Lhuillier
%A Philippe Reitz
%A Olivier Temam
%T Blob Computing
%B Computing Frontiers
%D 2004
%P 125--139
%I SIGMicro
%I ACM
%K genetic algorithms, genetic programming
%U http://blob.lri.fr/publication/2004-model-blob-machine.pdf
%X Current processor and multiprocessor architectures are almost all based on the Von Neumann paradigm. Based on this paradigm, one can build a general-purpose computer using
   very few transistors, e.g., 2250 transistors in the first Intel 4004 microprocessor. In other terms, the notion that on-chip space is a scarce resource is at the root of
   this paradigm which trades on-chip space for program execution time. Today, technology considerably relaxed this space constraint. Still, few research works question this
   paradigm as the most adequate basis for high-performance computers, even though the paradigm was not initially designed to scale with technology and space. In this article,
   we propose a different computing model, defining both an architecture and a language, that is intrinsically designed to exploit space; we then investigate the
   implementation issues of a computer based on this model, and we provide simulation results for small programs and a simplified architecture as a first proof of concept.
   Through this model, we also want to outline that revisiting some of the principles of today's computing paradigm has the potential of overcoming major limitations of
   current architectures.
%Z http://www.computingfrontiers.org/ http://blob.lri.fr/publication/listeARCHITECTURE.htm Conf. avec Actes - Numero de document : 11378 Blob is a dynamic collection of
   particles. Particles run on regular lattice of computer processing elements (PE). Cellular encoding used for dynamic positioning

%A Soren Grubov
%A Rasmus Hartvig
%T AI in Computer games
%R M.S. Thesis
%D 2005
%I 
%I Informatics and Mathematical Modelling, Technical University of Denmark, DTU
%C Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
%K genetic algorithms, genetic programming
%U http://www2.imm.dtu.dk/pubdb/p.php?3650
%X The aim of the project is to explore and demonstrate the potential of common AI techniques in computer games. We will be concentrating on some or all of the following: *
   Logic-based planning * Neural networks * Genetic programming * Machine learning We will be using game engines from IO Interactive as a framework for implementation, in
   order to demonstrate these techniques. The primary objective is to achieve a higher level of artificial intelligence in computer games by the usage of logic-based planning.
   This requires development of a multi agent system, for simulating human-like behaviour, within a computer game. The additionally mentioned techniques are regarded as
   secondary techniques, which are to be used in conjunction with planning, in order to facilitate more specific behavior like learning or adaptation. Combining one or more of
   the secondary techniques with the primary technique is a secondary objective. The extension of usage of secondary techniques will be decided at a later stage. Loosely
   formulated, the project objective is to bridge the gap between the AI planning field and the commercial computer game industry. Alternatively, to assess the distance
   between the AI field, and the emerging design patterns used in the gaming field. The project can be seen as an advanced application of multi agent theory, building on
   previous experiences from multi agent system projects.
%O Supervisor: Thomas Bolander \& Hans Bruun

%A S. Grunwald
%T Multi-criteria characterization of recent digital soil mapping and modeling approaches
%J Geoderma
%V 152
%N 3-4
%D 2009
%P 195--207
%I 
%K genetic algorithms, genetic programming, Digital soil mapping, Digital soil modelling, Pedometrics, Quantitative methods, Soils
%U http://www.sciencedirect.com/science/article/B6V67-4WSG2WJ-1/2/af92060815439203d2999e4ace2ae786
%X The history of digital soil mapping and modelling (DSMM) is marked by adoption of new mapping tools and techniques, data management systems, innovative delivery of soil
   data, and methods to analyse, integrate, and visualise soil and environmental datasets. DSMM studies are diverse with specialised, mathematical prototype models tested on
   limited geographic regions and/or datasets and simpler, operational DSMM used for routine mapping over large soil regions. Research-focused DSMM contrasts with need-driven
   DSMM and agency-operated soil surveys. Since there is no universal equation or digital soil prediction model that fits all regions and purposes the proposed strategy is to
   characterise recent DSMM approaches to provide recommendations for future needs at local, national and global scales. Such needs are not solely soil-centered, but consider
   broader issues such as land and water quality, carbon cycling and global climate change, sustainable land management, and more. A literature review was conducted to review
   90 DSMM publications from two high-impact international soil science journals -- Geoderma and Soil Science Society of America Journal. A selective approach was used to
   identify published studies that cover the multi-factorial DSMM space. The following criteria were used (i) soil properties, (ii) sampling setup, (iii) soil geographic
   region, (iv) spatial scale, (v) distribution of soil observations, (vi) incorporation of legacy/historic data, (vii) methods/model type, (viii) environmental covariates,
   (ix) quantitative and pedological knowledge, and (x) assessment method. Strengths and weaknesses of current DSMM, their potential to be operationalized in soil
   mapping/modelling programs, research gaps, and future trends are discussed. Modeling of soils in 3D space and through time will require synergistic strategies to converge
   environmental landscape data and denser soil data sets. There are needs for more sophisticated technologies to measure soil properties and processes at fine resolution and
   with accuracy. Although there are numerous quantitative models rooted in factorial models that predict soil properties with accuracy in select geographic regions they lack
   consistency in terms of environmental input data, soil properties, quantitative methods, and evaluation strategies. DSMM requires merging of quantitative, geographic and
   pedological expertise and all should be ideally in balance.
%Z survey

%A Cesar Guerra-Salcedo
%A Darrell Whitley
%T Genetic Search for Feature Subset Selection: A Comparison Between CHC and GENESIS
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 504--509
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Cesar Guerra-Salcedo
%A Darrell Whitley
%T Genetic Approach to Feature Selection for Ensemble Creation
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 236--243
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, data mining
%U http://www.cs.colostate.edu/~genitor/1999/gecco99c.pdf
%X boosting and bagging
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) CHC cataclysmic
   mutation, uniform crossover. EDT, k-means (KMA) Statlog and UCI, LandSat DNA Segment. Big study, difficult to follow. Lots of references.
%@ 1-55860-611-4

%A Alexis Guigue
%A Sofiane Oussedik
%A Daniel Delahaye
%T Sequencing Aircraft Landings by Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 788
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-880.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Sumit Gulwani
%T Dimensions in program synthesis
%B Proceedings of the 12th international ACM SIGPLAN symposium on Principles and practice of declarative programming
%D 2010
%P 13--24
%I ACM
%C Hagenberg, Austria
%K genetic algorithms, genetic programming, Deductive Synthesis, Inductive Synthesis, Programming by Examples, Programming by Demonstration, SAT Solving, SMT Solving, Machine
   Learning, Probabilistic Inference, Belief Propagation
%U http://research.microsoft.com/en-us/um/people/sumitg/pubs/ppdp10-synthesis.pdf
%X Program Synthesis, which is the task of discovering programs that realise user intent, can be useful in several scenarios: enabling people with no programming background to
   develop utility programs, helping regular programmers automatically discover tricky/mundane details, program understanding, discovery of new algorithms, and even teaching.
   This paper describes three key dimensions in program synthesis: expression of user intent, space of programs over which to search, and the search technique. These concepts
   are illustrated by brief description of various program synthesis projects that target synthesis of a wide variety of programs such as standard undergraduate textbook
   algorithms (e.g., sorting, dynamic programming), program inverses (e.g., decoders, deserializers), bitvector manipulation routines, deobfuscated programs, graph algorithms,
   text-manipulating routines, mutual exclusion algorithms, etc.
%O Invited talk
%8 October
%Z Formal Methods in Computer-Aided Design (FMCAD 2010) see also tutorial slides http://research.microsoft.com/en-us/um/people/sumitg/pubs/synthesis.html Programming
   assistance. bitvectors: Warren, Hacker's Delight, Addison Wesley, 2002. 'we can restrict sampling to the basis inputs' [Knuth]. Also known as \cite5770924

%A Hong Guo
%A Lindsay B. Jack
%A Asoke K. Nandi
%T Feature generation using genetic programming with application to fault classification
%J IEEE Transactions on Systems, Man, and Cybernetics, Part B
%V 35
%N 1
%D 2005
%P 89--99
%I 
%K genetic algorithms, genetic programming
%X One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data
   in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform
   the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and
   thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. A
   GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then
   used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover automatically the
   different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results-using GP extracted features with artificial
   neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM-have been obtained. This GP-based approach is used for bearing
   fault classification for the first time and exhibits superior searching power over other techniques. Additionally, it significantly reduces the time for computation
   compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.
%8 February

%A Hong Guo
%A Asoke K. Nandi
%T Breast cancer diagnosis using genetic programming generated feature
%J Pattern Recognition
%V 39
%N 5
%D 2006
%P 980--987
%I 
%K genetic algorithms, genetic programming, Feature extraction, Fisher discriminant analysis, Pattern recognition
%X This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP). We developed a new feature extraction measure
   (modified Fisher linear discriminant analysis (MFLDA)) to overcome the limitation of Fisher criterion. GP as an evolutionary mechanism provides a training structure to
   generate features. A modified Fisher criterion is developed to help GP optimise features that allow pattern vectors belonging to different categories to distribute
   compactly and disjoint regions. First, the MFLDA is experimentally compared with some classical feature extraction methods (principal component analysis, Fisher linear
   discriminant analysis, alternative Fisher linear discriminant analysis). Second, the feature generated by GP based on the modified Fisher criterion is compared with the
   features generated by GP using Fisher criterion and an alternative Fisher criterion in terms of the classification performance. The classification is carried out by a
   simple classifier (minimum distance classifier). Finally, the same feature generated by GP is compared with a original feature set as the inputs to multi-layer perceptrons
   and support vector machine. Results demonstrate the capability of this method to transform information from high-dimensional feature space into one-dimensional space and
   automatically discover the relationship among data, to improve classification accuracy.
%8 May

%A Hong Guo
%A Qing Zhang
%A Asoke K. Nandi
%T Breast Cancer Detection using Genetic Programming
%B Proceedings of the First International Conference on Biomedical Electronics and Devices, BIOSIGNALS 2008
%E Pedro Encarna\c c\~ao and Ant\'onio Veloso
%V 2
%D 2008
%P 334--341
%I INSTICC - Institute for Systems and Technologies of Information, Control and Communication
%C Funchal, Madeira, Portugal
%K genetic algorithms, genetic programming
%X Breast cancer diagnosis have been investigated by different machine learning methods. This paper proposes a new method for breast cancer diagnosis using a single feature
   generated by Genetic Programming(GP). GP as an evolutionary mechanism that provides a training structure to generate features. The presented approach is experimentally
   compared with some kernel feature extraction methods: The Kernel Principal Component Analysis (KPCA) and Kernel Generalised Discriminant Analysis (KGDA). Results
   demonstrate the capability of this method to transform information from high dimensional feature space into one dimensional space for breast cancer diagnosis.
%8 January  28-31
%Z http://www.biosignals.biostec.org/Abstracts/2008/BIOSIGNALS_2008_Abstracts.htm

%A Ling Guo
%A Daniel Rivero
%A Julian Dorado
%A Cristian R. Munteanu
%A Alejandro Pazos
%T Automatic feature extraction using genetic programming: An application to epileptic EEG classification
%J Expert Systems with Applications
%V 38
%N 8
%D 2011
%P 10425--10436
%I 
%K genetic algorithms, genetic programming, Feature extraction, K-nearest neighbour classifier (KNN), Discrete wavelet transform (DWT), Epilepsy, EEG classification
%U http://www.sciencedirect.com/science/article/B6V03-5265S7J-6/2/7bccfdf0fc39adebbc6851a1c6c408a3
%X This paper applies genetic programming (GP) to perform automatic feature extraction from original feature database with the aim of improving the discriminatory performance
   of a classifier and reducing the input feature dimensionality at the same time. The tree structure of GP naturally represents the features, and a new function generated in
   this work automatically decides the number of the features extracted. In experiments on two common epileptic EEG detection problems, the classification accuracy on the
   GP-based features is significant higher than on the original features. Simultaneously, the dimension of the input features for the classifier is much smaller than that of
   the original features.

%A Pei Fang Guo
%A Prabir Bhattacharya
%T An evolutionary approach to feature function generation in application to biomedical image patterns
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1883--1884
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Poster
%X A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions, based on the
   primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Experiments show that the propose algorithm achieves an average
   performance of 90.20percent recognition rate on diagnosis, while reducing the number of feature dimensions from 11 primitive features to the space of a single generated
   feature.
%8 8-12  July
%Z Oculopharyngeal Muscular Dystrophy, CellDB grayscale images, histogram region of interest by thresholds (HROIT). GECCO-2009 A joint meeting of the eighteenth international
   conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.

%A Pei-Fang Guo
%A Prabir Bhattacharya
%A Nawwaf Kharma
%T An efficient image pattern recognition system using an evolutionary search strategy
%B IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
%D 2009
%P 599--604
%I IEEE
%C San Antonio, Texas, USA
%K genetic algorithms, genetic programming, EM, GP, Gaussian mixture estimation, HROIT, OPMD disease diagnosis, efficiency 90.20 percent, evolutionary genetic programming,
   evolutionary search strategy, expectation maximization algorithm, feature function generation, histogram region, image pattern recognition system, image thresholding,
   oculopharyngeal muscular dystrophy, primitive texture feature extraction, support vector machine, Gaussian processes, diseases, expectation-maximisation algorithm, eye,
   feature extraction, image recognition, image segmentation, image texture, medical image processing, muscle, search problems
%X A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions automatically, based
   on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Prior to the feature function generation, we introduce a novel
   technique of the primitive texture feature extraction, which deals with non-uniform images, from the histogram region of interest by thresholds (HROIT). Compared with the
   performance achieved by support vector machine (SVM) using the whole primitive texture features, the GP-EM methodology, as a whole, achieves a better performance of
   90.20percent recognition rate on diagnosis, while projecting the hyperspace of the primitive features onto the space of a single generated feature.
%8 October
%Z Also known as \cite5346614

%A Pei-Fang Guo
%A Prabir Bhattacharya
%A Nawwaf Kharma
%T Automated synthesis of feature functions for pattern detection
%B 23rd Canadian Conference on Electrical and Computer Engineering (CCECE), 2010
%D 2010
%I 
%K genetic algorithms, genetic programming, Gaussian mixture model, automated synthesis, breast cancer detection, data modelling, expectation maximization algorithm, feature
   extraction, feature functions, inductive machine learning, logistic regression, multilayer perceptrons, pattern detection systems, primitive feature vector nonlinear
   transformations, support vector machine, cancer, data models, expectation-maximisation algorithm, feature extraction, medical computing, object detection, pattern
   classification, vectors
%X In pattern detection systems, the general techniques of feature extraction and selection perform linear transformations from primitive feature vectors to new vectors of
   lower dimensionality. At times, new extracted features might be linear combinations of some primitive features that are not able to provide better classification accuracy.
   To solve this problem, we propose the integration of genetic programming and the expectation maximisation algorithm (GP-EM) to automatically synthesise feature functions
   based on primitive input features for breast cancer detection. With the Gaussian mixture model, the proposed algorithm is able to perform nonlinear transformations of
   primitive feature vectors and data modelling simultaneously. Compared to the performance of other algorithms, such us the support vector machine, multi-layer perceptrons,
   inductive machine learning and logistic regression, which all used the entire primitive feature set, the proposed algorithm achieves a higher recognition rate by using one
   single synthesised feature function.
%8 2-5  May
%Z Pei-Fang Guo PhD A Gaussian Mixture-Based Approach to Synthesizing Nonlinear Feature Functions for Automated Object Detection (not GP?) Concordia University 2010
   http://users.encs.concordia.ca/~kharma/ResearchWeb/html/people/graduate%20students.html#pf_guo Also known as \cite5575224

%A Binod Gupta
%T Context-Free Grammar Generation Using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 180--187
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Nirmal Kumar Gupta
%A Mukesh Kumar Rohil
%T Using Genetic Algorithm for Unit Testing of Object Oriented Software
%B Proceedings of the 1st International Conference on Emerging Trends in Engineering and Technology (ICETET '08)
%D 2008
%P 308--313
%I IEEE
%K genetic algorithms, genetic programming, object-oriented methods, program testing, object oriented software unit testing, test case generation
%X Genetic algorithms have been successfully applied in the area of software testing. The demand for automation of test case generation in object oriented software testing is
   increasing. Genetic algorithms are well applied in procedural software testing but a little has been done in testing of object oriented software. In this paper, we propose
   a method to generate test cases for classes in object oriented software using a genetic programming approach. This method uses tree representation of statements in test
   cases. Strategies for encoding the test cases and using the objective function to evolve them as suitable test case are proposed.
%8 July
%Z Also known as \cite4579916 Java, HTMLparser

%A Darryl Gurganious
%T Adaptive Beamformer Weight Estimation Using Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 49--57
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Leo Gusel
%A Miran Brezocnik
%T Genetic modeling of electrical conductivity of formed material
%J Materials and technology
%V 39
%N 4
%D 2005
%P 107--111
%I 
%K genetic algorithms, genetic programming, copper alloys, electrical conductivity, cold forming, modelling, genetsko programiranje, modeliranje, hladno preoblikovanje,
   elektricna prevodnost, bakrove zlitine
%U http://ctklj.ctk.uni-lj.si/kovine/izvodi/mit054/gusel.pdf
%X V prispevku smo predstavili metodo genetskega programiranja za uspesno dolocitev natancnih modelov spremembe elektricne prevodnosti hladno preoblikovane zlitine CuCrZr.
   Glavna znacilnost metode genetskega programiranja, ki spada med evolucijske metode modeliranja, je, da resitev ne iscemo po vnaprej dolocenih poteh ter da socasno
   obravnavamo mnozico enostavnih objektov. Cedalje natancnejsim resitvam smo se priblizevali postopoma, med postopkom simulirane evolucije. V prispevku smo predstavili le
   nekatere najuspesnejse oziroma najprimernejse genetske modele. Natancnost genetskih modelov je bila preverjena na mnozici preskusnih tock. Primerjali smo tudi natancnost
   genetsko dobljenih modelov in modela, dobljenega po deterministicni metodi regresije. Primerjava je pokazala, da se genetski modeli dosti manj odmikajo od eksperimentalnih
   rezultatov in da so bolj raznoliki. Prav raznolikost nam omogoca, da se, glede na zahteve, odlocimo za optimalen model, s katerim lahko matematicno opisemo ali napovedujemo
   spremembo elektricne prevodnosti zlitine v okviru eksperimentalnega okolja.

%A Leo Gusel
%A Miran Brezocnik
%T Modeling of impact toughness of cold formed material by genetic programming
%J Computational Materials Science
%V 37
%N 4
%D 2006
%P 476--482
%I 
%K genetic algorithms, genetic programming, evolutionary computing, metal forming, modelling, impact toughness, copper alloy
%X In the paper, an approach completely different from the conventional methods for determination of accurate models for the change of properties of cold formed material, is
   presented. This approach is genetic programming (GP) method which is based on imitation of natural evolution of living organisms. The main characteristic of GP is its
   non-deterministic way of computing. No assumptions about the form and size of expressions were made in advance, but they were left to the self organisation and intelligence
   of evolutionary process. First, copper alloy rods were cold drawn under different conditions and then impact toughness of cold drawn specimens was determined by Charpy
   tests. The values of independent variables (effective strain, coefficient of friction) influence the value of the dependent variable, impact toughness. On the basis of
   training data, different prediction models for impact toughness were developed by GP. Only the best models, gained by genetic programming were presented in the paper.
   Accuracy of the best models was proved with the testing data set. The comparison between deviation of genetic model results and regression model results concerning the
   experimental results has showed that genetic models are more precise and more varied then regression models.
%8 October

%A Steven M. Gustafson
%A William H. Hsu
%T Genetic programming for strategy learning in soccer playing agents: A KDD-based architecture
%B Graduate Student Workshop
%E Conor Ryan and Una-May O'Reilly and William B. Langdon
%D 2000
%P 277--280
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2000.pdf
%8 8  July
%Z GECCO-2000WKS Part of \citewu:2000:GECCOWKS

%A Steven M. Gustafson
%A William H. Hsu
%T Layered Learning in Genetic Programming for a Co-operative Robot Soccer Problem
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 291--301
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Layered Learning, Hierarchical abstractions, Robot soccer, Robots, Multiagent systems
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=291
%X We present an alternative to standard genetic programming (GP) that applies layered learning techniques to decompose a problem. GP is applied to subproblems sequentially,
   where the population in the last generation of a subproblem is used as the initial population of the next subproblem. This method is applied to evolve agents to play
   keep-away soccer, a subproblem of robotic soccer that requires cooperation among multiple agents in a dynnamic environment. The layered learning paradigm allows GP to
   evolve better solutions faster than standard GP. Results show that the layered learning GP outperforms standard GP by evolving a lower fitness faster and an overall better
   fitness. Results indicate a wide area of future research with layered learning in GP.
%8 18-20  April
%Z EuroGP'2001, part of miller:2001:gp. See also \citegustafson:mastersthesis
%@ 3-540-41899-7

%A Steven M. Gustafson
%T Layered learning in genetic programming for a co-operative robot soccer problem
%R M.S. Thesis
%D 2000
%I 
%I Kansas State University
%C Manhattan, KS, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/450396.html
%X We present an alternative to standard genetic programming (GP) that applies layered learning techniques to decompose a problem. GP is applied to subproblems sequentially,
   where the population in the last generation of a subproblem is used as the initial population of the next subproblem. This method is applied to evolve agents to play
   keep-away soccer, a subproblem of robotic soccer that requires cooperation among multiple agents in a dynamic environment. The layered learning paradigm allows GP to evolve
   better solutions faster than standard GP. Results show that the layered learning GP outperforms standard GP by evolving a lower tness faster and an overall better tness.
   Results indicate a wide area of future research with layered learning in GP.
%8 Decemeber
%Z Related Publications from Masters Thesis: William H. Hsu and Steven M. Gustafson. Wrappers for automatic parameter tuning in multi-agent optimization by genetic
   programming. In IJCAI-2001 Workshop on Wrappers for Performance Enhancement in Knowledge Discovery in Databases (KDD), Seattle, Washington, USA, 4 August 2001.
   \citehsu:2001:waptmaoGP W. H. Hsu and S. M. Gustafson. Genetic Programming for Layered Learning of Multi-agent Tasks. In Late-Breaking Papers of the Genetic and
   Evolutionary Computation Conference (GECCO-2001), San Francisco, CA, June, 2001. \citehsu:2001:gpllmt S. M. Gustafson and W. H. Hsu. Layered learning in genetic programming
   for a co-operative robot soccer problem. In J. F. Miller et al, editors, Proceedings of EuroGP'2001, v. 2038 of LNCS,p ages 291--301, Lake Como, Italy, 18-20 April 2001.
   Springer-Verlag. \citegustafson:2001:EuroGP

%A Edmund Burke
%A Steven Gustafson
%A Graham Kendall
%T A Puzzle to Challenge Genetic Programming
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 238--247
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780238.pdf
%X This report represents an initial investigation into the use of genetic programming to solve the N-prisoners puzzle. The puzzle has generated a certain level of interest
   among the mathematical community. We believe that this puzzle presents a significant challenge to the field of evolutionary computation and to genetic programming in
   particular. The overall aim is to generate a solution that encodes complex decision making. Our initial results demonstrate that genetic programming can evolve good
   solutions. We compare these results to engineered solutions and discuss some of the implications. One of the consequences of this study is that it has highlighted a number
   of research issues and directions and challenges for the evolutionary computation community.We conclude the article by presenting some of these directions which range over
   several areas of evolutionary computation, including multi-objective fitness, coevolution and cooperation, and problem representations.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP Best poster
%@ 3-540-43378-3

%A Edmund K. Burke
%A Steven Gustafson
%A Graham Kendall
%A Natalio Krasnogor
%T Is increased diversity in genetic programming beneficial? An analysis of the effects on performance
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 1398--1405
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%X A selection strategy based on genetic lineages is used to increase genetic diversity. A genetic lineage is defined as the path from an individual to individuals which were
   created from its genetic material. The method is applied to three problem domains: Artificial Ant, Even-5-Parity and symbolic regression of the Binomial-3 function. We
   examine how increased diversity affects problems differently and draw conclusions about the types of diversity which are more important for each problem. Results indicate
   that diversity in the Ant problem helps to overcome deception, while elitism in combination with diversity is likely to benefit the Parity and regression problems.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Steven Gustafson
%A Edmund K. Burke
%A Graham Kendall
%T Sampling of Unique Structures and Behaviours in Genetic Programming
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 279--288
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-sampling-2004.pdf
%X We examine the sampling of unique structures and behaviours in genetic programming. A novel description of behaviour is used to better understand the solutions visited
   during genetic programming search. Results provide new insight about deception that can be used to improve the algorithm and demonstrate the capability of genetic
   programming to sample different large tree structures during the evolutionary process.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Steven Gustafson
%T An Analysis of Diversity in Genetic Programming
%R Ph.D. Thesis
%D 2004
%I 
%I School of Computer Science and Information Technology, University of Nottingham
%C Nottingham, England
%K genetic algorithms, genetic programming
%U http://www.gustafsonresearch.com/thesis_html/
%X Genetic programming is a metaheuristic search method that uses a population of variable-length computer programs and a search strategy based on biological evolution. The
   idea of automatic programming has long been a goal of artificial intelligence, and genetic programming presents an intuitive method for automatically evolving programs.
   However, this method is not without some potential drawbacks. Search using procedural representations can be complex and inefficient. In addition, variable sized solutions
   can become unnecessarily large and difficult to interpret. The goal of this thesis is to understand the dynamics of genetic programming that encourages efficient and
   effective search. Toward this goal, the research focuses on an important property of genetic programming search: the population. The population is related to many key
   aspects of the genetic programming algorithm. In this programme of research, diversity is used to describe and analyse populations and their effect on search. A series of
   empirical investigations are carried out to better understand the genetic programming algorithm. the relationship between diversity and search. The effect of increased
   population diversity and a metaphor of search are then examined. This is followed by an investigation into the phenomenon of increased solution size and problem difficulty.
   The research concludes by examining the role of diverse individuals, particularly the ability of diverse individuals to affect the search process and ways of improving the
   genetic programming algorithm. (1) An analysis shows the complexity of the issues of diversity and the relationship between diversity and fitness, (2) The genetic
   programming search process is characterised by using the concept of genetic lineages and the sampling of structures and behaviours, (3) A causal model of the varied rates
   of solution size increase is presented, (4) A new, tunable problem demonstrates the contribution of different population members during search, and (5) An island model is
   proposed to improve the search by speciating dissimilar individuals into better-suited environments. Currently, genetic programming is applied to a wide range of problems
   under many varied contexts. From artificial intelligence to operations research, the results presented in this thesis will benefit population-based search methods, methods
   based on the concepts of evolution and search methods using variable-length representations.
%8 February

%A Steven Gustafson
%A Aniko Ekart
%A Edmund Burke
%A Graham Kendall
%T Problem Difficulty and Code Growth in Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 5
%N 3
%D 2004
%P 271--290
%I 
%K genetic algorithms, genetic programming, population diversity, code growth, problem difficulty
%U http://www.gustafsonresearch.com/research/publications/gustafson-gpem2004.pdf
%X the relationship between code growth and problem difficulty in genetic programming. The symbolic regression problem domain is used to investigate this relationship using
   two different types of increased instance difficulty. Results are supported by a simplified model of genetic programming and show that increased difficulty induces higher
   selection pressure and less genetic diversity, which both contribute toward an increased rate of code growth.
%8 September
%Z Article ID: 5272970

%A Edmund K. Burke
%A Steven Gustafson
%A Graham Kendall
%T Diversity in Genetic Programming: An Analysis of Measures and Correlation with Fitness
%J IEEE Transactions on Evolutionary Computation
%V 8
%N 1
%D 2004
%P 47--62
%I IEEE Press
%K genetic algorithms, genetic programming
%U http://www.cs.nott.ac.uk/~smg/research/publications/gustafson-ieee2004-preprint.ps

%A Steven Gustafson
%A Leonardo Vanneschi
%T Operator-Based Distance for Genetic Programming: Subtree Crossover Distance
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 178--189
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=178
%X This paper explores distance measures based on genetic operators for genetic programming using tree structures. The consistency between genetic operators and distance
   measures is a crucial point for analytical measures of problem difficulty, such as fitness distance correlation, and for measures of population diversity, such as entropy
   or variance. The contribution of this paper is the exploration of possible definitions and approximations of operator-based edit distance measures. In particular, we focus
   on the subtree crossover operator. An empirical study is presented to illustrate the features of an operator-based distance. This paper makes progress toward improved
   algorithmic analysis by using appropriate measures of distance and similarity.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Steven Gustafson
%A Edmund K. Burke
%A Natalio Krasnogor
%T The Tree-String Problem: An Artificial Domain for Structure and Content Search
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 215--226
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=215
%X This paper introduces the Tree-String problem for genetic programming and related search and optimisation methods. To improve the understanding of optimisation and search
   methods, we aim to capture the complex dynamic created by the interdependencies of solution structure and content. Thus, we created an artificial domain that is amenable
   for analysis, yet representative of a wide-range of real-world applications. The Tree-String problem provides several benefits, including: the direct control of both
   structure and content objectives, the production of a rich and representative search space, the ability to create tunably difficult and random instances and the flexibility
   for specialisation.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Steven Gustafson
%A Edmund K. Burke
%A Natalio Krasnogor
%T On Improving Genetic Programming for Symbolic Regression
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 1
%D 2005
%P 912--919
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X reports an improvement to genetic programming (GP) search for the symbolic regression domain, based on an analysis of dissimilarity and mating. GP search is generally
   difficult to characterise for this domain, preventing well motivated algorithmic improvements. We first examine the ability of various solutions to contribute to the search
   process. Further analysis highlights the numerous solutions produced during search with no change to solution quality. A simple algorithmic enhancement is made that reduces
   these events and produces a statistically significant improvement in solution quality. We conclude by verifying the generalisability of these results on several other
   regression instances.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Steven Gustafson
%A Edmund K. Burke
%T The Speciating Island Model: An alternative parallel evolutionary algorithm
%J Journal of Parallel and Distributed Computing
%V 66
%N 8
%D 2006
%P 1025--1036
%I 
%K genetic algorithms, genetic programming, Parallel evolutionary algorithms, Islands
%X This paper presents an investigation of a novel model for parallel evolutionary algorithms (EAs) based on the biological concept of species. In EA population search, new
   species represent solutions that could lead to good solutions but are disadvantaged due to their dissimilarity from the rest of the population. The Speciating Island Model
   (SIM) attempts to exploit new species when they arise by allocating them to new search processes executing on other islands (other processors). The long term goal of the
   SIM is to allow new species to diffuse throughout a large (conceptual) parallel computer network, where idle and unimproving processors initiate a new search process with
   them. In this paper, we focus on the successful identification and exploitation of new species and show that the SIM can achieve improved solution quality as compared to a
   canonical parallel EA.
%O Parallel Bioinspired Algorithms
%8 August

%A Steven Gustafson
%A Leonardo Vanneschi
%T Crossover-Based Tree Distance in Genetic Programming
%J IEEE Transactions on Evolutionary Computation
%V 12
%N 4
%D 2008
%P 506--524
%I 
%K genetic algorithms, genetic programming, evolutionary computation, trees (mathematics)crossover-based tree distance, distance metrics, evolutionary algorithms, fitness
   sharing algorithm, fitness-distance correlation, genetic programming syntax trees
%X In evolutionary algorithms, distance metrics between solutions are often useful for many aspects of guiding and understanding the search process. A good distance measure
   should reflect the capability of the search: if two solutions are found to be close in distance, or similarity, they should also be close in the search algorithm sense,
   i.e., the variation operator used to traverse the search space should easily transform one of them into the other. This paper explores such a distance for genetic
   programming syntax trees. Distance measures are discussed, defined and empirically investigated. The value of such measures is then validated in the context of analysis
   (fitness-distance correlation is analyzed during population evolution) as well as guiding search (results are improved using our measure in a fitness sharing algorithm) and
   diversity (new insights are obtained as compared with standard measures).
%8 August
%Z also known as \cite4459225

%A Aytac Guven
%A Mustafa Gunal
%T Genetic Programming Approach for Prediction of Local Scour Downstream of Hydraulic Structures
%J Journal of Irrigation and Drainage Engineering
%V 134
%N 2
%D 2008
%P 241--249
%I American Society of Civil Engineers
%K genetic algorithms, genetic programming
%X This is a pioneer study that presents genetic programming (GP) as a new tool for prediction of local scour downstream of grade-control structures. The objective of this
   study is to provide an alternative formulation to conventional regression based equations and verify the superiority of GP over regression analysis. The training and
   testing patterns of the proposed GP formulation are based on well established and widely dispersed experimental results from the literature. Linear and nonlinear
   regression-based equations were derived throughout regression analysis on dimensionless parameters obtained from dimensional analysis. The GP-based formulation results are
   compared with experimental results and other equations and found to be more accurate.
%8 March /  April
%Z 1Research Assistant, Dept. of Civil Engineering, Faculty of Engineering, Univ. of Gaziantep, 27310 Gaziantep, Turkey 2Associate Professor, Dept. of Civil Engineering, Univ.
   of Gaziantep, 27310 Gaziantep, Turkey.

%A Aytac Guven
%A Ali Aytek
%A M. Ishak Yuce
%A Hafzullah Aksoy
%T Genetic Programming-Based Empirical Model for Daily Reference Evapotranspiration Estimation
%J CLEAN - Soil, Air, Water
%V 36
%N 10-11
%D 2008
%P 905--912
%I 
%K genetic algorithms, genetic programming, Evapotranspiration Artificial intelligence, Gene expression programming
%X Genetic programming (GP) is presented as a new tool for the estimation of reference evapotranspiration by using daily atmospheric variables obtained from the California
   Irrigation Management Information System (CIMIS) database. The variables employed in the model are daily solar radiation, daily mean temperature, average daily relative
   humidity and wind speed. The results obtained are compared to seven conventional reference evapotranspiration models including: (1) the Penman-Monteith equation modified by
   CIMIS, (2) the Penman-Monteith equation modified by the Food and Agricultural Organization (FAO 56), (3) the Hargreaves-Samani equation, (4) the solar radiation-based ET0
   equation, (5) the Jensen-Haise equation, (6) the Jones-Ritchie equation, and (7) the Turc method. Statistical measures such as average, standard deviation, minimum and
   maximum values, as well as criteria such as mean square error and determination coefficient are used to measure the performance of the model developed by employing GP.
   Statistics and scatter plots indicate that the new equation produces quite satisfactorily results and can be used as an alternative to the conventional models.
%Z Acta hydrochimica et hydrobiologica Correspondence to Ali Aytek, Gaziantep University, Department of Civil Engineering, Hydraulics Division, Gaziantep, Turkey

%A Aytac Guven
%T Linear genetic programming for time-series modelling of daily flow rate
%J Journal of Earth System Science
%V 118
%N 2
%D 2009
%P 137--146
%I Springer
%K genetic algorithms, genetic programming, neural networks, daily flows, flow forecasting
%U http://www.ias.ac.in/jess/apr2009/137.pdf
%X In this study linear genetic programming (LGP),which is a variant of Genetic Programming,and two versions of Neural Networks (NNs)are used in predicting time-series of
   daily flow rates at a station on Schuylkill River at Berne,PA,USA.Daily flow rate at present is being predicted based on different time-series scenarios.For this
   purpose,various LGP and NN models are calibrated with training sets and validated by testing sets.Additionally,the robustness of the proposed LGP and NN models are
   evaluated by application data,which are used neither in training nor at testing stage.The results showed that both techniques predicted the flow rate data in quite good
   agreement with the observed ones,and the predictions of LGP and NN are challenging.The performance of LGP,which was moderately better than NN,is very promising and hence
   supports the use of LGP in predicting of river flow data.
%8 April
%Z Civil Engineering Department, Gaziantep University, 27310 Gaziantep, Turkey.

%A Aytac Guven
%A Ali Aytek
%T New Approach for Stage-Discharge Relationship: Gene-Expression Programming
%J Journal of Hydrologic Engineering
%V 14
%N 8
%D 2009
%P 812--820
%I 
%K genetic algorithms, genetic programming, gene expression programming
%X This study presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to modelling stage discharge relationship.
   The results obtained are compared to more conventional methods, stage rating curve and multiple linear regression techniques. Statistical measures such as average, standard
   deviation, minimum and maximum values, as well as criteria such as mean square error and determination coefficient, the coefficient of efficiency, and the adjusted
   coefficient of efficiency are used to measure the performance of the models developed by employing GEP. Also, the explicit formulations of the developed GEP models are
   presented. Statistics and scatter plots indicate that the proposed equations produce quite satisfactory results and perform superior to conventional models.
%8 August

%A Aytac Guven
%A H. Md. Azamathulla
%A N. A. Zakaria
%T Linear genetic programming for prediction of circular pile scour
%J Ocean Engineering
%V 36
%N 12-13
%D 2009
%P 985--991
%I 
%K genetic algorithms, genetic programming, Scour, Neuro-fuzzy, Circular pile, Regression
%U http://www.sciencedirect.com/science/article/B6V4F-4WCTX10-3/2/805df81deb25d8c99465f876a03fc1e5
%X Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents linear genetic programming (LGP),
   which is an extension to GP, as an alternative tool in the prediction of scour depth around a circular pile due to waves in medium dense silt and sand bed. Field
   measurements were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP
   models were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at circular piles. The results
   were tabulated in terms of statistical error measures and illustrated via scatter plots.

%A Aytac Guven
%A Ozgur Kisi
%T Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming
%J Water Resources Management
%V 25
%N 2
%D 2011
%P 691--704
%I Springer
%K genetic algorithms, genetic programming, gene expression programming, Suspended sediment yield, Modelling, Linear genetic programming, ANN, Neural networks
%X Estimation of suspended sediment yield is subject to uncertainty and bias. Many methods have been developed for estimating sediment yield but they still lack accuracy and
   robustness. This paper investigates the use of a machine-coded linear genetic programming (LGP) in daily suspended sediment estimation. The accuracy of LGP is compared with
   those of the Gene-expression programming (GEP), which is another branch of GP, and artificial neural network (ANN) technique. Daily streamflow and suspended sediment data
   from two stations on the Tongue River in Montana, USA, are used as case studies. Root mean square error (RMSE) and determination coefficient (R2) statistics are used for
   evaluating the accuracy of the models. Based on the comparison of the results, it is found that the LGP performs better than the GEP and ANN techniques. The GEP was also
   found to be better than the ANN. For the upstream and downstream stations, it is found that the LGP models with RMSE = 175 ton/day, R2 = 0.941 and RMSE = 254 ton/day, R2 =
   0.959 in test period is superior in estimating daily suspended sediments than the best accurate GEP model with RMSE = 231 ton/day, R2 = 0.941 and RMSE = 331 ton/day, R2 =
   0.934, respectively.
%8 January

%A Aytac Guven
%A Ozgur Kisi
%T Daily pan evaporation modeling using linear genetic programming technique
%J Irrigation Science
%V 29
%N 2
%D 2011
%P 135--145
%I Springer
%K genetic algorithms, genetic programming, gene expression programming
%X This paper investigates the ability of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily pan evaporation modelling.
   The daily climatic data, air temperature, solar radiation, wind speed, pressure and humidity of three automated weather stations, Fresno, Los Angeles and San Diego in
   California, are used as inputs to the LGP to estimate pan evaporation. The LGP estimates are compared with those of the Gene-expression programming (GEP), which is another
   branch of GP, multilayer perceptrons (MLP), radial basis neural networks (RBNN), generalised regression neural networks (GRNN) and Stephens-Stewart (SS) models. The
   performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R 2) statistics. Based on the
   comparisons, it was found that the LGP technique could be employed successfully in modeling evaporation process from the available climatic data.

%A Baris Guyaguler
%T Regression on Petroleum Well Test Data with the Reservoir Model as a Parameter
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 188--197
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A E. Haasdijk
%A P. Vogt
%A A. E. Eiben
%T Social Learning in Population-Based Adaptive Systems
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X The subject of the present investigation is Population-based Adaptive Systems (PAS), as implemented in the NEW TIES platform. In many existing PASs two adaptation
   mechanisms are combined, (non-Lamarckian) evolution and individual learning, inevitably raising the issue of `forgetful populations': individually learnt knowledge
   disappears when the individual that learnt it dies. We propose social learning by explicit knowledge transfer to overcome this problem. Our mechanism is based on (1) direct
   communication among agents in the population, (2) messages carrying rules that the sender agent uses in its controller, and (3) the ability of the recipient agent to
   incorporate foreign rules into its controller. Thus, knowledge can be disseminated and multiplied within the same generation, making the population a knowledge reservoir
   for individually acquired knowledge. We present an initial assessment of this idea and show that this social mechanism is capable of efficiently distributing knowledge and
   improving the performance of the population.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Mike Haberman
%T Altrusitic Ants
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 34--43
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z Ant World mazes This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at
   Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Tim Hackworth
%T India and Pakistan, a classic ``Richardson'' Arms Race: A Genetic Algorithmic approach
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1543--1550
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-700.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Tim Hackworth
%T Genetic algorithms; Some effects of redundancy in chromosomes
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 99--106
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms
%8 13  July
%Z GECCO-99LB

%A Pauline C. Haddow
%T Introduction: special issue on evolvable hardware challenges
%J Genetic Programming and Evolvable Machines
%V 12
%N 3
%D 2011
%P 181--182
%I 
%K genetic algorithms, evolvable hardware
%O EDITORIAL
%8 September

%A Pauline C. Haddow
%A Andy M. Tyrrell
%T Challenges of evolvable hardware: past, present and the path to a promising future
%J Genetic Programming and Evolvable Machines
%V 12
%N 3
%D 2011
%P 183--215
%I 
%K genetic algorithms, genetic programming, evolvable hardware, EHW, Future technology, Scalability, Computation medium, Review
%X Nature is phenomenal. The achievements in, for example, evolution are everywhere to be seen: complexity, resilience, inventive solutions and beauty. Evolvable Hardware (EH)
   is a field of evolutionary computation (EC) that focuses on the embodiment of evolution in a physical media. If EH could achieve even a small step in natural evolutionĄÇs
   achievements, it would be a significant step for hardware designers. Before the field of EH began, EC had already shown artificial evolution to be a highly competitive
   problem solver. EH thus started off as a new and exciting field with much promise. It seemed only a matter of time before researchers would find ways to convert such
   techniques into hardware problem solvers and further refine the techniques to achieve systems that were competitive with or better than human designs. However, 15 years on
   it appears that problems solved by EH are only of the size and complexity of that achievable in EC 15 years ago and seldom compete with traditional designs. A critical
   review of the field is presented. Whilst highlighting some of the successes, it also considers why the field is far from reaching these goals. The paper further redefines
   the field and speculates where the field should go in the next 10 years.
%8 September
%Z Brief mention of GP, mostly Koza, Lohn and Miller. Claims to define EHW. randomspice, ngspice. JPL, Heidelberg FTPA, Xilinx, Altera FPGA, systolic arrays, MOGA, adaptive
   clock skew. Overhype, secure funding, lack of theoretical work. Complexity Kolmogorov, Lempel-Ziv. Reliability, multiple faults. we need to take a fresh look at the way we
   think about evolvable hardware

%A Fatima Zohra Hadjam
%A Claudio Moraga
%A Mohamed Benmohamed
%T Cluster-based evolutionary design of digital circuits using all improved multi-expression programming
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2007)
%E Peter A. N. Bosman
%D 2007
%P 2475--2482
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, improved multi-expression programming, islands model
%U http://ls1-www.cs.uni-dortmund.de/pdf/Veroeffentlichungen/GECCO-2007.pdf
%X Evolutionary Electronics (EE) is a research area which involves application of Evolutionary Computation in the domain of electronics. EE algorithms are generally able to
   find good solutions to rather small problems in a reasonable amount of time, but the need for solving more and more complex problems increases the time required to find
   adequate solutions. This is due to the large number of individuals to be evaluated and to the large number of generations required until the convergence process leads to
   the solution. As a consequence, there have been multiple efforts to make EE faster, and one of the most promising choices is to use distributed implementations. In this
   paper, we propose a cluster-based evolutionary design of digital circuits using a distributed improved multi expression programming method (DIMEP). DIMEP keeps, in
   parallel, several sub-populations that are processed by Improved Multi-Expression Programming algorithms, with each one being independent from the others. A migration
   mechanism produces a chromosome exchange between the subpopulations using MPI (Message Passing Interface) on a dedicated cluster of workstations (Lido Cluster, Dortmund
   University). This paper presents the main ideas and shows preliminary experimental results.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A Fatima Z. Hadjam
%A Claudio Moraga
%T Evolutionary design of reversible digital circuits using IMEP the case of the even parity problem
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Reversible logic is an emerging research area and has attracted significant attention in recent years. Developing systematic logic synthesis algorithms for reversible logic
   is still an area of research. Unlike other areas of application, there are relatively few publications on applications of genetic programming -(evolutionary algorithms in
   general) -to reversible logic synthesis. In this paper, we are introducing a new method; a variant of IMEP. The case of digital circuits for the even-parity problem is
   investigated. The type of gate used to evolve such a problem is the Fredkin gate.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586252

%A Christian Hafner
%A Juerg Froehlich
%A Hansueli Gerber
%T Generalized Genetic Program
%D 1996
%I 
%K genetic algorithms, genetic programming
%U http://alphard.ethz.ch/gp.htm
%X A novel hybrid approach for the Symbolic Regression problem is presented. First, the classical series expansion approach and the traditional Genetic Programming approach
   are outlined. In order to overcome the specific problems of them, a combination is analyzed and two specific implementations are presented. Both the Extended Genetic
   Programming and the Generalized Genetic Programming approach are based on series expansions with genetic optimizations of the basis functions combined with linear and
   nonlinear parameter optimizations, but they exhibit important differences in their 'philosophy' and in the details of the implementation. The advantages of our approaches
   are demonstrated with simple examples that are hard to solve with traditional Genetic Programming. It is demonstrated that the performance can drastically be improved.
%O Submitted to the 'Evolutionary Computation' Journal
%Z postscript generated by MS word appears to be faulty. GGP See GPP manual http://alphard.ethz.ch/Hafner/ggp/ggpmanu.htm

%A Christian Hafner
%A Jurg Frohlich
%T Generalized Function Analysis Using Hybrid Evolutionary Algorithms
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 1
%D 1999
%P 287--294
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, time series, evolutionary computation, generalized function analysis, hybrid evolutionary algorithms, time series prediction,
   prominent codes, future data, symbolic regression, series expansions, parameter optimization techniques, highly complex codes, physics, economy
%U http://ieeexplore.ieee.org/iel5/6342/16952/00781938.pdf
%X Two novel codes for the prediction of time series are presented. Unlike most of the prominent codes based on finding a process that predicts the future data, these codes
   are based on function analysis and symbolic regression. Both codes are based on a generalization and combination of series expansions, parameter optimization techniques,
   and genetic programming. These highly complex codes are outlined and applied to different examples of physics and economy.
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143 Extrapolation. GCP v. EGP. Sunspot, Dow
   Jones, stock price prediction. Full Binary trees of depth 3. On http://alphard.ethz.ch/Hafner/ggp/gp.htm there is more information on GGP with links for downloading the
   software.
%@ 0-7803-5537-7 (Microfiche)

%A John G. Hagedorn
%A Judith E. Devaney
%T A Genetic Programming System with a Procedural Program Representation
%B 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Erik D. Goodman
%D 2001
%P 152--159
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming
%U http://math.nist.gov/mcsd/savg/papers/g2001.ps.gz
%8 9-11  July
%Z GECCO-2001LB, NIST

%A Masami Hagiya
%T Towards Autonomous Molecular Computers
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 691--699
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K DNA Computing
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Gary L. Haith
%A Silvano P. Colombano
%A Jason D. Lohn
%A Dimitris Stassinopoulos
%T Coevolution for Problem Simplification
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 244--251
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-896.ps
%X predator-prey
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A A. Halaby
%A M. Awad
%A R. Khanna
%T Guided Search Space Genetic Programming for identifying energy aware microarchitectural designs
%B 2010 International Conference on Energy Aware Computing (ICEAC)
%D 2010
%I 
%K genetic algorithms, GSS-GP, energy aware microarchitectural design, fitness function, guided search space genetic programming, machine learning technique, resource use,
   convergence, learning (artificial intelligence), power aware computing, search problems
%X Genetic Programming (GP) is being proposed as a machine learning technique in design space exploration. An evolutionary but heuristic approach by default, GP basically
   searches the whole input space for suboptimal values, which often translates into long convergence times, more processing and thus inefficient resource usage. We propose in
   this paper a Guided Search Space GP (GSS-GP) approach that improves convergence time and accuracy because of the limited search space it uses and the fitness function
   designed to account for the class disproportionality. Experimental results to identify energy aware microarchitectural designs show the merit of GSS-GP and motivate follow
   on research.
%8 16-18  Decemeber
%Z fixed representation classifier. Electrical & Computer Engineering, American University of Beirut, Beirut, Lebanon. Also known as \cite5702307

%A Curt Hall
%A Paul Harmon
%T AI in Software Development: Genetic Programming, Fuzzy Logic, and Neural Nets
%D 1995
%I cutter
%K genetic algorithms, genetic programming
%U http://www.cutter.com/itgroup/reports/aisoft.htm
%X Neural network products are already being used for character recognition, real estate evaluation, "what-if" simulations for manufacturing, allocating airline seats, trading
   stocks and bonds, and detecting credit-card fraud. Two more cutting-edge technologies -- genetic programming and fuzzy-logic techniques -- are just entering the
   marketplace, promising many more innovative applications. AI in Software Development presents a clear overview of these exciting developments ... without hype and
   exaggerated projections. Drawn from issues of the monthly newsletter Intelligent Software Strategies, this practical report demonstrates the in-depth expertise and clear
   explanations that Curt Hall and Paul Harmon are known for.
%Z lovering@cutter.com

%A John M. Hall
%A Terence Soule
%T Does Genetic Programming Inherently Adopt Structured Design Techniques?
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 159--174
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, design, function choice, root node
%U http://www.cs.uidaho.edu/~tsoule/research/doesDesign.ps
%X Basic genetic programming (GP) techniques allow individuals to take advantage of some basic top-down design principles. In order to evaluate the effectiveness of these
   techniques, we define a design as an evolutionary frozen root node. We show that GP design converges quickly based primarily on the best individual in the initial random
   population. This leads to speculation of several mechanisms that could be used to allow basic GP techniques to better incorporate top-down design principles.
%O 10
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2 A version of Santa Fe trail artificial ant, 6-even parity (given XOR!), intertwined spirals, sin(x), Battleship GP robust to forced choice
   of root node. Differences in means small compared to variation between runs. In population of 100 root node almost always converges.
%@ 0-387-23253-2

%A Hatem Hamda
%A Francois Jouve
%A Evelyne Lutton
%A Marc Schoenauer
%A Michele Sebag
%T Compact Unstructured Representations for Evolutionary Design
%J International Journal of Applied Intelligence
%V 16
%N 2
%D 2002
%P 139--155
%I Springer Netherlands
%K genetic algorithms, evolution strategies, Computer Science
%U http://www.wkap.nl/prod/j/0924-669X
%X This paper proposes a few steps to escape structured extensive representations for evolutionary solving of Topological Optimum Design (TOD) problems: early results have
   shown the ability of Evolutionary methods to find numerical solutions to yet unsolved TOD problems, but those approaches were limited because the complexity of the
   representation was that of a fixed underlying mesh. Different compact unstructured representations are introduced, the complexity of which is self-adaptive, i.e. is evolved
   by the algorithm itself. The Voronoi-based representations are variable length lists of alleles that are directly decoded into shapes, while the IFS representation, based
   on fractal theory, involves a much more complex morphogenetic process. First results demonstrates that Voronoi-based representations allow one to push further the limits of
   Evolutionary Topological Optimum Design by actually removing the correlation between the complexity of the representations and that of the discretization. Further
   comparative results among all these representations on simple test problems indicate that the complex causality in the IFS representation disfavor it compared to the
   Voronoi-based representations.
%O Special Issue on Creative Evolutionary Systems
%Z Bentely and Corne Special issue

%A Lutz Hamel
%T Breeding Algebraic Structures---An Evolutionary Approach To Inductive Equational Logic Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 748--755
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, algebraic specification, concept learning, equational logic, inductive logic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Lutz Hamel
%A Chi Shen
%T An Inductive Programming Approach to Algebraic Specification
%B Proceedings of the ECML 2007 Workshop on Approaches and Applications of Inductive Programming (AAIP'07)
%D 2007
%P 3--15
%I 
%C Warsaw
%K genetic algorithms, genetic programming
%U http://www.ecmlpkdd2007.org/CD/workshops/AAIP/hamel_shen/hamel_shen.pdf
%X Inductive machine learning suggests an alternative approach to the algebraic specification of software systems: rather than using test cases to validate an existing
   specification we use the test cases to induce a specification. In the algebraic setting test cases are ground equations that represent specific aspects of the desired
   system behavior or, in the case of negative test cases, represent specific behavior that is to be excluded from the system. We call this inductive equational logic
   programming. We have developed an algebraic semantics for inductive equational logic programming where hypotheses are cones over specification diagrams. The induction of a
   hypothesis or specification can then be viewed as a search problem in the category of cones over a specific specification diagram for a cone that satisfies some pragmatic
   criteria such as being as general as possible. We have implemented such an induction system in the functional part of the Maude specification language using evolutionary
   computation as a search strategy.
%8 17-21  September
%Z Department of Computer Science and Statistics University of Rhode Island Kingston, RI 02881, USA

%A Molly Hammell
%T Computational methods to identify miRNA targets
%J Seminars in Cell \& Developmental Biology
%V 21
%N 7
%D 2010
%P 738--744
%I 
%K genetic algorithms, genetic programming, miRNA, miRNA target prediction, Computational methods
%U http://www.sciencedirect.com/science/article/B6WX0-4Y5GY3K-2/2/ee338722f9ce7b4b87a41bdd717fc22e
%X MicroRNAs (miRNAs) are short RNA molecules that regulate the post-transcriptional expression of their target genes. This regulation may take the form of stable
   translational or degradation of the target transcript, although the mechanisms governing the outcome of miRNA-mediated regulation remain largely unknown. While it is
   becoming clear that miRNAs are core components of gene regulatory networks, elucidating precise roles for each miRNA within these networks will require an accurate means of
   identifying target genes and assessing the impact of miRNAs on individual targets. Numerous computational methods for predicting targets are currently available. These
   methods vary widely in their emphasis, accuracy, and ease of use for researchers. This review will focus on a comparison of the available computational methods in animals,
   with an emphasis on approaches that are informed by experimental analysis of microRNA:target complexes.
%8 September
%Z survey

%A Richard Hampo
%T Genetic Programming: A New Paradigm for Control and Analysis
%B Third ASME Symposium on Transportation Systems
%D 1992
%P 155--163
%I 
%C Anaheim, California, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/hampo_1992_new.pdf
%O Invited Paper at ASME Winter Annual Meeting
%8 9--13  November

%A R. J. Hampo
%A K. A. Marko
%T Application of Genetic Programming to Control of Vehicle Systems
%B Proceedings of the Intelligent Vehicles '92 Symposium
%D 1992
%P 191--195
%I IEEE
%C Detroit, Mi, USA
%K genetic algorithms, genetic programming
%X The development of sophisticated and complex `intelligent' systems often requires effective means to process information and control complicated systems. An `intelligent'
   system gathers information and interacts with its environment under the control of microprocessors programmed to process information and to execute control actions or
   responses to sensory inputs. The authors review briefly the basic principles of genetic algorithms and examine some potential applications of genetic programming for
   intelligent vehicle systems. They demonstrate the potential of this method by examining a particular problem in detail; the discovery of a control algorithm for an active
   suspension system
%8 June  29 -  July  1
%@ 0-7803-0747-X

%A R. J. Hampo
%T The Genetic Programming Paradigm: A New Tool for Analysis and Control
%D 1992
%I 
%K genetic algorithms, genetic programming
%O Ford Proprietary
%8 6  March
%Z Ford Technical Report SR-92-114

%A Richard J. Hampo
%A Bruce D. Bryant
%A Kenneth A. Marko
%T IC Engine Misfire Detection Algorithm Generation Using Genetic Programming
%B EUFIT'94
%D 1994
%P 1674--1678
%I ELITE-Foundation
%C Promenade 9, D-52076, Aachen, Germany
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/misfire-detection.PS.Z
%8 20--23  September
%Z Presents 2 GPs and a Neural Net detecting engine missfires from test data. Both GPs better than NN. Not clear what distinction is between two GPs. Uses existing, processed
   input signals from engine. Says GP easier to implement in existing vehical computer. Author's address: Ford Motor Company Scientific Research Laboratory, PO BOX 2053, 20000
   Rotunda Drive, MD 2036, Dearborn, Michigan 48121-2053, USA

%A Karim Hamza
%A Kazuhiro Saitou
%T Optimization of Constructive Solid Geometry Via a Tree-Based Multi-objective Genetic Algorithm
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 981--992
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030981.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Pu Han
%A Shiliang Zhou
%A Dongfeng Wang
%T A Multi-objective Genetic Programming/ NARMAX Approach to Chaotic Systems Identification
%B The Sixth World Congress on Intelligent Control and Automation, WCICA 2006
%V 1
%D 2006
%P 1735--1739
%I IEEE
%C Dalian
%K genetic algorithms, genetic programming
%X A chaotic system identification approach based on genetic programming (GP) and multi-objective optimisation is introduced. NARMAX (Nonlinear Auto Regressive Moving Average
   with exogenous inputs) model representation is used for the basis of the hierarchical tree encoding in GP. Criteria related to the complexity, performance and chaotic
   invariants obtained by chaotic time series analysis of the models are considered in the fitness evaluation, which is achieved using the concept of the non-dominated
   solutions. So the solution set provides a trade-off between the complexity and the performance of the models, and derived model were able to capture the dynamic
   characteristics of the system and reproduce the chaotic motion. The simulation results show that the proposed technique provides an efficient method to get the optimum
   NARMAX difference equation model of chaotic systems
%Z Dept. of Autom., North China Electr. Power Univ., Baoding
%@ 1-4244-0332-4

%A Todd Han
%T Generating Hard Satisfiability Problems with Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 198--205
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Charles Hand
%T Genetic Nets
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A David J. Hand
%T Evolutionary computation
%J Statistics and Computing
%V 4
%N 2
%D 1994
%P 158
%I 
%K genetic algorithms, genetic programming
%O Book review of Koza's ``Genetic Programming''
%8 June
%Z Special issue on Evolutionary Programming. Favourable review of \citekoza:book

%A David J. Hand
%T Book Review: Data Mining and Knowledge Discovery with Evolutionary Programs
%J Genetic Programming and Evolvable Machines
%V 4
%N 3
%D 2003
%P 287--289
%I 
%K genetic algorithms
%8 September
%Z Review of Alex A. Freitas' \citefreitas:2002:book Article ID: 5141125

%A Hisashi Handa
%A Osamu Katai
%A Tadataka Konishi
%A Mitsuru Baba
%T Coevolutionary Genetic Algorithms for Solving Dynamic Constraint Satisfaction Problems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 252--257
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-394.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Simon Handley
%T Automatic Learning of a Detector for alpha-helices in Protein Sequences Via Genetic Programming
%B Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93
%E Stephanie Forrest
%D 1993
%P 271--278
%I Morgan Kaufmann
%C University of Illinois at Urbana-Champaign
%K genetic algorithms, genetic programming
%U http://www-leland.stanford.edu/~shandley/postscript/alpha-helices.ps.gz broken
%X This paper reports preliminary results from an attempt to predict the secondary structure of globular proteins. The genetic programming system was used to evolve programs
   that classified each residue in ten proteins as being either in an a-helix or in a "coil" (everything else). The proteins were chosen to be non-homologous and to contain
   mostly a-helices. The ten proteins were divided in half into a training set, that was used to drive the evolution, and a testing set, that was used to test the resultant
   programs. The fitness of the programs was based on the correlation coefficient between the observed and the predicted a-helicity of the residues. The fittest program
   produced by the genetic programming system had a correlation of 0.316 between the observed classifications and the classifications predicted by the program (on the proteins
   in the testing set).
%8 17-21  July
%Z GP based upon balkiness and hydrophilicity of the 7 amino acid residues closest to a point along the chain (repeat for whole chain). Train on five known P test on five
   more. NOT GOOD, GP learns structure of the training set well but this is not a very good predictor for the others

%A Simon Handley
%T The genetic planner: The automatic generation of plans for a mobile robot via genetic programming
%B Proceedings of the Eighth IEEE International Symposium on Intelligent Control
%D 1993
%P 190--195
%I IEEE
%I The IEEE Control System Society
%C Chicago, USA
%K genetic algorithms, genetic programming, Automatic control, Calculus, Computational modeling, Computer science, Computer simulation, Particle measurements, Proportional
   control, Robotics and automation, mobile robots, path planning, artificial selection, fitness proportionate reproduction, genetic planner, mobile robot, recombination,
   sexual mixing
%X Planning is the creation of programs to control an agent, such as a robot. Traditionally, planners have maintained a logical model of the agent's world and planned by
   reasoning about what plans do to that world. The Genetic Planner uses artificial selection, sexual mixing (recombination) and fitness proportionate reproduction to breed
   computer programs (i.e., to plan). The Genetic Planner uses a simulation of the world to execute candidate computer programs (i.e., candidate plans). This paper describes
   The Genetic Planner and shows it at work on a simple problem: a robot on a 2-D grid.
%8 August
%Z Chicago, IL, USA

%A S. Handley
%T The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions
%B Proceedings of the Fifth Workshop on Neural Networks: An International Conference on Computational Intelligence: Neural Networks, Fuzzy Systems, Evolutionary Programming,
   and Virtual Reality
%D 1991
%I 
%I The Society for Computer Simulation
%K genetic algorithms, genetic programming

%A Simon G. Handley
%T The Automatic Generations of Plans for a Mobile Robot via Genetic Programming with Automatically Defined Functions
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 391--407
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%X Planning is the creation of programs to control an agent, such as a robot. Traditionally, planners have maintained a logical model of the agent's world and planned by
   reasoning about what plans do to that world. In this chapter I describe a new planner, the Genetic Planner, that uses artificial selection, sexual mixing (recombination)
   and fitness proportionate reproduction to breed computer programs (i.e., to plan). This planner uses a simulation of the world to execute candidate computer programs (i.e.,
   candidate plans). I first describe this planner and then I show it at work on a simple problem---a robot on a 2-D grid. Also, Koza's Automatically Defined Functions (ADFs)
   are used and the results compared with the non-ADF genetic programming system.
%O 18
%Z Move about 49 by 49 world, move boxes, fails to switch on light. Describes Genetic Planner (=GP plus fitness function based upon hiow close to succeeding multiple
   predicates are)

%A S. Handley
%T On the use of a directed acyclic graph to represent a population of computer programs
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 154--159
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, DAG
%X This paper demonstrates a technique that reduces the time and space requirements of genetic programming. The population of parse trees is stored as a directed acyclic graph
   (DAG), rather than as a forest of trees. This saves space by not duplicating structurally identical subtrees. Also, the value computed by each subtree for each fitness case
   is cached, which saves computation both by not recomputing subtrees that appear more than once in a generation and by not recomputing subtrees that are copied from one
   generation to the next. I have implemented this technique for a number of problems and have seen a 15- to 28-fold reduction in the number of nodes extant per generation and
   an 11- to 30-fold reduction in the number of nodes evaluated per run (for populations of size 500).
%8 27-29  June
%Z Converts whole GP population to a directed Acyclic Graph, which is functionally equivelent. With primatives that have NO SIDE EFFECTS is able to cache earlier sub tree
   evaluations so they donot have to be re-evaluated, even if occur in a different individual. Claims speed ups of 11-30 fold.

%A S. Handley
%T Automated learning of a detector for the cores of a-helices in protein sequences via genetic programming
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 474--479
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%X I used Koza's genetic programming to evolve programs that classified contiguous regions of proteins as being a-helix cores or not. I snipped positive and negative examples
   of a-helix core regions out of a set of 90 proteins. These proteins were chosen from the Brookhaven Protein Data Bank to be non-homologous. The fitness of the programs was
   defined as the correlation coefficient between the observed and the predicted a-helicity of the above regions. The fittest program produced by the genetic programming
   system that predicted the training set at least as well as the testing set had a correlation of 0.4818 between the observed classifications and the classifications
   predicted by the program (on the proteins in the testing set).
%8 27-29  June

%A Simon G. Handley
%T The prediction of the degree of exposure to solvent of amino acid residues via genetic programming
%B Second International Conference on Intelligent Systems for Molecular Biology
%D 1994
%I AAAI Press
%C Stanford University, Stanford, CA, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Library/ISMB/ismb94contents.php
%X In this paper I evolve programs that predict the degree of exposure to solvent (the buriedness) of amino acid residues given only the primary structure. I use genetic
   programming to evolve programs that take as input the primary structure and that output the buriedness of each residue. I trained these programs on a set of 82 proteins
   from the Brookhaven Protein Data Bank (PDB) and cross-validated them on a separate testing set of 40 proteins, also from the PDB. The best program evolved had a correlation
   of 0.434 between the predicted and observed buriednesses on the testing set.

%A Simon G. Handley
%A Tod Klingler
%T Automated learning of a detector for a-helices in protein sequences via genetic programming
%B Artificial Life at Stanford 1993
%E John R. Koza
%D 1993
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 Decemeber
%Z Part of koza:1993:alife Student works for course "Artificial Life" (Computer Science 425) at Stanford University offered during 1994
   http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-171957-6

%A Simon Handley
%T Predicting Whether or Not a 60-base DNA Sequence Contains a Centrally-Located Splice Site Using Genetic Programming
%B Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications
%E Justinian P. Rosca
%D 1995
%P 98--103
%I 
%C Tahoe City, California, USA
%K genetic algorithms, genetic programming
%U http://www.cs.rochester.edu/u/rosca/ml95.htm
%X An evolutionary computation technique, genetic programming, was used to create programs that classify DNA sequences into one of three classes: (1) contains a
   centrally-located donor splice site, (2) contains a centrally-located acceptor splice site, and (3) contains neither a donor nor an acceptor. The performance of the
   programs created are competitive with previous work.
%8 9  July
%Z Pop size 64,000 part of \citerosca:1995:ml

%A Simon Handley
%T Classifying Nucleic Acid Sub-Sequences as Introns or Exons Using Genetic Programming
%B Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology (ISMB-95)
%E Christopher Rawlins and Dominic Clark and Russ Altman and Lawrence Hunter and Thomas Lengauer and Shoshana Wodak
%D 1995
%P 162--169
%I AAAI Press Menlo Park, CA, USA
%C Cambridge, UK
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Library/ISMB/ismb95contents.php
%X An evolutionary computation technique, genetic programming, was used to create programs that classify messenger RNA sequences into one of two classes: (1) the sequence is
   expressed as (part of) a protein (called an exon), or (2) not expressed as protein (called an intron).
%Z PMID: 7584433

%A Simon Handley
%T Predicting Whether or not a Nucleic Acid Sequence is an E. coli Promoter Region using Genetic Programming
%B Proceedings of the First International Symposium on Intelligence in Neural and Biological Systems INBS-95
%D 1995
%P 122--127
%I IEEE Computer Society Press Los Alamitos, California, USA
%I IEEE Comitteee on Pattern Analysis and Machine Intelligence (PAMI)
%C Herndon, Virginia, USA
%K genetic algorithms, genetic programming
%X This paper shows that an evolutionary computing technique, genetic programming, can create programs that classify DNA sequences as E. coli promoter vs non-E. coli promoter.
   The performance of the programs are competitive with pervious work.
%8 29-31  May
%Z Pop size 32,000

%A Simon Handley
%T Predicting Whether Or Not a 60-Base DNA Sequence Contains a Centrally-Located Splice Site Using Genetic Programming
%B Working Notes for the AAAI Symposium on Genetic Programming
%E E. V. Siegel and J. R. Koza
%D 1995
%P 17--22
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025, USA
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Library/Symposia/Fall/fs95-01.php
%X An evolutionary computation technique, genetic programming, was used to create programs that classify DNA sequences into one of three classes: (1) contains a
   centrally-located donor splice site, (2) contains a centrally-located acceptor splice site, and (3) contains neither donor nor an acceptor. The performance of the programs
   created are competitive with previous work.
%8 10--12  November
%Z AAAI-95f GP. Part of \citesiegel:1995:aaai-fgp \em Telephone: 415-328-3123 \em Fax: 415-321-4457 \em email info@aaai.org \em URL: http://www.aaai.org/

%A Simon Handley
%T The Prediction of the Degree of Exposure to Solvent of Amino Acid Residues via Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 297--300
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Simon Handley
%T A New Class of Function Sets for Solving Sequence Problems
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 301--308
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Simon Handley
%T Automatically Discovering Solutions that Flexibly Combine Iterative and non-Iterative Computations
%R Ph.D. Thesis
%D 1998
%I 
%I Department of Computer Science, Stanford University
%K genetic algorithms, genetic programming

%A Tony Handstad
%A Arne J H Hestnes
%A Pal Saetrom
%T Motif kernel generated by genetic programming improves remote homology and fold detection
%J BMC Bioinformatics
%V 8
%N 23
%D 2007
%I BioMed Central Ltd.
%K genetic algorithms, genetic programming, GPkernel, SVM, MISD, boosting
%U http://www.biomedcentral.com/1471-2105/8/23
%X Background Protein remote homology detection is a central problem in computational biology. Most recent methods train support vector machines to discriminate between
   related and unrelated sequences and these studies have introduced several types of kernels. One successful approach is to base a kernel on shared occurrences of discrete
   sequence motifs. Still, many protein sequences fail to be classified correctly for a lack of a suitable set of motifs for these sequences. Results We introduce the
   GPkernel, which is a motif kernel based on discrete sequence motifs where the motifs are evolved using genetic programming. All proteins can be grouped according to
   evolutionary relations and structure, and the method uses this inherent structure to create groups of motifs that discriminate between different families of evolutionary
   origin. When tested on two SCOP benchmarks, the superfamily and fold recognition problems, the GPkernel gives significantly better results compared to related methods of
   remote homology detection. Conclusion The GPkernel gives particularly good results on the more difficult fold recognition problem compared to the other methods. This is
   mainly because the method creates motif sets that describe similarities among subgroups of both the related and unrelated proteins. This rich set of motifs give a better
   description of the similarities and differences between different folds than do previous motif-based methods.
%8 January ~25
%Z PMID: 1794419 Undergraduate thesis: Protein Remote Homology Detection using Motifs made with Genetic Programming Handstad, Tony.
   http://urn.ub.uu.se/resolve?urn=urn:nbn:no:ntnu:diva-1030 (2007-03-30) 118 pages. Binary feature vectors. 2 seconds run time (PC+search chip). GPboost, SCOP, eMOTIF kernel,
   ROC, classifier combination. 'GPkernel performs significantly better than the other motif-based methods' p5. GPextended. evolves regular expressions. 'In addition to [the
   20] amino acid characters, the motifs are also made from the disjunction operator (|) wildcard (.) and Hamming distance :p>=x that specifies the minimum number of
   characters that must match in the pattern.' p13.

%A Mark S. Hanh
%T Simulating Evolution In a Kolmogorov Predator-Prey Model With Genetic Extensions
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 44--53
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford
   University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A K. Hanselmann
%A G. W. Barton
%A B. McKay
%A M. J. Willis
%T Modelling a Transformer Oil Regeneration Process Using Genetic Programming
%B Chemeca 96: Excellence in Chemical Engineering; Proceedings of the 24th Australian and New Zealand Chemical Engineering Conference and Exhibition
%S National conference publication
%E Gordon Weiss
%N 96/13
%D 1996
%P 9--84 [in volume 2]
%I Institution of Engineers Australia
%C Barton, ACT, Australia
%K genetic algorithms, genetic programming, Data processing, Neural networks (Computer science), Mathematical models, Linear programming, Mathematical models, Offshore oil
   industry, Electric insulators and insulation, Oils
%U http://search.informit.com.au/documentSummary;dn=894065266629714;res=IELENG
%X Genetic programming and neural network techniques were both used to predict the product distribution and yield of product oil from a reactor in a transformer oil
   regeneration process. All reactor models were developed by fitting laboratory-scale data. For the (relatively) small experimental data set available, it was found that the
   accuracy of the reactor model was significantly better when using genetic programming than neural network modelling techniques. A flowsheet of a pilot-scale version of the
   process was developed (using commercial simulation packages) based on the reactor model obtained using genetic programming, and the optimal operating conditions determined
   so as to give the maximum yield of regenerated transformer oil.
%Z http://lorien.ncl.ac.uk/ming/infer/inferrefs.htm (1) CSIRO Division of Coal and Energy Technology, Lucas Heights, Sydney, Australia (2) Department of Chemical Engineering,
   University of Sydney, Australia (3) Department of Chemical Engineering, University of Sydney, Australia (4) Department of Chemical and Process Engineering, University of
   Newcastle, UK
%@ 0-85825-658-4

%A James V. Hansen
%T Genetic Programming Experiments with Standard and Homologous Crossover Methods
%J Genetic Programming and Evolvable Machines
%V 4
%N 1
%D 2003
%P 53--66
%I 
%K genetic algorithms, genetic programming, homologous crossover, regression, classifications
%X While successful applications have been reported using standard GP crossover, limitations of this approach have been identified by several investigators. Among the most
   compelling alternatives to standard GP crossover are those that use some form of homologous crossover, where code segments that are exchanged are structurally or
   syntactically aligned in order to preserve context and worth. This paper reports the results of an empirical comparison of GP using standard crossover methods with GP using
   homologous crossover methods. Ten problems are tested, five each of pattern recognition and regression. Results suggest that in terms of generalisation accuracy, homologous
   crossover does generate consistently better performance. In addition, there is a consistently lower fraction of introns that are generated in the solution code.
%8 March
%Z Article ID: 5113072

%A James V. Hansen
%T Genetic search methods in air traffic control
%J Computers and Operations Research
%V 31
%N 3
%D 2004
%P 445--459
%I 
%K genetic algorithms, genetic programming, Aircraft traffic control, Genetic search, Heuristics, Scheduling
%U http://www.sciencedirect.com/science/article/B6VC5-480622F-4/2/468055c77aed02e9629b07b8dc6b0dbe
%X Of primary importance to the efficient operation and profitability of an airline is adherence to its flight schedule. This paper examines that segment of air traffic
   control, termed traffic management adviser (TMA), which is charged with the complex task of scheduling arriving aircraft to available runways in a manner that minimises
   delays and satisfies safety constraints. In particular, we investigate the effectiveness and efficiency of using genetic search methods to support the scheduling decisions
   made by TMA. Four different genetic search methods are tested on TMA problems suggested by recent work at the NASA Ames Research Center. For problems of realistic size,
   optimal or near-optimal assignments of aircraft to runways are achieved in real time. Scope and purpose. We report the application of genetic search algorithms to solve
   certain complexities associated with air traffic control. Air traffic control is an important practical problem that is difficult to solve by other methods because of
   non-convex, non-linear, or non-analytic characteristics. Four genetic search algorithms are applied, with consistent advantage being demonstrated by an algorithm based on
   genetic programming functions. Good results are achieved, with evidence that solutions can be achieved in real time.

%A James V. Hansen
%A Paul Benjamin Lowry
%A Rayman D. Meservy
%A Daniel M. McDonald
%T Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection
%J Decision Support Systems
%V 43
%N 4
%D 2007
%P 1362--1374
%I 
%K genetic algorithms, genetic programming, Cyberterrorism, Homologous crossover, Intrusion detection, Pattern recognition, Information security
%X Because malicious intrusions into critical information infrastructures are essential to the success of cyberterrorists, effective intrusion detection is also essential for
   defending such infrastructures. Cyberterrorism thrives on the development of new technologies; and, in response, intrusion detection methods must be robust and adaptive, as
   well as efficient. We hypothesise that genetic programming algorithms can aid in this endeavour. To investigate this proposition, we conducted an experiment using a very
   large dataset from the 1999 Knowledge Discovery in Database (KDD) Cup data, supplied by the Defense Advanced Research Projects Agency (DARPA) and MIT's Lincoln
   Laboratories. Using machine-coded linear genomes and a homologous crossover operator in genetic programming, promising results were achieved in detecting malicious
   intrusions. The resulting programs execute in real time, and high levels of accuracy were realised in identifying both positive and negative instances.
%O Special Issue Clusters
%8 August

%A Tuan-Hao Hoang
%A Daryl Essam
%A R. I. McKay
%A Xuan Hoai Nguyen
%T Developmental evaluation in genetic programming: A TAG-based framework
%B Proceedings of the Third Asian-Pacific workshop on Genetic Programming
%E The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen
%D 2006
%P 86--97
%I 
%C Military Technical Academy, Hanoi, VietNam
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/haodtag3p_new.pdf
%X We build on our previous feasibility studies [16, 17], which demonstrated the impact of evaluation during development in the DEVTAG system, and here present a full-fledged
   developmental system DTAG3P, with developmental evaluation, based on Tree-Adjoining Grammars (TAG). While DEVTAG used only a trivial developmental process, DTAG3P uses
   L-systems to encode TAG derivation trees, the L-systems permitting a full developmental process. DEVTAG was previously shown to dramatically out-perform standard Genetic
   Programming (GP) on some structured families of problems; here, we examine DTAG3P's performance on one of these families, and find a further major increment in performance
   over DEVTAG. DTAG3P achieves this despite dispensing with two extra control parameters which it was necessary to introduce into DEVTAG.
%Z http://www.aspgp.org

%A Akira Hara
%A Tomoharu Nagao
%T Emergence of the cooperative behavior using ADG; Automatically Defined Groups
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1039--1046
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-415.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Larry Hardesty
%T The mathematics of taste
%J MIT news
%D 2012
%I 
%K genetic algorithms, genetic programming
%U http://web.mit.edu/newsoffice/2012/what-smells-good-0124.html
%X By using 'genetic programming' to crossbreed algorithms, researchers help flavour companies figure out what their customers like.
%8 January  24
%Z See \citeVeeramachaneni:2012:GPEM MIT News Office 77 Massachusetts Avenue, Room 11-400, Cambridge, MA 02139-4307, 617.253.2700

%A Simon Harding
%A Julian Francis Miller
%T A Scalable Platform for Intrinsic Hardware and in materio Evolution
%B 2003 NASA/DoD Conference on Evolvable Hardware
%E Jason Lohn and Ricardo Zebulum and James Steincamp and Didier Keymeulen and Adrian Stoica and Michael I. Ferguson
%D 2003
%P 221--224
%I IEEE Computer Society 10662 Los Vaqueros Circle, P.O. Box 3014, Los Alamitos, CA, 90720-1314, USA
%I NASA Ames Research Center
%C Chicago, Illinois
%U EHW http://ehw.jpl.nasa.gov
%8 9-11  July
%Z EH2003 http://ic.arc.nasa.gov/projects/eh2003/
%@ 0-7695-1977-6

%A Simon Harding
%A Julian F. Miller
%T Evolution of Robot Controller Using Cartesian Genetic Programming
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 62--73
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming, cartesian genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=62
%X Cartesian Genetic Programming is a graph based representation that has many benefits over traditional tree based methods, including bloat free evolution and faster
   evolution through neutral search. Here, an integer based version of the representation is applied to a traditional problem in the field : evolving an obstacle avoiding
   robot controller. The technique is used to rapidly evolve controllers that work in a complex environment and with a challenging robot design. The generalisation of the
   robot controllers in different environments is also demonstrated. A novel fitness function based on chemical gradients is presented as a means of improving evolvability in
   such tasks.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Simon Harding
%A Julian F. Miller
%T Evolution In Materio : A Real-Time Robot Controller in Liquid Crystal
%B Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
%E Jason Lohn and David Gwaltney and Gregory Hornby and Ricardo Zebulum and Didier Keymeulen and Adrian Stoica
%D 2005
%P 229--238
%I IEEE Press IEEE Service Center 445 Hoes Lane Asia P.O. Box 1331 Piscataway, NJ 08855-1331
%I NASA, DoD
%C Washington, DC, USA
%K genetic algorithms, genetic programming, EHW
%X Although intrinsic evolution has been shown to be capable of exploiting the physical properties of materials to solve problems, most researchers have chosen to limit
   themselves to using standard electronic components. However, it has been previously argued that because such components are human designed and intentionally have
   predictable responses, they may not be the most suitable medium to use when trying to get a naturally inspired search technique to solve a problem. Indeed allowing computer
   controlled evolution (CCE) to manipulate novel physical media can allow much greater scope for the discovery of unconventional solutions. Last year the authors
   demonstrated, for the first time, that CCE could manipulate liquid crystal to perform signal processing tasks (i.e frequency discrimination). In this paper we show that CCE
   can use liquid crystal to solve the much harder problem of controlling a robot in real time to navigate in an environment to reach an obstructed destination point.
%8 29  June -1  July
%Z EH2005 IEEE Computer Society Order Number P2399
%@ 0-7695-2399-4

%A Simon Harding
%A Wolfgang Banzhaf
%T Fast genetic programming on GPUs
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 90--101
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming, Cartesian genetic programming
%X As is typical in evolutionary algorithms, fitness evaluation in GP takes the majority of the computational effort. In this paper we demonstrate the use of the Graphics
   Processing Unit (GPU) to accelerate the evaluation of individuals. We show that for both binary and floating point based data types, it is possible to get speed increases
   of several hundred times over a typical CPU implementation. This allows for evaluation of many thousands of fitness cases, and hence should enable more ambitious solutions
   to be evolved using GP.
%8 11-13  April
%Z NVidia GForce 7300 Go. p95 'GP interpreter', microsoft .NET C# visual studio, windowsXP 'The Accelerator tool kit compiles each individuals GP expression into a shader
   program.' floating point x^6-2x^4+x^2, C# boolean type. Two spirals. Nuclear proteins \citelangdon:2005:CS Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with
   EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Simon L. Harding
%A Julian F. Miller
%A Wolfgang Banzhaf
%T Self-modifying cartesian genetic programming
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 1
%D 2007
%P 1021--1028
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Cartesian Genetic Programming, Generative and Developmental Systems, evolution, self modification
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1021.pdf
%X In nature, systems with enormous numbers of components (i.e. cells) are evolved from a relatively small genotype. It has not yet been demonstrated that artificial evolution
   is sufficient to make such a system evolvable. Consequently researchers have been investigating forms of computational development that may allow more evolvable systems.
   The approaches taken have largely used re-writing, multi-cellularity, or genetic regulation. In many cases it has been difficult to produce general purpose computation from
   such systems. In this paper we introduce computational development using a form of Cartesian Genetic Programming that includes self-modification operations. One advantage
   of this approach is that ab initio the system can be used to solve computational problems. We present results on a number of problems and demonstrate the characteristics
   and advantages that self-modification brings.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A S. L. Harding
%A W. Banzhaf
%T Fast Genetic Programming and Artificial Developmental Systems on GPUs
%B 21st International Symposium on High Performance Computing Systems and Applications (HPCS'07)
%D 2007
%P 2
%I IEEE Computer Society
%C Canada
%K genetic algorithms, genetic programming, GPU
%X In this paper we demonstrate the use of the Graphics Processing Unit (GPU) to accelerate Evolutionary Computation applications, in particular Genetic Programming
   approaches. We show that it is possible to get speed increases of several hundred times over a typical CPU implementation, catapulting GPU processing for these applications
   into the realm of HPC. This increase in performance also extends to artificial developmental systems, where evolved programs are used to construct cellular systems.
   Feasibility of this approach to efficiently evaluate artificial developmental systems based on cellular automata is demonstrated.
%@ 0-7695-2813-9

%A Simon Harding
%T Evolution of Image Filters on Graphics Processor Units Using Cartesian Genetic Programming
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 1921--1928
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, Cartesian Genetic Programming, GPU
%X Graphics processor units are fast, inexpensive parallel computing devices. Recently there has been great interest in harnessing this power for various types of scientific
   computation, including genetic programming. In previous work, we have shown that using the graphics processor provides dramatic speed improvements over a standard CPU in
   the context of fitness evaluation. In this work, we use Cartesian Genetic Programming to generate shader programs that implement image filter operations. Using the GPU, we
   can rapidly apply these programs to each pixel in an image and evaluate the performance of a given filter. We show that we can successfully evolve noise removal filters
   that produce better image quality than a standard median filter.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A S. Harding
%A W. Banzhaf
%T Genetic programming on GPUs for image processing
%J International Journal of High Performance Systems Architecture
%V 1
%N 4
%D 2008
%P 231--240
%I 
%K genetic algorithms, genetic programming, GPU, graphics processing units, image filters, image processing, parallel processing, reverse engineering
%U http://www.inderscience.com/search/index.php?action=record&rec_id=24207&prevQuery=&ps=10&m=or
%X The evolution of image filters using genetic programming is a relatively unexplored task. This is most likely due to the high computational cost of evaluating the evolved
   programs. The parallel processors available on modern graphics cards can be used to greatly increase the speed of evaluation. Previous papers in this area dealt with tasks
   such as noise reduction and edge detection. Here we demonstrate that other more complicated processes can also be successfully evolved and that we can 'reverse engineer'
   the output from filters used in common graphics manipulation programs.
%Z IJHPSA

%A Simon Harding
%A Julian Miller
%A Wolfgang Banzhaf
%T Self Modifying Cartesian Genetic Programming: Fibonacci, Squares, Regression and Summing
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 133--144
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming, cartesian genetic programming, developmental systems
%U http://www.evolutioninmaterio.com/preprints/eurogp_smcgp_1.ps.pdf
%X Self Modifying CGP (SMCGP) is a developmental form of Cartesian Genetic Programming(CGP). It is able to modify its own phenotype during execution of the evolved program.
   This is done by the inclusion of modification operators in the function set. Here we present the use of the technique on several different sequence generation and
   regression problems.
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A S. Harding
%A J. F. Miller
%A W. Banzhaf
%T Self Modifying Cartesian Genetic Programming: Parity
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 285--292
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, cartesian genetic programming
%X Self Modifying CGP (SMCGP) is a developmental form of Cartesian Genetic Programming(CGP). It differs from CGP by including primitive functions which modify the program.
   Beginning with the evolved genotype the self-modifying functions produce a new program (phenotype) at each iteration. In this paper we have applied it to a well known
   digital circuit building problem: even-parity. We show that it is easier to solve difficult parity problems with SMCGP than either with CGP or Modular CGP, and that the
   increase in efficiency grows with problem size. More importantly, we prove that SMCGP can evolve general solutions to arbitrary-sized even parity problems.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Simon Harding
%A Julian Francis Miller
%A Wolfgang Banzhaf
%T Evolution, development and learning using self-modifying cartesian genetic programming
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 699--706
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, cartesian genetic programming
%X Self-Modifying Cartesian Genetic Programming (SMCGP) is a form of genetic programming that integrates developmental (self-modifying) features as a genotype-phenotype
   mapping. This paper asks: Is it possible to evolve a learning algorithm using SMCGP?
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Simon L. Harding
%A Wolfgang Banzhaf
%T Distributed Genetic Programming on GPUs using CUDA
%B Workshop on Parallel Architectures and Bioinspired Algorithms
%E Ignacio Hidalgo and Francisco Fernandez and Juan Lanchares
%D 2009
%P 1--10
%I Universidad Complutense de Madrid
%C Raleigh, NC, USA
%K genetic algorithms, genetic programming, GPU
%U http://www.evolutioninmaterio.com/preprints/CudaParallelCompilePP.pdf
%X Using of a cluster of Graphics Processing Unit (GPU) equipped computers, it is possible to accelerate the evaluation of individuals in Genetic Programming. Program
   compilation, fitness case data and fitness execution are spread over the cluster of computers, allowing for the efficient processing of very large datasets. Here, the
   implementation is demonstrated on datasets containing over 10 million rows and several hundred megabytes in size. Populations of candidate individuals are compiled into
   NVidia CUDA programs and executed on a set of client computers - each with a different subset of the dataset. The paper discusses the implementation of the system and acts
   as a tutorial for other researchers experimenting with genetic programming and GPUs.
%8 13  September
%Z mono dot net. WPABA'09 http://bioinspired.dacya.ucm.es/doku.php?id=workshops

%A Simon Harding
%A Julian F. Miller
%A Wolfgang Banzhaf
%T Developments in Cartesian Genetic Programming: self-modifying CGP
%J Genetic Programming and Evolvable Machines
%V 11
%N 3/4
%D 2010
%P 397--439
%I 
%K genetic algorithms, genetic programming, Cartesian Genetic Programming, Developmental systems
%X Self-modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Genetic Programming founded on Cartesian Genetic Programming.
   In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. This means that programs can be iterated to
   produce an infinite sequence of programs (phenotypes) from a single evolved genotype. It also allows programs to acquire more inputs and produce more outputs during this
   iteration. We discuss how SMCGP can be used and the results obtained in several different problem domains, including digital circuits, generation of patterns and sequences,
   and mathematical problems. We find that SMCGP can efficiently solve all the problems studied. In addition, we prove mathematically that evolved programs can provide general
   solutions to a number of problems: n-input even-parity, n-input adder, and sequence approximation to pi
%O Tenth Anniversary Issue: Progress in Genetic Programming and Evolvable Machines
%8 September

%A Simon Harding
%A Julian F. Miller
%A Wolfgang Banzhaf
%T Self modifying cartesian genetic programming: finding algorithms that calculate pi and e to arbitrary precision
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 579--586
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, cartesian genetic programming, Generative and developmental systems
%X Self Modifying Cartesian Genetic Programming (SMCGP) aims to be a general purpose form of developmental genetic programming. The evolved programs are iterated thus allowing
   an infinite sequence of phenotypes (programs) to be obtained from a single evolved genotype. In previous work this approach has already shown that it is possible to obtain
   mathematically provable general solutions to certain problems. We extend this class in this paper by showing how SMCGP can be used to find algorithms that converge to
   mathematical constants (pi and e). Mathematical proofs are given that show that some evolved formulae converge to pi and e in the limit as the number of iterations
   increase.
%8 7-11  July
%Z Also known as \cite1830591 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Simon Harding
%A Wolfgang Banzhaf
%A Julian F. Miller
%T A Survey of Self Modifying Cartesian Genetic Programming
%B Genetic Programming Theory and Practice VIII
%S Genetic and Evolutionary Computation
%E Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva
%V 8
%D 2010
%P 91--107
%I Springer
%C Ann Arbor, USA
%K genetic algorithms, genetic programming, cartesian genetic programming
%U http://www.springer.com/computer/ai/book/978-1-4419-7746-5
%O 6
%8 20-22  May
%Z part of \citeRiolo:2010:GPTP

%A Simon Harding
%A Julian F. Miller
%A Wolfgang Banzhaf
%T SMCGP2: self modifying cartesian genetic programming in two dimensions
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1491--1498
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, cartesian genetic programming, developmental systems
%X Self Modifying Cartesian Genetic Programming is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. Using a combination of computational
   functions and special functions that can modify the phenotype at runtime, it has been employed to find general solutions to certain Boolean circuits and mathematical
   problems. In the present work, a new version, of SMCGP is proposed and demonstrated. Compared to the original SMCGP both the representation and the function set have been
   simplified. However, the new representation is also two-dimensional and it allows evolution and development to have more ways to solve a given problem. Under most
   situations we show that the new method makes the evolution of solutions to even parity and binary addition faster than with previous version of SMCGP.
%8 12-16  July
%Z hill climbing. General solution to parity. Also known as \cite2001777 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011)
   and the sixteenth annual genetic programming conference (GP-2011)

%A Simon Harding
%A Julian F. Miller
%A Wolfgang Banzhaf
%T SMCGP2: finding algorithms that approximate numerical constants using quaternions and complex numbers
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 197--198
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, cartesian genetic programming: Poster
%X Self Modifying Cartesian Genetic Programming 2 (SMCGP2) is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. Using a combination of
   computational functions and special functions that can modify the phenotype at runtime, it has been employed to find general solutions to a number of computational
   problems. Here, we apply the new SMCGP technique to find mathematical relationships between well known mathematical constants (i.e. pi, e, phi, omega etc) using a variety
   of functions sets. Some of formulae obtained are distinctly unusual and may be unknown in mathematics.
%8 12-16  July
%Z Also known as \cite2001968 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Simon Harding
%A Wolfgang Banzhaf
%T Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET
%B GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)
%E Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis
%D 2011
%P 463--470
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, cartesian genetic programming, GPU
%X This paper investigates the use of a new Graphics Processing Unit (GPU) programming tool called 'GPU.NET' for implementing a Genetic Programming fitness evaluator. We find
   that the tool is able to help write software that accelerates fitness evaluation. For the first time, Cartesian Genetic Programming (CGP) was used with a GPU-based
   interpreter. With its code reuse and compact representation, implementing CGP efficiently on the GPU required several innovations. Further, we tested the system on a very
   large data set, and showed that CGP is also suitable for use as a classifier.
%8 12-16  July
%Z Also known as \cite2002034 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Simon L. Harding
%A Julian F. Miller
%A Wolfgang Banzhaf
%T Self-Modifying Cartesian Genetic Programming
%B Cartesian Genetic Programming
%S Natural Computing Series
%E Julian F. Miller
%D 2011
%P 101--124
%I Springer
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%U http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7
%X Self-modifying Cartesian genetic programming (SMCGP) is a general purpose, graph-based, form of genetic programming founded on Cartesian genetic programming. In addition to
   the usual computational functions, it includes functions that can modify the program encoded in the genotype. SMCGP has high scalability in that evolved programs encoded in
   the genotype can be iterated to produce an infinite sequence of programs (phenotypes). It also allows programs to acquire more inputs and produce more outputs during
   iterations. Another attractive feature of SMCGP is that it facilitates the evolution of provably general solutions to various computational problems.
%O 4
%Z part of \citeMiller:CGP

%A Simon L. Harding
%A Wolfgang Banzhaf
%T Hardware Acceleration for CGP: Graphics Processing Units
%B Cartesian Genetic Programming
%S Natural Computing Series
%E Julian F. Miller
%D 2011
%P 231--253
%I Springer
%K genetic algorithms, genetic programming, Cartesian Genetic Programming, GPU
%U http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7
%X As with other forms of genetic programming, evaluation of the fitness function in CGP is a major bottleneck. Recently there has been a lot of interest in exploiting the
   parallel processing capabilities of the Graphics Processing Units that are found on modern graphics cards. Using these processors it is possible to greatly accelerate
   evaluation of CGP individuals.
%O 8
%Z part of \citeMiller:CGP

%A Nicholas E. Hardison
%A Theresa J. Fanelli
%A Scott M. Dudek
%A David M. Reif
%A Marylyn D. Ritchie
%A Alison A. Motsinger-Reif
%T A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 353--354
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, gene-gene interactions, grammatical evolution, neural networks, single nucleotide polymorphism, Bioinformatics, computational
   biology: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p353.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389159

%A Nicholas E. Hardison
%A Alison A. Motsinger-Reif
%T The power of quantitative grammatical evolution neural networks to detect gene-gene interactions
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 299--306
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems, and synthetic biology
%X Applying grammatical evolution to evolve neural networks (GENN) has been increasing used in genetic epidemiology to detect gene-gene or gene-environment interactions, also
   known as epistasis, in high dimensional data. GENN approaches have previously been shown to be highly successful in a range of simulated and real case-control studies, and
   has recently been applied to quantitative traits. In the current study, we evaluate the potential of an application of GENN to quantitative traits (QTGENN) to a range of
   simulated genetic models. We demonstrate the power of the approach, and compare this power to more traditional linear regression analysis approaches. We find that the
   QTGENN approach has relatively high power to detect both single-locus models as well as several completely epistatic two-locus models, and favourably compares to the
   regression methods.
%8 12-16  July
%Z Also known as \cite2001618 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Yorick Hardy
%A W.-H. Steeb
%T Gene Expression Programming and One-dimensional chaotic maps
%J International Journal of Modern Physics C
%V 13
%N 1
%D 2002
%P 25--30
%I 
%K genetic algorithms, genetic programming, Gene expression programming, chromosomes, replication, chaotic maps
%X Gene expression programming is applied to find one-dimensional maps. A survey on gene expression programming is also given.
%8 January
%Z Computational Physics and Physical Computation Quantum? International School for Scientific Computing, at the Rand Afrikaans University, Auckland Park 2006, South Africa
   World Scientific Publishing Company

%A Sai sri Krishna Haridass
%A David H. K. Hoe
%T Fault tolerant Block Based Neural Networks
%B 42nd Southeastern Symposium on System Theory (SSST 2010)
%D 2010
%P 357--361
%I 
%C University of Texas at Tyler, USA
%K genetic algorithms, genetic programming, EHW, correcting logic, divide-and-conquer approach, evolvable hardware, fault tolerant block based neural networks, massive
   parallelism, online detection, reconfigurable fabrics, transient errors, fault tolerant computing, neural nets, reconfigurable architectures
%X Block Based Neural Networks (BBNNs) have shown to be a practical means for implementing evolvable hardware on reconfigurable fabrics for solving a variety of problems that
   take advantage of the massive parallelism offered by a neural network approach. This paper proposes a method for obtaining a fault tolerant implementation of BBNNs by using
   a biologically inspired layered design. At the lowest level, each block has its own online detection and correcting logic combined with sufficient spare components to
   ensure recovery from permanent and transient errors. Another layer of hierarchy combines the blocks into clusters, where a redundant column of blocks can be used to replace
   blocks that cannot be repaired at the lowest level. The hierarchical approach is well-suited to a divide-and-conquer approach to genetic programming whereby complex
   problems are subdivided into smaller parts. The overall approach can be implemented on a reconfigurable fabric.
%8 7-9  March
%Z Is this a GP? Also known as \cite5442804

%A Georges R. Harik
%A Fernando G. Lobo
%T A parameter-less genetic algorithm
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 258--265
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U ftp://ftp-illigal.ge.uiuc.edu/pub/papers/Publications/lobo/parameter-less-ga.ps.Z
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Mark Harman
%T Search Based Software Engineering for Program Comprehension
%B 15th International Conference on Program Comprehension (ICPC 2007)
%E Kenny Wong
%D 2007
%I IEEE
%C Banff, Canada
%K genetic algorithms, genetic programming
%U http://www.dcs.kcl.ac.uk/staff/mark/icpc07.ps
%O Invited paper
%8 26-29  June
%Z http://www-user.cs.ualberta.ca/conferences/icpc2007/

%A Mark Harman
%A Yue Jia
%A William B. Langdon
%T A Manifesto for Higher Order Mutation Testing
%B Mutation 2010
%E Lydie du Bousquet and Jeremy Bradbury and Gordon Fraser
%D 2010
%P 80--89
%I IEEE Computer Society
%C Paris
%K genetic algorithms, genetic programming, SBSE
%U http://www.dcs.kcl.ac.uk/pg/jiayue/publications/papers/HarmanJL10.pdf
%X We argue that higher order mutants are potentially better able to simulate real faults and to reveal insights into bugs than the restricted class of first order mutants.
   the Mutation Testing community has previously shied away from Higher Order Mutation Testing believing it to be too expensive and therefore impractical. However, this paper
   argues that Search Based Software Engineering can provide a solution to this apparent problem, citing results from recent work on search based optimization techniques for
   constructing higher order mutants. We also present a research agenda for the development of Higher Order Mutation Testing.
%O Keynote
%8 6  April
%Z http://www.st.cs.uni-saarland.de/mutation2010/ held in conjunction with the 3rd International Conference on Software Testing, Verfication, and Validation (ICST'10) (6-9
   April 2010). Also known as \citeHarmanJL10

%A Mark Harman
%T Automated Patching Techniques: The Fix Is In
%J Communications of the ACM
%V 53
%N 5
%D 2010
%P 108
%I ACM
%C New York, NY, USA
%K genetic algorithms, genetic programming, SBSE
%X Finding bugs is technically demanding and yet economically vital. How much more difficult yet valuable would it be to automatically fix bugs?
%8 June
%Z Technical Perspective. technical perspective. Intro to \citeWeimer:2010:ACM Also known as \cite1735248

%A Mark Harman
%T Software Engineering Meets Evolutionary Computation
%J Computer
%V 44
%N 10
%D 2011
%P 31--39
%I 
%K genetic algorithms, genetic programming, SBSE, evolutionary computation, realistic algorithm, software design, software engineering
%X The concept of evolutionary computation has affected virtually every area of software design, not merely as a metaphor, but as a realistic algorithm for exploration,
   insight, and improvement.
%O Cover feature
%8 October
%Z also known as \cite6036090

%A Stefan Harmeling
%T Solving Satisfiability Problems with Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 206--213
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Robin Harper
%A Alan Blair
%T A Structure Preserving Crossover In Grammatical Evolution
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2537--2544
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming, Grammatical Evolution
%X Grammatical Evolution is an algorithm for evolving complete programs in an arbitrary language. By using a Backus Naur Form grammar the advantages of typing are achieved. A
   separation of genotype and phenotype allows the implementation of operators that manipulate (for instance by crossover and mutation) the genotype (in Grammatical Evolution
   - a sequence of bits) irrespective of the genotype to phenotype mapping (in Grammatical Evolution - an arbitrary grammar). This paper introduces a new type of crossover
   operator for Grammatical Evolution. The crossover operator uses information automatically extracted from the grammar to minimise any destructive impact from the crossover.
   The information, which is extracted at the same time as the genome is initially decoded, allows the swapping between entities of complete expansions of non-terminals in the
   grammar without disrupting useful blocks of code on either side of the two point crossover. In the domains tested, results confirm that the crossover is (i) more productive
   than hill-climbing; (ii) enables populations to continue to evolve over considerable numbers of generations without intron bloat; and (iii) allows populations (in the
   domains tested) to reach higher fitness levels, quicker.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. LHS Replacement operator. Minesweeper, Taxi problems. Santa Fe ant too easy for GE. 2500 generations.
%@ 0-7803-9363-5

%A Robin Harper
%A Alan Blair
%T A Self-Selecting Crossover Operator
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 5569--5576
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X compares the efficacy of different crossover operators for Grammatical Evolution across a typical numeric regression problem and a typical data classification problem.
   Grammatical Evolution is an extension of Genetic Programming, in that it is an algorithm for evolving complete programs in an arbitrary language. Each of the two main
   crossover operators struggles (for different reasons) to achieve 100per cent correct solutions. A mechanism is proposed, allowing the evolutionary algorithm to self-select
   the type of crossover used and this is shown to improve the rate of generating 100per cent successful solutions.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Robin Harper
%A Alan Blair
%T Dynamically Defined Functions In Grammatical Evolution
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%P 9188--9195
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, grammatical evolution, grammars, search problems, Backus Naur form grammar, arbitrary language, genotype, phenotype
%X Grammatical Evolution is an extension of Genetic Programming, in that it is an algorithm for evolving complete programs in an arbitrary language. a Backus Naur Form grammar
   the advantages of typing are achieved as well as a separation of genotype and phenotype. introduces a meta-grammar into Grammatical Evolution allowing the grammar to
   dynamically define functions, self adaptively at the individual level without the need for special purpose operators or constraints. The user need not determine the
   architecture of the dynamically defined functions. As the search proceeds through genotype/phenotype space the number and use of the functions can vary. The ability of the
   grammar to dynamically define such functions allows regularities in the problem space to be exploited even where such regularities were not apparent when the problem was
   set up.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. minesweeper Also known as \cite1688638. IEEE Xplore gives pages 2638--2645.
%@ 0-7803-9487-9

%A Robin Thomas Ross Harper
%T Enhancing Grammatical Evolution
%R Ph.D. Thesis
%D 2009
%I 
%I School of Computer Science and Engineering, The University of New South Wales
%C Sydney 2052, Australia
%K genetic algorithms, genetic programming, grammatical evolution
%X Grammatical Evolution (GE) is a method of using a general purpose evolutionary algorithm to evolve programs written in an arbitrary BNF grammar. This thesis extends GE as
   follows: GE as an extension of Genetic Programming (GP) A novel method of automatically extracting information from the grammar is introduced. This additional information
   allows the use of GP style crossover which in turn allows GE to perform identically to a strongly typed GP system as well as a non-typed (or canonical) GP system. Two test
   problems are presented one which is more easily solved by the GP style crossover and one which favours the tradition GE Ripple Crossover. With this new crossover operator
   GE can now emulate GP (as well as retaining its own unique features) and can therefore now be seen as an extension of GP. Dynamically Defined Functions An extension to the
   BNF grammar is presented which allows the use of dynamically defined functions (DDFs). DDFs provide an alternative to the traditional approach of Automatically Defined
   Functions (ADFs) but have the advantage that the number of functions and their parameters do not need to be specified by the user in advance. In addition DDFs allow the
   architecture of individuals to change dynamically throughout the course of the run without requiring the introduction of any new form of operator. Experimental results are
   presented confirming the effectiveness of DDFs. Self-Selecting (or Variable) Crossover. A self-selecting operator is introduced which allows the system to determine, during
   the course of the run, which crossover operator to apply; this is tested over several problem domains and (especially where small populations are used) is shown to be
   effective in aiding the system to overcome local optima. Spatial Co-Evolution in Age Layered Planes (SCALP) A method of combining Hornby's ALPS metaheuristic and the
   spatial co-evolution system introduced by Mitchell is presented; the new SCALP system is tested over three problem domains of increasing difficulty and performs extremely
   well in each of them.

%A Robin Harper
%T Genetic Programming -To much P and not enough G?
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X This paper re-visits the minesweeper problem, one of the problems used by Koza in his 1994 book, Genetic Programming II, Advances in Genetic Programming. The minesweeper
   problem was one of the many problems used to demonstrate how the Automatically Defined Function methodology could solve problems not able to be solved (in this case) with a
   no function GP. By taking advantage of advances in computing power it has become easier to allow the problem to run for many more generations. If this is done it is seen
   that the no function version easily outperforms the ADF alternative. A variation to the problem, which might require a more general-purpose minesweeper to be evolved
   (rather than one which can learn two maps) is examined and it appears that the ADF methodology solves this alternative problem more readily than the no function version.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586050

%A Robin Harper
%T Spatial co-evolution in Age Layered Planes (SCALP)
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X This paper introduces a method of combining Greg Hornby's Age Layered Protocol System with a form of spatial co-evolution. The combined system (SCALP) is compared to these
   two systems and a canonical GP tournament selection scheme over three well understood domains, the sextic regression problem, a two variable regression problem and a
   variation on the classic minesweeper problem. In each case SCALP avoided premature convergence; solving every run of these particular problems.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586342

%A Robin Harper
%T GE, explosive grammars and the lasting legacy of bad initialisation
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming, Grammatical Evolution
%X This paper explores some of the initialisation schemes that can be used to create the starting population of a Grammatical Evolution (GE) run. It investigates why two
   typical initialisation schemes (random bit and ramped half and half) produce very different, but in each case skewed, tree types. A third methodology, Sean Luke's
   Probabilistic Tree-Creation version 2 (PTC2), is also examined and is shown to produce a wider variety of trees. Two experiments on different problem sets are carried out
   and it is shown that for each of these test cases, where the ``wrong'' initialisation method is used, the chance of achieving a successful run is decreased even if the runs
   are continued long enough for the populations to stagnate. This would seem to suggest that the system does not typically recover from a ``bad'' start.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586336

%A Robin Harper
%T Co-evolving robocode tanks
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1443--1450
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution
%X Robocode is a Java based programming platform where robot tanks, controlled by programs written in Java, compete. In this paper Grammatical Evolution is used to evolve Java
   programs to control a Robocode robot. This paper demonstrates how Grammatical Evolution together with spatial co-evolution in age layered planes (SCALP) can harness
   co-evolution to evolve relatively complex behaviour, including robots capable of beating Robocode's sample robots as well as some more complex human coded robots. The
   results of the co-evolution are similar to the results obtained by direct evolution against a range of human coded robots. This indicates that co-evolution alone is able to
   evolve robots of a similar standard to those evolved against graded human coded robots.
%8 12-16  July
%Z Also known as \cite2001770 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Robin Harper
%T Dynamic L-systems in GE
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 209--210
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution, Generative and developmental systems: Poster
%X In this paper, I describe how to use Grammatical Evolution to implement a parametrised Lindenmayer System (L-System), where the number of production rules of the L-System
   is determined by the genome of the individual, rather than being determined by the user before hand. This leaves the number of production rules as a free parameter and
   allows the underlying topology of the system to be optimised by the evolutionary algorithm.
%8 12-16  July
%Z Also known as \cite2001975 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Laura J. Harrell
%A S. Ranji Ranjithan
%T Evaluation of Alternative Penalty Function Implementations in a Watershed Management Design Problem
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1551--1558
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-736.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Kim Harries
%A Peter Smith
%T Exploring Alternative Operators and Search Strategies in Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 147--155
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/harries.gp97_paper.ps.gz
%8 13-16  July
%Z GP-97 even-4-parity, even-5-parity, artificial ant (Santa Fe trail), regression of x^4-3x^3+9x^2-27x Depth-based crossover (depth fair, SameDepths and DiffDepths) NoBias
   and combinations of crossovers. SameDepths does badly on even-5-parity otherwise crossovers similar to each other. 58,100 runs Several different types of mutation (and
   combination of mutation) used as stochastic "hill climbers". Steady state, tournament size=2, limit of 1000 nodes, kinnear's Hoist and mutation both at 1 percent. GP
   mutation generally good hill climber, small and self-crossover generally awful. See sec 4 discussion. )

%A K. Harries
%A P. W. H. Smith
%T Code Growth, Explicitly Defined Introns and Alternative Selection Schemes
%D 1998
%I 
%K genetic algorithms, genetic programming, Introns, Bloat, Parsimony
%U http://citeseer.ist.psu.edu/harries98code.html
%X Previous work on introns and code growth in genetic programming is expanded on and tested experimentally. Explicitly Defined Introns are introduced to tree-based
   representations as an aid to measuring and evaluating intron behaviour, and it is shown that though introns do create code growth they are not the only cause of it and
   removing them merely decreases the growth rate, not eliminates it. By systematically negating various forms of intron behaviour a deeper understanding of the causes of code
   growth is obtained, leading to the development of a system that keeps unnecessary bloat to a minimum. Alternative selection schemes and recombination operators are examined
   and improvements demonstrated over the standard methods in terms of both performance and parsimony.
%O www
%O Earlier version of Evolutionary Computation 6 (4), 336-360, 1998
%Z Final version is \citePWHSmith:1998:cgediass

%A George G. Harrigan
%A Roxanne H. LaPlante
%A Greg N. Cosma
%A Gary Cockerell
%A Royston Goodacre
%A Jane F. Maddox
%A James P. Luyendyk
%A Patricia E. Ganey
%A Robert A. Roth
%T Application of high-throughput Fourier-transform infrared spectroscopy in toxicology studies: contribution to a study on the development of an animal model for
   idiosyncratic toxicity
%J Toxicology Letters
%V 146
%N 3
%D 2004
%P 197--205
%I 
%K genetic algorithms, genetic programming, Bacterial lipopolysaccharide, High-throughput infrared spectroscopy, Idiosyncratic toxicity, Metabonomics
%X An evaluation of high-throughput Fourier-transform infrared spectroscopy (FT-IR) as a technology that could support a "metabonomics" component in toxicological studies of
   drug candidates is presented. The hypothesis tested in this study was that FT-IR had sufficient resolving power to discriminate between urine collected from control rat
   populations and rats subjected to treatment with a potent inflammatory agent, bacterial lipopolysaccharide (LPS). It was also hypothesized that co-administration of LPS
   with ranitidine, a drug associated with reports of idiosyncratic susceptibility, would induce hepatotoxicity in rats and that this could be detected non-invasively by an
   FT-IR-based metabonomics approach. The co-administration of LPS with "idiosyncratic" drugs represents an attempt to develop a predictive model of idiosyncratic toxicity and
   FT-IR is used herein to support characterization of this model. FT-IR spectra are high dimensional and the use of genetic programming to identify spectral sub-regions that
   most contribute to discrimination is demonstrated. FT-IR is rapid, reagentless, highly reproducible and inexpensive. Results from this pilot study indicate it could be
   extended to routine applications in toxicology and to supporting characterization of a new animal model for idiosyncratic susceptibility.
%8 2  February
%Z Pharmacia Corporation, GMax-Bio

%A Kyle Ira Harrington
%T Predicting reactions from amino acid sequences in S. cerevisiae: an evolutionary computation approach
%B Genetic and Evolutionary Computation Conference (GECCO2007) workshop program
%E Tina Yu
%D 2007
%P 2725--2728
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, GP^2, push, PushGP
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2725.pdf
%X Evolutionary computation has been used many times for protein function prediction. In this paper a new approach is taken by constraining the problem to predicting the
   products of enzyme catalysis. Genetic programming with the Push programming language is used to evolve predictors within multiple search spaces. Predictors are evolved
   within multiple search spaces to reduce the complexity of solutions and represent sequence analysis, protein domain recognition, protein folding, and informatic approaches.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A Andrew T. Harris
%A Anxhela Lungari
%A Christopher J. Needham
%A Stephen L. Smith
%A Michael A. Lones
%A Sheila E. Fisher
%A Xuebin Yang
%A Nicola Cooper
%A Jennifer Kirkham
%A D. Alastair Smith
%A Dominic P. Martin-Hirsch
%A Alec S. High
%T Potential for Raman Spectroscopy to Provide Cancer Screening Using a Peripheral Blood Sample
%J Head \& Neck Oncology
%V 1
%D 2009
%P 34
%I 
%K genetic algorithms, genetic programming
%U http://www.headandneckoncology.org/content/1/1/34
%X Cancer poses a massive health burden with incidence rates expected to double globally over the next decade. In the United Kingdom screening programmes exists for cervical,
   breast, and colorectal cancer. The ability to screen individuals for solid malignant tumours using only a peripheral blood sample would revolutionise cancer services and
   permit early diagnosis and intervention. Raman spectroscopy interrogates native biochemistry through the interaction of light with matter, producing a high definition
   biochemical 'fingerprint' of the target material. This paper explores the possibility of using Raman spectroscopy to discriminate between cancer and non-cancer patients
   through a peripheral blood sample. Forty blood samples were obtained from patients with Head and Neck cancer and patients with respiratory illnesses to act as a positive
   control. Raman spectroscopy was carried out on all samples with the resulting spectra being used to build a classifier in order to distinguish between the cancer and
   respiratory patients' spectra; firstly using principal component analysis (PCA)/linear discriminant analysis (LDA), and secondly with a genetic evolutionary algorithm. The
   PCA/LDA classifier gave a 65percent sensitivity and specificity for discrimination between the cancer and respiratory groups. A sensitivity score of 75percent with a
   specificity of 75percent was achieved with a 'trained' evolutionary algorithm. In conclusion this preliminary study has demonstrated the feasibility of using Raman
   spectroscopy in cancer screening and diagnostics of solid tumours through a peripheral blood sample. Further work needs to be carried out for this technique to be
   implemented in the clinical setting.
%8 September
%Z Also known as \cite19761601

%A Christopher Harris
%A Bernard Buxton
%T Evolving Edge Detectors
%R Research Note RN/96/3
%D 1996
%I 
%I UCL
%C Gower Street, London, WC1E 6BT, UK
%K genetic algorithms, genetic programming, Edge Detection
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/edgegp.ps.gz
%X Edge detection is the process of detecting discontinuities in signals and images. We apply Genetic Programming techniques to the production of high-performance edge
   detectors for 1-D signals and image profiles. The method, which it is intended to extend to the development of practical edge detectors for use in image processing and
   machine vision, uses theoretical performance measures as criteria for the experimental design.
%8 January

%A Christopher Harris
%A Bernard Buxton
%T Evolving Edge Detectors with Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 309--315
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X Edge detection is the process of detecting discontinuities in signals and images. We apply genetic programming techniques to the production of highperformance edge
   detectors for 1-D signals and image profiles. The method, which it is intended to extend to the development of practical edge detectors for use in image processing and
   machine vision, uses theoretical performance measures as criteria for the experimental design.
%8 28--31  July
%Z GP-96

%A Christopher Harris
%A Bernard Buxton
%T GP-COM: A Distributed, Component-Based Genetic Programming System in C++
%R Research Note RN/96/2
%D 1996
%I 
%I UCL
%C Gower Street, London, WC1E 6BT, UK
%K genetic algorithms, genetic programming, Software System
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gpcom.ps
%X Widespread adoption of Genetic Programming techniques as a domain-independent problem solving tool depends on a good underlying software structure. A system is presented
   that mirrors the conceptual make-up of a GP system. Consisting of a loose collection of software components, each with strict interface definitions and roles, the system
   maximises flexibility and minimises effort when applied to a new problem domain.
%8 January

%A Christopher Harris
%A Bernard Buxton
%T GP-COM: A Distributed, Component-Based Genetic Programming System in C++
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 425
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Christopher Harris
%A Bernard Buxton
%T Low-level Edge Detection Using Genetic Programming: performance, specificity and application to real-world signals
%R Research Note RN/97/7
%D 1997
%I 
%I UCL
%C Gower Street, London, WC1E 6BT, UK
%K genetic algorithms, genetic programming, Edge Detection
%U http://citeseer.ist.psu.edu/404512.html
%Z 404512.html PDF link broken 22 Oct 2004

%A Christopher Harris
%T Strongly Types GP to promote hierarchy through explicit syntax constraints
%B Late Breaking Papers at the GP-97 Conference
%E John Koza
%D 1997
%P 72--80
%I Stanford Bookstore Stanford, California, 94305-3079 USA
%C Stanford, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/C.Harris/stgp_structure.ps.gz broken
%8 13-16  July
%Z GP-97LB It's ms-word postscript, so use pageview to look at it rather than ghostview, should print fine.

%A Christopher Harris
%T Enforcing Hierarchy on Solutions with Strongly Typed Genetic Programming
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 292
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Christopher Harris
%T An investigation into the Application of Genetic Programming techniques to Signal Analysis and Feature Detection
%R Ph.D. Thesis
%D 1997
%I 
%I University College, London
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/c.harris/thesisps.zip
%8 26  September

%A E. L. Harris
%A V. Babovic
%A R. A. Falconer
%T Velocity predictions in compound channels with vegetated floodplains using genetic programming
%J International Journal of River Basin Management
%V 1
%N 2
%D 2003
%P 117--123
%I 
%K genetic algorithms, genetic programming, Evolutionary computation, hydrodynamic processes, floodplain vegetation
%X Data collection and storage methods have improved vastly over recent years, however the processes of information and knowledge extraction from data have not mirrored this.
   The application of computer supported scientific knowledge discovery processes to carefully collected observations aims to improve the understanding of the processes that
   generated or produced these data. In this paper, these new techniques have been applied to the complex and poorly understood phenomena of flow through idealised vegetation.
   The ability to predict, with improved accuracy, velocities within wetlands and other vegetated areas would be advantageous as these regions are increasingly being
   recognised for their natural flood alleviation properties. In this study, laboratory data collected in a flume with steady flows over a deep channel with relatively shallow
   vegetated floodplains were used to induce the formulation of expressions using a data driven discovery technique, namely genetic programming (GP). The objective of the
   study was not only to gain an understanding of the effect of vegetation on velocity distributions across a channel but moreover to demonstrate an alternative discovery
   process. The performance of the genetic program is reported for three variations of the GP. The reported results of the experiments were found to be encouraging and further
   work is detailed.
%Z PhD 2003 Environmental Hydroinformatics Tools for Water Quality Management

%A S. D. Harris
%A R. Mustata
%A L. Elliott
%A D. B. Ingham
%A D. Lesnic
%T Parameter Identification Within Rocks Using Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1779
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-758_2.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A S. D. Harris
%A L. Elliott
%A D. B. Ingham
%A M. Pourkashanian
%A C. W. Wilson
%T The Retrieval of Chemical Reaction Rates Using Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1780
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-759_2.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Sarah Harris
%T Genetically-Learned 7-Input Parity Function by an 8 x 8 FPGA
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 214--220
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A William R. Harris
%A Sumit Gulwani
%T Spreadsheet table transformations from examples
%B Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation, PLDI'11
%D 2011
%P 317--328
%I ACM New York, NY, USA
%C San Jose, California, USA
%K genetic algorithms, genetic programming, end-user programming, program synthesis, programming by example, spreadsheet programming, table manipulation, user intent
%Z Also known as \citeHarris:2011:STT:1993498.1993536

%A William R. Harris
%A Sumit Gulwani
%T Spreadsheet table transformations from examples
%J ACM SIGPLAN Notices
%V 46
%D 2011
%P 317--328
%I ACM
%K genetic algorithms, genetic programming, end-user programming, program synthesis, programming by example, spreadsheet programming, table manipulation, user intent
%X Every day, millions of computer end-users need to perform tasks over large, tabular data, yet lack the programming knowledge to do such tasks automatically. In this work,
   we present an automatic technique that takes from a user an example of how the user needs to transform a table of data, and provides to the user a program that implements
   the transformation described by the example. In particular, we present a language of programs TableProg that can describe transformations that real users require.We then
   present an algorithm ProgFromEx that takes an example input and output table, and infers a program in TableProg that implements the transformation described by the example.
   When the program is applied to the example input, it reproduces the example output. When the program is applied to another, potentially larger, table with a 'similar'
   layout as the example input table, then the program produces a corresponding table with a layout that is similar to the example output table. A user can apply ProgFromEx
   interactively, providing multiple small examples to obtain a program that implements the transformation that the user desires. Moreover, ProgFromEx can help identify
   'noisy' examples that contain errors. To evaluate the practicality of TableProg and ProgFromEx, we implemented ProgFromEx as a module for the Microsoft Excel spreadsheet
   program. We applied the module to automatically implement over 50 table transformations specified by end users through examples on on line Excel help forums. In seconds,
   ProgFromEx found programs that satisfied the examples and could be applied to larger input tables. This experience demonstrates that TableProg and ProgFromEx can
   significantly automate the tasks over tabular data that users need to perform.
%8 June
%Z As \citeHarris:2011:PLDI ? Also known as \citeHarris:2011:STT:1993316.1993536

%A Gregory Anthony Harrison
%A Eric W. Worden
%T Genetically programmed learning classifier system description and results
%B Genetic and Evolutionary Computation Conference (GECCO2007) workshop program
%E Tina Yu
%D 2007
%P 2729--2736
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, agent learning, autonomous agent, bucket brigade, evolutionary computation, genetics-based machine learning (GBML), intelligent
   agent, learning classifier system (LCS), reinforcement learning
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2729.pdf
%X An agent population can be evolved in a complex environment to perform various tasks and optimise its job performance using Learning Classifier System (LCS) technology. Due
   to the complexity and knowledge content of some real-world systems, having the ability to use genetic programming, GP, to represent the LCS rules provides a great benefit.
   Methods have been created to extend LCS theory into operation across the power-set of GP-enabled rule content. This system uses a full bucketbrigade system for GP-LCS
   learning. Using GP in the LCS system allows the functions and terminals of the actual problem environment to be used internally directly in the rule set, enabling more
   direct interpretation of the operation of the LCS system. The system was designed and built, and underwent independent testing at an advanced technology research
   laboratory. This paper describes the top-level operation of the system, and includes some of the results of the testing effort, and performance figures.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A Michael L. Harrison
%A James A. Foster
%T Co-evolving Faults to Improve the Fault Tolerance of Sorting Networks
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 57--66
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=57
%X Co-evolving Faults to Improve the Fault Tolerance of Sorting Networks Fault tolerance is an important objective for circuit design, so it is natural to apply genetic
   programming techniques that are already being used for circuit design to enhance fault tolerance. We present preliminary evidence that co-evolving faults with circuits
   enhances the masking of faults in evolved circuits. Our test systems are sorting networks, since these are simple enough to analyse. We show that the overall impact of
   faults in an evolved sorting network can be reduced proportionally to the strength of co-evolutionary pressure.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Emma Hart
%A Peter Ross
%T An Immune System Approach to Scheduling in Changing Environments
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1559--1566
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-723.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Emma Hart
%A Peter Ross
%T Exploiting the Analogy between the Immune System and Sparse Distributed Memories
%J Genetic Programming and Evolvable Machines
%V 4
%N 4
%D 2003
%P 333--358
%I 
%K artificial immune systems, sparse distributed memory, data-clustering
%X The relationship between immunological memory and a class of associative memories known as sparse distributed memories (SDM) is well known. This paper proposes a new model
   for clustering non-stationary data based on a combination of salient features from the two metaphors. The resulting system embodies the important principles of both types
   of memory; it is self-organising, robust, scalable, dynamic and can perform anomaly detection, and is shown to be a more faithful model of the biological system than a
   standard SDM. The model is first applied to clustering static benchmark data-sets, and is shown to outperform another system based on immunological principles. It is then
   applied to clustering non-stationary data-sets with promising results. The system is also shown to be scalable therefore is of potential for clustering real-world
   data-sets.
%8 Decemeber
%Z Special issue on artificial immune systems Article ID: 5144847

%A Emma Hart
%A Peter Ross
%A David Corne
%T Evolutionary Scheduling: A Review
%J Genetic Programming and Evolvable Machines
%V 6
%N 2
%D 2005
%P 191--220
%I 
%O Early and seminal work which applied evolutionary computing methods to scheduling problems from 1985 onwards laid a strong and exciting foundation for the work which has
   been reported over the past decade or so. A survey of the current state-of-the-art was produced in 1999 for the European Network of Excellence on Evolutionary Computing
   EVONET
%8 June

%A William E. Hart
%T Comparing Evolutionary Programs and Evolutionary Pattern Search Algorithms: A Drug Docking Application
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 855--862
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%U ftp://ftp.cs.sandia.gov/pub/papers/wehart/1999/Har99-gecco.ps.gz
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jonathan Joseph Hart
%T The Application of Genetic Programming to Cooperative Movement Planning and Execution
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 86--95
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A John Hart
%A Martin Shepperd
%T Evolving Software with Multiple Outputs and Multiple Populations
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 223--227
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp Tries to evolve controller for fridge. Variable length string. See also \citehart:2002:TR02-06

%A John Hart
%A Martin Shepperd
%T Evolving Software with Multiple Outputs and Multiple Populations
%R Technical Report TR02-06
%D 2002
%I 
%I School of Design, Engineering and Computing, Bournemouth University
%C Royal London House, Christchurch Rd, Bournemouth, BH1 3LT, UK
%K genetic algorithms, genetic programming, evolutionary algorithms, search, embedded system
%U http://dec.bournemouth.ac.uk/ESERG/Technical_Reports/TR02-06/TR02-06.pdf
%X In this research we are concerned with the automatic evolution of programs for control applications, the particular example we use being software for a simple fridge device
   with two inputs and three outputs. By careful choice of the target programming language - in a similar vein to a RISC processor - we are able to represent programs as
   variable length strings and use evolutionary computing techniques to search for fitter individuals. We used a fitness function that summed the fitness of each output
   channel, by various methods, in an attempt to encourage a total solution using a single population of candidate solutions. In general we were able to successfully evolve
   suitable solutions, however, the search sometimes suffered from premature convergence once the functionality for two out of the three output channels had evolved. More
   complex fitness assessment schemes, using mechanisms such as dynamically modifying the fitness associated with an output channel without additional benefit. These
   difficulties in attempting to do too much with a single population pointed to a `divide and conquer' approach whereby one (or more) populations are dedicated to solving for
   one output channel alone - whilst being exposed to all inputs. This is seen to be an acceptable approach due to the growth in multi-tasking operating systems and
   multiprocessor platforms.
%8 July
%Z as \citehart:2002:gecco:lbp

%A John K. Hart
%T Automatic control program creation using concurrent Evolutionary Computing
%R Ph.D. Thesis
%D 2004
%I 
%I Bournemouth University
%C UK
%K genetic algorithms, genetic programming
%8 January
%Z related publications \citehart:2002:gecco:lbp \citehart:2004:eurogp

%A John Hart
%A Martin Shepperd
%T The Evolution of Concurrent Control Software Using Genetic Programming
%R Technical Report TR03-08
%D 2003
%I 
%I Empirical Software Engineering Research Group School of Design, Engineering \& Computing, Bournemouth University
%C Royal London House, Christchurch Rd, Bournemouth, BH1 3LT, UK
%K genetic algorithms, genetic programming, linear genetic programming, embedded software
%U http://dec.bournemouth.ac.uk/ESERG/Technical_Reports/TR03-08/TR03-08.pdf
%X Despite considerable progress in GP over the past 10 years, there are many outstanding challenges that need to be addressed before it will be widely deployed for developing
   useful software. In this paper we suggest a method for the automatic creation of concurrent control software using Linear Genetic Programming (LGP) and a `divide and
   conquer' approach. The method involves decomposing the whole problem into a multi-task solution with multiple inputs and multiple outputs - similar to the process used to
   implement embedded control solutions. We describe the necessary architecture of typical embedded control systems and their relevance to this work, the software evolution
   scheme used and lastly demonstrate the technique for an embedded software problem, namely a washing machine controller.
%Z See also \citehart:2004:eurogp

%A John Hart
%A Martin Shepperd
%T The Evolution of Concurrent Control Software Using Genetic Programming
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 289--298
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=289
%X Despite considerable progress in GP over the past 10 years, there are many outstanding challenges that need to be addressed before it will be widely deployed for developing
   useful software. We suggest a method for the automatic creation of concurrent control software using Linear Genetic Programming (LGP) and a divide and conquer approach. The
   method involves decomposing the whole problem into a multi-task solution with multiple inputs and multiple outputs -- similar to the process used to implement embedded
   control solutions. We describe the necessary architecture of typical embedded control systems and their relevance to this work, the software evolution scheme used and
   lastly demonstrate the technique for an embedded software problem, namely a washing machine controller.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004 See also \citehart:2004:eurogpTR
%@ 3-540-21346-5

%A Adrian R. Hartley
%T Accuracy-based fitness allows similar performance to humans in static and dynamic classification environments
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 266--273
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bris.ac.uk/~kovacs/lcs.archive/Hartley1999a.ps.gz
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Morten Hartmann
%A Frode Eskelund
%A Pauline C. Haddow
%A Julian F. Miller
%T Evolving Fault Tolerance On An Unreliable Technology Platform
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 171--177
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K evolvable hardware, digital circuits, fault tolerance, noise robustness
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-04.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Brad Harvey
%A James A. Foster
%A Deborah Frincke
%T Byte Code Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/547985.html
%X This paper explores the idea of using Genetic Programming (GP) to evolve Java Virtual Machine (JVM) byte code to solve a sample symbolic regression problem. The
   evolutionary process is done completely in memory using a standard Java environment.
%8 22-25  July
%Z GP-98LB

%A Inman Harvey
%T Open the Box
%D 1997
%I 
%C East Lansing, MI, USA
%K genetic algorithms, variable size representation, SAGA
%O Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97
%8 20  July

%A Brad Harvey
%A James Foster
%A Deborah Frincke
%T Towards Byte Code Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1234
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-418.ps
%X his paper uses the GP paradigm to evolve linear genotypes (individuals) that consist of Java byte code. Our prototype GP system is implemented in Java using a standard Java
   development kit (JDK). The evolutionary process is done completely in memory and the fitness of individuals is determined by directly executing them in the Java Virtual
   Machine (JVM). We validate our approach by solving a functional regression problem with a fourth degree polynomial, and a classification problem diagnosing thyroid disease.
   Our implementation provides a fast, effective means for evolving native machine code for the JVM.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A K. Burton Harvey
%A Chrisila C. Pettey
%T The Outlaw Method for Solving Multimodal Functions with Split Ring Parallel Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 274--280
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-382.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Neal R. Harvey
%A Simon Perkins
%A Steven P. Brumby
%A James Theiler
%A Reid B. Porter
%A A. Cody Young
%A Anil K. Varghese
%A John J. Szymanski
%A Jeffrey J. Bloch
%T Finding Golf Courses: The Ultra High Tech Approach
%B Real-World Applications of Evolutionary Computing
%S LNCS
%E Stefano Cagnoni and Riccardo Poli and Yun Li and George Smith and David Corne and Martin J. Oates and Emma Hart and Pier Luca Lanzi and Egbert J. W. Boers and Ben Paechter
   and Terence C. Fogarty
%V 1803
%D 2000
%P 54--64
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.genie.lanl.gov/green/publications/harveyEvoIASP2000.pdf
%X The search for a suitable golf course is a very important issue in the travel plans of any modern manager. Modern management is also infamous for its penchant for high-tech
   gadgetry. Here we combine these two facets of modern management life. We aim to provide the cutting edge manager with a method of finding golf courses from space! In this
   paper, we present Genie: a hybrid evolutionary algorithm-based system that tackles the general problem of finding features of interest in multi-spectral remotely-sensed
   images, including, but not limited to, golf courses. Using this system we are able to successfully locate golf courses in 10-channel satellite images of several desirable
   US locations.
%8 17  April
%Z EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings
%@ 3-540-67353-9

%A N. R. Harvey
%A S. P. Brumby
%A S. J. Perkins
%A R. B. Porter
%A J. Theiler
%A A. C. Young
%A J. J. Szymanski
%A J. J. Bloch
%T Parallel evolution of image processing tools for multispectral imagery
%B Imaging Spectrometry VI, Procceedings of SPIE
%E Michael R. Descour and Sylvia S. Shen
%V 4132
%D 2000
%P 72--82
%I 
%K genetic algorithms, genetic programming, GENIE, ALADDIN
%U http://public.lanl.gov/jt/Papers/harveySPIE4132.ps.gz
%X We describe the implementation and performance of a parallel, hybrid evolutionary-algorithm based system, which optimises image processing tools for feature-finding tasks
   in multi-spectral imagery (MSI) data sets. Our system uses an integrated spatio-spectral approach and is capable of combining suitably-registered data from different
   sensors. We investigate the speed-up obtained by parallelisation of the evolutionary process via multiple processors (a workstation cluster) and develop a model...

%A Neal R. Harvey
%A James Theiler
%A Steven P. Brumby
%A Simon Perkins
%A John J. Szymanski
%A Jeffrey J. Bloch
%A Reid B. Porter
%A Mark Galassi
%A A. Cody Young
%T Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction
%J IEEE Transactions on Geoscience and Remote Sensing
%V 40
%N 2
%D 2002
%P 393--404
%I 
%K genetic algorithms, genetic programming, Supervised Classification, Image Processing, Evolutionary Algorithms, Multispectral Imagery, Remote Sensing, feature extraction,
   geophysical signal processing, geophysical techniques, geophysics computing, image classification, multidimensional signal processing, terrain mapping, GENIE, GENetic
   Imagery Exploitation, IR, feature extraction, geophysical measurement technique, hybrid evolutionary algorithm, image classification, image processing, infrared, land
   surface, multispectral remote sensing, supervised classifier, terrain mapping, visible
%U http://citeseer.ist.psu.edu/561309.html
%X We have developed an automated feature detection/ classification system, called Genie (GENetic Imagery Exploitation), which has been designed to generate image processing
   pipelines for a variety of feature detection/ classification tasks. Genie is a hybrid evolutionary algorithm that addresses the general problem of finding features of
   interest in multi-spectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of Genie with several conventional
   supervised classification techniques, for a number of classification tasks using multi-spectral remotely-sensed imagery.
%8 February
%Z On line version not identical to IEEE version Inspec Accession Number: 7265352, CODEN: IGRSD2

%A Michael Harwerth
%T Experiments on Islands
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 239--249
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming: poster
%X The use of segmented populations (Islands) has proved to be advantageous for Genetic Programming (GP). This paper discusses the application of segmentation and migration
   strategies to a system for Linear Genetic Programming (LGP). Besides revisiting migration topologies, a modification for migration strategies is proposed --- migration
   delay. It is found that highly connected topologies yield better results than those with segments coupled more loosely, and that migration delays can further improve the
   effect of migration.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Samiul Hasan
%A Sabine Daugelat
%A P. S. Srinivasa Rao
%A Mark Schreiber
%T Prioritizing Genomic Drug Targets in Pathogens: Application to Mycobacterium tuberculosis
%J PLoS Computational Biology
%V 2
%N 6
%D 2006
%P e61
%I 
%K genetic algorithms
%U http://compbiol.plosjournals.org/archive/1553-7358/2/6/pdf/10.1371_journal.pcbi.0020061-L.pdf
%X We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this
   software to produce three prioritised drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately
   needed. Each list is based on an individual criterion. The first list prioritises metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome
   (metabolic choke points) and their similarity to known druggable protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small
   molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the
   Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant
   state referred to as persistence. The final list aims to prioritise potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current,
   candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritising studied targets.
   It can also be shown that targets can be prioritised by using evolutionary programming to optimise the weights of each desired property. We demonstrate this approach in
   prioritizing persistence targets.
%8 June

%A Yasuhisa Hasegawa
%A Toshio Fukuda
%T Motion Generation of Two-link Brachiation Robot
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 407--412
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Artifical life and evolutionary robotics
%8 13-16  July
%Z GP-97

%A Yoshihiko Hasegawa
%A Hitoshi Iba
%T Optimizing Programs with Estimation of Bayesian Network
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 5527--5534
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X Genetic Programming (GP) is a powerful optimisation algorithm and has been applied to many problems. GP is an extension of Genetic Algorithm (GA) which can handle programs,
   functions, etc. GP evolves with genetic operators such as crossover and mutation. The crossover operator in GP however selects sub-trees randomly and this selection is done
   regardless of the problem. This gives rise to the destruction of good building blocks. Recently, probabilistic model building techniques have been applied to GP to estimate
   the building blocks properly. This type of algorithm is called Probabilistic Model Building GP (PMBGP). Because GP uses many types of nodes, prior PMBGPs have been faced
   with the problem of huge CPT (Conditional Probability Table) size. The large CPT not only consumes a lot of memory but also requires many samples to construct networks. We
   propose a new PMBGP that uses Bayesian network for generating new individuals. In our approach, a special chromosome called expanded
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Yoshihiko Hasegawa
%A Hitoshi Iba
%T Estimation of Bayesian network for program generation
%B Proceedings of the Third Asian-Pacific workshop on Genetic Programming
%E The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen
%D 2006
%P 35--46
%I 
%C Military Technical Academy, Hanoi, VietNam
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/hasegawa.pdf
%X Genetic Programming (GP) is a powerful optimisation algorithm, which employs crossover for a main genetic operator. Because a crossover operator in GP selects sub-trees
   randomly, the building blocks may be destroyed by crossover. Recently, algorithms called PMBGPs (Probabilistic Model Building GP) based on probabilistic techniques have
   been proposed in order to improve the problem above. We propose a new PMBGP employing Bayesian network for generating new individuals with a special chromosome called
   expanded parse tree, which much reduces the number of possible symbols at each node. Although the large number of symbols gives rise to the large conditional probability
   table and requires a lot of samples to estimate the interactions among nodes, a use of the expanded parse tree overcomes these problems. A computational experiment on a
   deceptive MAX problem (DMAX problem) demonstrates that our new PMBGP is superior to other program evolution methods.
%Z http://www.aspgp.org

%A Yoshihiko Hasegawa
%A Hitoshi Iba
%T Estimation of Distribution Algorithm Based on Probabilistic Grammar with Latent Annotations
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 1043--1050
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Genetic Programming (GP) which mimics the natural evolution to optimise functions and programs, has been applied to many problems. In recent years, evolutionary algorithms
   are seen from the viewpoint of the estimation of distribution. Many algorithms called EDAs (Estimation of Distribution Algorithms) based on probabilistic techniques have
   been proposed. Although probabilistic context free grammar (PCFG) is often used for the function and program evolution, it assumes the independence among the production
   rules. With this simple PCFG, it is not able to induce the building-blocks from promising solutions. We have proposed a new function evolution algorithm based on PCFG using
   latent annotations which weaken the independence assumption. Computational experiments on two subjects (the royal tree problem and the DMAX problem) demonstrate that our
   new approach is highly effective compared to prior approaches.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Yoshihiko Hasegawa
%A Hitoshi Iba
%T A Bayesian Network Approach to Program Generation
%J IEEE Transactions on Evolutionary Computation
%V 12
%N 6
%D 2008
%P 750--764
%I 
%K genetic algorithms, genetic programming, belief networks, probability, trees (mathematics)Bayesian network, conditional probability table, evolutionary algorithms, expanded
   parse tree, powerful optimization algorithm, probabilistic techniques, program generation
%X Genetic programming (GP) is a powerful optimization algorithm that has been applied to a variety of problems. This algorithm can, however, suffer from problems arising from
   the fact that a crossover, which is a main genetic operator in GP, randomly selects crossover points, and so building blocks may be destroyed by the action of this
   operator. In recent years, evolutionary algorithms based on probabilistic techniques have been proposed in order to overcome this problem. In the present study, we propose
   a new program evolution algorithm employing a Bayesian network for generating new individuals. It employs a special chromosome called the expanded parse tree , which
   significantly reduces the size of the conditional probability table (CPT). Prior prototype tree-based approaches have been faced with the problem of huge CPTs, which not
   only require significant memory resources, but also many samples in order to construct the Bayesian network. By applying the present approach to three distinct
   computational experiments, the effectiveness of this new approach for dealing with deceptive problems is demonstrated.
%8 Decemeber
%Z POLE, EPT, Kullback-Leibler. Max problem \citelangdon:1997:MAX. DMAX deceptive max problem. Royal tree problem. Also known as \cite4470578

%A Yoshihiko Hasegawa
%A Hitoshi Iba
%T Latent Variable Model for Estimation of Distribution Algorithm Based on a Probabilistic Context-Free Grammar
%J IEEE Transactions on Evolutionary Computation
%V 13
%N 4
%D 2009
%P 858--878
%I 
%K genetic algorithms, genetic programming, EM algorithm, estimation of distribution algorithm, variational Bayes.context-sensitive grammars, probability context freedom
   assumption, distribution algorithm estimation, evolutionary algorithm, function evolution, genetic operator, genetic programming techniques, latent variable model,
   probabilistic context-free grammar, probabilistic program evolution, probabilistic techniques
%8 August

%A Ghada Hassan
%A Christopher D. Clack
%T Multiobjective robustness for portfolio optimization in volatile environments
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1507--1514
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, dynamic environment, finance, multiobjective optimisation, portfolio optimisation, robustness, Real-World application
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1507.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389387

%A Ghada Hassan
%T Non-linear factor model for asset selection using multi objective genetic programming
%B GECCO-2008 Workshop: Advanced Research Challenges in Financial Evolutionary Computing (ARC-FEC)
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 1859--1862
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, Factor models, finance, multiobjective optimisation, portfolio optimisation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1859.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1388990

%A Ghada Hassan
%A Christopher D. Clack
%T Robustness of multiple objective GP stock-picking in unstable financial markets: real-world applications track
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1513--1520
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X Multiple Objective Genetic Programming (MOGP) is a promising stock-picking technique for fund managers, because the Pareto front approximates the risk/reward Efficient
   Frontier and simplifies the choice of investment model for a given client's attitude to risk. Unfortunately GP solutions don't work well if used in an environment that is
   different from the training environment, and the financial markets are notoriously unstable, often lurching from one market context to another (e.g. "bull" to "bear"). This
   turns out to be a hard problem -- simple dynamic adaptation methods are insufficient and robust behaviour of solutions becomes extremely important. In this paper we provide
   the first known empirical results on the robustness of MOGP solutions in an unseen environment consisting of real-world financial data. We focus on two well-known
   mechanisms to determine which leads to the more robust solutions: Mating Restriction, and Diversity Preservation. We introduce novel metrics for Pareto front robustness,
   and a novel variation on Mating Restriction, both based on phenotypic cluster analysis.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Ghada Nasr Aly Hassan
%T Multiobjective genetic programming for financial portfolio management in dynamic environments
%R Ph.D. Thesis Doctoral
%D 2010
%I 
%I University College London
%C UK
%K genetic algorithms, genetic programming
%U http://eprints.ucl.ac.uk/20456/
%X Multiobjective (MO) optimisation is a useful technique for evolving portfolio optimisation solutions that span a range from high-return/high-risk to low-return/low-risk.
   The resulting Pareto front would approximate the risk/reward Efficient Frontier [Mar52], and simplifies the choice of investment model for a given client's attitude to
   risk. However, the financial market is continuously changing and it is essential to ensure that MO solutions are capturing true relationships between financial factors and
   not merely over fitting the training data. Research on evolutionary algorithms in dynamic environments has been directed towards adapting the algorithm to improve its
   suitability for retraining whenever a change is detected. Little research focused on how to assess and quantify the success of multiobjective solutions in unseen
   environments. The multiobjective nature of the problem adds a unique feature to be satisfied to judge robustness of solutions. That is, in addition to examining whether
   solutions remain optimal in the new environment, we need to ensure that the solutions relative positions previously identified on the Pareto front are not altered. This
   thesis investigates the performance of Multiobjective Genetic Programming (MOGP) in the dynamic real world problem of portfolio optimisation. The thesis provides new
   definitions and statistical metrics based on phenotypic cluster analysis to quantify robustness of both the solutions and the Pareto front. Focusing on the critical period
   between an environment change and when retraining occurs, four techniques to improve the robustness of solutions are examined. Namely, the use of a validation data set;
   diversity preservation; a novel variation on mating restriction; and a combination of both diversity enhancement and mating restriction. In addition, preliminary
   investigation of using the robustness metrics to quantify the severity of change for optimum tracking in a dynamic portfolio optimisation problem is carried out. Results
   show that the techniques used offer statistically significant improvement on the solutions' robustness, although not on all the robustness criteria simultaneously.
   Combining the mating restriction with diversity enhancement provided the best robustness results while also greatly enhancing the quality of solutions.

%A Yasser Fouad Hassan
%T Rough Set Genetic Programming
%J International Journal of Computers and Their Applications
%V 17
%N 3
%D 2010
%P 161--171
%I 
%K genetic algorithms, genetic programming
%U http://www.isca-hq.org/j-list.htm

%A Toshiharu Hatanaka
%A Katsuji Uosaki
%T Hammerstein Model Identification Method Based on Genetic Programming
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 1430--1435
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, System identification, Hammerstein models, Nonlinear systems, Evolutionary computation
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . AIC Akaike
   information criterion
%@ 0-7803-6658-1

%A Koichi Hatta
%A Shin'ichi Wakabayashi
%A Tetsushi Koide
%T Adapting Parameters Based on Pedigree of Individuals in a Genetic Algorithm
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 510--517
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Justin C. Haugh
%T Evolution of Life Cycle Differentiation using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 102--110
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2002/Haugh.pdf
%X This paper describes the emergence of age and sexual differentiation among computer programs in a digital ecosystem. Programs to control the behavior of simulated mice are
   randomly generated, and are evolved over time using a steadystate genetic programming system with tournament selection. Problems and early failures are described, and
   solutions are discussed. Evolved programs demonstrating life stage differentiation are examined with a comparison of relative fitness
%8 June
%Z part of \citekoza:2002:gagp 10 by 10 world. Snakes and mice. lilgp problem -> gpc++ 0.40

%A Ami Hauptman
%A Moshe Sipper
%T GP-EndChess: Using Genetic Programming to Evolve Chess Endgame Players
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 120--131
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=120
%X We apply genetic programming to the evolution of strategies for playing chess endgames. Our evolved programs are able to draw or win against an expert human-based strategy,
   and draw against CRAFTY---a world-class chess program, which finished second in the 2004 Computer Chess Championship.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Ami Hauptman
%A Moshe Sipper
%T Analyzing the Intelligence of a Genetically Programmed Chess Player
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2005)
%E Franz Rothlauf
%D 2005
%I 
%C Washington, D.C., USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/21-hauptmann.pdf
%X We investigate a strong chess endgame player, previously evolved by us through genetic programming [1]. Its performance is analysed across four games, demonstrating the
   chess-playing capabilities developed through evolution. We end with a discussion of our GP-evolved player\'s pros and cons
%8 25-29  June
%Z Distributed on CD-ROM at GECCO-2005

%A Ami Hauptman
%A Moshe Sipper
%T Evolution of an Efficient Search Algorithm for the Mate-In-N Problem in Chess
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 78--89
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X We propose an approach for developing efficient search algorithms through genetic programming. Focusing on the game of chess we evolve entire game-tree search algorithms to
   solve the Mate-In-N problem: find a key move such that even with the best possible counterplays, the opponent cannot avoid being mated in (or before) move N. We show that
   our evolved search algorithms successfully solve several instances of the Mate-In-N problem, for the hardest ones developing 47percent less game-tree nodes than CRAFTY---a
   state-of-the-art chess engine with a ranking of 2614 points. Improvement is thus not over the basic alpha-beta algorithm, but over a world-class program using all standard
   enhancements.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Ami Hauptman
%A Achiya Elyasaf
%A Moshe Sipper
%A Assaf Karmon
%T GP-rush: using genetic programming to evolve solvers for the rush hour puzzle
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 955--962
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X We evolve heuristics to guide IDA* search for the 6x6 and 8x8 versions of the Rush Hour puzzle, a PSPACE-Complete problem, for which no efficient solver has yet been
   reported. No effective heuristic functions are known for this domain, and--before applying any evolutionary thinking--we first devise several novel heuristic measures,
   which improve (non-evolutionary) search for some instances, but hinder search substantially for many other instances. We then turn to genetic programming (GP) and find that
   evolution proves immensely efficacious, managing to combine heuristics of such highly variable utility into composites that are nearly always beneficial, and far better
   than each separate component. GP is thus able to beat both the human player of the game and also the human designers of heuristics.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Thomas D. Haynes
%T A Simulation of Adaptive Agents in a Hostile Environment
%R M.S. Thesis
%D 1994
%I 
%I University of Tulsa
%C Tulsa, OK, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/2240.html
%X The Genetic Programming Algorithm is used to construct an Autonomous Agent capable of learning how to survive a hostile environment. Randomly generated programs, which
   control the interaction of the Agent with its environment, are recombined to form better programs. Each generation of the population of Agents is placed into the Simulator
   with the ultimate goal of producing an Agent capable of surviving any environment. The Simulator determines the raw fitness of each Agent by interpreting the associated
   program. General programs are evolved to solve this problem. Different environmental setups are presented to show the generality of the solution. Certain constructs always
   appear to facilitate the solution of subproblems of the task. This is evidenced in similar responses of the Average Fitness per Generation curves for the different runs.
%8 April

%A Thomas Haynes
%A Roger Wainwright
%A Sandip Sen
%T Evolving Cooperation Strategies
%R Technical Report UTULSA-MCS-94-10
%D 1994
%I 
%I The University of Tulsa
%C Tulsa, OK, USA
%K genetic algorithms, genetic programming, ccoperation strategies
%U http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icmas95.pdf
%X The identification, design, and implementation of strategies for cooperation is a central research issue in the field of Distributed Artificial Intelligence (DAI). We
   propose a novel approach to the construction of cooperation strategies for a group of problem solvers based on the Genetic Programming (GP) paradigm. GP's are a class of
   adaptive algorithms used to evolve solution structures that optimize a given evaluation criterion. Our approach is based on designing a representation for cooperation
   strategies that can be manipulated by GPs. We present results from experiments in the predator-prey domain, which has been extensively studied as an easy-to-describe but
   difficult-to-solve cooperation problem domain. They key aspect of our approach is the minimal reliance on domain knowledge and human intervention in the construction of
   good cooperation strategies. Promising comparison results with prior systems lend credence to the viability of this approach.
%8 16  Decemeber

%A Thomas D. Haynes
%A Roger L. Wainwright
%T A Simulation of Adaptive Agents in Hostile Environment
%B Proceedings of the 1995 ACM Symposium on Applied Computing
%E K. M. George and Janice H. Carroll and Ed Deaton and Dave Oppenheim and Jim Hightower
%D 1995
%P 318--323
%I ACM Press
%C Nashville, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/2240.html
%X In this paper we use the genetic programming technique to evolve programs to control an autonomous agent capable of learning how to survive in a hostile environment. In
   order to facilitate this goal, agents are run through random environment configurations. Randomly generated programs, which control the interaction of the agent with its
   environment, are recombined to form better programs. Each generation of the population of agents is placed into the Simulator with the ultimate goal of producing an agent
   capable of surviving any environment. The environment that an agent is presented consists of other agents, mines, and energy. The goal of this research is to construct a
   program which when executed will allow an agent (or agents) to correctly sense, and mark, the presence of items (energy and mines) in any environment. The Simulator
   determines the raw fitness of each agent by interpreting the associated program. General programs are evolved to solve this problem. Different environmental setups are
   presented to show the generality of the solution. These environments include one agent in a fixed environment, one agent in a fluctuating environment, and multiple agents
   in a fluctuating environment cooperating together. The genetic programming technique was extremely successful. The average fitness per generation in all three environments
   tested showed steady improvement. Programs were successfully generated that enabled an agent to handle any possible environment.
%Z Agent has access to memory holding information on locations it has already visited. Agents are run through random environment configurations. Environment contains other
   agents, lethal mines and energy. Agents aims to sense and mark these. One example: multiple agents cooperating in a fluctating environment. GP generated an "agent to handle
   any possible enironment".

%A Thomas D. Haynes
%A Roger L. Wainwright
%A Sandip Sen
%T Evolving Cooperating Strategies
%B Proceedings of the first International Conference on Multiple Agent Systems
%E Victor Lesser
%D 1995
%P 450
%I AAAI Press/MIT Press
%C San Francisco, USA
%K genetic algorithms, genetic programming, evolutionary computation, cooperation strategies, poster
%U http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icmas95.pdf
%X The identification, design, and implementation of strategies for cooperation is a central research issue in the field of Distributed Artificial Intelligence (DAI). We
   propose a novel approach to the construction of cooperation strategies for a group of problem solvers based on the Genetic Programming (GP) paradigm. GP's are a class of
   adaptive algorithms used to evolve solution structures that optimize a given evaluation criterion. Our approach is based on designing a representation for cooperation
   strategies that can be manipulated by GPs. We present results from experiments in the predator-prey domain, which has been extensively studied as an easy-to-describe but
   difficult-to-solve cooperation problem domain. They key aspect of our approach is the minimal reliance on domain knowledge and human intervention in the construction of
   good cooperation strategies. Promising comparison results with prior systems lend credence to the viability of this approach.
%O Poster
%8 12--14  June
%Z 13 page version available via url
%@ 0-262-62102-9

%A Thomas Haynes
%A Roger Wainwright
%A Sandip Sen
%A Dale Schoenefeld
%T Strongly typed genetic programming in evolving cooperation strategies
%B Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)
%E Larry J. Eshelman
%D 1995
%P 271--278
%I Morgan Kaufmann San Francisco, CA, USA
%C Pittsburgh, PA, USA
%K genetic algorithms, genetic programming
%U http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icga95.pdf
%X A key concern in genetic programming (GP) is the size of the state-space which must be searched for large and complex problem domains. One method to reduce the state-space
   size is by using Strongly Typed Genetic Programming (STGP). We applied both GP and STGP to construct cooperation strategies to be used by multiple predator agents to pursue
   and capture a prey agent on a grid-world. This domain has been extensively studied in Distributed Artificial Intelligence (DAI) as an easy-to-describe but
   difficult-to-solve cooperation problem. The evolved programs from our systems are competitive with manually derived greedy algorithms. In particular the STGP paradigm
   evolved strategies in which the predators were able to achieve their goal without explicitly sensing the location of other predators or communicating with other predators.
   This represents an improvement over previous research in this area. The results of our experiments indicate that STGP is able to evolve programs that perform significantly
   better than GP evolved programs. In addition, the programs generated by STGP were easier to understand.
%8 15-19  July
%Z Our printers barf at graph on page 8.
%@ 1-55860-370-0

%A Thomas Haynes
%A Sandip Sen
%T Evolving Behavioral Strategies in Predators and Prey
%B IJCAI-95 Workshop on Adaptation and Learning in Multiagent Systems
%E Sandip Sen
%D 1995
%P 32--37
%I Morgan Kaufmann San Francisco, CA, USA
%I IJCAII,AAAI,CSCSI
%C Montreal, Quebec, Canada
%K genetic algorithms, genetic programming, cooperation strategies
%U http://citeseer.ist.psu.edu/haynes96evolving.html
%X The predator/prey domain is used to conduct research in Distributed Artificial Intelligence. Genetic Programing is used to evolve behavioral strategies for the predator
   agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not
   surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms.
%8 20-25  August
%Z see also \citeHaynes:1996:EBS

%A Thomas Haynes
%A Sandip Sen
%A Dale Schoenefeld
%A Roger Wainwright
%T Evolving a Team
%B Working Notes for the AAAI Symposium on Genetic Programming
%E E. V. Siegel and J. R. Koza
%D 1995
%P 23--30
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025, USA
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Library/Symposia/Fall/fs95-01.php#23
%X We introduce a cooperative co--evolutionary system to facilitate the development of teams of agents. Specifically, we deal with the credit assignment problem of how to
   fairly split the fitness of a team to all of its participants. We believe that $k$ different strategies for controlling the actions of a group of $k$ agents can combine to
   form a cooperation strategy which efficiently results in attaining a global goal. A concern is the amount of time needed to either evolve a good team or reach convergence.
   We present several crossover mechanisms to reduce this time. Even with this mechanisms, the time is large; which precluded the gathering of sufficient data for a
   statistical base.
%8 10--12  November
%Z AAAI-95f GP. Part of \citesiegel:1995:aaai-fgp \em Telephone: 415-328-3123 \em Fax: 415-321-4457 \em email info@aaai.org \em URL: http://www.aaai.org/

%A Thomas Haynes
%A Sandip Sen
%T Evolving Behavioral Strategies in Predators and Prey
%B Adaptation and Learning in Multiagent Systems
%S Lecture Notes in Artificial Intelligence
%E Gerhard Wei\ss and Sandip Sen
%V 1042
%D 1995
%P 113--126
%I Springer Verlag
%C Berlin, Germany
%K genetic algorithms, genetic programming, STGP
%X The predator/prey domain is used to conduct research in Distributed Artificial Intelligence. Genetic Programming is used to evolve behavioural strategies for the predator
   agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not
   surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms.
%Z Published in 1996?

%A Thomas Haynes
%A Sandip Sen
%A Dale Schoenefeld
%A Roger Wainwright
%T Evolving Multiagent Coordination Strategies with Genetic Programming
%R Technical Report UTULSA-MCS-95-04
%D 1995
%I 
%I The University of Tulsa
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/26626.html
%X The design and development of behavioral strategies to coordinate the actions of multiple agents is a central issue in multiagent systems research. We propose a novel
   approach of evolving, rather than handcrafting, behavioral strategies. The evolution scheme used is a variant of the Genetic Programming (GP) paradigm. As a proof of
   principle, we evolve behavioral strategies in the predator-prey domain that has been studied widely in the Distributed Artificial Intelligence community. We use the GP to
   evolve behavioral strategies for individual agents, as prior literature claims that communication between predators is not necessary for successfully capturing the prey.
   The evolved strategy, when used by each predator, performs better than all but one of the handcrafted strategies mentioned in literature. We analyze the shortcomings of
   each of these strategies. The next set of experiments involve co-evolving predators and prey. To our surprise, a simple prey strategy evolves that consistently evades all
   of the predator strategies. We analyze the implications of the relative successes of evolution in the two sets of experiments and comment on the nature of domains for which
   GP based evolution is a viable mechanism for generating coordination strategies. We conclude with our design for concurrent evolution of multiple agent strategies in
   domains where agents need to communicate with each other to successfully solve a common problem.
%8 May  31

%A Thomas D. Haynes
%A Dale A. Schoenefeld
%A Roger L. Wainwright
%T Type Inheritance in Strongly Typed Genetic Programming
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 359--376
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/376
%X Genetic Programming (GP) is an automatic method for generating computer programs, which are stored as data structures and manipulated to evolve better programs. An
   extension restricting the search space is Strongly Typed Genetic Programming (STGP), which has, as a basic premise, the removal of closure by typing both the arguments and
   return values of functions, and by also typing the terminal set. A restriction of STGP is that there are only two levels of typing. We extend STGP by allowing a type
   hierarchy, which allows more than two levels of typing.
%O 18
%@ 0-262-01158-1

%A Thomas Haynes
%A Rose Gamble
%A Leslie Knight
%A Roger Wainwright
%T Entailment for Specification Refinement
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 90--97
%I MIT Press Cambridge, MA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X Specification refinement is part of formal program derivation, a method by which software is directly constructed from a provably correct specification. Because program
   derivation is an intensive manual exercise used for critical software systems, an automated approach would allow it to be viable for many other types of software systems.
   The goal of this research is to determine if genetic programming (GP) can be used to automate the specification refinement process. The initial steps toward this goal are
   to show that a well--known proof logic for program derivation can be encoded such that a GP--based system can infer sentences in the logic for proof of a particular
   sentence. The results are promising and indicate that GP can be useful in aiding program derivation.
%8 28--31  July
%Z GP-96

%A Thomas Haynes
%T Clique Detection via Genetic Programming
%R Technical Report UTULSA-MCS-95-02
%D 1995
%I 
%I The University of Tulsa
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/135522.html
%X Genetic Programming is used as a technique for detecting cliques in a network. Candidate cliques are represented in lists, and the lists are manipulated such that larger
   cliques are formed from the candidates. The clique detection problem has some interesting implications to the Strongly Typed Genetic Programming paradigm, namely in forming
   a class hierarchy. The problem is also useful in that it is easy to add noise.
%8 April  24

%A Thomas Haynes
%A Dale Schoenefeld
%T Clique Detection via Genetic Programming
%R Technical Report UTULSA-MCS-96-05
%D 1996
%I 
%I The University of Tulsa
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/haynes95clique.html
%X Genetic programming is applied to the task of finding all of the cliques in a graph. Nodes in the graph are represented as tree structures, which are then manipulated to
   form candidate cliques. The intrinsic properties of clique detection complicates the design of a good fitness evaluation. We analyze those properties, and show the clique
   detector is found to be better at finding the maximum clique in the graph, not the set of all cliques.
%8 March  15
%Z Full version of GP'96 poster

%A Thomas Haynes
%A Dale Schoenefeld
%T Clique Detection via Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 426
%I MIT Press Cambridge, MA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X Genetic programming is applied to the task of finding all of the cliques in a graph. Nodes in the graph are represented as tree structures, which are then manipulated to
   form candidate cliques. The intrinsic properties of clique detection complicates the design of a good fitness evaluation. We analyze those properties, and show the clique
   detector is found to be better at finding the maximum clique in the graph, not the set of all cliques.
%8 28--31  July
%Z GP-96 see also technical report Haynes:1995:CDGb

%A Thomas Haynes
%T Duplication of Coding Segments in Genetic Programming
%R Technical Report UTULSA-MCS-96-03
%D 1996
%I 
%I The University of Tulsa
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/haynes96duplication.html
%X Research into the utility of non--coding segments, or introns, in genetic--based encodings has shown that they expedite the evolution of solutions in domains by protecting
   building blocks against destructive crossover. We consider a genetic programming system where non--coding segments can be removed, and the resultant chromosomes returned
   into the population. This parsimonious repair leads to premature convergence, since as we remove the naturally occurring non--coding segments, we strip away their
   protective backup feature. We then duplicate the coding segments in the repaired chromosomes, and place the modified chromosomes into the population. The duplication method
   significantly improves the learning rate in the domain we have considered. We also show that this method can be applied to other domains.
%8 March  11
%Z Longer version of AAAI '96 paper \citeHaynes:1996:DCSb

%A Thomas Haynes
%T Duplication of Coding Segments in Genetic Programming
%B Proceedings of the Thirteenth National Conference on Artificial Intelligence
%V 1
%D 1996
%P 344--349
%I AAAI Press / MIT Press
%C Portland, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/haynes96duplication.html
%X Research into the utility of non--coding segments, or introns, in genetic--based encodings has shown that they expedite the evolution of solutions in domains by protecting
   building blocks against destructive crossover. We consider a genetic programming system where non--coding segments can be removed, and the resultant chromosomes returned
   into the population. This parsimonious repair leads to premature convergence, since as we remove the naturally occurring non--coding segments, we strip away their
   protective backup feature. We then duplicate the coding segments in the repaired chromosomes, and place the modified chromosomes into the population. The duplication method
   significantly improves the learning rate in the domain we have considered. We also show that this method can be applied to other domains.
%8 4-6  August
%Z see also tech report \citeHaynes:1996:DCSa
%@ 0-262-51091-X

%A Thomas Haynes
%A Sandip Sen
%T Evolving Behavioral Strategies in Predators and Prey
%B Adaptation and Learning in Multi--Agent Systems
%S Lecture Notes in Artificial Intelligence
%E Gerhard Wei\ss and Sandip Sen
%D 1996
%P 113--126
%I Springer Verlag
%C Berlin, Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/haynes96evolving.html
%X The predator/prey domain is used to conduct research in Distributed Artificial Intelligence. Genetic Programing is used to evolve behavioral strategies for the predator
   agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not
   surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms.
%Z see also \citeHayes:1995:ebspp

%A Thomas Haynes
%A Sandip Sen
%T Cooperation of the Fittest
%R Technical Report UTULSA-MCS-96-09
%D 1996
%I 
%I The University of Tulsa
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/haynes96cooperation.html
%X We introduce a cooperative co-evolutionary system to facilitate the development of teams of heterogeneous agents. We believe that $k$ different behavioral strategies for
   controlling the actions of a group of $k$ agents can combine to form a cooperation strategy which efficiently achieves global goals. We examine the on-line adaption of
   behavioral strategies using genetic programming. Specifically, we deal with the credit assignment problem of how to fairly split the fitness of a team to all of its
   participants. We present several crossover mechanisms in a genetic programming system to facilitate the evolution of more than one member in the team during each crossover
   operation. Our goal is to reduce the time needed to either evolve a good team or reach convergence.
%8 April  12
%Z evolution of cooperation (multi-agent,multi-tree) NOT coevolution of fitness function evolution. Our printer barfs on page 9.

%A Thomas Haynes
%T Collective Memory Search
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 38--46
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 see \citehaynes:1997:cms
%@ 0-18-201031-7

%A Thomas Haynes
%A Sandip Sen
%T Cooperation of the Fittest
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 47--55
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-201031-7

%A Thomas Haynes
%T Collective Memory Search
%B Proceedings of the 1997 ACM Symposium on Applied Computing
%E Barrett Bryant and Janice Carroll and Dave Oppenheim and Jim Hightower and K. M. George
%D 1997
%P 217--222
%I Association for Computing Machinery New York
%C Hyatt Sainte Claire Hotel, San Jose, California, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/haynes97collective.html
%X Collective action has been examined to expedite search in optimisation problems [ Dorigo et al., 1996 ] . Collective memory has been applied to learning in multiagent
   systems [ Garland and Alterman, 1996 ] . We integrate the simplicity of collective action with the pattern detection of collective memory to significantly improve both the
   gathering and processing of knowledge. We investigate the augmentation of distributed search in genetic programming based systems with collective memory. Four...
%8 28  February -2  March
%Z ACM SAC-97 0-89791-850-9 citeseer says twsu.edu/~thomas/collect.ps see \citehaynes:1996:cms

%A Thomas Haynes
%T On-line Adaptation of Search via Knowledge Reuse
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 156--161
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming, distributed search
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.3381
%X We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system
   significantly improves search as problem complexity is increased. In collective adaptation, search agents gather knowledge of their environment and deposit it in a central
   information repository. Process agents are then able to manipulate that focused knowledge, exploiting the exploration of the search agents. Communication is oneway, from
   the search agents to the process agents. As the process agents are able to refine the knowledge gathered by the search agents, we investigate two-way communication. Such
   communication directs the genetic programming based engine of the search agents.
%8 13-16  July
%Z GP-97

%A Thomas Haynes
%A Sandip Sen
%T Crossover Operators for Evolving A Team
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 162--167
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.mcs.utulsa.edu/~sandip/gp97.ps
%8 13-16  July
%Z GP-97

%A Thomas Haynes
%T Competitive Computational Agent Society
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 293
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB PHD Students' workshop The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Thomas Haynes
%T Phenotypical Building Blocks for Genetic Programming
%B Genetic Algorithms: Proceedings of the Seventh International Conference
%E Thomas Back
%D 1997
%P 26--33
%I Morgan Kaufmann San Francisco, CA, USA
%C Michigan State University, East Lansing, MI, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/haynes97phenotypical.html
%X The theoretical foundations of genetic algorithms (GA) rest on the shoulders of the Schema Theorem, which states that the building blocks, highly fit compact subsets of the
   chromosome, are more likely to survive from one generation to the next. The theory of genetic programming (GP) is tenuous, borrowing heavily from that of GA. As the GP can
   be considered to be a GA operating on a tree structure, this borrowing is adequate for most. Part of the problem of tying GP theory to the schema theorem is in the
   identification of building blocks. We discuss how a building block can be represented in a GP chromosome and the characteristics of building blocks in GP chromosomes. We
   also present the clique detection domain for which the detection of building blocks is easier than in previous domains used in GP research. We illustrate how the clique
   detection domain facilitates the construction of fitness landscapes similar to those of the Royal Road functions in GA research.
%8 19-23  July
%Z ICGA-97 citeseer says twsu.edu/~thomas/pbb_gp.ps
%@ 1-55860-487-1

%A Thomas Haynes
%T Augmenting Collective Adaptation with Simple Process Agents
%B Papers from the AAAI Workshop on Multiagent Learning
%E Sandip Sen
%D 1997
%P 41--46
%I 
%I AAAI
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Papers/Workshops/1997/WS-97-03/WS97-03-008.pdf
%X We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system
   significantly improves search as problem complexity is increased. However, there is still considerable scope for improvement. In collective adaptation, search agents gather
   knowledge of their environment and deposit it in a central information repository. Process agents are then able to manipulate that focused knowledge, exploiting the
   exploration of the search agents. We examine the utility of increasing the capabilities of the centralised process agents.
%O Published in AAAI Technical Report WS-97-03
%Z http://www.aaai.org/Library/Workshops/ws97-03.php

%A Thomas Haynes
%T A Comparision of Random Search versus Genetic Programming as Engines for Collective Adaptation
%B Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%E V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben
%V 1447
%D 1998
%P 683--692
%I Springer-Verlag Berlin
%I Natural Selection, Inc.
%C Mission Valley Marriott, San Diego, California, USA
%K genetic algorithms, genetic programming
%X We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system
   significantly improves search as problem complexity is increased. Since the pure GP approach does not scale well with problem complexity, a natural question is which of the
   two components is actually contributing to the search process. We investigate a collective memory search which uses a random search engine and find that it significantly
   outperforms the GP based search engine. We examine the solution space and show that as problem complexity and search space grow, a collective adaptive system will perform
   better than a collective memory search employing random search as an engine.
%8 25-27  March
%Z EP-98. "With collective adaptation".... "A random search engine is more effective than a GP based one, but only at low problem complexity. As the complexity increases, the
   competetiveness of the GP search engine is more effective than the wide ranging exploration of random search." pages 10-11.
%@ 3-540-64891-7

%A Thomas Dunlop Haynes
%T Collective Adaptation: The Sharing of Building Blocks
%R Ph.D. Thesis
%D 1998
%I 
%I Department of Mathematical and Computer Sciences, University of Tulsa
%C Tulsa, OK, USA
%K genetic algorithms, genetic programming
%X Weak search heuristics use minimal domain knowledge during the search process. Genetic algorithms (GA) and genetic programming (GP) are population based weak search
   heuristics which represent candidate solutions as chromosomes. The Schemata Theorem forms the basis of the theory of how GAs process building blocks during the domain
   independent search for a solution to a given problem. Building blocks are templates describing subsets of the chromosome which have a small defining length and are highly
   fit. The main differences between typical GP and GA implementations are a variable length tree versus a fixed length linear string representation and a n-ary versus a
   binary alphabet. A consequence of the differences is that what constitutes a building block has been difficult to answer for GP and has led to theories that the Schemata
   Theorem does not hold for GP. This thesis defines building blocks to be coding segments, which are those subsets of the chromosome that contribute fitness to the evaluation
   of the chromosome. Building blocks can be extracted from chromosomes and stored in a collective memory, which becomes a repository of partial solutions for both recently
   discovered building blocks and those discovered earlier. The contributions of this thesis are the mechanisms by which building blocks can be effectively shared both inside
   and outside chromosomes. The duplication of building blocks inside a chromosome is shown to increase the exploratory power of the weak search heuristics. The perturbation
   of a candidate solution will affect one copy of the building blocks and if the fitness of the perturbed copy is not better than the original, the duplicate copies may still
   maintain the overall fitness of the chromosome. The duplication of coding segments is significant in finding better partial solutions with the following weak search
   heuristics: GP, GA, random search (RS), hill climbing (HC), and simulated annealing (SA). Each algorithm is systematically validated in the clique detection domain against
   a particular family of graphs, which have the properties that the set of partial solutions is known, the set of partial solutions is larger than viable chromosome lengths,
   and pruning algorithms are not effective. Collective adaptation is the addition of the collective memory to the weak search heuristic. The solution no longer has to be
   found inside the chromosomes; the chromosomes can collectively contribute partial solutions such that the overall solution is formed inside the collective memory. Strong
   search heuristics can extend the partial solutions inside the collective memory and these partial solutions can be transfered back into the chromosomes. The thesis
   empirically demonstrates that collective adaptation finds significantly better partial solutions with weak search heuristics (GP, GA, RS, HC, and SA).
%8 April
%Z a ROUGH DRAFT available via http://citeseer.ist.psu.edu/haynes96explicit.html

%A Thomas Haynes
%T Augmenting Collective Adaptation with Simple Process Agents
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 116--121
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/haynes97augmenting.html
%X We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system
   significantly improves search as problem complexity is increased. However, there is still considerable scope for improvement. In collective adaptation, search agents gather
   knowledge of their environment and deposit it in a central information repository. Process agents are then able to manipulate that focused knowledge, exploiting...
%8 22-25  July
%Z GP-98 citeseer says utulsa.edu/~haynes/active.ps
%@ 1-55860-548-7

%A Thomas Haynes
%T Perturbing the Representation, Decoding, and Evaluation of Chromosomes
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 122--127
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/haynes98perturbing.html
%X We investigate different genetic algorithm and genetic programming variants of representation, decoding, and evaluation of chromosomes for clique detection in graph. Small
   changes can drastically impact finding the evolutionary process, making fair comparisons difficult. 1 Introduction While research into the interactions of function and
   terminal set size is sparse to non--existent (for examples, see [ Montana, 1995 ] and [ Haynes et al., 1995 ] ), a rule of thumb for GP researchers is to...
%8 22-25  July
%Z GP-98 citeseer says twsu.edu/~thomas/cook.ps
%@ 1-55860-548-7

%A Thomas Haynes
%T Collective Adaptation: The Exchange of Coding Segments
%J Evolutionary Computation
%V 6
%N 4
%D 1998
%P 311--338
%I 
%K genetic algorithms, genetic programming, collective adaptation, coding segments, duplication of coding segments, collective memory
%U http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.311
%X Coding segments are those subsegments of the chromosome that contribute positively to the fitness evaluation of the chromosome. Clique detection is a NP-complete problem in
   which we can detect such coding segments. We extract coding segments from chromosomes, and we investigate the duplication of coding segments inside the chromosome and the
   collection of coding segments outside of the chromosome. We find that duplication of coding segments inside the chromosomes provides a back-up mechanism for the search
   heuristics. We further find local search in a collective memory of coding segments outside of the chromosome, collective adaptation, enables the search heuristic to
   represent partial solutions that are larger than realistic chromosomes lengths and to express the solution outside of the chromosome.
%8 Winter
%Z Evolutionary Computation (Journal) Special Issue: Variable-Length Representation and Noncoding Segments for Evolutionary Algorithms Edited by Annie S. Wu and Wolfgang
   Banzhaf

%A Thomas Haynes
%T Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem
%B Proceedings of the 1999 ACM Symposium on Applied Computing
%E Janice Carroll and Hisham Haddad and Dave Oppenheim and Barrett Bryant and Gary B. Lamont
%D 1999
%P 339--343
%I ACM Press
%K genetic algorithms, genetic programming
%U http://delivery.acm.org/10.1145/300000/298377/p339-haynes.pdf
%X We use collective memory to integrate weak and strong search heuristics to find cliques in FC, a family of graphs. We construct FC such that pruning partial solutions will
   be ineffective. Each weak heuristic maintains a local cache of the collective memory. We examine the impact on the distributed search of the distribution of the collective
   memory, the search algorithms, and our family of graphs. We find the distributed search performs better than the individual searches, even though the space of partial
   solutions is combinatorial.
%Z (GA track)

%A Thomas Haynes
%T Distributing Collective Adaptation via Message Passing
%B Proceedings of the 1999 ACM Symposium on Applied Computing
%E Janice Carroll and Hisham Haddad and Dave Oppenheim and Barrett Bryant and Gary B. Lamont
%D 1999
%P 501--505
%I ACM Press
%K genetic algorithms, genetic programming
%X We describe an architecture for implementing a distributed access to a collective memory on a cluster of PC workstations running Linux. The basic memory hierarchy of
   register, cache, RAM, and main storage is modeled. The message passing interface (MPI) provides the functionality of a virtual bus between the various layers of memory.
%Z (PC Cluster track)

%T Foundations of Genetic Programming
%E Thomas Haynes and William B. Langdon and Una-May O'Reilly and Riccardo Poli and Justinian Rosca
%D 1999
%P 52
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/workshop.html
%8 13  July
%Z GECCO'99 WKSHOP, GECCO-99WKS Part of wu:1999:GECCOWKS

%A Amaury Hazan
%A Rafael Ramirez
%A Esteban Maestre
%A Alfonso Perez
%A Antonio Pertusa
%T Modelling Expressive Performance: a Regression Tree Approach Based on Strongly Typed Genetic Programming
%B Applications of Evolutionary Computing, EvoWorkshops2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC
%S LNCS
%E Franz Rothlauf and Jurgen Branke and Stefano Cagnoni and Ernesto Costa and Carlos Cotta and Rolf Drechsler and Evelyne Lutton and Penousal Machado and Jason H. Moore and
   Juan Romero and George D. Smith and Giovanni Squillero and Hideyuki Takagi
%V 3907
%D 2006
%P 676--687
%I Springer Verlag Berlin
%C Budapest
%K genetic algorithms, genetic programming, STGP
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3907&spage=676
%X Strongly-Typed Genetic Programming approach for building Regression Trees in order to model expressive music performance. The approach consists of inducing a Regression
   Tree model from training data (monophonic recordings of Jazz standards) for transforming an inexpressive melody into an expressive one. The work presented in this paper is
   an extension of [1], where we induced general expressive performance rules explaining part of the training examples. Here, the emphasis is on inducing a generative model
   (i.e. a model capable of generating expressive performances) which covers all the training examples. We present our evolutionary approach for a one-dimensional regression
   task: the performed note duration ratio prediction. We then show the encouraging results of experiments with Jazz musical material, and sketch the milestones which will
   enable the system to generate expressive music performance in a broader sense.
%8 10-12  April
%Z part of \citeevows06
%@ 3-540-33237-5

%A Alex Hazell
%A Stephen L. Smith
%T Towards an objective assessment of Alzheimer's disease: the application of a novel evolutionary algorithm in the analysis of figure copying tasks
%B GECCO-2008 Workshop: MedGEC Medical Applications of Genetic and Evolutionary Computation
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 2073--2080
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, Alzheimer's disease, cartesian genetic programming, evolutionary algorithm(s), image analysis, medical applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2073.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1389024

%A Jingsong He
%A Xufa Wang
%A Min Zhang
%A Jiying Wang
%A Qiansheng Fang
%T New Research on Scalability of Lossless Image Compression by GP Engine
%B Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
%E Jason Lohn and David Gwaltney and Gregory Hornby and Ricardo Zebulum and Didier Keymeulen and Adrian Stoica
%D 2005
%P 160--164
%I IEEE Press IEEE Service Center 445 Hoes Lane Asia P.O. Box 1331 Piscataway, NJ 08855-1331
%I NASA, DoD
%C Washington, DC, USA
%K genetic algorithms, genetic programming, EHW
%X By introducing the optimal linear predictive code technic into the dynamic issue of loss less image compression, this paper presented a less complexity fitness function for
   Genetic Programming engine, which can reduce the cost of computational time in each evaluation for individual greatly, and can also provide further benefit with the
   scalability issue. To make the speed of large image compression faster in condition of not increasing the cost of computational resource and time, evaluating mechanism in
   the field of machine learning was used to help Genetic Programming, and the scalability issue was mapped to the task of making the approach accuracy best from lower speed
   sampling to higher speed sampling in the field of signal processing. In experiments for compressing large images, the cost of computational time was reduced evidently and
   efficiently.
%8 29  June -1  July
%Z EH2005 IEEE Computer Society Order Number P2399
%@ 0-7695-2399-4

%A Qiang He
%A Jun Ma
%A Shuaiqiang Wang
%T Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm
%B Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10
%D 2010
%P 1449--1452
%I ACM
%C Toronto, ON, Canada
%K genetic algorithms, genetic programming, clonal selection algorithm, information retrieval, learning to rank, machine learning, ranking function: Poster
%X One fundamental issue of learning to rank is the choice of loss function to be optimised. Although the evaluation measures used in Information Retrieval (IR) are ideal
   ones, in many cases they can't be used directly because they do not satisfy the smooth property needed in conventional machine learning algorithms. In this paper a new
   method named RankCSA is proposed, which tries to use IR evaluation measure directly. It employs the clonal selection algorithm to learn an effective ranking function by
   combining various evidences in IR. Experimental results on the LETOR benchmark datasets demonstrate that RankCSA outperforms the baseline methods in terms of P@n, MAP and
   NDCG@n.

%A Mingyi He
%A Yifan Zhang
%A Yuzhen Xie
%A Na Liang
%A Changyun Wen
%T Classification of Multi-spectral/Hyperspectral Data using Genetic Programming and Error-correcting Output Codes
%B 1ST IEEE Conference on Industrial Electronics and Applications
%D 2006
%P 1--6
%I IEEE
%C Singapore
%K genetic algorithms, genetic programming
%X Genetic programming (GP) and error-correcting output codes (ECOC) are combined to develop a new classification method (GP-ECOC) for the multi-class problem solving in this
   paper. Some additional improvements on the algorithm, modified codeword matrix and group division before classification, are also proposed to settle several existing
   problems in multi-spectral and hyperspectral data classification. Experimental tests using both multi-spectral and hyper-spectral data are carried out for verification and
   illustration. It is observed from the obtained results that the classification precision with the newly proposed method is greatly enhanced compared with some existing
   methods using GP, and the proposed improvements are also effective. The algorithm of GP-ECOC and its improved versions can also be run on multi-terminals, which saves
   computational cost effectively
%8 24-26  May
%Z INSPEC Accession Number: 9096919 Sch. of Electron. & Inf., Northwestern Polytech Univ., Xi'an;
%@ 0-7803-9514-X

%A Pei He
%A Lishan Kang
%A Ming Fu
%T Formality Based Genetic Programming
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 4080--4087
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, program verification, approximate program, automatic programming, formality based genetic programming, software testing
%X Genetic programming (GP) is an illogical method for automatic programming. It shows creativity in discovering a desired program to solve problem, but in essence bases its
   searching principle on software testing. This paper is dedicated to establishing a novel GP which combines classical GP and formal approaches like Hoare's logic, model
   checking, and automaton, etc. The result indicates these methods can collaborate in the framework pretty well. As has been demonstrated by the experiment, they work in a
   way that preserves their advantages while each compensates for the deficiencies of the other. So, once an approximate program is obtained, we can say with certainty it is
   correct with respect to its corresponding pre- and post-conditions.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Robert B. Heckendorn
%A Soraya Rana
%A Darrell Whitley
%T Polynomial Time Summary Statistics for a Generalization of MAXSAT
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 281--288
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.colostate.edu/~genitor/1999/maxsat99.pdf
%X NK landscape, Walsh analysis
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Michael Hecker
%A Sandro Lambeck
%A Susanne Toepfer
%A Eugene {van Someren}
%A Reinhard Guthke
%T Gene regulatory network inference: Data integration in dynamic models--A review
%J Biosystems
%V 96
%N 1
%D 2009
%P 86--103
%I 
%K genetic algorithms, genetic programming, Systems biology, Reverse engineering, Biological modelling, Knowledge integration
%U http://www.sciencedirect.com/science/article/B6T2K-4V7MSTS-1/2/db669ac3459da19bab3535dc038303d5
%X Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems
   biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene
   regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays.
   However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network
   inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are
   discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and
   outlines current challenges in GRN modelling.
%Z survey

%A Abdel-Rahman Hedar
%A Mostafa Kamel Osman
%T Scatter Programming
%B 2nd International Conference on Computer Technology and Development (ICCTD), 2010
%D 2010
%P 451--455
%I 
%C Cairo
%K genetic algorithms, genetic programming, cartesian genetic programming, grammatical evolution, artificial intelligence, machine learning, scatter programming, learning
   (artificial intelligence)
%X The core of artificial intelligence and machine learning is to get computers to solve problems automatically. One of the great tools that attempt to achieve that goal is
   Genetic Programming (GP). As alternatives to GP, Scatter Programming (SP) is proposed in this paper. One of the main features of SP is to exploit local search in order to
   overcome some recently addressed drawbacks of GP, especially its highly disruption of its main operations; crossover and mutation. This work shows that SP has promising
   performance and results in solving machine learning problems.
%8 2-4  November
%Z symbolic regression, 6-mux. Also known as \cite5645839

%A Abdel-Rahman Hedar
%A Emad Mabrouk
%A Masao Fukushima
%T Tabu Programming: a New Problem Solver through Adaptive Memory Programming over Tree Data Structures
%J International Journal of Information Technology and Decision Making
%V 10
%N 2
%D 2011
%P 373--406
%I 
%K genetic algorithms, genetic programming
%X Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is
   considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and
   the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical
   Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the
   drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark
   problems. The results of those experiments show that the TP algorithm compares favourably to recent versions of the GP algorithm in terms of computational efforts and the
   rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.

%A Sara Resse Hedberg
%T Evolutionary computing: the rise of electronic breeding
%J Intelligent Systems
%V 20
%N 6
%D 2005
%P 12--15
%I 
%K genetic algorithms, genetic programming, biological evolution, electronic breeding, evolutionary computing
%X GAs and their relations, which fall under the umbrella term evolutionary computing, are being harnessed to optimise designs of all sorts. GAs mimics the mechanisms of
   biological evolution. Populations of individuals evolve by means of reproduction, inheritance, mutation, natural selection, and recombination or crossover (two organisms
   swap a portion of their genetic code). The result is computational methods that build a population of individuals or designs based on a set of criteria and constraints.
%8 November -  Decemeber
%Z high level

%A Amine Heddad
%A Markus Brameier
%A Robert M. MacCallum
%T Evolving Regular Expression-based Sequence Classifiers for Protein Nuclear Localisation
%B Applications of Evolutionary Computing, EvoWorkshops2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC
%S LNCS
%E Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori
   and Franz Rothlauf and George D. Smith and Giovanni Squillero
%V 3005
%D 2004
%P 31--40
%I Springer Verlag Berlin
%C Coimbra, Portugal
%K genetic algorithms, genetic programming, evolutionary computation, perl, grammar, BNF, linear GP, LGP, RE, regular expressions
%U http://www.sbc.su.se/~maccallr/publications/heddad-evobio2004.pdf
%X A number of bioinformatics tools use regular expression (RE) matching to locate protein or DNA sequence motifs that have been discovered by researchers in the laboratory.
   For example, patterns representing nuclear localisation signals (NLSs) are used to predict nuclear localisation. NLSs are not yet well understood, and so the set of
   currently known NLSs may be incomplete. Here we use genetic programming (GP) to generate RE-based classifiers for nuclear localisation. While the approach is a supervised
   one (with respect to protein location), it is unsupervised with respect to already known NLSs. It therefore has the potential to discover new NLS motifs. We apply both tree
   based and linear GP to the problem. The inclusion of predicted secondary structure in the input does not improve performance. Benchmarking shows that our majority
   classifiers are competitive with existing tools. The evolved REs are usually "NLS like" and work is underway to analyse these for novelty.
%8 5-7  April
%Z EvoWorkshops2004, perlGP, grammar (not needed, cf p39?). http://www.sbc.su.se/~maccallr/nucpred/ perl eval(), grammar, stgp, matches(),, pdiv, plog, multiple classifier
   combination majority vote. 'No crossover is allowed between REs' p38. Removing ineffective code. 'LGP very close to PerlGP' p38. RE matching done in C. cf.
   \citebrameier:nucpred
%@ 3-540-21378-3

%A Karl Hedman
%A David Persson
%A Per Skoglund
%A Dan Wiklund
%A Krister Wolff
%A Peter Nordin
%T Sensing And Direction In Locomotion Learning With A Random Morphology Robot
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 1297
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, evolutionary robotics, poster paper, evolutionary algorithm, random morphology
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-23.pdf
%X We describe the first instance in sensing and direction with a learning Random Morphology robot. Using GP, it learns to locomote itself in different directions and by
   letting different solutions master the robot in different situations it can thus follow an arbitrary path.
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Vilhelm Heiberg
%T Learning Bayesian Networks Using a Genetic Algorithm
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 86--97
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%X for learning baysian networks .... intelligent Greedy Search outperforms GA and SA
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A A Geert Heidema
%A Jolanda M A Boer
%A Nico Nagelkerke
%A Edwin C M Mariman
%A Daphne L {van der A}
%A Edith J M Feskens
%T The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases
%J BMC Genetics
%R Commentary
%V 7
%N 23
%D 2006
%I BioMed Central Ltd.
%K genetic algorithms, genetic programming
%U http://www.biomedcentral.com/1471-2156/7/23
%X Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of
   genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analysing the
   relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss
   logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimised neural networks (GPNN) and several
   non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor
   dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and
   neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the
   non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model
   important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association
   approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important
   contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be
   related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the
   case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a
   useful strategy to find the important genes and interaction patterns involved in complex diseases.
%8 April ~21
%Z Open Access

%A Verena Heidrich-Meisner
%A Christian Igel
%T Neuroevolution strategies for episodic reinforcement learning
%J Journal of Algorithms
%V 64
%N 4
%D 2009
%P 152--168
%I 
%K genetic algorithms, genetic programming, Reinforcement learning, Evolution strategy, Covariance matrix adaptation, Partially observable Markov decision process, Direct
   policy search
%U http://www.sciencedirect.com/science/article/B6WH3-4W7RY8J-3/2/22f7075bc25dab10a8ff3714e2fee303
%X Because of their convincing performance, there is a growing interest in using evolutionary algorithms for reinforcement learning. We propose learning of neural network
   policies by the covariance matrix adaptation evolution strategy (CMA-ES), a randomised variable-metric search algorithm for continuous optimisation. We argue that this
   approach, which we refer to as CMA Neuroevolution Strategy (CMA-NeuroES), is ideally suited for reinforcement learning, in particular because it is based on ranking
   policies (and therefore robust against noise), efficiently detects correlations between parameters, and infers a search direction from scalar reinforcement signals. We
   evaluate the CMA-NeuroES on five different (Markovian and non-Markovian) variants of the common pole balancing problem. The results are compared to those described in a
   recent study covering several RL algorithms, and the CMA-NeuroES shows the overall best performance.
%O Special Issue: Reinforcement Learning
%Z compared against CE \citegruau:1996:ceVdeGNN

%A Carl Hein
%A Alex Meystel
%T A genetic technique for robotic trajectory planning
%J Telematics and Informatics
%V 11
%N 4
%D 1994
%P 351--364
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V1H-48V1Y16-6/2/1a0f7979e649fe0ff30f590d6fc5e0b5
%X There are many multi-stage optimisation problems that are not easily solved through any known direct method when the stages are coupled. For instance, the problem of
   planning a vehicle's control sequence to negotiate obstacles and reach a goal in minimum time is investigated. The vehicle has a known mass, and the controlling forces have
   finite limits. A genetic programming technique is developed that finds admissible control trajectories that tend to minimise the vehicle's transit time through the obstacle
   field. The immediate application is that of a space robot that must rapidly traverse around two or three dimensional structures via application of a rotating thruster or
   non-rotating on-off thrusters. (An air-bearing floor test-bed for such vehicles is located at the Marshal Space Flight Center in Huntsville, Alabama.) It appears that the
   developed method is applicable to a general set of optimization problems in which the cost function and the multi-dimensional multi-state system can be any non-linear
   functions that are continuous in the operating regions. Other applications include: the planning of optimal navigation pathways through a traversability graph, the planning
   of control input for underwater manoeuvring vehicles which have complex control state-space relationships, the planning of control sequences for milling and manufacturing
   robots, the planning of control and trajectories for automated delivery vehicles, and the optimisation of control for racing vehicles and athletic training in slalom
   sports.

%A D. Heiss-Czedik
%T Is Genetic Programming Dependent on High-level Primitives?
%B Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97
%E George D. Smith and Nigel C. Steele and Rudolf F. Albrecht
%D 1997
%I Springer-Verlag
%C University of East Anglia, Norwich, UK
%K genetic algorithms, genetic programming
%O published in 1998
%Z Dorothea Heiss, nee Czedik-Eysenberg http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html
%@ 3-211-83087-1

%A Terry M. Helm
%A Steve W. Painter
%A W. Robert Oakes
%T A Comparison of Three Optimization Methods for Scheduling Maintenance of High Cost, Long-Lived Capital Assets
%B Proceedings of the 2002 Winter Simulation Conference
%E E. Yucesan and C.-H. Chen and J. L. Snowdon and J. M. Charnes
%V 2
%D 2002
%P 880--1884
%I 
%K genetic algorithms, genetic programming, constraint handling, financial data processing, investment, minimisation, scheduling, constraint programming, costs, investments,
   long-lived capital assets, maintenance scheduling, minimization, optimization
%U http://www.informs-sim.org/wsc02papers/259.pdf
%X A range of minimization methods exist enabling planners to tackle tough scheduling problems. We compare three scheduling techniques representative of old or standard
   technologies, evolving technologies, and advanced technologies. The problem we address includes the complications of scheduling long-term upgrades and refurbishments
   essential to maintaining expensive capital assets. We concentrate on the costs of being able to do maintenance work. Using a standard technology as the baseline technique,
   Constraint Programming (CP) produces a 50-yr maintenance approach that is 31percent less costly. Genetic Programming produces an approach that is 60percent less costly

%A Guy Helmer
%A Johnny Wong
%A Vasant Honavar
%A Les Miller
%T Feature Selection Using a Genetic Algorithm for Intrusion Detection
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1781
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-737.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Martin Helmer
%A Martin Hemberg
%T Moving a Snake Robot using Genetic Programming
%D 1999
%I 
%K genetic algorithms, genetic programming
%U http://www.dd.chalmers.se/~f96mahe/evcomp.html broken
%X We have constructed a snake robot with five servos. Our goal was to make the snake move using an evolutionary algorithm. For fitness we attached a mouse by the tail of the
   snake. We used the "30-monkeys-in-a-bus" algorithm for selection. It was found possible to develop a forward movement of the snake, however not without problems. One of the
   biggest problems was to prevent the snake from cheating, which it often did by wagging its tail a lot or by ending in a curled-up position.
%O www
%8 15  Decemeber

%A Thomas Helmuth
%A Lee Spector
%A Brian Martin
%T Size-based tournaments for node selection
%B GECCO 2011 Graduate students workshop
%E Miguel Nicolau
%D 2011
%P 799--802
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X In genetic programming, the reproductive operators of crossover and mutation both require the selection of nodes from the reproducing individuals. Both unbiased random
   selection and Koza 90/10 mechanisms remain popular, despite their arbitrary natures and a lack of evidence for their effectiveness. It is generally considered problematic
   to select from all nodes with a uniform distribution, since this causes terminal nodes to be selected most of the time. This can limit the complexity of program fragments
   that can be exchanged in crossover, and it may also lead to code bloat when leaf nodes are replaced with larger new subtrees during mutation. We present a new node
   selection method that selects nodes based on a tournament, from which the largest participating subtree is selected. We show this method of size-based tournaments improves
   performance on three standard test problems with no increases in code bloat as compared to unbiased and Koza 90/10 selection methods.
%8 12-16  July
%Z Also known as \cite2002095 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Erik Hemberg
%A Conor Gilligan
%A Michael O'Neill
%A Anthony Brabazon
%T A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 1--11
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X The ability of Genetic Programming to scale to problems of increasing difficulty operates on the premise that it is possible to capture regularities that exist in a problem
   environment by decomposition of the problem into a hierarchy of modules. As computer scientists and more generally as humans we tend to adopt a similar divide-and-conquer
   strategy in our problem solving. In this paper we consider the adoption of such a strategy for Genetic Algorithms. By adopting a modular representation in a Genetic
   Algorithm we can make efficiency gains that enable superior scaling characteristics to problems of increasing size. We present a comparison of two modular Genetic
   Algorithms, one of which is a Grammatical Genetic Programming algorithm, the meta-Grammar Genetic Algorithm (mGGA), which generates binary string sentences instead of
   traditional GP trees. A number of problems instances are tackled which extend the Checkerboard problem by introducing different kinds of regularity and noise. The results
   demonstrate some limitations of the modular GA (MGA) representation and how the mGGA can overcome these. The mGGA shows improved scaling when compared the MGA.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Erik Hemberg
%A Michael O'Neill
%A Anthony Brabazon
%T Altering Search Rates of the Meta and Solution Grammars in the mGGA
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 362--373
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Erik Hemberg
%A Michael O'Neill
%A Anthony Brabazon
%T Grammatical Bias and Building Blocks in Meta-Grammar Grammatical Evolution
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, grammatical evolution
%X This paper describes and tests the utility of a meta Grammar approach to Grammatical Evolution (GE). Rather than employing a fixed grammar as is the case with canonical GE,
   under a meta Grammar approach the grammar that is used to specify the construction of a syntactically correct solution is itself allowed to evolve. The ability to evolve a
   grammar in the context of GE means that useful bias towards specific structures and solutions can be evolved and directly incorporated into the grammar during a run. This
   approach facilitates the evolution of modularity and reuse both on structural and symbol levels and consequently could enhance both the scalability of GE and its adaptive
   potential in dynamic environments. In this paper an analysis of the extent that building block structures created in the grammars are used in the solution is undertaken. It
   is demonstrated that building block structures are incorporated into the evolving grammars and solutions at a rate higher than would be expected by random search.
   Furthermore, the results indicate that grammar design can be an important factor in performance.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Erik Hemberg
%T An exploration of learning and grammars in grammatical evolution
%B GECCO-2009 Graduate student workshop
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 2705--2708
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, grammatical evolution
%X This paper is concerned with the challenge of learning solutions to problems. The method employed here is a grammar based heuristic, where domain knowledge is encoded in a
   generative grammar, while evolution drives the update of the population of solutions. Furthermore the method can adapt to the environment by altering the grammar. The
   implementation consists of the grammar-based Genetic Programming approach of Grammatical Evolution (GE). A number of different constructions of grammars and operators for
   manipulating the grammars and the evolutionary algorithm are investigated, as well as a meta-grammar GE which allows a more flexible grammar. The results show some benefit
   of using meta-grammars in GE and re-emphasize the grammar's impact on GE's performance.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Erik Hemberg
%A Nic McPhee
%A Michael O'Neill
%A Anthony Brabazon
%T Pre-, In- and Postfix grammars for Symbolic Regression in Grammatical Evolution
%B IEEE Workshop and Summer School on Evolutionary Computing
%E T. M. McGinnity
%D 2008
%P 18--22
%I 
%C University of Ulster, Derry, Northern Ireland
%K genetic algorithms, genetic programming, Grammatical Evolution
%8 18-22  August
%Z http://isel.infm.ulst.ac.uk/conference/wssec2008/wssec08.htm

%A Erik Hemberg
%A Michael O'Neill
%A Anthony Brabazon
%T An investigation into automatically defined function representations in Grammatical Evolution
%B 15th International Conference on Soft Computing, Mendel'09
%E R. Matousek and L. Nolle
%D 2009
%I 
%C Brno, Czech Republic
%K genetic algorithms, genetic programming, grammatical evolution
%8 24-26  June
%Z ID09045 http://www.mendel-conference.org/tmp/ScheduleMendel2009b.pdf Also in electronic form ISSN 1803-3814

%A Erik Anders Pieter Hemberg
%T An Exploration of Grammars in Grammatical Evolution
%R Ph.D. Thesis
%D 2010
%I 
%I University College Dublin
%C Ireland
%K genetic algorithms, genetic programming, grammatical evolution
%U http://ncra.ucd.ie/papers/exploration_of_grammars_in_grammatical_evolution.pdf
%X The grammar in the grammar-based Genetic Programming (GP) approach of Grammatical Evolution (GE) is explored. The GE algorithm solves problems by using a grammar
   representation and an automated and parallel trial-and-error approach, Evolutionary Computation (EC). The search for solutions in EC is driven by evaluating each solution,
   selecting the fittest and replacing these into a population of solutions which are modified to further guide the search. Representations have a strong impact on the
   efficiency of search and by using a generative grammar domain knowledge is encoded into the population of solutions. The grammar in GE biases the search for solutions, and
   in combination with a linear representation this is what distinguishes GE from other GP-systems. After a review of grammars in EC and a description of GE, several different
   constructions of grammars and operators for manipulating the grammars and the evolutionary algorithm are studied. The thesis goes on to study a meta-grammar GE, which
   allows a larger grammar with different bias. By adopting a divide-and-conquer strategy the goal is to investigate how a modular GE approach solves problems of increasing
   size and in dynamically changing environments. The results show some benefit from using meta-grammars in GE, for the meta-grammar Genetic Algorithm (mGGA) and they
   re-emphasise the grammar's impact on GE's performance. In addition, GE and meta-grammars are more formally described. The bias, both declarative and search, arising from
   the use of a Context-Free Grammar representation and the constraints of GE and the mGGA are analysed and their implications are examined. This is done by studying the
   effects of the mapping and operations on the input, single and multiple changes in input, as well as the preservation of output after a change. Furthermore, a matrix view
   of a grammar and different suggestions for measurements of grammars are investigated, in order to allow the practitioner to get an alternative view of the mapping process
   and of how operations work.
%8 17  September

%A Erik Hemberg
%A Lester Ho
%A Michael O'Neill
%A Holger Claussen
%T A symbolic regression approach to manage femtocell coverage using grammatical genetic programming
%B 3rd symbolic regression and modeling workshop for GECCO 2011
%E Steven Gustafson and Ekaterina Vladislavleva
%D 2011
%P 639--646
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution
%X We present a novel application of Grammatical Evolution to the real-world application of femtocell coverage. A symbolic regression approach is adopted in which we wish to
   uncover an expression to automatically manage the power settings of individual femtocells in a larger femtocell group to optimise the coverage of the network under time
   varying load. The generation of symbolic expressions is important as it facilitates the analysis of the evolved solutions. Given the multi-objective nature of the problem
   we hybridise Grammatical Evolution with NSGA-II connected to tabu search. The best evolved solutions have superior power consumption characteristics than a fixed coverage
   femtocell deployment.
%8 12-16  July
%Z Also known as \cite2002061 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Martin Hemberg
%T GENR8 - A Design Tool for Surface Generation
%R M.S. Thesis
%D 2001
%I 
%I Department of Physical Resource Theory
%C Chalmers University, Sweden
%K genetic algorithms, genetic programming, lindenmayer system, development, grammatical evolution
%U http://www.ai.mit.edu/projects/emergentDesign/genr8/main.pdf
%X GENR8 is an architect's design tool that generates surfaces. It is powerful and innovative because it fuses expressively powerful universes of growth languages with
   evolutionary search. Unlike traditional CAD-tools, GENR8 can create new designs and help the user to come up with new ideas. Developed via the API of AliasjWavefront's
   Maya, it combines 3D map L-systems, that are extended to an abstract physical environment with evolutionary computation. GENR8 uses Grammatical Evolution and a BNF of the
   grammar to specify the grammar that governs the growth. GENR8 addresses key issues arising from exploiting evolutionary adaption within a creative interactive tool
   framework. EAs typically adapt `off-line' but GENR8 is designed to sensitively accommodate the nature of the back and forth control exchange between user and tool during
   on-line evolutionary adaptation. GENR8 addresses how users may interrupt, intervene and then resume an EA tool. It also forgoes interactive subjective design evaluation for
   computationalized multi-criteria evaluation that permits wider search in shorter time spans.
%8 June  29
%Z Master of Science Engineering Physics

%A Martin Hemberg
%A Una-May O'Reilly
%A Peter Nordin
%T GENR8 - A Design Tool for Surface Generation
%B 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Erik D. Goodman
%D 2001
%P 160--167
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, architecture, Lindenmayer systems, BNF grammar, HEMLS, grammatical evolution, Alias|Wavefront Maya
%U http://www.ai.mit.edu/projects/emergentDesign/genr8/lateGecco.pdf
%8 9-11  July
%Z GECCO-2001LB

%A Martin Hemberg
%A Una-May O'Reilly
%T GENR8 - A Design Tool for Surface Generation
%B Graduate Student Workshop
%E Conor Ryan
%D 2001
%P 413--416
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming
%8 7  July
%Z GECCO-2001WKS Part of heckendorn:2001:GECCOWKS, see \citehemberg:2001:adtsg, GENR8

%A Martin Hemberg
%A Una-May O'Reilly
%T GENR8 - Using Grammatical Evolution In A Surface Design Tool
%B GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2002
%P 120--123
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.ai.mit.edu/projects/emergentDesign/genr8/gecco2002.pdf
%8 8  July
%Z Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic
   Programming Conference (GP-2002) part of barry:2002:GECCO:workshop

%A Martin Hemberg
%A Una-May O'Reilly
%T Extending Grammatical Evolution to Evolve Digital Surfaces with Genr8
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 299--308
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=299
%X Genr8 is a surface design tool for architects. It uses a grammar-based generative growth model that produces surfaces with an organic quality. Grammatical Evolution is used
   to help the designer search the universe of possible surfaces. We describe how we have extended Grammatical Evolution, in a general manner, in order to handle the grammar
   used by Genr8.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004 GENR8 http://www.ai.mit.edu/projects/emergentDesign/genr8/euroGPposter.pdf
%@ 3-540-21346-5

%A Martin Hemberg
%A Una-May O'Reilly
%T Using Generative Growth Systems to Design Architectural Form
%B Workshop and Tutorial Proceedings Ninth International Conference on the Simulation and Synthesis of Living Systems(Alife XI)
%E Mark Bedau and Phil Husbands and Tim Hutton and Sanjeev Kumar and Hideaki Sizuki
%D 2004
%P 33--36
%I 
%C Boston, Massachusetts
%K genetic algorithms, genetic programming, gramatical evolution, Genr8, HEMLS, Lindenmayer (L-systems), BNF
%U http://www.cs.ucl.ac.uk/staff/S.Kumar/hemberg-oreilly.zip
%X Inspired by biological growth, we are using generative systems influenced by simulated environmental factors to create scalable and complex form designs. We describe how a
   generative system language in combination with simulated physics can crudely mimic biology with respect to parallel, non-linear spatial growth reacting to the environment.
   We also present a categorization of selected creative design tools in terms of how they address environment, genomic representation, search and development.
%O Self-organisation and development in artificial and natural systems workshop.
%8 12  September
%Z http://www.alife9.org/ ALIFE9 http://www.cs.ucl.ac.uk/staff/S.Kumar/sodans.htm 3D surfaces. Nice (concise) survey of creative design tools (CDT) generative architectural
   systems. \citebroughton:1999:e3DwlsGPwww Rosenman and John S Gero, GADES, GENRE, The Groningen Twister, Jackson, J. Frazer, MoSS, AgencyGP \citeo'reilly:2001:aagpd, Genr8
   \citehemberg:2001:masters

%A J. I. {van Hemert}
%T Applying Adaptive Evolutionary Algorithms to Hard Problems
%R M.S. Thesis Master's thesis
%D 1998
%I 
%I Leiden University
%K constraint satisfaction; data mining
%U http://www.vanhemert.co.uk/publications/IR-98-19.ps.gz
%X Supervised by A.E. Eiben and E. Marchiori
%8 31  August

%A J. I. {van Hemert}
%A M. L. M. Jansen
%T An Engineering Approach to Evolutionary Art
%R Technical Report TR-01-01
%D 2001
%I 
%I Leiden University
%K genetic algorithms, genetic programming, evolutionary art
%U http://www.vanhemert.co.uk/publications/tr01-01.An_Engineering_Approach_to_Evolutionary_Art.ps.gz
%X We present a general system that evolves art on the Internet. The system runs on a server which enables it to collect information about its usage world wide; its core uses
   operators and representations from genetic programming. The output consists of images that are decoded from tree structures. We show how this general system can be used to
   evolve two types of art: A Mondriaan like art and a type known as mandala. Both types are implemented with the mind of an engineer.
%8 31  January

%A Jano I. {van Hemert}
%A Clarissa {Van Hoyweghen}
%A Eduard Lukschandl
%A Katja Verbeeck
%T A ``Futurist'' approach to dynamic environments
%R Technical Report TR-01-02
%D 2001
%I 
%I Leiden University
%K genetic algorithms, genetic programming, dynamic problems, interactive evolution
%U http://www.vanhemert.co.uk/publications/tr01-02.A_Futurist_Approach_to_Dynamic_Environments.ps.gz
%X We present a general system that evolves art on the Internet. The system runs on a server which enables it to collect information about its usage world wide; its core uses
   operators and representations from genetic programming. The output consists of images that are decoded from tree structures. We show how this general system can be used to
   evolve two types of art: A Mondriaan like art and a type known as mandala. Both types are implemented with the mind of an engineer.
%8 31  January

%A J. I. {van Hemert}
%A M. L. M. Jansen
%T An Engineering Approach to Evolutionary Art
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 177
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster, art, abstract, Internet, human induced fitness function, subjective, gene bank, evolutionary art
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%X We present a general system that evolves art on the Internet. The system runs on a server which enables it to collect information about its usage world wide; its core uses
   operators and representations from genetic programming. We show two types of art that can be evolved using this general system.
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO See also \citetr-01-01
%@ 1-55860-774-9

%A Robert W. Henderson
%A Robert Powell
%T West Indian Herpetoecology
%B Caribbean Amphibians and Reptiles
%E Brian I. Crother
%D 1999
%P 223--268
%I Academic Press
%C San Diego
%U http://www.sciencedirect.com/science/article/B87C3-4PN0BJP-K/2/14f280906c919939952ffbddf6b96c6c
%Z Not on GP

%A S. Hengpraprohm
%A P. Chongstitvatana
%T Selective Crossover in Genetic Programming
%B ISCIT International Symposium on Communications and Information Technologies
%D 2001
%I 
%C ChiangMai Orchid, ChiangMai Thailand
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/536164.html
%X Performance of Genetic Programming depends its genetic operators, especially the crossover operator. The simple crossover randomly swaps subtrees of the parents. The "good"
   subtree can be destroyed by an inappropriate choice of the crossover point. This work proposes a crossover operator that identifies a good subtree by measuring its impact
   on the fitness value and recombines good subtrees from parents. The proposed operator, called selective crossover, has been tested on two problems with satisfactory
   results.
%O The Pennsylvania State University CiteSeer Archives
%8 14-16  November
%Z http://www.ecti.or.th/conferences/ISCIT/ Chulalongkorn University, Thailand

%A S. Hengpraprohm
%A P. Chongstitvatana
%T Selecting Informative Genes from Microarray Data for Cancer Classification with Genetic Programming Classifier Using K-Means Clustering and SNR Ranking
%B Proceedings of the 2007 International Conference Frontiers in the Convergence of Bioscience and Information Technologies (FBIT 2007)
%D 2007
%P 211--218
%I IEEE Press
%C Jeju Island, Korea
%K genetic algorithms, genetic programming
%U http://www.computer.org/portal/web/csdl/doi/10.1109/FBIT.2007.84
%X This paper presents a method for selecting informative features using K-Means clustering and SNR ranking. The performance of the proposed method was tested on cancer
   classification problems. Genetic Programming is employed as a classifier. The experimental results indicate that the proposed method yields higher accuracy than using the
   SNR ranking alone and higher than using all of the genes in classification. The clustering step assures that the selected genes have low redundancy, hence the classifier
   can exploit these features to obtain better performance.
%8 October  11-13
%Z DOI broken 9 May 2010 Dept. of Comput. Eng., Chulalongkorn Univ., Chulalongkorn

%A Supoj Hengpraprohm
%A Prabhas Chongstitvatana
%T A Genetic Programming Ensemble Approach to Cancer Microarray Data Classification
%B 3rd International Conference on Innovative Computing Information and Control, ICICIC '08
%D 2008
%P 340--340
%I 
%C Dalian, Liaoning China
%K genetic algorithms, genetic programming, K-means clustering, cancer microarray data classification, ensemble approach, evolutionary algorithm, feature selection, machine
   learning, cancer, feature extraction, learning (artificial intelligence), medical computing, pattern classification, pattern clustering
%U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4603529
%X This paper presents a method for building an ensemble of classifiers for cancer microarray data. The proposed method exploits the advantage of a clustering technique,
   namely K-means clustering, combined with a feature selection technique, namely SNR feature selection. An evolutionary algorithm, namely Genetic Programming, is used to
   construct a number of classifiers which are assembled into an ensemble. The performance of the proposed method was tested on six cancer microarray data sets. The
   experimental results indicate that the proposed method yields a good prediction accuracy with a small standard deviation.
%8 June  18-  June  20
%Z Also known as \cite4603529

%A Kenneth Hennessy
%A Michael G. Madden
%A Jennifer Conroy
%A Alan G. Ryder
%T An improved genetic programming technique for the classification of Raman spectra
%J Knowledge Based Systems
%V 18
%N 4-5
%D 2005
%P 217--224
%I 
%K genetic algorithms, genetic programming, Machine learning, Neural networks, Spectroscopy, Raman
%X The aim of this study is to evaluate the effectiveness of genetic programming relative to that of more commonly-used methods for the identification of components within
   mixtures of materials using Raman spectroscopy. A key contribution of the genetic programming technique proposed in this research is that it explicitly aims to optimise the
   certainty levels associated with discovered rules, so as to minimize the chance of misclassification of future samples.
%O AI-2004, Cambridge, England, 13th-15th December 2004
%8 August

%A Kelvin C. Henry
%T Exploring Cellular Automata Using a Two-Dimensional Genetic Algorithm
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 57--66
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, life, GENESIS
%8 Decemeber
%Z GA generates rules for Cellular Automata aimming to select those that support proporgating structures. Life used as comparison. This volume contains 20 papers written and
   submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer Science 426) at Stanford University offered
   during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-187263-3

%A Almir Heralic
%A Krister Wolff
%A Mattias Wahde
%T Central Pattern Generators for Gait Generation in Bipedal Robots
%B Humanoid Robots: New Developments
%E Armando Carlos de Pina Filho
%D 2007
%P 285--304
%I I-Tech Education and Publishing
%C Vienna, Austria
%K genetic algorithms, genetic programming
%U http://www.intechopen.com/articles/show/title/central_pattern_generators_for_gait_generation_in_bipedal_robots
%X An obvious problem confronting humanoid robotics is the generation of stable and efficient gaits. Whereas wheeled robots normally are statically balanced and remain upright
   regardless of the torques applied to the wheels, a bipedal robot must be actively balanced, particularly if it is to execute a human-like, dynamic gait. The success of gait
   generation methods based on classical control theory, such as the zero-moment point (ZMP) method (Takanishi et al., 1985), relies on the calculation of reference
   trajectories for the robot to follow. In the ZMP method, control torques are generated in order to keep the zero-moment point within the convex hull of the support area
   defined by the feet. When the robot is moving in a well-known environment, the ZMP method certainly works well. However, when the robot finds itself in a dynamically
   changing real-world environment, it will encounter unexpected situations that cannot be accounted for in advance. Hence, reference trajectories can rarely be specified
   under such circumstances. In order to address this problem, alternative, biologically inspired control methods have been proposed, which do not require the specification of
   reference trajectories. The aim of this chapter is to describe one such method, based on central pattern generators (CPGs), for control of bipedal robots.
%O 17
%O Invited book chapter
%8 June

%A German Hernandez
%A Jerome A. Goldstein
%A Fernando Niao
%T Stochastic Differential Model for Evolutionary Algorithms over Continuous Spaces
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 863--870
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Julio Cesar Hernandez
%A Andre Seznec
%A Pedro Isasi
%T On the design of state-of-the-art pseudorandom number generators by means of genetic programming
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 1510--1516
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Evolutionary Computation in Cryptology and Computer Security, cellular automata, fitness function, pseudorandom number generators,
   cellular automata, random number generation
%X The design of pseudorandom number generators by means of evolutionary computation is a classical problem. To day, it has been mostly and better accomplished by means of
   cellular automata and not many proposals, inside or outside this paradigm, could claim to be both robust (passing many statistical tests, including the most demanding ones)
   and fast, as is the case of the proposal we present. Furthermore, we use a radically new approach, where our fitness function is not at all based in any measure of
   randomness, as is frequently the case in the literature, but of non-linearity. Efficiency is assured by using only very efficient operators, and by limiting the number of
   terminals in the Genetic Programming implementation.
%8 20-23  June
%Z PRNG. Also known as \cite1331075. CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Arturo Hernandez-Aguirre
%A Bill P. Buckles
%A Carlos A. Coello-Coello
%T Gate-level Synthesis of Boolean Functions using Binary Multiplexers and Genetic Programming
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 675--682
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, hybrid systems
%U http://citeseer.ist.psu.edu/309980.html
%X This paper presents a genetic programming approach for the synthesis of logic functions by means of multiplexers. The approach uses the 1-control line multiplexer as the
   only design unit. Any logic function (defined by a truth table) can be produced through the replication of this single unit. Our fitness function works in two stages:
   first, it finds feasible solutions, and then it concentrates on the minimization of the circuit. The proposed approach does not require any knowledge from the application
   domain.
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Julio C. Hernandez-Castro
%A Juan M. Estevez-Tapiador
%A Arturo Ribagorda-Garnacho
%A Benjamin Ramos-Alvarez
%T Wheedham: An Automatically Designed Block Cipher by means of Genetic Programming
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 499--506
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X we present a general scheme for the design of block ciphers by means of Genetic Programming. In this vein, we try to evolve highly nonlinear and efficient functions to be
   used for the key expansion and the F-function of a Feistel network. Following this scheme, we propose a new block cipher design called Wheedham, that operates on 512 bit
   blocks and keys of 256 bits, of which we offer its C code (directly translated from the GP Trees) and some preliminary security results.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Shany Hershkovitz
%A Sioma Baltianski
%A Yoed Tsur
%T Nb-Doped Barium Titanate: Concentration-Properties Relations
%B 9th Biennial Conference on Engineering Systems Design and Analysis (ESDA2008)
%V 1
%D 2008
%P 499--504
%I ASME
%C Haifa, Israel
%K genetic algorithms, genetic programming
%X Nb doped barium titanate (BT) experiences unique phenomena over a range of dopant concentrations. One important phenomenon is the resistivity behaviour as a function of
   donor concentration. The role of the grains and the grain boundaries in this system is not fully established yet. There are diverse opinions on this subject, since this
   system is usually only in partial equilibrium and hence very complex. We examine the system using Impedance Spectroscopy (IS). Two new analysis methods for IS based on
   evolutionary programming techniques, which are inspired by biological evolution, have been developed in our lab. Those evolutionary programming techniques are called
   Genetic Programming (GP) and Genetic Algorithm (GA). This is an approach to solve (or in the case of GA suggest solution for) such ill-posed inverse problems. By
   implementation and improvement of the use of those techniques for analysing IS results, we believe that the role of the grains and the grain boundaries can be separated and
   the physical processes occur can be analysed.
%8 July  7-9
%Z Technion-Israel Institute of Technology, Haifa, Israel

%A Shany Hershkovitz
%A Sioma Baltianski
%A Yoed Tsur
%T Harnessing evolutionary programming for impedance spectroscopy analysis: A case study of mixed ionic-electronic conductors
%J Solid State Ionics
%V 188
%N 1
%D 2011
%P 104--109
%I 
%K genetic algorithms, genetic programming, Impedance spectroscopy, Warburg elements, Parametric analysis
%U http://www.sciencedirect.com/science/article/B6TY4-51D5RFW-2/2/78396a47420bfca2e3d664e88b21c461
%X A modified Genetic Programming (GP) method has been developed for the analysis of impedance spectroscopy data. It gives a functional form of the distribution function of
   relaxation times (DFRT) in the sample. The evolution force is composed of lowering the discrepancy between the model's prediction and the measured data, while keeping the
   model simple in terms of the number of free parameters. The DFRT that the program seeks for has the form of a peak or a sum of several peaks. All the peaks are known
   mathematical functions (e.g., Gaussians). The user can let the program search for many types of peaks or to limit the search. Finding a functional form of the underlying
   DFRT has two main assets. (a) DFRT is unique and (b) a functional form makes it possible to develop a physical model and compare it to the function. In addition, if more
   than one peak is present and each peak can be related to a different phenomenon, the peaks can be directly separated for further analysis. The analysis method is
   demonstrated using synthetic data as well as experimental data of Gd0.1Ce0.9O1.95 (GDC).
%O 9th International Symposium on Systems with Fast Ionic Transport

%A Javier Hervas
%A Paul L. Rosin
%T Image Thresholding For Landslide Detection By Genetic Programming
%B Proceedings of the First International Workshop on Multitemporal Remote Sensing Images
%E Lorenzo Bruzzone and Paul Smits
%D 2001
%I World Scientific Publishing
%C University of Trento, Italy
%K genetic algorithms, genetic programming
%8 13-14  September
%Z see also \citeoai:CiteSeerPSU:555070
%@ 981-02-4955-1

%A Javier Hervas
%A Paul L. Rosin
%T Image Thresholding For Landslide Detection By Genetic Programming
%D 2003
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/555070.html
%X This paper describes an approach to image thresholding that combines various multiscale and morphological features, including texture, shape and edge filtering, by using
   genetic programming, to detect the presence of landslides and their active sectors in change detected multitemporal aerial images
%O The Pennsylvania State University CiteSeer Archives
%8 January ~02
%Z see also \citehervas:2001:MTRSI http://www.worldscibooks.com/compsci/4997.html

%A Magnus Lie Hetland
%A Pal Saetrom
%T Temporal Rule Discovery using Genetic Programming and Specialized Hardware
%B Proceedings of the 4th International Conference on Recent Advances in Soft Computing
%E Ahmad Lotfi and Jon Garibaldi and Robert John
%D 2002
%P 182--188
%I The Nottingham Trent University Nottingham, United Kingdom
%C Nottingham, United Kingdom
%K genetic algorithms, genetic programming, Time series, sequence mining, rule discovery, pattern matching hardware
%U http://citeseer.ist.psu.edu/549830.html
%X Discovering association rules is a well-established problem in the field of data mining, with many existing solutions. In later years, several methods have been proposed
   for mining rules from sequential and temporal data. This paper presents a novel technique based on genetic programming and specialized pattern matching hardware. The
   advantages of this method are its exibility and adaptability, and its ability to produce intelligible rules of considerable complexity.
%8 12-13  Decemeber
%Z RASC http://www.rasc2002.info/ See also \citehetland:2005:ML
%@ 1-84233-076-4

%A Magnus Lie Hetland
%A Pal Saetrom
%T Evolutionary Rule Mining in Time Series Databases
%J Machine Learning
%V 58
%N 2-3
%D 2005
%P 107--125
%I 
%K genetic algorithms, genetic programming, sequence mining, knowledge discovery, time series, specialised hardware
%X Data mining in the form of rule discovery is a growing field of investigation. A recent addition to this field is the use of evolutionary algorithms in the mining process.
   While this has been used extensively in the traditional mining of relational databases, it has hardly, if at all, been used in mining sequences and time series. In this
   paper we describe our method for evolutionary sequence mining, using a specialized piece of hardware for rule evaluation, and show how the method can be applied to several
   different mining tasks, such as supervised sequence prediction, unsupervised mining of interesting rules, discovering connections between separate time series, and
   investigating tradeoffs between contradictory objectives by using multiobjective evolution.
%8 February

%A Adam Hewgill
%T COSC 4P77 Final Project Improvements to lilgp Genetic Programming System
%I 
%K genetic algorithms, genetic programming
%U http://www.cosc.brocku.ca/Offerings/5P71/bstlilgp/bstlilgp_unix/lilgp%20Improvments.pdf
%O www
%O Brock Strongly Typed lilgp
%Z bstlilgp-0.5.0 http://www.cosc.brocku.ca/Offerings/5P71/bstlilgp/bstlilgp_unix/bstlilgp-0.5.0.zip

%A Adam Hewgill
%T Real-Time Competitive Evolutionary Computation
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 228--232
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming, alife
%U http://www.cosc.brocku.ca/files/downloads/research/cs0217.pdf
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp lil-gp used to evolve brains for robot fish in simulated aquarium

%A Adam Hewgill
%A Brian J. Ross
%T Procedural 3D Texture Synthesis Using Genetic Programming
%R Technical Report CS-03-06
%D 2003
%I 
%I Brock University, Department of Computer Science
%C St. Catharines, Ontario, Canada L2S 3A1
%K genetic algorithms, genetic programming, procedural textures, evolution
%U http://citeseer.ist.psu.edu/559621.html
%X The automatic synthesis of procedural textures for 3D surfaces using genetic programming is investigated. Genetic algorithms employ a search strategy inspired by Darwinian
   natural evolution. Genetic programming uses genetic algorithms on tree structures, which are interpretable as computer programs or mathematical formulae. We use a texture
   generation language as a target language for genetic programming, and then use it to evolve textures having particular characteristics of interest. The texture generation
   language used here includes operators useful for texture creation, for example, mathematical operators, and colour and noise functions. In order to be practical for 3D
   model rendering, the language includes primitives that access surface information for the point being rendered, such as coordinates values, normal vectors, and surface
   gradients. A variety of experiments successfully generated procedural textures that displayed visual characteristics similar to the target textures used during training.
%8 April  2003
%Z see also \citehewgill:2004:CG

%A Adam Hewgill
%A Brian J. Ross
%T The Evolution of 3D Procedural Textures
%B Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Bart Rylander
%D 2003
%P 146--147
%I 
%C Chicago, USA
%K genetic algorithms, genetic programming, STGP
%U http://adamhewgill.com/research/gen3d_LBP.pdf
%8 12  July
%Z GECCO-2003LB, lilgp

%A Adam Hewgill
%A Brian J. Ross
%T Procedural 3D Texture Synthesis Using Genetic Programming
%J Computers and Graphics
%V 28
%N 4
%D 2004
%P 569--584
%I 
%K genetic algorithms, genetic programming, Procedural textures, Evolution, grammar BNF
%U http://www.sciencedirect.com/science/article/B6TYG-4CS4FCT-1/2/b8a5d381a1371ba6545d194a470dfa89
%X The automatic synthesis of procedural textures for 3D surfaces using genetic programming is investigated. Genetic algorithms employ a search strategy inspired by Darwinian
   natural evolution. Genetic programming uses genetic algorithms on tree structures, which are interpretable as computer programs or mathematical formulae. We define a
   texture generation language in the genetic programming system, which is then used to evolve textures having particular characteristics of interest. The texture generation
   language used here includes operators useful for texture creation, for example, mathematical operators, colour functions and noise functions. In order to be practical for
   3D model rendering, the language includes primitives that access surface information for the point being rendered, such as coordinates values, normal vectors, and surface
   gradients. A variety of experiments successfully generated procedural textures that displayed visual characteristics similar to the target textures used during training.
%8 August
%Z \citehewgill:2003:06 is prelimary version. lilgp. Ten runs in parallel on 16-CPU Silicon Graphics Origin 2000 server. Fig 9 Woman's clothing training points

%A William R. Hewlett
%T Reynolds Numbers: Using Genetic Programming and Vite to find Formulas to Describe Organizations
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 20--28
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A M. I. Heywood
%A A. N. Zincir-Heywood
%T Register Based Genetic Programming on FPGA Computing Platforms
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 44--59
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://users.cs.dal.ca/~mheywood/X-files/Publications/EuroGP-2k0.pdf
%X The use of FPGA based custom computing platforms is proposed for implementing linearly structured Genetic Programs. Such a context enables consideration of micro
   architectural and instruction design issues not normally possible when using classical Von Neumann machines. More importantly, the desirability of minimising memory
   management overheads results in the imposition of additional constraints to the crossover operator. Specifically, individuals are described in terms of the number of pages
   and page length, where the page length is common across individuals of the population. Pairwise crossover therefore results in the swapping of equal length pages, hence
   minimising memory overheads. Simulation of the approach demonstrates that the method warrants further study.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP http://users.cs.dal.ca/~mheywood/X-files/Publications/EuroGP-2k0.pdf has additional revisions.
%@ 3-540-67339-3

%A M. I. Heywood
%A A. N. Zincir-Heywood
%T Page-based linear genetic programming
%B Systems, Man, and Cybernetics, 2000 IEEE International Conference
%V 5
%D 2000
%P 3823--3828
%I 
%C IEEE Press
%K genetic algorithms, genetic programming, page-based linear genetic programming, evolutionary computation, computational overheads, fitness of individuals, crossover
   operator, equal length code fragments, register-machine, a priori internal register external output definitions
%U http://ieeexplore.ieee.org/iel5/7099/19140/00886606.pdf?isNumber=19140
%X Genetic programming arguably represents the most general form of evolutionary computation. However, such generality is not without significant computational overheads.
   Particularly, the cost of evaluating the fitness of individuals in any form of evolutionary computation represents the single most significant computational bottleneck. A
   less widely acknowledged computational overhead in GP involves the implementation of the crossover operator. To this end a page-based definition of individuals is used to
   restrict crossover to equal length code fragments. Moreover, by using a register-machine context, the significance of a priori internal register external output definitions
   is emphasized.
%8 8-11  October
%@ 0-7803-6583-6

%A M. I. Heywood
%A A. N. Zincir-Heywood
%T Dynamic Page Based Crossover in Linear Genetic Programming
%J IEEE Transactions on Systems, Man, and Cybernetics: Part B - Cybernetics
%V 32
%N 3
%D 2002
%P 380--388
%I 
%K genetic algorithms, genetic programming, linear genetic programming
%X Page-based Linear Genetic Programming (GP) is proposed in which individuals are described in terms of a number of pages. Pages are expressed in terms of a fixed number of
   instructions, constant for all individuals in the population. Pairwise crossover results in the swapping of single pages, thus individuals are of a fixed number of
   instructions. Head-to-head comparison with Tree structured GP and block-based Linear GP indicates that the page-based approach evolves succinct solutions without penalizing
   generalization ability.
%8 June

%A H. G. Hiden
%A M. J. Willis
%A P. Turner
%A M. T. Tham
%A G. A. Montague
%T Non-linear Principal Components Analysis Using Genetic Programming
%R Technical Report
%D 1996
%I 
%I Chemical Engineering, Newcastle University
%C UK
%K genetic algorithms, genetic programming
%X The recent explosion of low-cost computing power and information storage has brought with it a corresponding mushrooming in the amount of data on almost any subject
   conceivable that is available. The philosophy that you cant have enough information seems to have been applied to every situation with great enthusiasm. By adopting such an
   approach, much useful data can be gathered, however it is all too frequently swamped by irrelevant information. The distinction must be made between useful information and
   information for the sake of having it. The chemical industry also has not been immune to the data collection bug. The equipment required to collect, process and store data
   is more affordable than ever, a fact which the designers of chemical processes are beginning to exploit. Unfortunately, this data is not particularly useful on its own. It
   is very easy to collect data, but difficult to analyse it productively. It is this situation that has spawned a wide variety of data analysis tools, the objective of which
   is to determine underlying relationships and structures within large data sets.
%O Extended Abstract, ICANNGA '97, Norwich, UK
%Z MSword postscript not compatible with unix.

%A Hugo Hiden
%A Mark Willis
%A Ben McKay
%A Gary Montague
%T Non-Linear And Direction Dependent Dynamic Modelling Using Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 168--173
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/hiden_1997_ndddmGP.pdf
%8 13-16  July
%Z GP-97

%A Hugo Hiden
%A Mark Willis
%A Ming Tham
%A Paul Turner
%A Gary Montague
%T Non-Linear Principal Components Analysis using Genetic Programming
%B Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA
%E Ali Zalzala
%D 1997
%P 302--307
%I Institution of Electrical Engineers Savoy Place, London WC2R 0BL, UK
%C University of Strathclyde, Glasgow, UK
%K genetic algorithms, genetic programming
%U http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019970CP446000302000001&idtype=cvips&prog=normal
%X Principal Components Analysis (PCA) is a standard statistical technique, which is frequently employed in the analysis of large highly correlated data-sets. As it stands,
   PCA is a linear technique which can limit its relevance to the highly non-linear systems frequently encountered in the chemical process industries. Several attempts to
   extend linear PCA to cover non-linear data sets have been made, and will be briefly reviewed in this paper. We propose a symbolically oriented technique for non-linear PCA,
   which is based on the Genetic Programming (GP) paradigm. Its applicability will be demonstrated using two simple non-linear systems and industrial data collected from a
   distillation column. It is suggested that the use of the GP based non-linear PCA algorithm achieves the objectives of non-linear PCA, while giving high a degree of
   structural parsimony.
%8 1-4  September
%Z GALESIA'97 see also \citehiden:1999:CCE
%@ 0-85296-693-8

%A H. G. Hiden
%A M. J. Willis
%A G. A. Montague
%T Using Genetic Programming to Develop Non-Linear Dynamic Models of Chemical Process Systems
%B IChemE Jubilee Research Event
%V 2
%D 1997
%P 789--792
%I 
%I Institute of Chemical Engineers
%C Nottingham, UK
%K genetic algorithms, genetic programming
%8 8-9  April
%Z Comparison of GP with feedforward ANN and finite Impulse response model

%A Hugo Hiden
%A Ben McKay
%A Mark Willis
%A Gary Montague
%T Non-Linear Partial Least Squares using Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 128--133
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A H. G. Hiden
%A M. J. Willis
%A M. T. Tham
%A G. A. Montague
%T Non-linear principal components analysis using genetic programming
%J Computers and Chemical Engineering
%V 23
%N 3
%D 1999
%P 413--425
%I 
%K genetic algorithms, genetic programming, data analysis, multivariate statistics, statistical methods, data reduction, mathematical programming, distillation columns,
   nonlinear systems, chemical operations, chemical plants, principal component analysis, multivariate statistics
%X Principal components analysis (PCA) is a standard statistical technique, which is frequently employed in the analysis of large highly correlated data sets. As it stands,
   PCA is a linear technique which can limit its relevance to the non-linear systems frequently encountered in the chemical process industries. Several attempts to extend
   linear PCA to cover non-linear data sets have been made, and will be briefly reviewed in this paper. We propose a symbolically oriented technique for non-linear PCA, which
   is based on the genetic programming (GP) paradigm. Its applicability will be demonstrated using two simple non-linear systems and data collected from an industrial
   distillation column.
%8 28  February
%Z Matlab, Maple, pop=60

%A Hugo George Hiden
%T Data-based modelling using genetic programming
%R Ph.D. Thesis
%D 1998
%I 
%I University of Newcastle upon Tyne
%K genetic algorithms, genetic programming
%Z Ming Tham - I don't think the thesis is online.

%A Tetsuya Higuchi
%A Hitoshi Iba
%A Bernard Manderick
%T Applying Evolvable Hardware to Autonomous Agents
%B Parallel Problem Solving from Nature III
%S LNCS
%E Yuval Davidor and Hans-Paul Schwefel and Reinhard M\"anner
%V 866
%D 1994
%P 524--533
%I Springer-Verlag Berlin, Germany
%C Jerusalem
%K genetic algorithms, reinforcement learning, Evovable Hardware
%U http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6
%X In this paper, we describe a parallel processing architecture for Evolvable Hardware (EHW) which changes its own hardware structure in order to adapt to the environment in
   which it is embedded. This adaptation process is a combination of genetic learning with reinforcement learning. As an example of EHW applications, the arbitration in
   behaviour-based robot is discussed. Our goal by implementing adaptation in hardware is to produce a flexible and fault-tolerant architecture which responds in real-time to
   a changing environment.
%8 9-14  October
%Z Describes software reconfigurable logic device which changes its own hardware to adapt to its environment. PPSN3
%@ 3-540-58484-6

%A Tomofumi Hikage
%A Hitoshi Hemmi
%A Katsunori Shimohara
%T Comparison of Evolutionary Methods for Smoother Evolution
%B Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware (ICES 98)
%S LNCS
%E Moshe Sipper and Daniel Mange and Andres Perez-Uribe
%V 1478
%D 1998
%P 115--124
%I Springer Verlag Berlin
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming, HDL
%U http://link.springer.de/link/service/series/0558/papers/1478/14780115.pdf
%X Hardware evolution methodologies come into their own in the construction of real-time adaptive systems. The technological requirements for such systems are not only
   high-speed evolution, but also steady and smooth evolution. This paper shows that the Progressive Evolution Model (PEM) and Diploid chromosomes contribute toward satisfying
   these requirements in the hardware evolutionary system AdAM (Adaptive Architecture Methodology). Simulations of an artificial ant problem using four combinations of two
   wets of variables - PEM vs. non-PEM, and Diploid AdAM vs. Haploid AdAM - show that the Diploid-PEM combination overwhelms the others.
%8 23-25  September
%Z ICES98 Chromosome is parse-tree for SFL (HDL). Simulation. Dominace recessive tags in trees. Ant is _assumed_ to be able to solve problem entirely from its current sensor
   readings, ie no memory
%@ 3-540-64954-9

%A Torsten Hildebrandt
%A Jens Heger
%A Bernd Scholz-Reiter
%T Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 257--264
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, Combinatorial optimization and metaheuristics
%X Developing dispatching rules for manufacturing systems is a process, which is time- and cost-consuming. Since there is no good general rule for different scenarios and
   objectives automatic rule search mechanism are investigated. In this paper an approach using Genetic Programming (GP) is presented. The priority rules generated by GP are
   evaluated on dynamic job shop scenarios from literature and compared with manually developed rules yielding very promising results also interesting for Simulation
   Optimisation in general.
%8 7-11  July
%Z hyperheuristic Also known as \cite1830530 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual
   genetic programming conference (GP-2010)

%A James A. Hilder
%A Andy M. Tyrrell
%T An evolutionary platform for developing next-generation electronic circuits
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2007)
%E Peter A. N. Bosman
%D 2007
%P 2483--2488
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, EHW, analogue circuit design, genetic algorithms, SPICE
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2483.pdf
%X In this paper, a new method for evolving simple electronic circuits is discussed, with the aim of improving the reliability and performance of basic circuit blocks.
   Next-generation CMOS device models will be used in the simulation of circuits. Circuits are mapped to a grid layout which reflects the appearance of conventional schematic
   blocks. The performance of the system at designing passive lowpass filters is discussed, with an outline given of the intended future steps, towards the goal of integrating
   sub 100 nm MOSFET models into the circuits.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A James A. Hilder
%A James Alfred Walker
%A Andy M. Tyrrell
%T Designing variability tolerant logic using evolutionary algorithms
%B Research in Microelectronics and Electronics, PRIME 2009. Ph.D.
%D 2009
%P 184--187
%I 
%K genetic algorithms, genetic programming, cartesian genetic programming, EHW, block-level designs, evolutionary algorithms, intrinsic variability, logic gates, robust logic
   circuit topology, variability tolerant logic, circuit optimisation, evolutionary computation, integrated circuit design, logic design
%X This paper describes an approach to create novel, robust logic-circuit topologies, using several evolution-inspired techniques over a number of design stages. A library of
   2-input logic gates are evolved and optimised for tolerance to the effects of intrinsic variability. Block-level designs are evolved using evolutionary methods (CGP). A
   method of selecting the optimal gates from the library to fit into the block-level designs to create variability-tolerant circuits is also proposed.
%8 12-17  July
%Z Also known as \cite5201345

%A James Hilder
%A James A. Walker
%A Andy Tyrrell
%T Use of a multi-objective fitness function to improve cartesian genetic programming circuits
%B 2010 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)
%D 2010
%P 179--185
%I 
%K genetic algorithms, genetic programming, cartesian genetic programming
%X This paper describes an approach of using a multi-objective fitness function to improve the performance of digital circuits evolved using CGP. Circuits are initially
   evolved for correct functionality using conventional CGP before the NSGA-II algorithm is used to extract circuits which are more efficient in terms of design complexity and
   delay. This approach is used to evolve typical digital-system building block circuits with results compared to standard-CGP, other evolutionary methods and conventional
   designs.
%8 15-18  June
%Z Hex to 7-Segment Display Driver. Also known as \cite5546262

%A David J. Hill
%A Barbara S. Minsker
%A Albert J. Valocchi
%A Vladan Babovic
%A Maarten Keijzer
%T Upscaling models of solute transport in porous media through genetic programming
%J Journal of Hydroinformatics
%V 9
%N 4
%D 2007
%P 251--266
%I IWA Publishing
%K genetic algorithms, genetic programming, data-driven modeling, knowledge discovery, solute transport
%U http://www.iwaponline.com/jh/009/0251/0090251.pdf
%X Due to the considerable computational demands of modeling solute transport in heterogeneous porous media, there is a need for upscaled models that do not require explicit
   resolution of the small-scale heterogeneity. This study investigates the development of upscaled solute transport models using genetic programming (GP), a
   domain-independent modelling tool that searches the space of mathematical equations for one or more equations that describe a set of training data. An upscaling methodology
   is developed that facilitates both the GP search and the implementation of the resulting models. A case study is performed that demonstrates this methodology by developing
   vertically averaged equations of solute transport in perfectly stratified aquifers. The solute flux models developed for the case study were analysed for parsimony and
   physical meaning, resulting in an up scaled model of the enhanced spreading of the solute plume, due to aquifer heterogeneity, as a process that changes from predominantly
   advective to Fickian. This case study not only demonstrates the use and efficacy of GP as a tool for developing upscaled solute transport models, but it also provides
   insight into how to approach more realistic multi-dimensional problems with this methodology.
%Z Synthetic aquifers, ALP, p264 'GP.. produce mathematical models that researchers can understand.'

%A David J. Hill
%T Data Mining Approaches to Complex Environmental Problems
%R Ph.D. Thesis
%D 2007
%I 
%I Environmental Engineering in Civil Engineering, University of Illinois at Urbana-Champaign
%C Urbana, Illinois, USA
%K genetic algorithms, genetic programming
%U http://gaia.rutgers.edu/docs/HillDissertation.pdf
%X Understanding and predicting the behaviour of large-scale environmental systems is necessary for addressing many challenging problems of environmental interest.
   Unfortunately, the challenge of scaling predictive models, as well as the difficulty of parametrise these models, makes it difficult to apply them to large-scale systems.
   This research addresses these issues through the use of data mining. Specifically, this dissertation addresses two problems: upscaling models of solute transport in porous
   media and detecting anomalies in streaming environmental data. Up scaling refers to the creation of models that do not need to explicitly resolve all scales of system
   heterogeneity. Upscaled models require significantly fewer computational resources than do models that resolve small-scale heterogeneity. This research develops an
   upscaling method based on genetic programming (GP), which facilitates both the GP search and the implementation of the resulting models, and demonstrates its use and
   efficacy through a case study. Anomaly detection is the task of identifying data that deviate from historical patterns. It has many practical applications, such as data
   quality assurance and control (QA/QC), focused data collection, and event detection. The second portion of this dissertation develops a suite of data-driven anomaly
   detection methods, based on autoregressive datadriven models (e.g. artificial neural networks) and dynamic Bayesian network (DBN) models of the sensor data stream. All of
   the developed methods perform fast, incremental evaluation of data as it becomes available; scale to large quantities of data; and require no a priori information,
   regarding process variables or types of anomalies that may be encountered. Furthermore, the methods can be easily deployed on large heterogeneous sensor networks. The
   anomaly detection methods are then applied to a sensor network located in Corpus Christi Bay, Texas, and their abilities to identify both real and synthetic anomalies in
   meteorological data are compared. Results of these case studies indicate that DBN-based detectors, using either robust Kalman filtering or Rao-Blackwellized particle
   filtering, are most suitable for the Corpus Christi meteorological data.
%8 23  July

%A Seamus Hill
%A Colm O'Riordan
%T A Genetic Algorithm with a multi-layered Genotype-Phenotype Mapping
%B Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)
%E Agostinho Rosa
%D 2010
%I 
%C Valencia, Spain
%K genetic algorithms
%X n this paper we investigate the introduction of a multiple-layer genotype-phenotype mapping to a Genetic Algorithm (GA) which attempts to mimic more closely, the effects of
   nature. The motivation for introducing multiple-layers into the genotype-phenotype mapping is to create a many-to-one genotype-phenotype mapping. The paper compares a
   traditional GA with a GA containing a multi-layered genotype-phenotype mapping using a number of well understood problems in an attempt to illustrate the potential benefits
   of including the multilayered mapping. Initial findings suggest that the multi-layered mapping between the genotype-phenotype used in conjunction with a binary
   representation outperforms existing traditional GA approaches on well known problems, while still allowing the use well understood genetic operators.
%8 24-26  October
%Z GP? http://www.icec.ijcci.org/ICEC2010/home.asp http://www.ecta.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm

%A Seamus Hill
%A Colm O'Riordan
%T Examining the use of a Non-Trivial Fixed Genotype-Phenotype Mapping in Genetic Algorithms to Induce Phenotypic Variability over Deceptive Uncertain Landscapes
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 1403--1410
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Representation and operators
%X In nature, living organisms can be viewed as the product of their genotype-phenotype mapping (GP-map). This paper presents a GP-map loosely based on the biological
   phenomena of transcription and translation, to create a multi-layered GP-map which increases the level of phenotypic variability. The aim of the paper is to examine through
   the use of a fixed non-trivial GP-map, the impact of increased phenotypic variability, on search over a set of deceptive landscapes. The GP-map allows for a non-injective
   genotype-phenotype relationship, and the phenotypic variability of a number of phenotypes, introduced by the GP-map, are advanced from the genotypes used to encode them
   through a basic interpretation of transcription and translation. We attempt to analyse the level of variability by measuring diversity, both at a genotypic and phenotypic
   level. The multi-layered GP-map is incorporated into a Genetic Algorithm, the multi-layered mapping GA (MMGA), and runs over a number of GA-Hard landscapes. Initial
   empirical results appear to indicate that over deceptive landscapes, as the level of problem difficulty increases, so too does the benefit of using the proposed GP-map to
   probe the search space.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Christopher J. Hillar
%A Friedrich T. Sommer
%T On the article ``Distilling free-form natural laws from experimental data''
%D 2009
%I 
%K genetic algorithms, genetic programming
%U http://www.msri.org/people/members/chillar/files/hs09b.pdf
%X A recent paper \citeScience09:Schmidt introduced the fascinating idea that natural symbolic laws (such as Lagrangians and Hamiltonians) could be learned from experimental
   measurements in a physical system (such as a pendulum)...
%O www
%Z See also \citeSchmidt:2009:rebuttal

%A W. Daniel Hillis
%T Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure
%B Artificial Life II
%S Santa Fe Institute Studies in the Sciences of Complexity
%E Christopher G. Langton and Charles E. Taylor and J. Doyne Farmer and Steen Rasmussen
%V X
%D 1992
%P 313--324
%I Addison-Wesley
%C Santa Fe Institute, New Mexico, USA
%K genetic algorithms
%X Evolves sorting networks. Tests evolved at same time lead to better solutions. Also aim to reduced testing effort.
%8 February  1990
%Z Not in index, see page 313-324

%A Mark Hinchliffe
%A Mark Willis
%A Hugo Hiden
%A Ming Tham
%T A comparison of two Genetic Programming Algorithms Applied to Chemical Process Systems Modelling
%R Technical Report
%D 1996
%I 
%I Chemical Engineering, Newcastle University
%C UK
%K genetic algorithms, genetic programming
%X Previous work by McKay et al (1996a,b,c) has shown that the Genetic programming (GP) methodology can be successfully applied to the development of non-linear steady state
   models of industrial chemical processes. Although a GP algorithm can identify the relevant input variables and evolve parsimonious system representations, the resulting
   model structures tend to contain little or no information relating to the mechanisms of the process itself. In this respect, the performance of the GP methodology is
   comparable to that of other black-box modelling techniques such as neural networks. Chemical process systems are often extremely complex and non-linear in nature.
   Phenomenological models are time consuming to develop and can be subject to inaccuracies caused by any simplifying assumptions made. Consequently, mechanistic models are
   costly to construct; an aspect which would make an automated procedure highly desirable. Phenomenological models are usually derived by applying the laws of conservation of
   mass, energy and momentum to the system. An examination of a number of steady-state mechanistic models shows that they tend to be made up of distinct sub-groups which, when
   added together, give the overall model structure. In the search for an automatic model generating algorithm, it would be extremely useful if the GP methodology could be
   used to identify these sub-groups. This could potentially enhance the GP algorithm's ability to evolve accurate chemical process models and also help to reveal hidden
   process knowledge. To achieve this goal, the standard GP algorithm used by McKay et al (1996a) was modified to accommodate the multiple gene model structure. The multiple
   gene structure was introduced by Altenberg (1994) in an attempt to enhance the learning capabilities of GA and GP algorithms. The work was inspired by the observation that,
   in nature, genetic information is stored on more than one gene. To demonstrate the feasibility of this new approach, real world examples are used to compare the performance
   of the algorithm with that of the standard GP algorithm.
%O Extended Abstract, submitted to: ICANNGA '97, Norwick, UK
%Z MSword postscript not camptible with unix

%A Mark Hinchliffe
%A Hugo Hiden
%A Ben McKay
%A Mark Willis
%A Ming Tham
%A Geoffery Barton
%T Modelling Chemical Process Systems Using a Multi-Gene Genetic Programming Algorithm
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 56--65
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%X In this contribution a multi-gene Genetic Programming (Gp) Algorithm is used to evolve input output models of chemical process systems. Three case studies are used to
   demonstrate the performance of the method when compared to a standard GP algorithm. A statistical analysis procedure is used to aid in the assessment of the results and
   suggest the number of independent runs required to obtain a successful result. It is concluded that the multi-gene algorithm provides superior performance, as partitioning
   the problem into sub-groups incorporates basic heuristic knowledge of the search space.
%8 28--31  July
%Z GP-96LB MSword .ps file not compatible with unix The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or
   800-533-2670
%@ 0-18-201031-7

%A Mark Hinchliffe
%A Mark Willis
%A Ming Tham
%T Chemical Process Sytems Modelling Using Multi-objective Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 134--139
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, MOGP
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Mark Hinchliffe
%A Mark Willis
%A Ming Tham
%T Dynamic Chemical Process Modelling Using a Multiple Basis Function Genetic Programming Algorithm
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1782
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, real world applications, poster papers, NARMAX
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-746.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Mark P. Hinchliffe
%T Dynamic Modelling Using Genetic Programming
%R Ph.D. Thesis
%D 2001
%I 
%I School of Chemical Engineering and Advanced Materials, University of Newcastle upon Tyne
%C UK
%K genetic algorithms, genetic programming, MOGA, MOGP, SOGP
%U http://www.ncl.ac.uk/ceam/postgrad/pg-theses.htm
%X Genetic programming (GP) is an evolutionary algorithm that attempts to evolve solutions to a problem by using concepts taken from the naturally occurring evolutionary
   process. This thesis introduces the concepts of GP model development by applying the technique to steady-state model evolution. A variation of the algorithm known as the
   multiple basis function GP (MBF-GP) algorithm is described and its performance compared with the standard algorithm. Results show that the MBF-GP algorithm requires
   significantly less computational effort to evolve models of comparable accuracy to the standard algorithm. The steady-state algorithm is then modified to enable the
   evolution of dynamic process models. Three case studies are used to demonstrate algorithm performance and show how the MBF-GP algorithm produces performance benefits
   similar to those observed in the steady-state modelling work. A comparison with neural networks reveals that GP is able to match the accuracy of the network predictions but
   is more expensive computationally. However, a significant advantage of the GP algorithm is that it can automatically evolve the time history of model terms required to
   account for process characteristics such as the system time delay. The model development process is not simply a case of reducing the error between the predicted and actual
   process output. The parallel nature of GP means that it is ideally suited to solving multi-objective problems. The MBF-GP algorithm is modified to incorporate a Pareto
   based ranking scheme that allows models to be compared using multiple performance criteria. The ranking scheme allows preference information in the form of goals and
   priorities to be specified in order to guide the search towards the desired region of the search space. Two case studies are used to demonstrate the performance of this
   technique. The first example uses the multi-objective algorithm to improve the parsimony of the evolved model structures. The second example demonstrates how a set residual
   correlation tests can be combined and used as an additional performance measure. In each case, the multi-objective algorithm performs significantly better than the single
   objective version. In addition, the inclusion of preference information overcomes some of the difficulties associated with conventional Pareto ranking and produces a
   greater number of acceptable solutions.
%8 September
%Z "the results do not provide sufficient evidence to suggest that GP will become as widely used as neural network modelling techniques." page 160.

%A Mark Hinchliffe
%A Mark Willis
%A Ming Tham
%A Gary Montague
%T Dynamic Chemical Process Modelling Using a Multiple Basis Function Genetic Programming Algorithm
%B Nineteenth IASTED International Conference, Modelling, Identification and Control
%D 2000
%I 
%C Innsbruck, Austria
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/263745.html
%O The Pennsylvania State University CiteSeer Archives
%8 February  14-17
%Z cited in \citehinchliffe:thesis

%A M. Hinchliffe
%A M. Willis
%T Dynamic Modelling Using Genetic Programming
%B Proceedings of the 15th IFAC World Congress
%D 2002
%I 
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%Z cited in \citehinchliffe:thesis

%A Mark P. Hinchliffe
%A Mark J. Willis
%T Dynamic systems modelling using genetic programming
%J Computers \& Chemical Engineering
%V 27
%N 12
%D 2003
%P 1841--1854
%I 
%K genetic algorithms, genetic programming, Neural networks, Dynamic modelling, Multi-objective
%U http://www.sciencedirect.com/science/article/B6TFT-49MDYGW-2/2/742bcc7f22240c7a0381027aa5ff7e73
%X In this contribution genetic programming (GP) is used to evolve dynamic process models. An innovative feature of the GP algorithm is its ability to automatically discover
   the appropriate time history of model terms required to build an accurate model. Two case studies are used to compare the performance of the GP algorithm with that of
   filter-based neural networks (FBNNs). Although the models generated using GP have comparable prediction performance to the FBNN models, a disadvantage is that they required
   greater computational effort to develop. However, we show that a major benefit of the GP approach is that additional model performance criteria can be included during the
   model development process. The parallel nature of GP means that it can evolve a set of candidate solutions with varying levels of performance in each objective. Although
   any combination of model performance criteria could be used as objectives within a multi-objective GP (MOGP) framework, the correlation tests outlined by Billings and Voon
   (Int. J. Control 44 (1986) 235) were used in this work.

%A Philip Hingston
%A Mike Preuss
%T Red Teaming with Coevolution
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 1160--1168
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Coevolutionary systems, Evolutionary simulation-based optimization, Real-world applications
%X In this paper we present a coevolutionary algorithm designed to be used as a computational tool to assist in red teaming studies. In these applications, analysts seek to
   understand the strategic and tactical options available to each side in a conflict situation. Combining scenario simulations with a coevolutionary search of parameter space
   is an approach that has many attractions. We argue that red teaming applications are sufficiently different from many others where coevolution is used so that specially
   designed algorithms can bring advantages. We illustrate by presenting a new algorithm that simultaneously evolves strong strategies along with dangerous counter-strategies.
   We test the new algorithm on two example problems: an abstract problem with some difficult characteristics; and a practical red teaming scenario. Experiments show that the
   new algorithm is able to solve the abstract problem well, and that it is able to provide useful insights on the red teaming scenario.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Kotaro Hirasawa
%A M. Okubo
%A J. Hu
%A J. Murata
%T Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP)
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 1276--1282
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, genetic programming Network, Evolution, Ant behaviors
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . GNP directed
   graph: judgement, time delay, processing nodes. Network genome. subnet swapping crossover. Ant pheremone square 32 by 32 grid world, food gathering.
%@ 0-7803-6658-1

%A Yoshikazu Hirayama
%A Tim Clarke
%A Julian Francis Miller
%T Fault tolerant control using Cartesian genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1523--1530
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, cartesian genetic programming, Fault Tolerance robotics, Real-World application
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1523.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389389

%A Tomoyuki Hiroyasu
%A Sosuke Fujita
%A Akihito Watanabe
%A Mitsunori Miki
%A Maki Ogura
%A Manabu Fukumoto
%T Comparison of GP and SAP in the image-processing filter construction using pathology images
%B 3rd International Congress on Image and Signal Processing (CISP 2010)
%V 2
%D 2010
%P 904--908
%I 
%K genetic algorithms, genetic programming, GP, SAP, image processing filter construction, medical image processing, pathology images, simulated annealing programming, medical
   image processing, simulated annealing
%X In this paper, programming methods of constructing filters for choosing target images from pathology images are discussed. Automatic construction of these filters would be
   very useful in the medical field. Image processing filters can be expressed as tree topology operations. Genetic Programming (GP) is an evolutionary computation algorithm
   that can design tree topology operations. Simulated Annealing Programming (SAP) is also an emergent algorithm that can create tree topology operations. These two
   algorithms, GP and SAP, were applied to construct Image Processing Filters and the characteristics of these two algorithms were compared. The results indicated that GP has
   strong search capability for finding the global optimum solution. However, in the latter part of the search, the diversity of solutions is lost and the program size becomes
   large. This can be avoided by removing introns. It is assumed that filters developed by GP have strong robustness for other images. On the other hand, SAP requires many
   iterations to find the optimum but the program size is small. Filters developed by SAP are relatively weak from the viewpoint of robustness for other images.
%8 16-18  October
%Z 'GP can derive the best solution with less evaluation time than SAP.' Also known as \cite5646895

%A Laurence Hirsch
%A Masoud Saeedi
%T Modelling exchange using the prisoner's dilemma and genetic programming
%B Proceedings of the Computer Society of Iran Computing Conference
%E Rasool Jalili
%D 1999
%I 
%C Sharif University of Technology, Tehran, Iran
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GPandIPDpaper1999Hirsch.pdf
%X In this paper we show how exchange, co-operation and other complex strategies found in nature can be modelled using the prisoners dilemma game and genetic programming. We
   are able to produce and evolve different strategies represented by computer programs that can play the prisoners' dilemma against a set of predefined strategies or against
   other programs in the population (co-evolution). Although the game is simple the number of possible strategies for playing it is huge. Genetic programming provides an
   efficient search mechanism capable of identifying and propagating strategies that do well in a particular environment. Our implementation provides a distinct advantage over
   previous investigations into the prisoner's dilemma using genetic algorithms. In particular strategies can be based upon the entire history of a game at any point, rather
   than on recent moves only. We incorporate the use of list data structures as terminals and provide list-searching capability in the function set so that potentially large
   volumes of data can be used by the evolved programs.
%8 26-28  January
%Z CSICC 98 http://persia.org/Conferences/conf3/cp.html

%A Laurence Hirsch
%A Masoud Saeedi
%A Robin Hirsch
%T Evolving Text Classifiers with Genetic Programming
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 309--317
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=309
%X We describe a method for using Genetic Programming (GP) to evolve document classifiers. GPs create regular expression type specifications consisting of particular sequences
   and patterns of N-Grams (character strings) and acquire fitness by producing expressions, which match documents in a particular category but do not match documents in any
   other category. Libraries of N-Gram patterns have been evolved against sets of pre-categorised training documents and are used to discriminate between new texts. We
   describe a basic set of functions and terminals and provide results from a categorisation task using the 20 Newsgroup data.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Laurence Hirsch
%A Masoud Saeedi
%A Robin Hirsch
%T Evolving Rules for Document Classification
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 85--95
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=85
%X We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs
   acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of
   functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a
   human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Laurence Hirsch
%A Masoud Saeedi
%A Robin Hirsch
%T Evolving Text Classification Rules with Genetic Programming
%J Applied Artificial Intelligence
%V 19
%N 7
%D 2005
%P 659--676
%I 
%K genetic algorithms, genetic programming
%U http://www.journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=0883-9514&volume=19&issue=7&spage=659
%X We describe a novel method for using genetic programming to create compact classification rules using combinations of N-grams (character strings). Genetic programs acquire
   fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of
   functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that the rules may have a number of other uses
   beyond classification and provide a basis for text mining applications.
%8 August

%A Laurence Hirsch
%A Robin Hirsch
%A Masoud Saeedi
%T Evolving Lucene search queries for text classification
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1604--1611
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, apache lucene, text classification
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1604.pdf
%X We describe a method for generating accurate, compact, human understandable text classifiers. Text datasets are indexed using Apache Lucene and Genetic Programs are used to
   construct Lucene search queries. Genetic programs acquire fitness by producing queries that are effective binary classifiers for a particular category when evaluated
   against a set of training documents. We describe a set of functions and terminals and provide results from classification tasks.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Laurie Hirsch
%T Evolved Apache Lucene SpanFirst queries are good text classifiers
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Human readable text classifiers have a number of advantages over classifiers based on complex and opaque mathematical models. For some time now search queries or rules have
   been used for classification purposes, either constructed manually or automatically. We have performed experiments using genetic algorithms to evolve text classifiers in
   search query format with the combined objective of classifier accuracy and classifier readability. We have found that a small set of disjunct Lucene SpanFirst queries
   effectively meet both goals. This kind of query evaluates to true for a document if a particular word occurs within the first N words of a document. Previously researched
   classifiers based on queries using combinations of words connected with OR, AND and NOT were found to be generally less accurate and (arguably) less readable. The approach
   is evaluated using standard test sets Reuters-21578 and Ohsumed and compared against several classification algorithms.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5585955

%A Haym Hirsh
%A Wolfgang Banzhaf
%A John R. Koza
%A Conor Ryan
%A Lee Spector
%A Christian Jacob
%T Genetic Programming
%J IEEE Intelligent Systems
%V 15
%N 3
%D 2000
%P 74--84
%I 
%K genetic algorithms, genetic programming, artificial computer code evolution, machine intelligence, automatic programming, arbitrary computational processes
%U http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf
%X The paper presents essays on genetic programming which involve topics such as: the artificial evolution of computer code, human-competitive machine intelligence by means of
   genetic programming, GP as automatic programming, GP application, the evolution of arbitrary computational processes and the art of genetic programming.
%8 May -  June
%Z Collection of essays by each author with introduction by Hirsh. See \citebanzhaf:2000:IS, \citekoza:2000:IS, \citeryan:2000:IS, \citespector:2000:IS, jacob:2000:IS.

%A Hideru Hiruma
%A Alex Fukunaga
%A Kazuki Komiya
%A Hitoshi Iba
%T Evolving an Effective Robot Tour Guide
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 137--144
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Evolutionary Robotics, Robotics, Emerging areas
%X Guiding visitors through an exhibit space such as a museum is an important, early application for mobile robots, and commercial robots designed for this purpose have become
   available. We consider the problem of using a single mobile robot to simultaneously direct multiple groups of visitors through a museum or exhibition, and formulate an
   objective function for this task. We show that an evolutionary robotics approach using a simple, low-fidelity simulator and genetic programming can automatically generate
   robot controllers which can perform this task better than hand-coded controllers as well as humans in both simulation and on a real robot.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Alex Ho
%A George Lumpkin
%T The Genetic Query Optimizer
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 67--76
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, Oracle Corporation, Relational Database Query
%X "For complex queries, we find that the genetic algorithm produces more efficient query plans in a running time comparable to that of conventional methods".
%8 Decemeber
%Z This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-187263-3

%A Lester T. W. Ho
%A Imran Ashraf
%A Holger Claussen
%T Evolving femtocell coverage optimization algorithms using genetic programming
%B IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications
%D 2009
%P 2132--2136
%I 
%K genetic algorithms, genetic programming, distributed algorithm, enterprise environment, femtocell coverage optimization, self-configuration capability, self-optimisation
   capability, cellular radio
%X The use of a group of femtocells to jointly provide coverage in an enterprise environment introduces several challenges in the introduction of self-configuration and
   self-optimisation capabilities required for plug-and-play styles of deployment. In this paper, an approach to automatically derive a distributed algorithm to dynamically
   optimise the coverage of a femtocell group using genetic programming is described. The resulting evolved algorithm showed the ability to optimize the coverage well, and is
   able to offer increased overall network capacity compared with a fixed coverage femtocell deployment.
%8 September
%Z Bell Labs., Alcatel-Lucent, Swindon, UK. Also known as \cite5450062

%A Shinn-Ying Ho
%A Xiao-I Chang
%T An Efficient Generalized Multiobjective Evolutionary Algorithm
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 871--878
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Shinn-Ying Ho
%A Li-Sun Shu
%A Hung-Ming Chen
%T Intelligent Genetic Algorithm with a New Intelligent Crossover Using Orthogonal Arrays
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 289--296
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Shinn-Ying Ho
%A Hung-Ming Chen
%A Li-Sun Shu
%T Solving Large Knowledge Base Partitioning Problems Using an Intelligent Genetic Algorithm
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1567--1572
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-747NEW.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Shinn-Ying Ho
%A Chih-Hung Hsieh
%A Hung-Ming Chen
%A Hui-Ling Huang
%T Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis
%J Biosystems
%V 85
%N 3
%D 2006
%P 165--176
%I 
%K genetic algorithms, genetic programming
%X An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive
   diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbour, and
   logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact
   fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimised: maximal classification accuracy, minimal number of
   rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters.
   The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class)
   on average in terms of test classification accuracy (87.9percent), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the
   existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers.
%8 September
%Z PMID: 16490299 [PubMed - in process]

%A X. H. Nguyen
%A R. I. (Bob) McKay
%T A Framework for Tree-adjunct Grammar Guided Genetic Programming
%B Post-graduate ADFA Conference on Computer Science
%D 2001
%P 93--100
%I 
%C Canberra, Australia
%K genetic algorithms, genetic programming
%U http://sc.snu.ac.kr/PAPERS/TAG3P.pdf
%Z Refereed Regional and National Conference and Workshop Papers

%A N. X. Hoai
%T Solving the Symbolic Regression with Tree-Adjunct Grammar Guided Genetic Programming: The Preliminary Results
%B Australasia-Japan Workshop on Intelligent and Evolutionary Systems
%E Nikola Kasabov and Peter Whigham
%D 2001
%I 
%C University of Otago, Dunedin, New Zealand
%K genetic algorithms, genetic programming
%8 19-21st  November
%Z http://divcom.otago.ac.nz/infosci/KEL/conferences/IESWorkshop/default.htm

%A N. X. Hoai
%T Solving Trignometric Identities with Tree Adjunct Grammar Guided Genetic Programming
%B 2001 International Workshop on Hybrid Intelligent Systems
%S LNCS
%E Ajith Abraham and Mario Koppen
%D 2001
%P 339--352
%I Springer-Verlag Berlin
%C Adelaide, Australia
%K genetic algorithms, genetic programming, Grammar Guided Genetic Progrogramming, Tree-Adjunct Grammars, Trigonometric Identity Discovery
%U http://www.amazon.com/Hybrid-Information-Systems-Ajith-Abraham/dp/3790814806/ref=sr_1_8?s=books&ie=UTF8&qid=1326475568&sr=1-8
%X Tree-adjunct grammar guided genetic programming (TAG3P) (Hoai and McKay 2001) is a grammar guided genetic programming system that uses context-free grammars along with
   tree-adjunct grammars as means to set language bias for the genetic programming system. In this paper, we show the result of TAG3P on the problem of discovering
   trigonometric identities, one of the benchmark problems in genetic programming (Koza 1992). The results show that although TAG3P did successfully discover all three popular
   trigonometric identities of the trigonometric function cos(2x), namely, sin(2x+p /2), sin(p /2 -2x) and 1-2sin 2 (x), it had a tendency to converge towards the first two
   identities.
%8 11-12  Decemeber
%Z HIS01
%@ 3-7908-1480-6

%A X. H. Nguyen
%A R. I. (Bob) McKay
%A D. L. Essam
%T Solving the Symbolic Regression Problem with Tree-Adjunct Grammar Guided Genetic Programming: The Comparative Results
%J The Australian Journal of Intelligent Information Processing Systems
%V 7
%N 3/4
%D 2001
%P 114--121
%I 
%K genetic algorithms, genetic programming
%U http://sc.snu.ac.kr/PAPERS/xuanetal.pdf
%X In this paper, we show some experimental results of tree-adjunct grammar guided genetic programming [6] (TAG3P) on the symbolic regression problem, a benchmark problem in
   genetic programming. We compare the results with genetic programming [9] (GP) and grammar guided genetic programming [14] (GGGP). The results show that TAG3P significantly
   outperforms GP and GGGP on the target functions attempted in terms of probability of success. Moreover, TAG3P still performed well when the structural complexity of the
   target function was scaled up.

%A Nguyen Xuan Hoai
%A R. I. McKay
%A D. Essam
%T Some Experimental Results with Tree Adjunct Grammar Guided Genetic Programming
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 228--237
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780228.pdf
%X Tree-adjunct grammar guided genetic programming (TAG3P) [5] is a grammar guided genetic programming system that uses context -free grammars along with tree-adjunct grammars
   as means to set language bias for the genetic programming system. In this paper, we show the experimental results of TAG3P on two problems: symbolic regression and
   trigonometric identity discovery. The results show that TAG3P works well on those problems.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A X. H. Nguyen
%A R. I. (Bob) McKay
%A D. L. Essam
%T Can Tree Adjunct Grammar Guided Genetic Programming be Good at Finding a Needle in a Haystack? A Case Study
%B IEEE International Conference on Communications, Circuits and Systems
%V 2
%D 2002
%P 1113--1117
%I IEEE Press Piscataway, NJ, USA
%C Chengdu, China
%K genetic algorithms, genetic programming
%U http://sc.snu.ac.kr/PAPERS/hoaietal.pdf
%8 July
%Z Refereed International Conference Papers

%A N. X. Hoai
%A R. I. McKay
%A D. Essam
%A R. Chau
%T Solving the Symbolic Regression Problem with Tree-Adjunct Grammar Guided Genetic Programming: The Comparative Results
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 1326--1331
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%X We show some experimental results of tree-adjunct grammar guided genetic programming [6] (TAG3P) on the symbolic regression problem, a benchmark problem in genetic
   programming. We compare the results with genetic programming [9] (GP) and grammar guided genetic programming [14] (GGGP). The results show that TAG3P significantly
   outperforms GP and GGGP on the target functions attempted in terms of probability of success. Moreover, TAG3P still performed well when the structural complexity of the
   target function was scaled up.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Nguyen Xuan Hoai
%A Yin Shan
%A Robert Ian McKay
%T Is Ambiguity Useful or Problematic for Grammar Guided Genetic Programming?
%B Procedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02)
%E Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and Jong-Hwan Kim and Xin Yao
%D 2002
%P 449--454
%I 
%C Orchid Country Club, Singapore
%K genetic algorithms, genetic programming
%U http://sc.snu.ac.kr/PAPERS/ambiguity.pdf
%X In [2] Antonisse made a conjecture that unambiguous grammars are better candidates for grammar-guided genetic learning. In this paper, we empirically show that it is not
   always the case, especially when the structural ambiguity is boosted by semantic redundancies in the grammar. We also show that the search space (or genotype space) of
   grammar guided genetic programming (GGGP) is truly tree sets rather than string sets of formalisms.
%8 18-22  November
%Z Refereed International Conference Papers
%@ 981-04-7522-5

%A Nguyen Xuan Hoai
%A R. I. McKay
%A H. A. Abbass
%T Tree Adjoining Grammars, Language Bias, and Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 335--344
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=335
%X In this paper, we introduce a new grammar guided genetic programming system called tree-adjoining grammar guided genetic programming (TAG3P+), where tree-adjoining grammars
   (TAGs) are used as means to set language bias for genetic programming. We show that the capability of TAGs in handling context-sensitive information and categories can be
   useful to set a language bias that cannot be specified in grammar guided genetic programming. Moreover, we bias the genetic operators to preserve the language bias during
   the evolutionary process. The results pace the way towards a better understanding of the importance of bias in genetic programming.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A X. H. Nguyen
%A R. I. (Bob) McKay
%A D. L. Essam
%T Finding Trigonometric Identities with Tree Adjunct Grammar Guided Genetic Programming
%B Innovations in Intelligent Systems and Applications
%S Springer Studies in Fuzziness and Soft Computing
%E A. Abraham and L. Jain and B. J. van der Zwaag
%V 140
%D 2004
%P 221--236
%I Springer-Verlag
%C Berlin, Germany
%K genetic algorithms, genetic programming
%U http://sc.snu.ac.kr/PAPERS/trigonometry.pdf
%X Introduction. Genetic programming (GP) may be seen as a machine learning method, which induces a population of computer programs by evolutionary means (Banzhaf et al.
   1998). Genetic programming has been used successfully in generating computer programs for solving a number of problems in a wide range of areas. In (Hoai and McKay 2001),
   we proposed a framework for a grammar-guided genetic programming system called Tree-Adjunct Grammar Guided Genetic Programming (TAG3P), which uses tree-adjunct grammars
   along with a context-free grammar to set language bias in genetic programming. The use of tree-adjunct grammars can be seen as a process of building context-free grammar
   guided programs in the two dimensional space. In this chapter, we show some results of TAG3P on the trigonometric identity discovery problem. The organisation of the
   remainder of the chapter is as follows. In section 2, we give a brief overview of genetic programming, grammar guided genetic programming, tree-adjunct grammars and TAG3P.
   The problem of finding trigonometric identities will be given in section 3. Section 4 contains the experiment and results of TAG3P on that problem. The nature of search
   space is empirically analysed and the bias by selective adjunction is introduced. The last section contains conclusion and future work.
%8 January
%Z Book Chapter
%@ 3-540-20265-X

%A Nguyen Xuan Hoai
%A R. I. (Bob) McKay
%A Daryl Essam
%A Hussein Abbass
%T Toward an Alternative Comparison between Different Genetic Programming Systems
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 67--77
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=67
%X We use multi-objective techniques to compare different genetic programming systems, permitting our comparison to concentrate on the effect of representation and separate
   out the effects of different search space sizes and search algorithms. Experimental results are given, comparing the performance and search behaviour of Tree Adjoining
   Grammar Guided Genetic Programming (TAG3P) and Standard Genetic Programming (GP) on some standard problems.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Nguyen Xuan Hoai
%A Robert Ian McKay
%T An Investigation on the Roles of Insertion and Deletion Operators in Tree Adjoining Grammar Guided Genetic Programming
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 472--477
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Theory of evolutionary algorithms
%X We investigate the roles of insertion and deletion as mutation operators and as local search operators in a Tree Adjoining Grammar Guided Genetic Programming (TAG3P) system
   [13]. The results show that, on three standard problems, these operators work better as mutation operators than the more standard sub-tree mutation originally used in [13,
   14]. Moreover, for some problems, insetion and deletion can also act effectively as local search operators, allowing TAG3P to solve problems with very small population
   sizes.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Nguyen Xuan Hoai
%A R. I. McKay
%T Softening the Structural Difficulty in Genetic Programming with TAG-Based Representation and Insertion/Deletion Operators
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 605--616
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030605.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Xuan Hoai Nguyen
%A R. I. (Bob) McKay
%A D. L. Essam
%A H. A. Abbass
%T Genetic Transposition in Tree-Adjoining Grammar Guided Genetic Programming: the Relocation Operator
%B 2004 Asia-Pacific Conference on Simulated Evolution and Learning
%D 2004
%I 
%C Busan, Korea
%K genetic algorithms, genetic programming
%U http://sc.snu.ac.kr/PAPERS/SEAL2004.pdf
%X We empirically investigate the use of relocation operator as a local search operator, in combination with genetic search, in a Tree Adjoining Grammar Guided Genetic
   Programming system (TAG3P). The results show that, on all the problems we tried, the use of the relocation operator as a local search operator in TAG3P outperforms TAG3P
   using purely crossover and mutation, and also outperforms standard genetic programming (GP). Moreover, it manages to solve problems with very small population sizes.
%8 October
%Z Refereed International Conference Papers

%A Nguyen Xuan Hoai
%T A Flexible Representation for Genetic Programming from Natural Language Processing
%R Ph.D. Thesis
%D 2004
%I 
%I Australian Defence force Academy, University of New South Wales
%C Australia
%K genetic algorithms, genetic programming, grammar-guided, genotype space, natural language processing, phenotype space, tree adjoining grammars (TAGs)
%U http://handle.unsw.edu.au/1959.4/38750
%X This thesis principally addresses some problems in genetic programming (GP) and grammar-guided genetic programming (GGGP) arising from the lack of operators able to make
   small and bounded changes on both genotype and phenotype space. It proposes a new and flexible representation for genetic programming, using a state-of-the-art formalism
   from natural language processing, Tree Adjoining Grammars (TAGs). It demonstrates that the new TAG-based representation possesses two important properties: non-fixed arity
   and locality. The former facilitates the design of new operators, including some which are bio-inspired, and others able to make small and bounded changes. The latter
   ensures that bounded changes in genotype space are reflected in bounded changes in phenotype space. With these two properties, the thesis shows how some well-known
   difficulties in standard GP and GGGP tree-based representations can be solved in the new representation. These difficulties have been previously attributed to the treebased
   nature of the representations; since TAG representation is also tree-based, it has enabled a more precise delineation of the causes of the difficulties. Building on the new
   representation, a new grammar guided GP system known as TAG3P has been developed, and shown to be competitive with other GP and GGGP systems. A new schema theorem,
   explaining the behaviour of TAG3P on syntactically constrained domains, is derived. Finally, the thesis proposes a new method for understanding performance differences
   between GP representations requiring different ways to bound the search space, eliminating the effects of the bounds through multi-objective approaches.
%8 Decemeber
%Z separate files

%A Nguyen Xuan Hoai
%A Robert I. McKay
%A Daryl Essam
%A Hoang Tuan Hao
%T Genetic Transposition in Tree-Adjoining Grammar Guided Genetic Programming: The Duplication Operator
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 108--119
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=108
%X We empirically investigate the use of dual duplication/truncation operators both as mutation operators and as generic local search operators, in combination with genetic
   search in a tree adjoining grammar guided genetic programming system (TAG3P). The results show that, on the problems tried, duplication/truncation works well as a mutation
   operator but not reliably when the complexity of the problem was scaled up. When using these dual operators as a generic local search operator, however, it helped TAG3P not
   only to solve the problems reliably but also cope well with scalability in problem complexity. Moreover, it managed to solve problems with very small population sizes.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Nguyen Xuan Hoai
%A R. I. (Bob) McKay
%A Daryl Essam
%T Representation and Structural Difficulty in Genetic Programming
%J IEEE Transactions on Evolutionary Computation
%V 10
%N 2
%D 2006
%P 157--166
%I 
%K genetic algorithms, genetic programming, Deletion, insertion, operator, representation, structural difficulty
%U http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/nguyen/Structdiff.pdf
%X Standard tree-based genetic programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very
   narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of
   tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be
   almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a
   consequence of the large step size of the operators in standard genetic programming, which is itself a consequence of the fixed-arity property embodied in its
   representation.
%8 April

%A Tuan-Hao Hoang
%T Representation and Data Preparation Issues in Ecological Time-Series Modeling using Genetic Programming
%R M.S. Thesis Maser S.c of Information Technology
%D 2004
%I 
%I School of Computer Science University College, University of New South Wales, Australian Defence Force Academy
%K genetic algorithms, genetic programming, TAG3P
%U http://seal.tst.adfa.edu.au/~z3106820/publications/masthesis.pdf
%X Many important ecological datasets are collected irregularly over time. In view of the fact that many time series modelling techniques require regularly spaced intervals,
   one common approach is to interpolate the data, and then build a model from the interpolated data. However, this may cause negative effects on the performance of models
   built on the interpolated data. This thesis has two aims, the first is to investigate the extent of those effect, by comparing models built on the original sample data (the
   irregular dataset of the phytoplankton in Lake Kasumigaura), and on interpolated data, whilst the second is to examine the effect of representation on modelling systems, in
   particular the differences between context-free and tree-adjoining grammar models.
%O Under the co-supervision of Daryl Essam and R.I. McKay (2004). School of IT and EE, University of New South Wales, ADFA, Canberra, Australia
%8 November

%A Hoang Tuan Hao
%A Nguyen Xuan Hoai
%A Robert I McKay
%T Does it Matter Where you Start? A Comparison of Two Initialisation Strategies for Grammar Guided Genetic Programming
%B Proceedings of The Second Asian-Pacific Workshop on Genetic Programming
%E R I Mckay and Sung-Bae Cho
%D 2004
%I 
%C Cairns, Australia
%K genetic algorithms, genetic programming, GGGP, TAG, TAG3P
%X we experimentally show that the initialisation process is very important for Grammar Guided Genetic Programming (GGGP). In particular, using different initialization
   strategies (algorithms) can lead to very different overall results with GGGP. This is in strong contrast with results previously reported for standard Genetic Programming
   [9]. We also show that on the problems tried, the initialisation algorithm from Tree Adjoining Grammar Guided Genetic Programming (TAG3P) helps GGGP improve its performance
   compared with the use of the standard initialisation algorithm proposed in [10, 11]
%8 6-7  Decemeber
%Z http://www.itee.adfa.edu.au/~rim/ASPGP/programme.html

%A Tuan Hao Hoang
%A Nguyen Xuan Hoai
%A R. I. (Bob) McKay
%A Daryl Essam
%T The Importance pf Local Search: A Grammar Based Approach to Environmental Time Series Modelling
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 159--175
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, local search, insertion, deletion, grammar guided, tree adjoining grammar, ecological modelling, time series
%X Standard Genetic Programming operators are highly disruptive, with the concomitant risk that it may be difficult to converge to an optimal structure. The Tree Adjoining
   Grammar (TAG) formalism provides a more flexible Genetic Programming tree representation which supports a wide range of operators while retaining the advantages of
   tree-based representation. In particular, minimal-change point insertion and deletion operators may be defined. Previous work has shown that point insertion and deletion,
   used as local search operators, can dramatically reduce search effort in a range of standard problems. Here, we evaluate the effect of local search with these operators on
   a real-World ecological time series modelling problem. For the same search effort, TAG-based GP with the local search operators generates solutions with significantly lower
   training set error. The results are equivocal on test set error, local search generating larger individuals which generalise only a little better than the less accurate
   solutions given by the original algorithm.
%O 11
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Tuan-Hao Hoang
%A Nguyen Xuan Hoai
%A Nguyen Thi Hien
%A R I McKay
%A Daryl Essam
%T ORDERTREE: a new test problem for genetic programming
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 807--814
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, benchmark problems, graph and tree search strategies, languages, problem difficulty, theory
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p807.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Tuan-Hao Hoang
%A Daryl Essam
%A R. I. (Bob) McKay
%A Xuan Hoai Nguyen
%T Solving Symbolic Regression Problems using Incremental Evaluation in Genetic Programming
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%P 7487--7494
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%U http://seal.tst.adfa.edu.au/~z3106820/publications/cec2006.devtag.pdf
%X we show some experimental results using Incremental Evaluation with Tree Adjoining Grammar Guided Genetic Programming (DEVTAG) on two symbolic regression problems, a
   benchmark polynomial fitting problem in genetic programming, and a Fourier series problem (saw-tooth problem). In our pilot study, we compare results with standard Genetic
   Programming (GP) and the original Tree Adjoining Grammar Guided Genetic Programming (TAG3P). Our results on the two problems are good, outperforming both standard GP and
   the original TAG3P.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-9487-9

%A Tuan-Hao Hoang
%A Daryl Essam
%A Robert Ian (Bob) McKay
%A Xuan Hoai Nguyen
%T Building on Success in Genetic Programming:Adaptive Variation \& Developmental Evaluation
%B Proceedings of the 2007 International Symposium on Intelligent Computation and Applications (ISICA)
%D 2007
%I China University of Geosciences Press Wuhan, China
%C Wuhan, China
%K genetic algorithms, genetic programming
%U http://sc.snu.ac.kr/PAPERS/dtag.pdf
%8 September  21-23
%Z Accepted, Refereed International Conference Papers

%A Tuan Hao Hoang
%A Daryl Essam
%A Bob McKay
%A Nguyen Xuan Hoai
%T Building on Success in Genetic Programming: Adaptive Variation and Developmental Evaluation
%B Proceedings of the Second International Symposium on Computation and Intelligence, ISICA 2007
%S Lecture Notes in Computer Science
%E Lishan Kang and Yong Liu and Sanyou Y. Zeng
%V 4683
%D 2007
%P 137--146
%I Springer
%C Wuhan, China
%K genetic algorithms, genetic programming, Developmental, Incremental Learning, Adaptive Mutation
%X We investigate a developmental tree-adjoining grammar guided genetic programming system (DTAG3P+), in which genetic operator application rates are adapted during evolution.
   We previously showed developmental evaluation could promote structured solutions and improve performance in symbolic regression problems. However testing on parity problems
   revealed an unanticipated problem, that good building blocks for early developmental stages might be lost in later stages of evolution. The adaptive variation rate in
   DTAG3P plus preserves good building blocks found in early search for later stages. It gives both good performance on small k-parity problems, and good scaling to large
   problems.
%8 September  21-23

%A Tuan-Hao Hoang
%A R. McKay
%A D. Essam
%A Xuan Hoai Nguyen
%T Developmental Evaluation in Genetic Programming: A Position Paper
%B Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007
%D 2007
%P 773--778
%I IEEE Press
%C Jeju City, Korea
%K genetic algorithms, genetic programming, grammars, trees (mathematics), L-systems, code duplication, code replication, developmental evaluation, developmental tree
   adjoining grammar guided GP, modularity selection, structural regularity, tree adjoining grammar guided derivation trees
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4524062&arnumber=4524205&count=165&index=142
%X Standard genetic programming genotypes are generally highly disorganised and poorly structured, with little code replication. This is also true of existing developmental
   genetic programming systems, which exploit regularity by using procedures, functional modules, or macros and parameters passing. By contrast, in biological developmental
   evolution, nature works through code duplication to generate modularity, regularity and hierarchy. Previous developmental approaches have only one level of evaluation for
   each individual - an approach which limits the advantages of modularity to the species rather than the individual, and hence inhibits selection of modularity. We argued
   that evaluation during development is necessary for structural regularity to emerge. To confirm the benefits of developmental evaluation and the contribution of code
   duplication to nature, our new developmental process uses a new representation. Developmental tree adjoining grammar guided GP (DTAG3P) uses L-systems to encode tree
   adjoining grammar guided (TAG) derivation trees, and has been investigated. We have demonstrated scalable solutions to difficult families of problems, and have evidence
   that this performance is linked to the generation and exploitation of structural regularities in the solutions.
%8 11-13  October
%Z FBIT 2007: http://ieeexplore.ieee.org/servlet/opac?punumber=4524061

%A Tuan-Hao Hoang
%A Daryl Essam
%A R. I. (Bob) McKay
%A Nguyen Xuan Hoai
%T Developmental evaluation in Genetic Programming: The TAG-based frame work
%J International Journal of Knowledge-Based and Intelligent Engineering Systems
%V 12
%N 1
%D 2008
%P 69--82
%I IOS Press
%K genetic algorithms, genetic programming
%U http://iospress.metapress.com/content/w4qu7136432k6733/
%X We build on our previous feasibility studies [18,20], which demonstrated the impact of evaluation during development in the DEVTAG system, and here present a full-fledged
   developmental system - Developmental Tree Adjoining Grammar Guided Genetic Programming (DTAG3P) with developmental evaluation, based on Tree-Adjoining Grammars (TAG). While
   DEVTAG used only a trivial developmental process, DTAG3P uses L-systems to encode TAG derivation trees, because the L-systems permit a full developmental process. DEVTAG
   was previously shown to dramatically out-perform standard Genetic Programming (GP) on some structured families of problems; here, we examine DTAG3P's performance on these
   families, and find a further major increment in performance over DEVTAG. DTAG3P achieves this despite dispensing with two extra control parameters which were necessary with
   DEVTAG.
%Z KES

%A Tuan Hao Hoang
%A R. I. (Bob) McKay
%A Daryl Essam
%A Nguyen Xuan Hoai
%T Learning General Solutions through Multiple Evaluations during Development
%B Proceedings of the 8th International Conference Evolvable Systems: From Biology to Hardware, ICES 2008
%S Lecture Notes in Computer Science
%E Gregory Hornby and Luk\'as Sekanina and Pauline C. Haddow
%V 5216
%D 2008
%P 201--212
%I Springer
%C Prague, Czech Republic
%K genetic algorithms, genetic programming, Developmental Genetic Programming, Hyper-heuristics, Generalisation Overfitting, Parsimony
%X In this paper, we investigate whether performing multiple evaluations during development, a technique we call Evolutionary Developmental Evaluation (EDE), could help
   developmental Genetic Programming (GP) evolve general solutions, solving not only the original (training) problem, but also unseen similar problems (with higher degrees of
   complexity). The hypothesis is tested on two families of regression problems, and the experimental results support the hypothesis.
%8 September  21-24

%A Tuan-Hao Hoang
%A R. I. McKay
%A Daryl Essam
%A Nguyen Xuan Hoai
%T On Synergistic Interactions Between Evolution, Development and Layered Learning
%J IEEE Transactions on Evolutionary Computation
%V 15
%N 3
%D 2011
%P 287--312
%I 
%K genetic algorithms, genetic programming, animal development, biological evolution, development learning, evolution learning, evolutionary developmental evaluation, learning
   theory perspective, lifelong layered learning, plant development, tree-adjoining grammar guided genetic programming, biology, genetic algorithms, learning systems
%X We investigate interactions between evolution, development and lifelong layered learning in a combination we call evolutionary developmental evaluation (EDE), using a
   specific implementation, developmental tree-adjoining grammar guided genetic programming (GP). The approach is consistent with the process of biological evolution and
   development in higher animals and plants, and is justifiable from the perspective of learning theory. In experiments, the combination is synergistic, outperforming
   algorithms using only some of these mechanisms. It is able to solve GP problems that lie well beyond the scaling capabilities of standard GP. The solutions it finds are
   simple, succinct, and highly structured. We conclude this paper with a number of proposals for further extension of EDE systems.
%8 June
%Z DTAG3P, TAG3P, tree adjoined grammar (TAG), symbolic regression, k-parity, ordertree Also known as \cite5898401

%A Cem Hocaoglu
%A Arthur C. Sanderson
%T Multi-dimensional Path Planning Evolutionary Computation using Evolutionary Computation
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%P 165--170
%I IEEE Press
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming
%X This paper describes a flexible and efficient multi-dimensional path planning algorithm based on evolutionary computation concepts. A novel iterative multi-resolution path
   representation is used as a basis for the GA coding. The use of a multi-resolution path representation can reduce the expected search length for the path planning problem.
   If a successful path is found early in the search hierarchy (at a low level of resolution), then further expansion of that portion of the path search is not necessary. This
   advantage is mapped into the encoded search space and adjusts the string length accordingly. The algorithm is flexible; it handles multi-dimensional path planning problems,
   accommodates different optimization criteria and changes in these criteria, and it uses domain specific knowledge for making decisions. In the evolutionary path planner,
   the individual candidates are evaluated with respect to the workspace so that computation of the configuration space is not required. The algorithm can be applied for
   planning paths for mobile robots, assembly, pianomovers problems and articulated manipulators. The effectiveness of the algorithm is demonstrated on a number of
   multi-dimensional path planning problems.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
%@ 0-7803-4869-9

%A Gregor Hochmuth
%T On the Genetic Evolution of a Perfect Tic-Tac-Toe Strategy
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 75--82
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2003/Hochmuth.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Theodore P. Hoehn
%A Chrisila C. Pettey
%T Parental and Cyclic-Rate Mutation in Genetic Algorithms: An Initial Investigation
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 297--304
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-383.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Carl C. Hoff
%T Pattern Recognition via Machine Learning with Genetic Decision-Programming
%R Ph.D. Thesis
%D 2005
%I 
%I Department of Computer Science and Engineering, Wright State University
%K genetic algorithms, genetic programming, Computer Science (0984), Pattern Recognition, Machine Learning, Evolutionary Computation, Genetic Decision-Programming
%U http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117
%X In the intersection of pattern recognition, machine learning, and evolutionary computation is a new search technique by which computers might program themselves. That
   technique is called genetic decision-programming. A computer can gain the ability to distinguish among the things that it needs to recognise by using genetic
   decision-programming for pattern discovery and concept learning. Those patterns and concepts can be easily encoded in the spines of a decision program (tree or diagram). A
   spine consists of two parts: (1) the test-outcome pairs along a path from the program's root to any of its leaves and (2) the conclusion in that leaf. The test-outcome
   pairs specify a pattern and the conclusion identifies the corresponding concept. Genetic decision-programming combines and extends discrete decision theory with the
   principles of genetics and natural selection. The resulting algorithm searches for those decision programs that best satisfy some user-defined criteria. Each program mates
   problem decompositions with subproblem solutions, and consists of overlapping spines. Those spines are manipulated by three context-sensitive operators. The context defines
   a subproblem and is determined by the operator's point of application within a decision program. Macro-mutation generates a new solution for that context; mini-mutation
   restructures the existing solution for that context; and spine crossover inserts another program's solution for that context. Those solutions are encoded in the spines.
   Thus the operators recompose, restructure and recombine spines as the search technique evolves a population of decision programs to satisfy the user-defined criteria.
   Genetic decision-programming overcomes the difficulties encountered when evolving decision programs with genetic programming techniques that rely on subtree crossover.
   Those impractical techniques require too much memory and computation. Subtree crossover exchanges random subtrees of broken spines without regard for context. Meaning is
   lost. In contrast, the spine crossover of genetic decision-programming crosses entire spines and uses them in context. Meaning is retained. This means that genetic
   decision-programming can be applied to practical problems. In an experiment, it consistently gave very good results without the variability from problem to problem of other
   more conventional decision-tree construction techniques.

%A Don Hoffman
%T Using Genetic Algorithms for Data Compression: Discovering Huffman Codes as Efficiently as Possible
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 58--67
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Frank Hoffmann
%T Incremental Tuning of Fuzzy Controllers by Means of an Evolution Strategy
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 843--851
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K Evolutionary Strategies
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Frank Hoffmann
%A Oliver Nelles
%T Genetic programming for model selection of TSK-fuzzy systems
%J Information Sciences
%V 136
%N 1-4
%D 2001
%P 7--28
%I 
%K genetic algorithms, genetic programming, Fuzzy modeling, Neuro-fuzzy system
%U http://citeseer.ist.psu.edu/459134.html
%X This paper compares a genetic programming (GP) approach with a greedy partition algorithm (LOLIMOT) for structure identification of local linear neuro-fuzzy models. The
   crisp linear conclusion part of a Takagi-Sugeno-Kang (TSK) fuzzy rule describes the underlying model in the local region specified in the premise. The objective of
   structure identification is to identify an optimal partition of the input space into Gaussian, axis-orthogonal fuzzy sets. The linear parameters in the rule consequent are
   then estimated by means of a local weighted least-squares algorithm. LOLIMOT is an incremental tree-construction algorithm that partitions the input space by
   axis-orthogonal splits. In each iteration it greedily adds the new model that minimizes the classification error. GP performs a global search for the optimal partition tree
   and is therefore able to backtrack in case of sub-optimal intermediate split decisions. We compare the performance of both methods for function approximation of a highly
   non-linear two-dimensional test function and an engine characteristic map.
%8 August

%A James P. Hoffmann
%A Christopher D. Ellingwood
%A Osei M. Bonsu
%A Daniel E. Bentil
%T Ecological Model Selection via Evolutionary Computation and Information Theory
%J Genetic Programming and Evolvable Machines
%V 5
%N 2
%D 2004
%P 229--241
%I 
%K genetic algorithms, genetic programming, model selection, parsimony, complexity-based fitness, variable-length representation
%X an evolutionary algorithm-based approach to model selection and demonstrates its effectiveness in using the information content of ecological data to choose the correct
   model structure. Experiments with a modified genetic algorithm are described that combine parsimony with a novel gene regulation mechanism. This combination creates
   evolvable switches that implement functional variable-length genomes in the GA that allow for simultaneous model selection and parameter fitting. In effect, the GA
   orchestrates a competition among a community of models. Parsimony is implemented via the Akaike Information Criterion, and gene regulation uses a modulo function to
   overload the gene values and create an evolvable binary switch. The approach is shown to successfully specify the correct model structure in experiments with a nested set
   of polynomial test models and complex biological simulation models, even when Gaussian noise is added to the data.
%8 June
%Z Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster (1) Botany, University of Vermont,
   Burlington, VT, 05405-0086 (2) Mathematics & Statistics, University of Vermont, Burlington, VT, 05401-0086-3357

%A Steven A. Hofmeyr
%A Stephanie Forrest
%T Immunity by Design: An Artificial Immune System
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1289--1296
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-039.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A S. Holden
%T Several Things all Genetic Programmers Should Know About Machine Learning
%R Research Note RN/98/1
%D 1998
%I 
%I Computer Science, University College, London
%8 January
%Z 6 Jan 2003. It exists only as a half-finished draft I'm afraid

%A Martin Holena
%A David Linke
%A Lukas Bajer
%T Case study: constraint handling in evolutionary optimization of catalytic materials
%B GECCO 2011 Evolutionary computation techniques for constraint handling
%E Carlos Artemio Coello Coello and Dara Curran and Thomas Jansen
%D 2011
%P 333--340
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X The paper presents a case study in an industrially important application domain the optimization of catalytic materials. Though evolutionary algorithms are the by far most
   frequent approach to optimization tasks in that domain, they are challenged by mixing continuous and discrete variables, and especially by a large number of constraints.
   The paper describes the various kinds of encountered constraints, and explains constraint handling in GENACAT, one of evolutionary optimization systems developed
   specifically for catalyst optimization. In particular, it is shown that the interplay between cardinality constraints and linear equality and inequality constraints allows
   GENACAT to efficienlty determine the set of feasible solutions, and to split the original optimization task into a sequence of discrete and continuous optimization.
   Finally, the genetic operations employed in the discrete optimization are sketched, among which crossover is based on an assumption about the importance of the choice of
   sets of continuous variables in the cardinality constraints.
%8 12-16  July
%Z Also known as \cite2002015 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Kenneth Holladay
%A Kay Robbins
%A Jeffery {von Ronne}
%T FIFTH: A Stack Based GP Language for Vector Processing
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 102--113
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X FIFTH, a new stack-based genetic programming language, efficiently expresses solutions to a large class of feature recognition problems. This problem class includes mining
   time-series data, classification of multivariate data, image segmentation, and digital signal processing (DSP). FIFTH is based on FORTH principles. Key features of FIFTH
   are a single data stack for all data types and support for vectors and matrices as single stack elements. We demonstrate that the language characteristics allow simple and
   elegant representation of signal processing algorithms while maintaining the rules necessary to automatically evolve stack correct and control flow correct programs. FIFTH
   supports all essential program architecture constructs such as automatically defined functions, loops, branches, and variable storage. An XML configuration file provides
   easy selection from a rich set of operators, including domain specific functions such as the Fourier transform (FFT). The fully-distributed FIFTH environment (GPE5) uses
   CORBA for its underlying process communication.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A K. L. Holladay
%A K. A. Robbins
%T Evolution of Signal Processing Algorithms using Vector Based Genetic Programming
%B 15th International Conference on Digital Signal Processing
%D 2007
%P 503--506
%I IEEE
%K genetic algorithms, genetic programming, signal classification, FIFTH, vector based genetic programming language, signal classification problem, signal processing
   algorithm, symbol rate estimation
%X This paper demonstrates that FIFTH, a new vector-based genetic programming (GP) language, can automatically derive very effective signal processing algorithms directly from
   signal data. Using symbol rate estimation as an example, we compare the performance of a standard algorithm against an evolved algorithm. The evolved algorithm uses a novel
   approach in developing a symbol transition feature vector and achieves an impressive 97.7% overall accuracy in the defined problem domain, far exceeding the performance of
   the standard algorithm. These results suggest that vector based GP approaches could be useful in developing more expressive features for a large class of signal processing
   and classification problems.
%8 July
%Z P1333 p506 GP human competitive against DPDT Also known as \cite4288629

%A Kenneth Holladay
%T Characterizing the genetic programming environment for fifth (GPE5) on a high performance computing cluster
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1363--1370
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X Solving complex, real-world problems with genetic programming (GP) can require extensive computing resources. However, the highly parallel nature of GP facilitates using a
   large number of resources simultaneously, which can significantly reduce the elapsed wall clock time per GP run. This paper explores the performance characteristics of an
   MPI version of the Genetic Programming Environment for FIFTH (GPE5) on a high performance computing cluster. The implementation is based on the island model with each node
   running the GP algorithm asynchronously. In particular, we examine the effect of several configurable properties of the system including the ratio of migration to
   crossover, the migration cycle of programs between nodes, and the number of processors used. The problems employed in the study were selected from the fields of symbolic
   regression, finite algebra, and digital signal processing.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Kenneth L. Holladay
%A John Marshall Sharp
%A Marc Janssens
%T Automatic pyrolysis mass loss modeling from thermo-gravimetric analysis data using genetic programming
%B 3rd symbolic regression and modeling workshop for GECCO 2011
%E Steven Gustafson and Ekaterina Vladislavleva
%D 2011
%P 655--662
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X Modelling to predict flame spread and fire growth is an active area of research in Fire Safety Engineering. A significant limitation to current approaches has been the lack
   of thermophysical material properties necessary for the simplified pyrolysis models ... Mode ling to predict flame spread and fire growth is an active area of research in
   Fire Safety Engineering. A significant limitation to current approaches has been the lack of thermophysical material properties necessary for the simplified pyrolysis
   models embedded within the models. Researchers have worked to derive physical properties such as density, specific heat capacity, and thermal conductivity from data
   obtained using bench-scale fire tests such as Thermo-Gravimetric Analysis (TGA). While Genetic Algorithms (GA) have been successfully used to solve for constants in
   empirical models, it has been shown that the resulting parameters are not valid individually as material properties, especially for complex materials such as wood. This
   paper describes an alternate approach using Genetic Programming (GP) to automatically derive a mass loss model directly from TGA data.
%8 12-16  July
%Z Also known as \cite2002063 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Gordon S. Hollingworth
%A Steve L. Smith
%A Andy M. Tyrrell
%T Design of Highly Parallel Edge Detection Nodes Using Evolutionary Techniques
%B Proceedings of the Seventh Euromicro Workshop on Parallel and Distributed Processing, PDP '99
%D 1999
%P 35--42
%I IEEE
%C Funchal
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/279876.html
%X This paper considers the application of bio-inspired systems in the design of a novel and highly parallel image processing tool to detect edges within conventional
   grey-scale images. The aim of the work is to implement a new image processing architecture through evolvable hardware that is able to adapt according to the particular
   images encountered. The simulation of such a system through the use of evolutionary algorithms and genetic programming is demonstrated for the conventional image processing
   operation of edge detection. Results are presented for this system and evaluated with respect to a conventional Sobel edge detector
%O The Pennsylvania State University CiteSeer Archives
%8 3-5  February

%A Gordon S. Hollingworth
%A Andy M. Tyrrell
%A Steve L. Smith
%T Simulation of Evolvable Hardware to Solve Low Level Image Processing Tasks
%B Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99
%S LNCS
%E Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty
%V 1596
%D 1999
%P 46--58
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/280684.html
%X The long term goal of the work described in this paper is the development of a bio-inspired system, employing evolvable hardware, that adapts according to the needs of the
   environment in which it is deployed. The application described here is the design of a novel and highly parallel image processing tool to detect edges within a wide range
   of conventional grey-scale images. We discuss the simulation of such a system based on a genetic programming paradigm, using a simple binary logic tree to implement the
   genetic string coding. The results acquired from the simulation are compared with those obtained from the application of a conventional Sobel edge detector, and although
   rudimentary, show the great potential of such bio-inspired systems.
%O The Pennsylvania State University CiteSeer Archives
%8 28  May
%@ 3-540-65837-8

%A Paul Holmes
%T The Odin Genetic Programming System
%R Tech Report RR-95-3
%D 1995
%I 
%I Computer Studies, Napier University
%C Craiglockhart, 216 Colinton Road, Edinburgh, EH14 1DJ
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/holmes95odin.html
%X A new paradigm for Genetic Programming (GP) is proposed. In the new paradigm the genetic representation is separated from the tree structure of the program with a layer of
   abstraction, and it is argued that this will allow more efficient evolution of large programs. A GP system which can evolve Turing-complete programs has been developed and
   is presented. Emphasis is placed on the evolution of real-time functional programs which handle input and output using lazy streams.
   http://docs.dcs.napier.ac.uk/DOCS/GET/holmes95a/document.html
%Z Fixed length chromosome, 8 bytes per line of code, Initial population seeded by individual written by user in Odin and compiled to Runes. Functional language, naturally
   recursive. Domiance bits used to arbitrate order iff conflict between which function to apply. Destructive translocation of genes (desctructive as fixed length) 8byte code
   interpretted by G-Machine (Antoni Diller) cf Peyton Jones. Standard GA (D-Genesis) crossover and mutation (does it respect opcodes and their boundaries?) Fitness function
   similarity of output (which may be list of some data type) with user supplied data (ie user also specifies functional language style type of output) page 49 "Its [Odin's]
   relative effectiveness remains to be tested."

%A Paul Holmes
%A Peter J. Barclay
%T Functional Languages on Linear Chromosomes
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 427
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96. See also \citeholmes:1995:odin
%@ 0-262-61127-9

%A John H. Holmes
%T Differential Negative Reinforcement Improves Classifier System Learning Rate in Two-Class Problems with Unequal Base Rates
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 635--642
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, classifiers, ROC
%U http://cceb.med.upenn.edu/holmes/gp98.ps.gz
%X The effect of biasing negative reinforcement levels on learning rate and classification accuracy in a learning classifier system (LCS) was investigated. Simulation data at
   five prevalences (base rates) were used to train and test the LCS. Erroneous decisions made by the LCS during training were punished differentially according to type: false
   positive (FP) or false negative (FN), across a range of four FP:FN ratios. Training performance was assessed by learning rate, determined from the number of iterations
   required to reach 95% of the maximum area under the receiver operating characteristic (ROC) curve obtained during learning. Learning rates were compared across the three
   biased ratios with those obtained at the unbiased ratio. Classification performance of the LCS at testing was evaluated by means of the area under the ROC curve. During
   learning, differences were found between the biased and unbiased penalty schemes, but only at unequal base rates. A linear relationship between bias level and base rate was
   suggested. With unequal base rates, biasing the FP:FN ratio improved the learning rate. Little effect was observed on testing the LCS with novel cases.
%8 22-25  July
%Z GP-98. My version of ghostview barfs with gp98.ps.gz AUC=probability classifier is correct on postive-negative test (Green and Swets, 1966). Wilcoxon statistic (Hanley and
   McNeil, 1982).
%@ 1-55860-548-7

%A John H. Holmes
%T Evaluating Learning Classifier System Performance In Two-Choice Decision Tasks: An LCS Metric Toolkit
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 789
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-389.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Emily Rose Holzinger
%A Carrie C. Buchanan
%A Scott M. Dudek
%A Eric C. Torstenson
%A Stephen D. Turner
%A Marylyn D. Ritchie
%T Initialization parameter sweep in ATHENA: optimizing neural networks for detecting gene-gene interactions in the presence of small main effects
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 203--210
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems and synthetic biology
%X Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved
   insufficient in extracting all of the information about the genetic components of common, complex human diseases. A contributing factor to the problem of analysis is that
   amongst the small main effects of each single gene on disease susceptibility, there are non-linear, gene-gene interactions that can be difficult for traditional, parametric
   analyses to detect. In addition, exhaustively searching all multi-locus combinations has proved computationally impractical. Novel strategies for analysis have been
   developed to address these issues. The Analysis Tool for Heritable and Environmental Network Associations (ATHENA) is an analytical tool that incorporates grammatical
   evolution neural networks (GENN) to detect interactions among genetic factors. Initial parameters define how the evolutionary process will be implemented. This research
   addresses how different parameter settings affect detection of disease models involving interactions. In the current study, we iterate over multiple parameter values to
   determine which combinations appear optimal for detecting interactions in simulated data for multiple genetic models. Our results indicate that the factors that have the
   greatest influence on detection are: input variable encoding, population size, and parallel computation.
%8 7-11  July
%Z Also known as \cite1830519 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Emily R. Holzinger
%A Scott M. Dudek
%A Alex T. Frase
%A Brooke Fridley
%A Prabhakar Chalise
%A Marylyn D. Ritchie
%T Comparison of methods for meta-dimensional data analysis using in silico and biological data sets
%B 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012
%S LNCS
%E Mario Giacobini and Leonardo Vanneschi and William S. Bush
%V 7246
%D 2012
%P 134--143
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, grammatical evolution, GENN, Systems biology, neural networks, evolutionary computation, data integration, human genetics
%X Recent technological innovations have catalysed the generation of a massive amount of data at various levels of biological regulation, including DNA, RNA and protein. Due
   to the complex nature of biology, the underlying model may only be discovered by integrating different types of high-throughput data to perform a 'meta-dimensional'
   analysis. For this study, we used simulated gene expression and genotype data to compare three methods that show potential for integrating different types of data in order
   to generate models that predict a given phenotype: the Analysis Tool for Heritable and Environmental Network Associations (ATHENA), Random Jungle (RJ), and Lasso. Based on
   our results, we applied RJ and ATHENA sequentially to a biological data set that consisted of genome-wide genotypes and gene expression levels from lymphoblastoid cell
   lines (LCLs) to predict cytotoxicity. The best model consisted of two SNPs and two gene expression variables with an r-squared value of 0.32.
%8 11-13  April
%Z Part of \citeGiacobini:2012:EvoBio EvoBio'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoMusArt2012 and EvoApplications2012

%A Abdollah Homaifar
%A Ed McCormick
%T Simultaneous Design of Membership Functions and Rule Sets for Fuzzy Controllers Using Genetic Algorithms
%J IEEE Transactions on Fuzzy Systems
%V 3
%N 2
%D 1995
%P 129--139
%I 
%K genetic algorithms, fuzzy control, control system synthesis, membership function design, fuzzy controllers, high-performance membership functions, simultaneous design
   procedure, rule set design, cart controller, truck controller
%U http://ieeexplore.ieee.org/iel4/91/8807/00388168.pdf?isNumber=8807
%X This paper examines the applicability of genetic algorithms (GA's) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous
   work using genetic algorithms has focused on the development of rule sets or high performance membership functions; however, the interdependence between these two
   components suggests a simultaneous design procedure would be a more appropriate methodology. When GA's have been used to develop both, it has been done serially, e.g.,
   design the membership functions and then use them in the design of the rule set. This, however, means that the membership functions were optimized for the initial rule set
   and not the rule set designed subsequently. GA's are fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need
   for human input in the design loop. This new method has been applied to two problems, a cart controller and a truck controller. Beyond the development of these controllers,
   we also examine the design of a robust controller for the cart problem and its ability to overcome faulty rules.
%8 May

%A Abdollah Homaifar
%A Daryl Battle
%A Edward Tunstel
%T Soft computing-based design and control for mobile robot path tracking
%B Computational Intelligence in Robotics and Automation, CIRA '99. Proceedings. 1999 IEEE International Symposium on
%D 1999
%P 35--40
%I 
%K genetic algorithms, genetic programming, evolutionary computation, soft computing-based design, mobile robot, robot path tracking, evolutionary algorithms, Darwinian
   concepts, automatic learning, nonlinear mappings, genetic programming, fuzzy control rules, autonomous vehicle, steering control problem, membership functions, rule bases,
   robustness, sensor measurement noise, nominal forward velocity
%U http://ieeexplore.ieee.org/iel5/6589/17587/00809943.pdf?isNumber=17587
%X A variety of evolutionary algorithms, operating according to Darwinian concepts, have been proposed to approximately solve problems of common engineering applications.
   Increasingly common applications involve automatic learning of nonlinear mappings that govern the behavior of control systems. In many cases where robot control is of
   primary concern, the systems used to demonstrate the effectiveness of evolutionary algorithms often do not represent practical robotic systems. In this paper, genetic
   programming (GP) is the evolutionary strategy of interest. It is applied to learn fuzzy control rules for a practical autonomous vehicle steering control problem, namely,
   path tracking. GP handles the simultaneous evolution of membership functions and rule bases for the fuzzy path tracker. As a matter of practicality, robustness of the
   genetically evolved fuzzy controller is demonstrated by examining the effects of sensor measurement noise and an increase in the robot's nominal forward velocity.
%8 8-9  November
%Z CIRA'99 http://web.nps.navy.mil/~yun/cira99/
%@ 0-7803-5806-6

%A Abdollah Homaifar
%A D. Battle
%A E. Tunstel
%A G. Dozier
%T Genetic Programming Design of Fuzzy Controllers for Mobile Robot Path Tracking
%J International Journal of Knowledge-Based Intelligent Engineering Systems
%V 4
%N 1
%D 2000
%P 33--52
%I 
%K genetic algorithms, genetic programming
%X Genetic programming (GP) is an evolutionary strategy that attempts to deal with the notion of how computers can learn to solve problems without being explicitly programmed.
   It has been demonstrated that GP, under the influence of Darwinian concepts, could genetically breed computer programs to approximately solve problems in a variety of
   applications. One primary example is its application to the problem of automatically learning nonlinear mappings that govern the behavior of control systems. It is
   demonstrated here that GP can formulate such nonlinear maps in the form of fuzzy control rules, which yield comparable or better performance than one derived through manual
   design using trial-and-error. The objective is to address the efficient implementation of GP for the discovery of knowledge bases intended for use in fuzzy logic controller
   applications. Efficiency is achieved with a C programming language implementation of GP, which is applied to a mobile robot steering control problem. Robot path following
   performance is compared to results obtained using an existing GP implementation in the LISP programming language. It is demonstrated that the C implementation has a
   definite advantage with regard to computational speed of evolution. In this work, we have extended the application of GP to handle simultaneous evolution of membership
   functions and rule bases for the same control problem. Furthermore, GP is used to handle selection of fuzzy t-norms. It is concluded that simultaneous evolution of rule
   bases and membership functions with t-norm selection results in enhanced performance of the evolved controllers. Finally, the robustness characteristics of the genetically
   evolved fuzzy controllers are investigated by examining the effects of sensor measurement noise and an increase in the robot's nominal forward velocity.
%8 January

%A Naohiro Hondo
%A Hitoshi Iba
%A Yukinori Kakazu
%T Sharing and Refinement for Reusable Subroutines of Genetic Programming
%B Proceedings of the 1996 IEEE International Conference on Evolutionary Computation
%V 1
%D 1996
%P 565--570
%I 
%I IEEE Neural Network Council
%C Nagoya, Japan
%K genetic algorithms, genetic programming, COAST,efficiency, reusable subroutines, subroutine library, subroutine refinement, subroutine sharing, wall-following problem,
   genetic algorithms, software libraries, software performance evaluation, software reusability, subroutines
%X Presents a new approach to genetic programming (GP). The aim of this study is to indicate an approach to make GP fit for practical use. The objective of our study
   originates in the fact that human-created programs tend to be divided into subroutines that are reused frequently. In traditional GP, the program is structured as a single
   sequence. Moreover, there is no room to reuse the subroutines in traditional GP. There have been a few techniques proposed for dividing such programs into subroutines,
   which attempt to discover certain subroutines. However, the reusability of genetic programs has not yet been discussed. In this paper, we propose an approach for
   reusability. The proposed method has a library for keeping the subroutines in order to share and reuse them. We make use of the wall-following problem to indicate the
   efficiency of the method experimentally
%8 20-22  May
%Z ICEC-96
%@ 0-7803-2902-3

%A Naohiro Hondo
%A Hitoshi Iba
%A Yukinori Kakazu
%T COAST: An Approach to Robustness and Reusability in Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 429
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Naohiro Hondo
%A Hitoshi Iba
%A Yukinori Kakazu
%T Robust GP in Robot Learning
%B Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation
%S LNCS
%E Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel
%V 1141
%D 1996
%P 751--760
%I Springer Verlag Heidelberg, Germany
%C Berlin, Germany
%K genetic algorithms, genetic programming
%X This paper presents a new approach to Genetic Programming (i.e. GP). Our goal is to realise robustness by means of the automatic discovery of functions. In traditional GP,
   techniques have been proposed which attempt to discover certain subroutines for the sake of improved efficiency. So far, however, the robustness of GP has not yet been
   discussed in terms of knowledge acquisition. We propose an approach for robustness named COAST, which has a library for storing certain subroutines for reuse. We make use
   of the Wall Following Problem to illustrate the efficiency of this method.
%8 22-26  September
%Z http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 COAST, Wall following problem
%@ 3-540-61723-X

%A Naohiro Hondo
%A Koji Nishikawa
%A Hiroshi Yokoi
%A Yukinori Kakazu
%T Multi-Agent Programming System for Starfish Robot Control
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 140--145
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A G. Hong
%A L. Hu
%A D. Xue
%A Y. L. Tu
%A Y. L. Xiong
%T Integrated Optimal Product Design and Process Planning for One-of-a-Kind Production
%B 26th Computers and Information in Engineering Conference
%D 2006
%I ASME
%C Philadelphia, Pennsylvania, USA
%K genetic algorithms, genetic programming
%X This research addresses the issues to identify the optimal product configuration and its parameters based on the requirements of customers on performance and costs of
   products in one-of-a-kind production (OKP) environment. In this work, variations of product configurations and parameters in an OKP product family are modelled by an AND-OR
   tree and parameters of the nodes in this tree. Different product configurations with different parameters are evaluated by performance and cost measures. These evaluation
   measures are converted into comparable customer satisfaction indices using the non-linear relations between the evaluation measures and the customer satisfaction indices.
   The optimal product configuration and its parameters with the maximum overall customer satisfaction index are identified by genetic programming and constrained
   Optimization.
%8 September  10-13
%Z Gang Hong, University of Calgary, Calgary, AB, Canada

%A G. Hong
%A P. R. Dean
%A W. Yang
%A Y. L. Tu
%A D. Xue
%T Integrated Optimal Product Design and Process Planning for One-of-a-Kind Production
%B 28th Computers and Information in Engineering Conference IDETC/CIE2008
%V 3
%D 2008
%P 111--120
%I ASME
%C Brooklyn, New York, USA
%K genetic algorithms, genetic programming
%X One-of-a-kind production (OKP) is a new manufacturing paradigm to produce customised products based on requirements of individual customers while maintaining the quality
   and efficiency of mass production. In this research, an integrated optimal product design and process planning approach is developed to satisfy customer requirements
   considering design and manufacturing constraints. In this work, a hybrid AND-OR graph is introduced to model the variations of design configurations/parameters and
   manufacturing processes/parameters in generic product family. Since different design configurations and parameters can be created from the same customer requirements, and
   each design can be further achieved through alternative manufacturing processes and parameters, co-evolutionary genetic programming and numerical Optimization are employed
   to identify the optimal product design configuration/parameters and manufacturing process/parameters. An industrial case study to identify the optimal design
   configuration/parameters and manufacturing process/parameters of custom window products in a local company is introduced to demonstrate the effectiveness of the developed
   method.
%8 August  3-6
%Z Gang Hong, University of Calgary, Calgary, AB, Canada

%A Gang Hong
%T Research on Product Design and Manufacture for One-of-a-Kind Production
%R Ph.D. Thesis
%D 2009
%I 
%I Department of Mechanical and Manufacturing Engineering, University of Calgary
%C Canada
%K genetic algorithms, genetic programming
%U http://schulich.ucalgary.ca/mechanical/files/mechanical/Gang%20Hong-PhD%20Abstract.pdf
%X To keep competitive advantages in today's global marketplace, many companies, especially the small and medium enterprises, have been embracing a production strategy, named
   one-of-a-kind production (OKP), which aims at satisfying individual customer requirements while maintaining the efficiency and quality of mass production. This thesis work
   contributes to a further understanding of one-of-a-kind production by addressing the following three objectives to improve the productivity in OKP companies: (1) customer
   information should be incorporated in the product modelling scheme; (2) design variations and manufacturing variations should be well integrated, and (3) the concurrent
   optimal custom product design and manufacturing should be quickly identified based on the individual customer requirements and manufacturing constraints. In this thesis
   work, a customer-driven product modeling scheme is introduced to incorporate customer information into OKP product family modeling. Through this modeling scheme, relations
   between customer categories and product categories are explored to facilitate the optimisation process to quickly identify the custom product. In order to provide products
   in a cost-effective way in addition to satisfying individual customer needs, a hybrid modelling scheme is introduced to model design variations and manufacturing variations
   in an integrated environment. Based on the hybrid modelling scheme, a new multi-level optimisation method is developed to identify the optimal custom product design and its
   optimal manufacturing process, where co-evolutionary programming is used for configuration design and numerical search is carried out for parameter design. Two prototype
   systems are developed to illustrate the effectiveness of the introduced methodologies.
%8 16  March
%Z Gang Tony Hong

%A G. Hong
%A L. Hu
%A D. Xue
%A Y. L. Tu
%A Y. L. Xiong
%T Identification of the optimal product configuration and parameters based on individual customer requirements on performance and costs in one-of-a-kind production
%J International Journal of Production Research
%V 46
%N 12
%D 2008
%P 3297--3326
%I Taylor \& Francis
%K genetic algorithms, genetic programming, One-of-a-kind production (OKP), Optimization, Customer requirements
%X One-of-a-kind production (OKP) aims at manufacturing products based on the requirements from individual customers while maintaining the high quality and efficiency of mass
   production. This research addresses the issues in identifying the optimal product configuration and its parameters based on individual customer requirements on performance
   and costs of products. In this work, variations of product configurations and parameters in an OKP product family are modelled by an AND-OR tree and parameters of the nodes
   in this tree. Different product configurations with different parameters are evaluated by performance and cost measures. These evaluation measures are converted into
   comparable customer satisfaction indices using the non-linear relations between the evaluation measures and the customer satisfaction indices. The optimal product
   configuration and its parameters with the maximum overall customer satisfaction index are identified by genetic programming and constrained optimisation. A case study to
   identify the optimal configuration and its parameters of window products in an industrial company is used to demonstrate the effectiveness of the introduced approach.
%Z Official Journal of the International Foundation for Production Research (IFPR)

%A Gang Hong
%A Deyi Xue
%A Yiliu Tu
%T Rapid identification of the optimal product configuration and its parameters based on customer-centric product modeling for one-of-a-kind production
%J Computers in Industry
%V 61
%N 3
%D 2010
%P 270--279
%I 
%K genetic algorithms, genetic programming, One-of-a-kind production, Customer-centric product modelling, Pattern recognition, Rough set, Optimisation
%U http://www.sciencedirect.com/science/article/B6V2D-4XHC68M-2/2/3d71e33179122a81965181a637daea9e
%X One-of-a-kind production (OKP) aims at manufacturing products based on the individual customer requirements while maintaining the high quality and efficiency of mass
   production. This paper presents a customer-centric product modelling scheme to model OKP product families by considering the relations between customer needs and OKP
   products. In this modeling scheme, an OKP product family is modelled by an AND-OR tree. In order to investigate the relations between customer needs and OKP products, data
   mining techniques are employed to achieve knowledge from the historical data. First, OKP products and customer requirements are grouped into product patterns and customer
   patterns, respectively, using a fuzzy pattern clustering method. Then, hybrid attribute reduction is carried out based on rough set theory to remove the irrelevant
   attributes for each product pattern. Finally, the relationships between product patterns and customer patterns are obtained. Based on the achieved knowledge, the different
   patterns of OKP products are modeled by different sub-AND-OR trees trimmed from the original AND-OR tree. Since only partial product descriptions in a product family are
   used to identify the optimal custom product based on customer requirements, the efficiency of custom product identification process can be improved considerably.

%A Hong S. Hong
%T Digbital Image Restoration Using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 68--75
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Jin-Hyuk Hong
%A Sung-Bae Cho
%T Effective Rule Discovery Using Genetic Programming for DNA Microarray Analysis
%B Proceedings of The First Asian-Pacific Workshop on Genetic Programming
%E Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan
%D 2003
%P 53--61
%I 
%C Rydges (lakeside) Hotel, Canberra, Australia
%K genetic algorithms, genetic programming
%8 8  Decemeber
%Z \citeaspgp03
%@ 0-9751724-0-9

%A Jin-Hyuk Hong
%A Sung-Bae Cho
%T Cancer Prediction Using Diversity-Based Ensemble Genetic Programming
%B Modeling Decisions for Artificial Intelligence, Second International Conference, MDAI 2005, Proceedings
%S Lecture Notes in Computer Science
%E Vicenc Torra and Yasuo Narukawa and Sadaaki Miyamoto
%V 3558
%D 2005
%P 294--304
%I Springer
%C Tsukuba, Japan
%K genetic algorithms, genetic programming
%X Combining a set of classifiers has often been exploited to improve the classification performance. Accurate as well as diverse base classifiers are prerequisite to
   construct a good ensemble classifier. Therefore, estimating diversity among classifiers has been widely investigated. This paper presents an ensemble approach that combines
   a set of diverse rules obtained by genetic programming. Genetic programming generates interpretable classification rules, and diversity among them is directly estimated.
   Finally, several diverse rules are combined by a fusion method to generate a final decision. The proposed method has been applied to cancer classification using gene
   expression profiles, which is one of the important issues in bioinformatics. Experiments on several popular cancer datasets have demonstrated the usability of the method.
   High performance of the proposed method has been obtained, and the accuracy has increased by diversity among the base classification rules.
%8 July  25-27
%@ 3-540-27871-0

%A Tzung-Pei Hong
%A Hong-Shung Wang
%A Wei-Chou Chen
%T Simultaneously Applying Multiple Crossover and Mutation Operators
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 790
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/ga305.ps
%8 13-17  July
%Z Information management dept. I-Shou University GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic
   programming conference (GP-99)
%@ 1-55860-611-4

%A Jin-Hyuk Hong
%A Sung Bae Cho
%T Lymphoma Cancer Classification Using Genetic Programming with SNR Features
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 78--88
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=78
%X Lymphoma cancer classification with DNA microarray data is one of important problems in bioinformatics. Many machine learning techniques have been applied to the problem
   and produced valuable results. However the medical field requires not only a high-accuracy classifier, but also the in-depth analysis and understanding of classification
   rules obtained. Since gene expression data have thousands of features, it is nearly impossible to represent and understand their complex relationships directly. We adopt
   the SNR (Signal-to-Noise Ratio) feature selection to reduce the dimensionality of the data, and then use genetic programming to generate cancer classification rules with
   the features. In the experimental results on Lymphoma cancer dataset, the proposed method yielded 96.6% test accuracy in average, and an excellent arithmetic classification
   rule set that classifies all the samples correctly is discovered by the proposed method.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Jin-Hyuk Hong
%A Sung-Bae Cho
%T Ensemble Genetic Programming for Classifying Gene Expression Data
%B Proceedings of The Second Asian-Pacific Workshop on Genetic Programming
%E R I Mckay and Sung-Bae Cho
%D 2004
%I 
%C Cairns, Australia
%K genetic algorithms, genetic programming
%8 6-7  Decemeber
%Z http://www.itee.adfa.edu.au/~rim/ASPGP/programme.html

%A Jin-Hyuk Hong
%A Sung-Bae Cho
%T The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming
%J Artificial Intelligence In Medicine
%V 36
%N 1
%D 2006
%P 43--58
%I 
%K genetic algorithms, genetic programming, Ensemble, Diversity, Classification
%X Object The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results,
   ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply
   conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a
   new ensemble approach that can enhance the successful classification of these datasets. Materials and methods An effective ensemble approach that does use diversity in
   genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating. Results
   Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of
   the proposed method in relation to the conventional approaches. Conclusion Diversity measured by comparing the structure of the classification rules obtained by genetic
   programming is useful to improve the performance of the ensemble classifier.
%8 January

%A Jin-Hyuk Hong
%A Sungsoo Lim
%A Sung-Bae Cho
%T Autonomous Language Development Using Dialogue-Act Templates and Genetic Programming
%J IEEE Transactions on Evolutionary Computation
%V 11
%N 2
%D 2007
%P 213--225
%I 
%K genetic algorithms, genetic programming, belief networks, finite state machines, knowledge acquisition, pattern matching, software agents, Bayesian networks, autonomous
   language development, autonomous machines, autonomous mental development, behavioural patterns, dialogue-act templates, finite-state machines, genetic programming,
   intelligent conversational agents, knowledge acquisition, knowledge bases, pattern matching
%X In recent years, the concept of autonomous mental development (AMD) has been applied to the construction of artificial systems such as conversational agents, in order to
   resolve some of the difficulties involved in the manual definition of their knowledge bases and behavioural patterns. AMD is a new paradigm for developing autonomous
   machines, which are adaptive and flexible to the environment. Language development, a kind of mental development, is an important aspect of intelligent conversational
   agents. we propose an intelligent conversational agent and its language development mechanism by putting together five promising techniques: Bayesian networks, pattern
   matching, finite-state machines, templates, and genetic programming (GP). Knowledge acquisition implemented by finite-state machines and templates, and language learning by
   GP are used for language development. Several illustrations and usability tests show the usefulness of the proposed developmental conversational agent
%8 April

%A Yoon-Seok Hong
%A Michael R. Rosen
%T Identification of an urban fractured-rock aquifer dynamics using an evolutionary self-organizing modelling
%J Journal of Hydrology
%V 259
%N 1-4
%D 2002
%P 89--104
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V6C-44KPK1K-4/2/cc33fdeeff7d3869ee62940e37e3e133
%X An urban fractured-rock aquifer system, where disposal of storm water is via 'soak holes' drilled directly into the top of fractured-rock basalt, has a highly dynamic
   nature where theories or knowledge to generate the model are still incomplete and insufficient. Therefore, formulating an accurate mechanistic model, usually based on first
   principles (physical and chemical laws, mass balance, and diffusion and transport, etc.), requires time- and money-consuming tasks. Instead of a human developing the
   mechanistic-based model, this paper presents an approach to automatic model evolution in genetic programming (GP) to model dynamic behaviour of groundwater level
   fluctuations affected by storm water infiltration. This GP evolves mathematical models automatically that have an understandable structure using function tree
   representation by methods of natural selection ('survival of the fittest') through genetic operators (reproduction, crossover, and mutation). The simulation results have
   shown that GP is not only capable of predicting the groundwater level fluctuation due to storm water infiltration but also provides insight into the dynamic behaviour of a
   partially known urban fractured-rock aquifer system by allowing knowledge extraction of the evolved models. Our results show that GP can work as a cost-effective modelling
   tool, enabling us to create prototype models quickly and inexpensively and assists us in developing accurate models in less time, even if we have limited experience and
   incomplete knowledge for an urban fractured-rock aquifer system affected by storm water infiltration.

%A Yoon-Seok Hong
%T Automatic Model Induction of a Biological Waste Water Treatment Process using Context-Free Grammar Genetic Programming
%B GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2003
%P 146--149
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C Chigaco
%K genetic algorithms, genetic programming
%8 11  July
%Z Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming
   Conference (GP-2003) part of barry:2003:GECCO:workshop

%A Yoon-Seok Hong
%A Rao Bhamidimarri
%T Evolutionary self-organising modelling of a municipal wastewater treatment plant
%J Water Research
%V 37
%N 6
%D 2003
%P 1199--1212
%I 
%K genetic algorithms, genetic programming, Municipal wastewater treatment plant, Self-organising modelling, Model evolution, Neural network, ASM2
%U http://www.sciencedirect.com/science/article/B6V73-47XW9PY-5/2/5581df84c89448cc706b69488765c7e1
%X Building predictive models for highly time varying and complex multivariable aspects of the wastewater treatment plant is important both for understanding the dynamics of
   this complex system, and in the development of optimal control support and management schemes. genetic programming as a self-organising modelling tool, to model dynamic
   performance of municipal activated-sludge wastewater treatment plants. Genetic programming evolves several process models automatically based on methods of natural
   selection ('survival of the fittest'), that could predict the dynamics of MLSS and suspended solids in the effluent. The predictive accuracy of the genetic programming
   approach was compared with a nonlinear state-space model with neural network and a well-known IAWQ ASM2. The genetic programming system evolved some models that were an
   improvement over the neural network and ASM2 and showed that the transparency of the model evolved may allow inferences about underlying processes to be made. This work
   demonstrates that dynamic nonlinear processes in the wastewater treatment plant may be successfully modelled through the use of evolutionary model induction algorithms in
   GP technique. Further, our results show that genetic programming can work as a cost-effective intelligent modelling tool, enabling us to create prototype process models
   quickly and inexpensively instead of an engineer developing the process model.
%Z PMID: 12598184

%A Yoon-Seok Timothy Hong
%A Paul A. White
%A David M. Scott
%T Automatic rainfall recharge model induction by evolutionary computational intelligence
%J Water Resources Research
%V 41
%N W08422
%D 2005
%I 
%K genetic algorithms, genetic programming, automatic rainfall recharge model induction, Canterbury Plains, evolutionary computational intelligence, New Zealand, soil moisture
   balance model, 0555 Computational Geophysics: Neural networks, fuzzy logic, machine learning; 1805 Hydrology: Computational hydrology; 1816 Hydrology: Estimation and
   forecasting; 1829 Hydrology: Groundwater hydrology; 1847 Hydrology: Modelling
%U http://www.agu.org/pubs/crossref/2005/2004WR003577.shtml
%X Genetic programming (GP) is used to develop models of rainfall recharge from observations of rainfall recharge and rainfall, calculated potential evapotranspiration (PET)
   and soil profile available water (PAW) at four sites over a 4 year period in Canterbury, New Zealand. This work demonstrates that the automatic model induction method is a
   useful development in modeling rainfall recharge. The five best performing models evolved by genetic programming show a highly nonlinear relationship between rainfall
   recharge and the independent variables. These models are dominated by a positive correlation with rainfall, a negative correlation with the square of PET, and a negative
   correlation with PAW. The best performing GP models are more reliable than a soil water balance model at predicting rainfall recharge when rainfall recharge is observed in
   the late spring, summer, and early autumn periods. The 'best' GP model provides estimates of cumulative sums of rainfall recharge that are closer than a soil water balance
   model to observations at all four sites.

%A Yoon-Seok Timothy Hong
%A Byeong-Cheon Paik
%T Evolutionary Multivariate Dynamic Process Model Induction for a Biological Nutrient Removal Process
%J Journal of Environmental Engineering
%V 12
%D 2007
%P 1126--1135
%I ASCE
%K genetic algorithms, genetic programming, Grammar-based genetic programming, wastewater treatment process
%X This paper proposes an automatic process model induction system using an evolutionary computational intelligence, called grammar-based genetic programming, that is
   specially designed to automatically discover multivariate dynamic process models that best fit observed process data. This automatic process model induction system combines
   an evolutionary self-organising system of genetic programming paradigm with various mathematical functions for a multivariate nonlinear model evolution using a grammar
   system via the mechanism of genetics and natural selection. The results demonstrate how the automatic process model induction system based on grammar-based genetic
   programming can be used to develop accurate and relatively cost-effective multivariate dynamic process models for the full-scale biological nutrient removal process.
   Multivariate dynamic process models are derived automatically in the form of understandable mathematical formulas that enable engineers to extract important knowledge
   hidden in the data and develop better operation and control strategies.
%8 Decemeber

%A Yoon-Seok Timothy Hong
%A Byeong-Cheon Paik
%T Inference model derivation with a pattern analysis for predicting the risk of microbial pollution in a sewer system
%J Stochastic Environmental Research and Risk Assessment
%I Springer
%K genetic algorithms, genetic programming, Fecal coliform bacteria, Water quality modelling, Multivariate inference model derivation, Neural network-based pattern analysis,
   Self-Organising Feature Maps, Evolutionary process model induction system, Grammar-based genetic programming
%X Developing a mathematical model for predicting fecal coliform bacteria concentration is very important because it can provide a basis for water quality management decisions
   that can minimise microbial pollution risk to the public. This paper introduces a hybrid modelling methodology which is a combined use of a neural network-based pattern
   analysis and an evolutionary process model induction system. The neural network-based pattern analysis technique is applied to extract knowledge on inter-relationships
   between fecal coliform concentrations and other measurable variables in a sewer system. Based on the result of neural network-based pattern analysis, an evolutionary
   process model induction system is used to derive mathematical inference models that can predict fecal coliform bacteria concentration from easily measurable variables
   instead of directly measuring fecal coliform bacteria concentration in a sewer system. The neural network-based pattern analysis extracts that temperature and ammonia
   concentration are the most important driving forces leading to an increase in fecal coliform bacteria concentration in the sewer system at Paraparaumu City, New Zealand.
   Fecal coliform bacteria concentration is also positively correlated with dissolved phosphorus and inversely with flow rate. The multivariate inference models that are able
   to predict fecal coliform bacteria concentration are successfully derived as functions of flow rate, temperature, ammonia, and dissolved phosphorus in the form of
   understandable mathematical formulae using the evolutionary process model induction system, even if a priori mathematical knowledge of the dynamic nature of fecal coliform
   bacteria is poor. The multivariate inference models evolved by the evolutionary process model induction system produce a slightly better performance than the multi-layer
   perceptron neural network model.
%O online first

%A Yuan Hongbo
%A Cai Zhenjiang
%A Cheng Man
%A Gao liai
%T Study on Camera Calibration for Binocular Vision Based on Genetic programming
%B 8th International Conference on Electronic Measurement and Instruments, ICEMI '07
%D 2007
%P 3--890--3--893
%I IEEE
%C Xian, China
%K genetic algorithms, genetic programming
%X In view of the camera calibration existent questiones, on the basis of Stereo Vision, a new method of camera calibration for binocular vision based on genetic programming
   is proposed. It is used to learn the relationships between the image information and the 3D information. For two-cameras system, the complicated relation between the
   cameras is established by training the genetic programming without the parameters of the cameras calibrated. It neither requires an accurate mathematical model nor needs
   any prior knowledge about the parameters. The 3D information of target is achieved from genetic programming output. The results of the experiment showed that this method
   was more accurate with traditional visual calibration methods.
%8 August  16-  July  18 ?????
%Z Mechanical and electricity of College Agriculture university of Hebei, Baoding, 071001 China

%A J.-B. Hoock
%A O. Teytaud
%T Racing-Based Genetic Programming
%B 4th Workshop on Theory of Randomized Search Heuristics, ThRaSH'2010
%E Anne Auger and Benjamin Doerr and Thomas Jansen and Per Kristian Lehre and Frank Neumann and Pietro S. Oliveto and Carsten Witt
%D 2010
%I 
%C Paris
%K genetic algorithms, genetic programming
%U http://trsh2010.gforge.inria.fr/abstracts/04Hoock.pdf
%8 March  24-25
%Z Multiple Simultaneous Hypothesis Testing (MSHT) effect. racing algorithms Co-located JET meeting at Universite Pierre et Marie Curie

%A Jean-Baptiste Hoock
%A Olivier Teytaud
%T Bandit-Based Genetic Programming
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 268--277
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X We consider the validation of randomly generated patterns in a Monte-Carlo Tree Search program. Our bandit-based genetic programming (BGP) algorithm, with proved
   mathematical properties, outperformed a highly optimized handcrafted module of a well-known computer-Go program with several world records in the game of Go.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Dale Hooper
%A Nicholas S. Flann
%T Improving the Accuracy and Robustness of Genetic Programming through Expression Simplification
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 428
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96. Occam's razor, bloat, introns, 200 edit rules
%@ 0-262-61127-9

%A Dale C. Hooper
%A Nicholas S. Flann
%A Stephanie R. Fuller
%T Recombinative Hill-Climbing: A Stronger Search Method for Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 174--179
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Hooper_1997_rhc.pdf
%8 13-16  July
%Z GP-97 Artificial Ant Santa Fe trail (only 400 time steps p177), Symbolic regression pi*x**2+ex+x**0.5 "RHC is shown to run about ten times faster than traditional GP for
   the same population size" p175 NB only on symbolic regression. Optional program simplification (cites \citehooper:1996:iarGPes) 0.5% mutation. Overfitting.

%A Kristopher Hoover
%A Rachel Marceau
%A Tyndall Harris
%A Nicholas Hardison
%A David Reif
%A Alison Motsinger-Reif
%T Optimization of grammatical evolution decision trees
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 35--36
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems, and synthetic biology: Poster
%X The detection of gene-gene and gene-environment interactions in genetic association studies presents a difficult computational and statistical challenge, especially as
   advances in genotyping technology have rapidly expanded the number of potential genetic predictors in such studies. The scale of these studies makes exhaustive search
   approaches infeasible, inspiring the application of evolutionary computation algorithms to perform variable selection and build classification models. Recently, an
   application of grammatical evolution to evolve decision trees (GEDT) has been introduced for detecting interaction models. Initial results were promising, but relied on
   arbitrary parameter choices for the evolutionary process. In the current study, we present the results of a parameter sweep evaluating the power of GEDT and show that
   improved parameter choices improves the performance of the method. The results of these experiments are important for the continued optimisation, evaluation, and comparison
   of this and related methods, and for proper application in real data.
%8 12-16  July
%Z Also known as \cite2001879 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Jeffrey Horn
%A David E. Goldberg
%T Natural Niching for Cooperative Learning in Classifier Systems
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 553--564
%I MIT Press
%C Stanford University, CA, USA
%K Classifier Systems, Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 Classifier paper

%A Jeffrey Horn
%T Controlling the Cooperative-Competitive Boundary in Niched Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 305--312
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Ga-830.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A G. S. Hornby
%A M. Fujita
%A S. Takamura
%A T. Yamamoto
%A O. Hanagata
%T Autonomous Evolution of Gaits with the Sony Quadruped Robot
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1297--1304
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents, robotics, evolutionary robotics, locomotion
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-011.ps
%X A trend in robotics is towards legged robots. One of the issues with legged robots is the development of gaits. Typically gaits are developed manually. In this paper we
   report our results of autonomous evolution of dynamic gaits for the Sony Quadruped Robot. Fitness is determined using the robot's digital camera and infrared sensors. Using
   this system we evolve faster dynamic gaits than previously manually developed
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Gregory S. Hornby
%A Brian Mirtich
%T Diffuse versus True Coevolution in a Physics-based World
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1305--1312
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents, co-evolution, pursuer-evader, neural networks
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-025.ps
%X We compare two types of coevolutionary tournaments, true and diffuse, in contests using a general-purpose, physics-based simulator. Previous work in coevolving agents has
   used true coevolution and found that populations tend to enter mediocre states. One hypothesis for alleviating these problems is to use diffuse coevolution. Our results
   show that agents evaluated with diffuse tournaments are more generalized than those evaluated with true tournaments.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Gregory S. Hornby
%A Jordan B. Pollack
%T The Advantages of Generative Grammatical Encodings for Physical Design
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 600--607
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, lindenmayer system, L-systems, generative encoding, design
%U http://www.demo.cs.brandeis.edu/papers/hornby_cec01.ps
%X One of the applications of evolutionary algorithms is the automatic creation of designs. For evolutionary techniques to scale to the complexities necessary for actual
   engineering problems, it has been argued that generative systems, where the genotype is an algorithm for constructing the final design, should be used as the encoding. We
   describe a system for creating generative specifications by combining Lindenmayer systems with evolutionary algorithms and apply it to the problem of generating table
   designs. Designs evolved by our system reach an order of magnitude more parts than previous generative systems. Comparing it against a non-generative encoding we find that
   the generative system produces designs with higher fitness and is faster than the non-generative system. Finally, we demonstrate the ability of our system to go from design
   to manufacture by constructing evolved table designs using rapid prototyping equipment.
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = The project
   page for this work is at: http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html
%@ 0-7803-6658-1

%A Gregory S. Hornby
%A Hod Lipson
%A Jordan B. Pollack
%T Evolution of Generative Design Systems for Modular Physical Robots
%B IEEE International Conference on Robotics and Automation
%D 2001
%I 
%K genetic algorithms, genetic programming, L-systems, generative encoding, design, robotics, P0L
%U http://www.demo.cs.brandeis.edu/papers/hornby_icra01.ps
%X Recent research has demonstrated the ability for automatic design of the morphology and control of real physical robots using techniques inspired by biological evolution.
   The main criticism of the evolutionary design approach, however, is that it is doubtful whether it will reach the high complexities necessary for practical engineering.
   Here we claim that for automatic design systems to scale in complexity the designs they produce must be made of re-used modules. Our approach is based on the use of a
   generative design grammar subject to an evolutionary process. Unlike a direct encoding of a design, a generative design specification can re-use components, giving it the
   ability to create more complex modules from simpler ones. Re-used modules are also valuable for improved efficiency in testing and construction. We describe a system for
   creating generative specifications capable of hierarchical modularity by combining Lindenmayer systems with evolutionary algorithms. Using this system we demonstrate for
   the first time a generative system for physical, modular, 2D locomoting robots and their controllers.
%Z The project page for this work is at: http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html

%A Gregory S. Hornby
%A Jordan B. Pollack
%T Body-Brain Co-evolution Using L-systems as a Generative Encoding
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 868--875
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, artificial life, adaptive behaviour, agents, L-systems, Lindenmayer grammar, generative encoding, ANN
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d07.pdf
%X We co-evolve the morphology and controller of artificial creatures using two integrated generative processes. L-systems are used as the common generative encoding for both
   body and brain. Combining the languages of both into a single L-system allows for linkage between the genotype of the controller and the parts of the morphology that it
   controls. Creatures evolved by this system are more complex than previous work, having an order of magnitude more parts and a higher degree of regularity.
%8 7-11  July
%Z A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO The project page for this work is at: http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html and the source code can be downloaded from here.
%@ 1-55860-774-9

%A Gregory S. Hornby
%A Jordan B. Pollack
%T Evolving L-Systems To Generate Virtual Creatures
%J Computers and Graphics
%V 25
%N 6
%D 2001
%P 1041--1048
%I Elsevier
%K genetic algorithms, genetic programming, animation, artificial life, representation, intelligent agents, Lindenmayer systems (L-systems)
%U http://www.demo.cs.brandeis.edu/papers/hornby_cag01.ps
%X Virtual creatures play an increasingly important role in computer graphics as special effects and background characters. The artificial evolution of such creatures
   potentially offers some relief from the difficult and time consuming task of specifying morphologies and behaviours. But, while artificial life techniques have been used to
   create a variety of virtual creatures, previous work has not scaled beyond creatures with 50 components and the most recent work has generated creatures that are unnatural
   looking. Here we describe a system that uses Lindenmayer systems (L-systems) as the encoding of an evolutionary algorithm (EA) for creating virtual creatures. Creatures
   evolved by this system have hundreds of parts, and the use of an L-system as the encoding results in creatures with a more natural look.
%Z The project page for this work is at: http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html

%A Gregory S. Hornby
%A Jordan B. Pollack
%T Creating High-Level Components with a Generative Representation for Body-Brain Evolution
%J Artificial Life
%V 8
%N 3
%D 2002
%P 223--246
%I 
%K genetic algorithms, genetic programming, Body-brain evolution, generative representations, representation, Lindenmayer systems, L-systems
%U http://mitpress.mit.edu/journals/pdf/alife_8_3_223_0.pdf
%X One of the main limitations of scalability in body-brain evolution systems is the representation chosen for encoding creatures. This paper defines a class of
   representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translation to the phenotype. This paper
   presents an example of a generative representation for the concurrent evolution of the morphology and neural controller of simulated robots, and also introduces GENRE, an
   evolutionary system for evolving designs using this representation. Applying GENRE to the task of evolving robots for locomotion and comparing it against a non-generative
   (direct) representation shows that the generative representation system rapidly produces robots with significantly greater fitness. Analyzing these results shows that the
   generative representation system achieves better performance by capturing useful bias from the design space and by allowing viable large scale mutations in the phenotype.
   Generative representations thereby enable the encapsulation, coordination, and reuse of assemblies of parts.
%8 Summer
%Z genetic variations are repeated if offspring fitness<0.1 parent

%A Gregory Scott Hornby
%T Generative Representations for Evolutionary Design Automation
%R Ph.D. Thesis
%D 2003
%I 
%I Brandeis University, Dept. of Computer Science
%C Boston, MA, USA
%K genetic algorithms, genetic programming, generative representation, evolutionary design
%U http://ic.arc.nasa.gov/people/hornby/genre/genre.html
%X In this thesis the class of generative representations is defined and it is shown that this class of representations improves the scalability of evolutionary design systems
   by automatically learning inductive bias of the design problem thereby capturing design dependencies and better enabling search of large design spaces. First, properties of
   representations are identified as: combination, control-flow, and abstraction. Using these properties, representations are classified as non-generative, or generative.
   Whereas non-generative representations use elements of encoded artifacts at most once in translation from encoding to actual artifact, generative representations have the
   ability to reuse parts of the data structure for encoding artifacts through control-flow (using iteration) and/or abstraction (using labelled procedures). Unlike
   non-generative representations, which do not scale with design complexity because they cannot capture design dependencies in their structure, it is argued that evolution
   with generative representations can better scale with design complexity because of their ability to hierarchically create assemblies of modules for reuse, thereby enabling
   better search of large design spaces. Second, GENRE, an evolutionary design system using a generative representation, is described. Using this system, a non-generative and
   a generative representation are compared on four classes of designs: three-dimensional static structures constructed from voxels; neural networks; actuated robots
   controlled by oscillator networks; and neural network controlled robots. Results from evolving designs in these substrates show that the evolutionary design system is
   capable of finding solutions of higher fitness with the generative representation than with the non-generative representation. This improved performance is shown to be a
   result of the generative representation's ability to capture intrinsic properties of the search space and its ability to reuse parts of the encoding in constructing
   designs. By capturing design dependencies in its structure, variation operators are more likely to be successful with a generative representation than with a non-generative
   representation. Second, reuse of data elements in encoded designs improves the ability of an evolutionary algorithm to search large design spaces.
%8 February
%Z Fri, 10 Sep 2004 01:13:34 EDT genetic_programming@yahoogroups.com GENREv1.1b source http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html#genre_source

%A Gregory S. Hornby
%T Creating Complex Building Blocks through Generative Representations
%B Computational Synthesis: From Basic Building Blocks to High Level Functionality: Papers from the 2003 AAAI Spring Symposium
%S AAAI technical report SS-03-02
%E Hod Lipson and Erik K. Antonsson and John R. Koza
%D 2003
%P 98--105
%I AAAI Press
%C Stanford, California, USA
%K genetic algorithms, genetic programming
%U http://ic.arc.nasa.gov/people/hornby/papers/abstracts.html#pollack_alife01
%X One of the main limitations for the functional scalability of computer automated design systems is the representation used for encoding designs. Using computer programs as
   an analogy, representations can be thought of as having the properties of combination, control-flow and abstraction. We define generative representations as those which
   have the ability to reuse elements in an encoding through either iteration or abstraction and argue that reuse improves functional scalability by allowing the
   representation to construct building-blocks and capture design dependencies. Next we describe GENRE, an evolutionary design system for evolving a variety of different types
   of designs. Using this system we compare the generative representation against a non-generative representation on evolving tables and robots and show that designs evolved
   with the generative representation have higher fitness than designs created with the non-generative representation. Further, we show that designs evolved with the
   generative representation are constructed in a modular way through the reuse of discovered building blocks.
%Z TR SS-03-02
%@ 1-57735-179-7

%A Gregory S. Hornby
%T Generative Representations for Evolving Families of Designs
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1678--1689
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, parametric Lindenmayer systems, evolving neural networks, ANN
%U http://ic.arc.nasa.gov/people/hornby/papers/abstracts.html#hornby_gecco03
%X Since typical evolutionary design systems encode only a single artifact with each individual, each time the objective changes a new set of individuals must be evolved. When
   this objective varies in a way that can be parameterized, a more general method is to use a representation in which a single individual encodes an entire class of
   artifacts. In addition to saving time by preventing the need for multiple evolutionary runs, the evolution of parameter-controlled designs can create families of artifacts
   with the same style and a reuse of parts between members of the family. In this paper an evolutionary design system is described which uses a generative representation to
   encode families of designs. Because a generative representation is an algorithmic encoding of a design, its input parameters are a way to control aspects of the design it
   generates. By evaluating individuals multiple times with different input parameters the evolutionary design system creates individuals in which the input parameter controls
   specific aspects of a design. This system is demonstrated on two design substrates: neural-networks which solve the 3/5/7-parity problem and three-dimensional tables of
   varying heights.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Gregory S. Hornby
%A Hod Lipson
%A Jordan B. Pollack
%T Generative Representations for the Automated Design of Modular Physical Robots
%J IEEE transactions on Robotics and Automation
%V 19
%N 4
%D 2003
%P 709--713
%I 
%K genetic algorithms, genetic programming, Design automation, evolutionary robotics, generative representations, Lindenmayer systems
%U http://ieeexplore.ieee.org/iel5/70/27428/01220719.pdf?isnumber=27428&arnumber=1220719
%X The field of evolutionary robotics has demonstrated the ability to automatically design the morphology and controller of simple physical robots through synthetic
   evolutionary processes. However, it is not clear if variation-based search processes can attain the complexity of design necessary for practical engineering of robots.
   Here, we demonstrate an automatic design system that produces complex robots by exploiting the principles of regularity, modularity, hierarchy, and reuse. These techniques
   are already established principles of scaling in engineering design and have been observed in nature, but have not been broadly used in artificial evolution. We gain these
   advantages through the use of a generative representation, which combines a programmatic representation with an algorithmic process that compiles the representation into a
   detailed construction plan. This approach is shown to have two benefits: it can reuse components in regular and hierarchical ways, providing a systematic way to create more
   complex modules from simpler ones; and the evolved representations can capture intrinsic properties of the design space, so that variations in the representations move
   through the design space more effectively than equivalent-sized changes in a nongenerative representation. Using this system, we demonstrate for the first time the
   evolution and construction of modular, three-dimensional, physically moving robots, comprising many more components than previous work on body-brain evolution.
%8 August
%Z INSPEC Accession Number: 7719817

%A Gregory S. Hornby
%T Shortcomings with Tree-Structured Edge Encodings for Neural Networks
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 495--506
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming, neural networks, graphs, representation
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030495.htm
%X In evolutionary algorithms a common method for encoding neural networks is to use a tree-structured assembly procedure for constructing them. Since node operators have
   difficulties in specifying edge weights and these operators are execution-order dependent, an alternative is to use edge operators. Here we identify three problems with
   edge operators: in the initialisation phase most randomly created genotypes produce an incorrect number of inputs and outputs; variation operators can easily change the
   number of input/output (I/O) units; and units have a connectivity bias based on their order of creation. Instead of creating I/O nodes as part of the construction process
   we propose using parameterised operators to connect to pre-existing I/O units. Results from experiments show that these parameterized operators greatly improve the
   probability of creating and maintaining networks with the correct number of I/O units, remove the connectivity bias with I/O units and produce better controllers for a
   goal-scoring task.
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
   football
%@ 3-540-22343-6

%A Gregory S. Hornby
%T Functional Scalability through Generative Representations: the Evolution of Table Designs
%J Environment and Planning B: Planning and Design
%V 31
%N 4
%D 2004
%P 569--587
%I 
%K genetic algorithms, genetic programming, representation, evolutionary design
%U http://www.envplan.com/epb/abstracts/b31/b3015.html
%X One of the main limitations for the functional scalability of automated design systems is the representation used for encoding designs. I argue that generative
   representations, those which are capable of reusing elements of the encoded design in the translation to the actual artifact, are better suited for automated design because
   reuse of building blocks captures some design dependencies and improves the ability to make large changes in design space. To support this argument I compare a generative
   and a nongenerative representation on a table-design problem and find that designs evolved with the generative representation have higher fitness and a more regular
   structure. Additionally the generative representation was found to capture better the height dependency between table legs and also produced a wider range of table designs.
%8 July

%A Gregory S. Hornby
%T Properties of Artifact Representations for Evolutionary Design
%B Workshop and Tutorial Proceedings Ninth International Conference on the Simulation and Synthesis of Living Systems(Alife XI)
%E Mark Bedau and Phil Husbands and Tim Hutton and Sanjeev Kumar and Hideaki Sizuki
%D 2004
%P -
%I 
%C Boston, Massachusetts
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/S.Kumar/hornby.pdf
%O Self-organisation and development in artificial and natural systems workshop.
%8 12  September
%Z http://www.alife9.org/ ALIFE9 http://www.cs.ucl.ac.uk/staff/S.Kumar/sodans.htm

%A Gregory S. Hornby
%T Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1729--1736
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, evolutionary algorithm, computer-automated design, design, open-ended design, evolutionary design, representations
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1729.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Gregory S. Hornby
%T ALPS: the age-layered population structure for reducing the problem of premature convergence
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 815--822
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, age, computer-automated design, evolutionary algorithm, open-ended design, premature convergence, reliability
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p815.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Gregory S. Hornby
%T Shortcomings with using edge encodings to represent graph structures
%J Genetic Programming and Evolvable Machines
%V 7
%N 3
%D 2006
%P 231--252
%I 
%K genetic algorithms, genetic programming, Circuits, Graphs, Neural networks, Representations, CEEL, PEEL, ANN
%U http://ic.arc.nasa.gov/publications/pdf/1212.pdf
%X There are various representations for encoding graph structures, such as artificial neural networks (ANNs) and circuits, each with its own strengths and weaknesses. Here we
   analyse edge encodings and show that they produce graphs with a node creation order connectivity bias (NCOCB). Additionally, depending on how input/ output (I/O) nodes are
   handled, it can be difficult to generate ANNs with the correct number of I/O nodes. We compare two edge encoding languages, one which explicitly creates I/O nodes and one
   which connects to pre-existing I/O nodes with parameterised connection operators. Results from experiments show that these parameterized operators greatly improve the
   probability of creating and maintaining networks with the correct number of I/O nodes, remove the connectivity bias with I/O nodes and produce better ANNs. These results
   suggest that evolution with a representation which does not have the NCOCB will produce better performing ANNs. Finally we close with a discussion on which directions hold
   the most promise for future work in developing better representations for graph structures.
%8 October
%Z 3-parity. goal scoring robot

%A Gregory S. Hornby
%A Sanjeev Kumar
%A Christian Jacob
%T Editorial introduction to the special issue on developmental systems
%J Genetic Programming and Evolvable Machines
%V 8
%N 2
%D 2007
%P 111--113
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%O Special issue on developmental systems
%8 June

%A Gregory S. Hornby
%T Improving the Scalability of Generative Representations
%B Genetic Programming Theory and Practice V
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2007
%P 127--144
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%X With the recent examples of the human-competitiveness of evolutionary design systems, it is not of interest to scale them up to produce more sophisticated designs. Here we
   argue that for computer-automated design systems to scale to producing more sophisticated results they must be able to produce designs with greater structure and
   organisation. By structure and organization we mean the characteristics of modularity, reuse and hierarchy (MR&amp;H), characteristics that are found both in man-made and
   natural designs. We claim that these characteristics are enabled by implementing the attributes of combination, control-flow and abstraction in the representation, and
   define metrics for measuring MR&amp;H and define two measures of overall structure and organisation by combining the measures of MR&amp;H. To demonstrate the merit of our
   complexity measures, we use an evolutionary algorithm to evolve solutions to different sizes for a table design problem, and compare the structure and organisation scores
   of the best tables against existing complexity measures. We find that our measures better correlate with the complexity of good designs than do others, which supports our
   claim that MR&amp;H are important components of complexity. We also compare evolution using five representations with different combinations of MR&amp;H, and find that the
   best designs are achieved when all three of these attributes are present. The results of this second set of experiments demonstrate that implementing representations with
   MR&amp;H can greatly improve search performance.
%O 8
%8 17-19  May
%Z part of \citeRiolo:2007:GPTP Published 2008

%A Gregory S. Hornby
%T Measuring Complexity by Measuring Structure and Organization
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 2017--2024
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming, L-system, GENRE, ALPS
%X Necessary for furthering the development of more powerful evolutionary design systems, capable of scaling to evolving more sophisticated and complex artifacts, is the
   ability to meaningfully and objectively compare these systems by applying complexity measures to the artifacts they evolve. Previously we have proposed measures of
   modularity, reuse and hierarchy (MR&amp;H), here we compare these measures to ones from the fields of Complexity, Systems Engineering and Computer Programming. In addition,
   we propose several ways of combining the MR&amp;H measures into a single measure of structure and organization. We compare all of these measures empirically as well as on
   three sample objects and find that the best measures of complexity are two of the proposed measures of structure and organization.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C 3d table from cubes
%@ 1-4244-1340-0

%A Gregory Hornby
%A William F. Kraus
%A Jason D. Lohn
%T Evolving MEMS Resonator Designs for Fabrication
%B Proceedings of the 8th International Conference Evolvable Systems: From Biology to Hardware, ICES 2008
%S Lecture Notes in Computer Science
%E Gregory Hornby and Luk\'as Sekanina and Pauline C. Haddow
%V 5216
%D 2008
%P 213--224
%I Springer
%C Prague, Czech Republic
%K genetic algorithms, genetic programming
%U http://idesign.ucsc.edu/pubs.html
%X Because of their small size and high reliability, microelectromechanical (MEMS) devices have the potential to revolution many areas of engineering. As with
   conventionally-sized engineering design, there is likely to be a demand for the automated design of MEMS devices. Here we present our work in using an evolutionary
   algorithm and generative representation to automatically create designs for a MEMS meandering resonator and describe what is involved in having these designs fabricated. To
   produce designs that are likely to transfer to reality, we give two ways to modify evaluation of designs: using fabrication noise, differences between the actual dimensions
   of the design and the design blueprint, which has helped us in our work in evolving antennas and robots; and including prestress, to model the warping that occurs during
   the extreme heat of fabrication. We have had the best evolved designs fabricated with a commercial MEMS fabrication process and are currently in the process of testing
   designs to verify how closely the actual devices compare to simulation performance.
%8 September  21-24

%A Gregory S. Hornby
%T A Steady-State Version of the Age-Layered Population Structure EA
%B Genetic Programming Theory and Practice VII
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Una-May O'Reilly and Trent McConaghy
%D 2009
%P 87--102
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Age, Evolutionary Design, Genetic Programming, Metaheuristic, Premature Convergence
%O 6
%8 14-16  May
%Z part of \citeRiolo:2009:GPTP

%A Gregory. S. Hornby
%A Jason D. Lohn
%A Derek S. Linden
%T Computer-Automated Evolution of an X-Band Antenna for NASA's Space Technology 5 Mission
%J Evolutionary Computation
%V 19
%N 1
%D 2011
%P 1--23
%I 
%K genetic algorithms, genetic programming, Antenna, automated design, computational design, evolutionary design, generative representation, spacecraft
%X Whereas the current practise of designing antennas by hand is severely limited because it is both time and labour intensive and requires a significant amount of domain
   knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be
   developed. Here we present our work in using evolutionary algorithms to automatically design an X-band antenna for NASA's Space Technology 5 (ST5) spacecraft. Two
   evolutionary algorithms were used: the first uses a vector of real-valued parameters and the second uses a tree-structured generative representation for constructing the
   antenna. The highest-performance antennas from both algorithms were fabricated and tested and both outperformed a hand-designed antenna produced by the antenna contractor
   for the mission. Subsequent changes to the spacecraft orbit resulted in a change in requirements for the spacecraft antenna. By adjusting our fitness function we were able
   to rapidly evolve a new set of antennas for this mission in less than a month. One of these new antenna designs was built, tested, and approved for deployment on the three
   ST5 spacecraft, which were successfully launched into space on 22 March 2006. This evolved antenna design is the first computer-evolved antenna to be deployed for any
   application and is the first computer-evolved hardware in space.
%8 Spring
%Z GP and GA approaches to 2 problems. NASA flew GP and traditional QHA microwave aerials in 2006. 20 gauge wire. VSWR part of multiplicative fitness (3 multi-objective
   components. Randomised to simulate manufacturing errors. Take _worse_ fitness in order to evolve robust designs)

%A Helmut Horner
%T A C++ Class Library for Genetic Programming: The Vienna University of Economics Genetic Programming Kernel
%D 1996
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.nj.nec.com/horner96class.html
%X This article gives a brief introduction in a variant of genetic programming (namely simple genetic algorithms over k-bounded context-free languages) and presents the most
   important genetic operators. A C++ class-library for genetic programming with context-free languages - the Vienna University of Economics Genetic Programming Kernel - is
   presented within this article. This program is flexible and includes the most important genetic operators. It is able to interpret every grammar in its Backus-NaurForm
   provided it is available in a file. In addition, this article deals with the problems of search-space-size calculations in connection with depth-bounded derivation trees.
%O citeseer
%8 29  May
%Z Only appears to be available via citeseer (oct 2001)

%A Jorng-Tzong Horng
%A Yu-Jan Chang
%A Cheng-Yen Kao
%T Applying evolutionary algorithms to materialized view selection in a data warehouse
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 107--115
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms
%8 13  July
%Z GECCO-99LB

%A Jorng-Tzong Horng
%A Chien-Chin Chen
%A Cheng-Yen Kao
%T Resolution of quadratic assignment problems using an evolutionary algorithm
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 116--124
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms, Evolutionary Strategies
%8 13  July
%Z GECCO-99LB

%A Hao-Sheng Hou
%A Shoou-Jinn Chang
%A Yan-Kuin Su
%T Economical passive filter synthesis using genetic programming based on tree representation
%B Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS)
%D 2005
%I IEEE Press
%K genetic algorithms, genetic programming, Passive Filter Synthesis, Circuit Representation
%U http://www.epapers.org//iscas2005/ESR/paper_details.php?PHPSESSID=3b6735b25d9602780e3827e15b1ee196&paper_id=4103
%X we propose a tree representation for RLC circuits. Genetic programming based on the tree representation is described and applied to passive filter synthesis problems. In
   addition, a way to minimize the size of synthesized circuits is presented. The results show that the proposed method can effectively generate not only compliant but also
   economical passive filters.
%Z National Cheng Kung University, Taiwan ROC

%A Hao-Sheng Hou
%A Shoou-Jinn Chang
%A Yan-Kuin Su
%T Practical Passive Filter Synthesis Using Genetic Programming
%J IEICE Transactions on Electronics
%V E88-C
%N 6
%D 2005
%P 1180--1185
%I 
%K genetic algorithms, genetic programming, passive filter synthesis, frequency-dependent component
%U http://ietele.oxfordjournals.org/cgi/reprint/E88-C/6/1180
%X proposes a genetic programming method to synthesise passive filter circuits. This method allows both the circuit topology and the component values to be evolved
   simultaneously. Experiments show that this method is fast and capable of generating circuits which are more economical than those generated by traditional design
   approaches. In addition, we take into account practical design considerations at high-frequency applications, where the component values are frequency-dependent and
   restricted to some discrete values. Experimental results show that our method can effectively generate not only compliant but also economical circuits for practical design
   tasks.
%Z Special Section on Analog Circuit and Device Technologies -- Papers -- CAD

%A Jia-Li Hou
%T Constructing Static and Dynamic Investment Strategy Portfolios by Genetic Programming
%R Ph.D. Thesis Doctoral Dissertation
%D 2008
%I 
%I Information Management, National Central University
%C Taiwan
%K genetic algorithms, genetic programming, Portfolio, Artificial Intelligence, Capital Allocation, Investment Strategy, Linear Capital Allocation, Non-Linear Capital
   Allocation
%U http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search/view_etd?URN=90443001
%X he study comes up with a framework of portfolio, dividing investment issues into four quadrants based on two dimensions: capital allocation frequency and allocation
   approach. In allocation approach, there are linear and non-linear. In capital allocation frequency selection approach, there are static and dynamic allocation approaches.
   In the framework, static allocation, based on the assumption that if investment duration is identical, is to complete capital allocation selection at the beginning of
   duration; dynamic allocation, based on the assumption that each investment period is different, is to allocate capital when needed. In traditional financial area,
   investment portfolios are linear and static investment issue, which is take all investment duration are the same, and to buy in at the beginning of period, therefore,
   invest decision is to directly allocate capital on multiple investment objectives by static allocation, in order to gain the greatest profit or minimize the risk
   probability.[Huang, 2008; Li, 2008] And reconsidering investment decision for next duration at the end of duration. The framework of the research takes investment strategy
   as investment objectives. The research is to make pairs of investment objectives and transaction rules, and allocate capital on investment strategies rather on investment
   objectives directly. And the research comes up a solution of non-linear capital allocation approach, including planning a capital allocation tree by soft computing and
   genetic algorithms, calculating every capital weight on every investment strategies, and providing static and dynamic capital frequency strategies. The research takes 30
   stocks in Dow Jones Industrial Average of U.S. stock market textbook academic researches and 9 technical indexes which are commonly used in investment markets to comprise
   81 simple transaction rules and constitute 2,430 investment strategies which are planned by genetic algorithms. And experiment test of research is based on 1999 to 2006
   stock market data, the outcome of experiment shows that static and dynamic and non-linear portfolios gains greater profit and smaller probability of risk, comparing to
   buy-in strategy.
%8 8  January
%Z Language zh-TW.Big5 Chinese. Locked for two years

%A Ryan Houlette
%T Evolving Communication using Genetic Programming in the Central-Place Foraging Problem
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 29--38
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Boye Annfelt Hoverstad
%T Simdist: a distribution system for easy parallelization of evolutionary computation
%J Genetic Programming and Evolvable Machines
%V 11
%N 2
%D 2010
%P 185--203
%I 
%K genetic algorithms, Distributed computing, Program development
%X This article introduces Simdist, a software tool for parallel execution of evolutionary algorithms (EAs) in a master-slave configuration on cluster architectures. Clusters
   have become a cost-effective parallel solution, and the potential computational capabilities are phenomenal. However, the transition from traditional R&D on a personal
   computer to parallel development and deployment can be a major step. Simdist simplifies this transition considerably, by separating the task of distributing data across the
   cluster network from the actual EA-related processing performed on the master and slave nodes. Simdist is constructed in the vein of traditional Unix command line tools; it
   runs in a separate process and communicates with EA child processes via standard input and output. As a result, Simdist is oblivious to the programming language(s) used in
   the EA, and the EA is similarly oblivious to the internals of Simdist.
%8 June
%Z http://simdist.sourceforge.net.

%A Janice How
%A Martin Ling
%A Peter Verhoeven
%T Does size matter? A genetic programming approach to technical trading
%J Quantitative Finance
%V 10
%N 2
%D 2010
%P 130--140
%I 
%K genetic algorithms, genetic programming
%U http://www.informaworld.com/smpp/title~db=all~content=g918916776
%Z School of Economics and Finance, Queensland University of Technology, Brisbane, Queenland 4001, Australia b General Electric, Auckland, New Zealand

%A Daniel Howard
%T Why Genetic Programming for solution of partial differential equations?
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Daniel Howard
%A Simon C. Roberts
%A Richard Brankin
%T Target Detection in SAR Imagery by Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Daniel Howard
%A Simon C. Roberts
%A Richard Brankin
%T Evolution of Ship Detectors for Satellite SAR Imagery
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 135--148
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=135
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP
%@ 3-540-65899-8

%A Daniel Howard
%A Simon C. Roberts
%T Evolving object detectors for infrared imagery: a comparison of texture analysis against simple statistics
%B Evolutionary Algorithms in Engineering and Computer Science
%E Kaisa Miettinen and Marko M. Makela and Pekka Neittaanmaki and Jacques Periaux
%D 1999
%P 79--86
%I John Wiley \& Sons Chichester, UK
%C Jyvaskyla, Finland
%K genetic algorithms, genetic programming
%U http://www.mit.jyu.fi/eurogen99/papers/howard.ps
%8 30  May  - 3  June
%Z EUROGEN'99 Multi-stage GP terminals based on fourier transforms found to be (marginally?) better than those based on simple stats (mean, standard deviation). Looking for
   parked cars from 300 feet.

%A Daniel Howard
%A Simon C. Roberts
%T A Staged Genetic Programming Strategy for Image Analysis
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1047--1052
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-461.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Daniel Howard
%A Simon C. Roberts
%A Richard Brankin
%T Target detection in SAR imagery by genetic programming
%J Advances in Engineering Software
%V 30
%N 5
%D 1999
%P 303--311
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V1P-3W1XV4H-1/1/6e7aee809f33757d0326c62a21824411
%X The automatic detection of ships in low-resolution synthetic aperture radar (SAR) imagery is investigated in this article. The detector design objectives are to maximise
   detection accuracy across multiple images, to minimise the computational effort during image processing, and to minimise the effort during the design stage. The results of
   an extensive numerical study show that a novel approach, using genetic programming (GP), successfully evolves detectors which satisfy the earlier objectives. Each detector
   represents an algebraic formula and thus the principles of detection can be discovered and reused. This is a major advantage over artificial intelligence techniques which
   use more complicated representations, e.g. neural networks.
%8 May

%A Daniel Howard
%A Simon C. Roberts
%T Evolution of Mesh Refinement Rules for Impact Dynamics
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 1297--1303
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, novel applications, impact (mechanical), evolutionary computation, learning (artificial intelligence), mechanical engineering
   computing, partial differential equations, mesh refinement rule evolution, impact dynamics, rule learning, adaptive mesh refinement, mesh cells, material densities, high
   speed impact, spherical ball, metal plate
%X Genetic programming (GP) was used in an experiment to investigate the possibility of learning rules that trigger adaptive mesh refinement. GP detected mesh cells that
   required refinement by evolving a formula involving cell quantities such as material densities. Various cell variable combinations were investigated in order to identify
   the optimal ones for indicating mesh refinement. The problem studied was the high speed impact of a spherical ball on a metal plate.
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Daniel Howard
%A Simon C. Roberts
%T Genetic Programming solution of the convection-diffusion equation
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 34--41
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, convection-diffusion, differential equations, WRM, FEM, numerical method
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO linear one dimensional second order partial differential equation, comparison of GP with known analytic solution. Evolve single polynomial
   approximation. Fitness based on analytical integration of differentials of polynomial. Polynomial is phenotype created by GP ADD, BACK WRITE functions on variable length
   vector of polynomial co-efficients. read and write memory (two cells). Peclet numbers. p37 GP with ADFs "did not significantly improve performance". Weighted residues
   method, WRM. p39 "This method cannot be recommended"
%@ 1-55860-774-9

%A Daniel Howard
%A Simon C. Roberts
%T The Prediction of Journey Times on Motorways using Genetic Programming
%B Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN
%S LNCS
%E Stefano Cagnoni and Jens Gottlieb and Emma Hart and Martin Middendorf and G"unther Raidl
%V 2279
%D 2002
%P 210--221
%I Springer-Verlag Berlin
%I EvoNet
%C Kinsale, Ireland
%K genetic algorithms, genetic programming, evolutionary computation, applications, MIDAS, London orbital motorway M25
%U http://link.springer-ny.com/link/service/series/0558/papers/2279/22790210.pdf
%X Considered is the problem of reliably predicting motorway journey times for the purpose of providing accurate information to drivers. This proof of concept experiment
   investigates: (a) the practicalities of using a Genetic Programming (GP) method to model/forecast motorway journey times; and (b) different ways of obtaining a journey time
   predictor. Predictions are compared with known times and are also judged against a collection of naive prediction formulae. A journey time formula discovered by GP is
   analysed to determine its structure, demonstrating that GP can indeed discover compact formulae for different traffic situations and associated insights. GP's felxibility
   allows it to self-determine the required level of modelling complexity.
%8 3-4  April
%Z EvoWorkshops2002, part of cagnoni:2002:ews Counter clockwise (ie south bound) between junction 15 (M4) and Junction 11 (Chertsey) Sepetember 1999. (Covered by variable
   speed limits)
%@ 3-540-43432-1

%A Daniel Howard
%A Simon C. Roberts
%A Conor Ryan
%T The Boru Data Crawler for Object Detection Tasks in Machine Vision
%B Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN
%S LNCS
%E Stefano Cagnoni and Jens Gottlieb and Emma Hart and Martin Middendorf and G"unther Raidl
%V 2279
%D 2002
%P 222--232
%I Springer-Verlag Berlin
%I EvoNet
%C Kinsale, Ireland
%K genetic algorithms, genetic programming, evolutionary computation, applications
%X A 'data crawler' is allowed to meander around an image deciding what it considers to be interesting and laying down flags in areas where its interest has been aroused.
   These flags can be analysed statistically as if the image was being viewed from afar to achieve object recognition. The guidance program for the crawler, the program which
   excites it to deposit a flag and how the flags are combined statistically, are driven by an evolutionary process which has as objective the minimisation of misses and false
   alarms. The crawler is represented by a tree-based Genetic Programming (GP) method with fixed architecture Automatically Defined Functions (ADFs). The crawler was used as a
   post-processor to the object detection obtained by a Staged GP method, and it managed to appreciably reduce the number of false alarms on a real-world application of
   vehicle detection in infrared imagery.
%8 3-4  April
%Z EvoWorkshops2002, part of cagnoni:2002:ews READMEM WRITEMEM working memory. Mark decisions branch. Flags. Second results branch. Looking for cars
%@ 3-540-43432-1

%A Daniel Howard
%A Simon C. Roberts
%T Application Of Genetic Programming To Motorway Traffic Modelling
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 1097--1104
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, real world applications, forecasting, incident detection, motorway traffic modelling, time series prediction
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Daniel Howard
%A Simon C. Roberts
%A Conor Ryan
%T Machine Vision: Exploring Context With Genetic Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 756--763
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, automatically defined functions, data crawler, image analysis, machine vision, target detection
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Daniel Howard
%A Karl Benson
%T Promoter Prediction with a GP-Automaton
%B Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM
%S LNCS
%E G\"unther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and
   Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf
%V 2611
%D 2003
%P 44--53
%I Springer-Verlag Berlin
%I EvoNet
%C University of Essex, England, UK
%K evolutionary computation, applications
%X A GP-automaton evolves motif sequences for its states; it moves the point of motif application at transition time using an integer that is stored and evolved in the
   transition; and it combines motif matches via logical functions that it also stores and evolves in each transition. This scheme learns to predict promoters in human genome.
   The experiments reported use 5-fold cross validation.
%8 14-16  April
%Z EvoWorkshops2003

%A Daniel Howard
%T Innovating with Automatic Programming
%J Journal of Defence Science
%V 8
%N 2
%D 2003
%P 76--82
%I 
%K genetic algorithms, genetic programming
%8 May
%Z pixel fusion tirrs. midas linda m25

%A Daniel Howard
%A Karl Benson
%T Evolutionary Computation Method for Promoter Site Prediction in DNA
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1690--1701
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming
%X develops an evolutionary method that learns inductively to recognize the makeup and the position of very short consensus sequences, which are a typical feature of promoters
   in eukaryotic genomes. This class of method can be used to discover candidate promoter sequences in primary sequence data. If further developed, it has the potential to
   discover genes which are regulated together.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Daniel Howard
%T Modularization by Multi-Run Frequency Driven Subtree Encapsulation
%B Genetic Programming Theory and Practice
%E Rick L. Riolo and Bill Worzel
%D 2003
%P 155--172
%I Kluwer
%K genetic algorithms, genetic programming, Modularization, Subtree Encapsulation, Multi-run, ADF, Subtree Database, Subtree Frequency, Parity Problem
%O 10
%Z Part of \citeRioloWorzel:2003
%@ 1-4020-7581-2

%A Daniel Howard
%A Karl Benson
%T Evolutionary computation method for pattern recognition of cis-acting sites
%J Biosystems
%V 72
%N 1-2
%D 2003
%P 19--27
%I 
%K genetic algorithms, genetic programming, Finite State Automata, DNA, human genome, promoter, evolutionary computation, bioinformatics
%U http://www.ncbi.nlm.nih.gov/PubMed/
%X This paper develops an evolutionary method that learns inductively to recognize the makeup and the position of very short consensus sequences, cis-acting sites, which are a
   typical feature of promoters in genomes. The method combines a Finite State Automata (FSA) and Genetic Programming (GP) to discover candidate promoter sequences in primary
   sequence data. An experiment measures the success of the method for promoter prediction in the human genome. This class of method can take large base pair jumps and this
   may enable it to process very long genomic sequences to discover gene specific cis-acting sites, and genes which are regulated together.
%O Special Issue on Computational Intelligence in Bioinformatics
%8 November

%A Daniel Howard
%T Top Down Modelling with Genetic Programming
%B Proceedings of the 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems Conference, KES 2004, Part III
%S Lecture Notes in Artificial Intelligence
%E Mircea Gh. Negoita and Robert J. Howlett and Lakhmi C. Jain
%V 3215
%D 2004
%P 217--223
%I Springer
%K genetic algorithms, genetic programming, top down modelling
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3215&spage=217
%X explores the connection between top down modelling and the artificial intelligence (AI) technique of Genetic Programming (GP). It provides examples to illustrate how the
   author and colleagues took advantage of this connection to solve real world problems. Following this account, the paper speculates about how GP may be developed further to
   meet more challenging real world problems. It calls for novel applications of GP to quantify a top down design in order to make rapid progress with the understanding of
   organisations.
%8 September  20-25
%@ 3-540-23205-2

%A Daniel Howard
%A Simon C. Roberts
%T Incident Detection on Highways
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 263--282
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, automatic incident detection, freeway, motorway, highways, traffic management, control office, low flow, high speed, occupancy,
   reversing vehicles, roadworks, HIOCC, California Algorithm, MIDAS, LINDA
%X This chapter discusses the development of the Low-occupancy INcident Detection Algorithm (LINDA) that detects night-time motorway incidents. LINDA is undergoing testing on
   live data and deployment on the M5, M6 and other motorways in the United Kingdom. It was developed by the authors using Genetic Programming.
%O 16
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A Daniel Howard
%A Joseph Kolibal
%T Solution of differential equations with Genetic Programming and the Stochastic Bernstein Interpolation
%N BDS-TR-2005-001
%D 2005
%I 
%I Biocomputing-Developmental Systems Group, University of Limerick
%C Ireland
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/hc2005/bds.pdf
%X This report introduces a method for the solution of the Convection-Diffusion equations (CDE) that combines Genetic Programming with Stochastic Bernstein Interpolation.
   Significantly, it is being used to solve a problem that has resisted analysis for a long time using other methods. Although the method in this report solves the
   one-dimensional CDE which has also been solved analytically and optimally, our strategy of combining the Stochastic Bernstein Interpolation method with GP allows for the
   method to extend to higher dimensions, and thus it shows how to construct GP based methods for solving a range of computational problems in multiple dimensions which have
   hitherto resisted numerical solution.
%8 June  19
%Z Honorable Mention 2005 HUMIES GECCO-2005

%A Daniel Howard
%A Simon C. Roberts
%A Conor Ryan
%T Pragmatic Genetic Programming strategy for the problem of vehicle detection in airborne reconnaissance
%J Pattern Recognition Letters
%V 27
%N 11
%D 2006
%P 1275--1288
%I 
%K genetic algorithms, genetic programming, Object detection, Method of stages, Reconnaissance, Discrete Fourier transform, Vehicle detection, Machine vision
%X A Genetic Programming (GP) method uses multiple runs, data decomposition stages, to evolve a hierarchical set of vehicle detectors for the automated inspection of infrared
   line scan imagery that has been obtained by a low flying aircraft. The performance on the scheme using two different sets of GP terminals (all are rotationally invariant
   statistics of pixel data) is compared on 10 images. The discrete Fourier transform set is found to be marginally superior to the simpler statistics set that includes an
   edge detector. An analysis of detector formulae provides insight on vehicle detection principles. In addition, a promising family of algorithms that take advantage of the
   GP method's ability to prescribe an advantageous solution architecture is developed as a post-processor. These algorithms selectively reduce false alarms by exploring
   context, and determine the amount of contextual information that is required for this task.
%O Evolutionary Computer Vision and Image Understanding
%8 August

%A Daniel Howard
%T Multiple Solutions by Means of Genetic Programming: A Collision Avoidance Example
%B Proceedings of the Second International Conference on Rough Sets and Knowledge Technology, RSKT 2007
%S Lecture Notes in Computer Science
%E Jingtao Yao and Pawan Lingras and Wei-Zhi Wu and Marcin S. Szczuka and Nick Cercone and Dominik Slezak
%V 4481
%D 2007
%P 508--517
%I Springer
%C Toronto, Canada
%K genetic algorithms, genetic programming, Multiple Solutions
%X Seldom is it practical to completely automate the discovery of the Pareto Frontier by genetic programming (GP). It is not only difficult to identify all of the optimization
   parameters a-priori but it is hard to construct functions that properly evaluate parameters. For instance, the ease of manufacture of a particular antenna can be determined
   but coming up with a function to judge this on all manner of GP-discovered antenna designs is impractical. This suggests using GP to discover many diverse solutions at a
   particular point in the space of requirements that are quantifiable, only a-posteriori (after the run) to manually test how each solution fares over the less tangible
   requirements e.g. ease of manufacture. Multiple solutions can also suggest requirements that are missing. A new toy problem involving collision avoidance is introduced to
   research how GP may discover a diverse set of multiple solutions to a single problem. It illustrates how emergent concepts (linguistic labels) rather than distance measures
   can cluster the GP generated multiple solutions for their meaningful separation and evaluation.
%8 May  14-16
%Z railway track, two train speeds, GP sets the points

%A Daniel Howard
%A Simon C. Roberts
%A Conor Ryan
%A Adrian Brezulianu
%T Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network
%J Journal of Biomedicine and Biotechnology
%V 2008
%D 2008
%P 526343
%I 
%K genetic algorithms, genetic programming
%X In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be
   visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes
   in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the
   mammograms as input data to a SONNET self organizing neural network. The paper discusses how SONNET was used to produce a taxonomic organization of the mammography archive
   in an unsupervised manner. This process is subject to certain choices of SONNET parameters, in these numerical experiments using the craniocaudal view, and typically
   produced O(10), for example, 39 mammogram classes, by analysis of features from O(103) mammogram images. The mammogram taxonomy captured typical subtleties to discriminate
   mammograms, and it is submitted that this may be exploited to aid the detection of mammographic anomalies, for example, by acting as a preprocessing stage to simplify the
   task for a computational detection scheme, or by ordering mammography examinations by mammogram taxonomic class prior to screening in order to encourage more successful
   visual examination during screening. The resulting taxonomy may help train screening radiologists and conceivably help to settle legal cases concerning a mammography
   screening examination because the taxonomy can reveal the frequency of mammographic patterns in a population.
%8 July  22
%Z PMID: "As the [previously evolved] data crawler has been developed for target detection in imagery, it is highly transferable to the problem of lesion detection in
   mammograms. The crawler could scrutinize mammogram areas which possess the greatest asymmetry and thus focus on candidate lesions. The evolutionary approach allows the
   crawler to discover its own multiscale features which best locate lesions."

%A Daniel Howard
%T A Method of Project Evaluation and Review Technique (PERT) Optimization by Means of Genetic Programming
%B 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security, BLISS '09
%D 2009
%P 132--135
%I 
%K genetic algorithms, genetic programming, PERT optimization, project control, project evaluation and review technique, scheduling problems, PERT, project management,
   scheduling
%X Genetic Programming is applied to solve scheduling problems. The resulting tool simulates the PERT method of project control, and Genetic Programming provides multiple
   acceptable solutions. This tool has a wide application in the management of large and complex projects. It is a bio-inspired means to obtain solution in many disparate
   areas of activity such as for computer gaming, and when a complex system needs to be understood and executed properly as in many types of security operation.
%8 August
%Z Also known as \cite5376803

%A Daniel Howard
%A Adrian Brezulianu
%A Joseph Kolibal
%T Genetic programming of the stochastic interpolation framework: convection-diffusion equation
%J Soft Computing
%V 15
%N 1
%D 2011
%P 71--78
%I 
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.1007/s00500-009-0520-3
%X The stochastic interpolation (SI) framework of function recovery from input data comprises a de-convolution step followed by a convolution step with row stochastic matrices
   generated by a mollifier, such as a probability density function. The choice of a mollifier and of how it gets weighted, offers unprecedented flexibility to vary both the
   interpolation character and the extent of influence of neighbouring data values. In this respect, a soft computing method such as a genetic algorithm or heuristic method
   may assist applications that model complex or unknown relationships between data by tuning the parameters, functional and component choices inherent in SI. Alternatively or
   additionally, the input data itself can be reverse engineered to recover a function that satisfies properties, as illustrated in this paper with a genetic programming
   scheme that enables SI to recover the analytical solution to a two-point boundary value convection-diffusion differential equation. If further developed, this nascent
   solution method could serve as an alternative to the weighted residual methods, as these are known to have inherent mathematical difficulties.

%A Daniel Howard
%A Adrian Brezulianu
%T Capturing expert knowledge of mesh refinement in numerical methods of impact analysis by means of genetic programming
%J Soft Computing
%V 15
%D 2011
%P 103--110
%I Springer Berlin / Heidelberg
%K genetic algorithms, genetic programming
%X The mesh refinement decisions of an experienced user of high-velocity impact numerical approximation finite differences computations are discovered as a set of
   comprehensible rules by means of Genetic Programming. These rules that could automatically trigger adaptive mesh refinement to mimic the expert user, detect mesh cells that
   require refinement by evolving a formula involving cell quantities such as material densities. Various cell variable combinations are investigated in order to identify the
   optimal ones for indicating mesh refinement. A high-velocity impact phenomena example of a tungsten ball that strikes a steel plate illustrates this methodology.

%A Daniel Howard
%A Conor Ryan
%A J. J. Collins
%T Attribute Grammar Genetic Programming Algorithm for Automatic Code Parallelization
%B Proceedings of the 5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011
%S Lecture Notes in Computer Science
%E Geuk Lee and Daniel Howard and Dominik Slezak
%V 6935
%D 2011
%P 250--257
%I Springer
%C Daejeon, Korea
%K genetic algorithms, genetic programming, Grammatical Evolution, Context Free Grammar, Attribute Grammar, Parallel Computing, Automatic Parallelisation, Evolutionary
   Computation, SBSE
%X A method is presented for evolving individuals that use an Attribute Grammar (AG) in a generative way. AGs are considerably more flexible and powerful than the closed ,
   context free grammars normally employed by GP. Rather than evolving derivation trees as in most approaches, we employ a two step process that first generates a vector of
   real numbers using standard GP, before using the vector to produce a parse tree. As the parse tree is being produced, the choices in the grammar depend on the attributes
   being input to the current node of the parse tree. The motivation is automatic parallelisation or the discovery of a re-factoring of a sequential code or equivalent
   parallel code that satisfies certain performance gains when implemented on a target parallel computing platform such as a multicore processor. An illustrative and a
   computed example demonstrate this methodology.
%8 September  22-24

%A Gerard David Howard
%A Larry Bull
%A Andrew Adamatzky
%T Cartesian Genetic Programming for Memristive Logic Circuits
%B Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012
%S LNCS
%E Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta
%V 7244
%D 2012
%P 37--48
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, cartesian genetic programming, Self-adaptation, Nanotechnology, Boolean logic, Memristors, Robotics
%X In this paper memristive logic circuits are evolved using Cartesian Genetic Programming. Graphs comprised of implication logic (IMP) nodes are compared to more ubiquitous
   NAND circuitry on a number of logic circuit problems and a robotic control task. Self-adaptive search parameters are used to provide each graph with autonomy with respect
   to its relative mutation rates. Results demonstrate that, although NAND-logic graphs are easier to evolve, IMP graphs carry benefits in terms of (i) numbers of memristors
   required (ii) the time required to process the graphs.
%8 11-13  April
%Z Part of \citeMoraglio:2012:GP EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012

%A Les M. Howard
%A Donna J. D'Angelo
%T The GA--P: A Genetic Algorithm and Genetic Programming hybrid
%J IEEE Expert
%V 10
%N 3
%D 1995
%P 11--15
%I 
%K genetic algorithms, genetic programming
%X Moreover, the examination is easily influenced by the researcher's desires and expectations. Statistical methods were among the first tools developed to help a researcher
   find the relationships of observed facts. Statistical methods are often based on such assumptions as these: (1) the data are normally distributed, (2) the equation relating
   the data is of a specific form (for example, linear, quadratic, or polynomial), and (3) the variables are independent. If the problem meets these assumptions, statistics
   are a valuable tool for providing static descriptors. But real-world problems seldom meet these criteria. Neural networks, an artificial intelligence technique, are not
   limited by these assumptions. They serve as strong predictive models that can uncover complex relationships, but they give little insight into the underlying mechanisms
   that describe a relationship. However, two other nonstatistical AI techniques, genetic algorithms and genetic programming, are more robust methods of exploring complex
   solution spaces. Independently, they have had some success at revealing the mechanisms relating data items. Recently, genetic algorithms, which use the principles of
   evolution through natural selection to solve problems, have established themselves as a powerful search and optimization technique. Most GAs are linear (the structure of an
   individual is a flat bit string). The basic GA proceeds as follows: 1. Create a population of random individuals, in which each individual represents a possible solution to
   the problem at hand. 2. Evaluate each individual's fitness--its ability to solve the specified problem. 3. Select individual population members to be parents. 4. Produce
   children by recombining parent material via crossover and mutation, and add them to the population. 5. Evaluate the children's fitness. 6. Repeat steps 3-5 until a solution
   with the desired fitness goal is obtained. GAs have been used for everything from multiple-fault diagnosis to medical-image registration. They have shown themselves to be a
   superior tool for developing rule-based systems, capable of gleaning knowledge from data inaccessible to statistical methods. Goldberg thoroughly discusses genetic
   algorithms and their use as a problem-solving and function optimization technique. Goldberg and Forrest give additional examples. Although linear GAs are adept at
   developing rule-based systems, they cannot develop equations. A recent addition to the evolutionary domain is genetic programming, which uses an evolutionary approach to
   generate symbolic expressions and perform symbolic regressions. However, the genetic-programming method of performing symbolic regressions has some limitations. It can
   modify only the structure of an expression, not its contents, which is generated by the implementation program when the genetic programming starts. In performing symbolic
   regressions, genetic programming cannot deal with nonnumeric variables. It also tends to produce convoluted equations because it cannot modify the coefficients it uses (for
   example, a genetic program might use (2.523+2.523)/2.523 to represent the number 2). We have developed a method combining the known strengths of traditional genetic
   algorithms with the new field of genetic programming to produce a superior tool for performing symbolic regressions. We call this tool the genetic algorithm-program, or the
   GA-P.
%8 June
%Z University of Georgia. IEEE Expert Special Track on Evolutionary Programming (P. J. Angeline editor) \citeangeline:1995:er

%A Abraham L. Howell
%A Roy T. R. McGrann
%A Richard R. Eckert
%A Hiroki Sayama
%A Eileen Way
%T Using RFID and a Low Cost Robot to Evolve Foraging Behavior
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006)
%E J\"orn Grahl
%D 2006
%I 
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp131.pdf
%X The process of developing genetic algorithms, genetic programs or training neural networks is a time consuming task. When the target device is an autonomous mobile robot,
   this development is often performed using software simulation. Software simulations are a cost effective tool and provide researchers with the ability to test out multiple
   algorithms quickly and efficiently. However, the end result is that the optimised algorithm(s) must be implemented and tested on an actual robot to evaluate performance in
   the real world. Significant cost can be associated with this final step. In this paper we propose to leverage Radio Frequency Identification (RFID) and a low-cost RFID
   capable mobile robot with the intent of creating basic foraging behaviour. Additionally, we will present experimental results that demonstrate the effectiveness of using
   Genetic Programming (GP) and a low-cost RFID capable robot to create foraging behaviour by presenting our experimental results.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2006

%A Abraham L. Howell
%A Roy T. R. McGrann
%A Richard R. Eckert
%T Teaching concepts in fuzzy logic using low cost robots, PDAs, and custom software
%B 38th Annual Frontiers in Education Conference, FIE 2008
%D 2008
%P T3H-7--T3H-11
%I 
%K GUI, PDA, artificial intelligence, bioengineering course, classical control theory, custom software, desktop computers, fuzzy logic libraries, low cost robots, machine
   learning, neural networks, personal digital assistant, robotics courses, software modules, control engineering education, educational courses, fuzzy logic, graphical user
   interfaces, learning (artificial intelligence), notebook computers, robots
%8 October
%Z not on GP

%A Andrew Howlett
%A Simon Colton
%A Cameron Browne
%T Evolving Pixel Shaders for the Prototype Video Game Subversion
%B The Thirty Sixth Annual Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB'10)
%D 2010
%I 
%C De Montfort University, Leicester, UK
%K genetic algorithms, genetic programming, GPU, OpenGL GLSL
%U http://www.doc.ic.ac.uk/~sgc/papers/howlett_aisb10.pdf
%X Pixel shaders can be used to create a variety of visual effects in 3D environments, far more efficiently than if produced using the standard graphics pipeline. For such
   efficiency reasons, pixel shaders are commonly used in video game rendering, to add artistic or other visual effects. We investigate the automated creation of novel shader
   programs for rendering scenes in the Subversion virtual game world, with a view to providing the player with a visually richer and more diverse 3D environment. We show how
   shader programs based on the OpenGL shading language may be represented in a hierarchical tree form. This representation admits an evolutionary approach to shader creation,
   and we show how the application of genetic programming techniques can lead to the evolution of new and interesting shaders. We harness this for an approach where the user
   supplies details of a fitness function for the overall look of the city environment. We experimented with a number of different fitness function setups in order to produce
   some preliminary results about this approach. While generally successful in the creation of novel and visually interesting shading effects with little effort, we find some
   drawbacks to the approach and suggest methods for improvement.
%O AI \& Games Symposium
%8 30th  March
%Z Colour as RGBA. http://www.aisb.org.uk/convention/aisb10/AISB2010.html http://staffwww.dcs.shef.ac.uk/people/D.Romano/AISB10/program.html www.introversion.co.uk/subversion

%A Brian Howley
%T Genetic Programming of Near-Minimum-Time Spacecraft Attitude Maneuvers
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 98--106
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 see also \citehowley:1996:samAIAA

%A Brian Howley
%T Genetic Programming of Spacecraft Attitude Maneuvers Under Reaction Wheel Control
%B AIAA Guidance Navigation and Control Conference
%D 1996
%I 1801 Alexander Bell Crive, Suite 500, Reston, VA 22091, USA
%I American Institute of Aeronautics and Astronautics
%C San Diego, CA, USA
%K genetic algorithms, genetic programming
%U http://www.aiaa.org/content.cfm?pageid=406&gTable=mtgpaper&gID=10524
%X A general solution for maneuvers with non-zero initial and final rates was not found, however, the GP solution out performs a hand crafted solution to the problem
%8 29--31  July
%Z AIAA 1996-3849 see also \citehowley:1996:GPsam

%A Brian Howley
%T Genetic Programming and Parametric Sensitivity: a Case Study In Dynamic Control of a Two Link Manipulator
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 180--185
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.9214
%X Minimum time control of a two link manipulator is used to investigate the sensitivity of genetic programming solutions to parametric design changes. Two methods of reducing
   sensitivity are considered. An aggregate fitness method in which results from multiple fitness cases are combined into a single fitness measure, and a bimodal selection
   method in which male and female parents are selected on the basis of fitness' derived from different parameter values. Results are preliminary. The genetically derived
   solutions perform poorly compared to numerical solutions. The poor performance may be due to an insufficiently large population. Population size was limited by simulation
   run time concerns.
%8 13-16  July
%Z GP-97

%A Brian Howley
%T Genetic Programming of Near Minimum Time Spacecraft Attitude Maneuvers
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 96--106
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Tom Howley
%A Michael G. Madden
%T The Genetic Kernel Support Vector Machine: Description and Evaluation
%J Artificial Intelligence Review
%V 24
%N 3-4
%D 2005
%P 379--395
%I 
%K genetic algorithms, genetic programming, classification, genetic Kernel SVM, Mercer Kernel, model selection, support vector machine
%X The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose
   a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper
   proposes a classification technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier. Results of
   initial experiments with the proposed technique are presented. These results are compared with those of a standard SVM classifier using the Polynomial, RBF and Sigmoid
   kernel with various parameter settings

%A Yarema Hrytsyshyn
%A Rostyslav Kryvyy
%A Sergiy Tkatchenko
%T Genetic Programming For Solving Cutting Problem
%B 9th International Conference on the Experience of Designing and Applications or CAD Systems in Microelectronics, CADSM '07
%D 2007
%P 280--282
%I IEEE
%C Polyana, Ukraine
%K genetic algorithms, genetic programming, CAD system, arbitrary shape platforms, automated arbitrary shape object arrangement, material cutting task, optimal cutting
   problem, CAD/CAM, cutting
%X This paper described the functioning of genetic algorithm for the automated arranging the arbitrary shape objects on the arbitrary shape platforms. The set of criteria for
   determination the sequence of selecting templates and platforms for arranging and also a set of criteria for selecting the optimum arranging of single template are
   suggested. The genetic algorithm for the selecting criteria manipulation and choice of necessary decisions is developed.
%8 20-24  February
%Z Yarema Hrytsyshyn a CAD/CAM Department, Lviv Polytechnic National University, 12, S. Bandery Str., Lviv, 79013, UKRAINE, E-mail: hrytsyshyn@gmail.com Rostyslav Kryvyy a
   CAD/CAM Department, Lviv Polytechnic National University, 12, S. Bandery Str., Lviv, 79013, UKRAINE, E-mail: rostyslav.kryvyy@gmail.com Sergiy Tkatchenko a CAD/CAM
   Department, Lviv Polytechnic National University, 12, S. Bandery Str., Lviv, 79013, UKRAINE8) Also known as \cite4297550

%A Chih-Ming Hsu
%T A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming
%J Expert Systems with Applications
%V In Press, Uncorrected Proof
%D 2011
%I 
%K genetic algorithms, genetic programming, Stock price prediction, Self-organising map
%U http://www.sciencedirect.com/science/article/B6V03-52T13T7-7/2/c2626c201c0da6cbc20628185936eaf3
%X Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerable attention received from both researchers and
   practitioners. Stock price series have properties of high volatility, complexity, dynamics and turbulence, thus the implicit relationship between the stock price and
   predictors is quite dynamic. Hence, it is difficult to tackle the stock price prediction problems effectively by using only single soft computing technique. This study
   hybridises a self-organizing map (SOM) neural network and genetic programming (GP) to develop an integrated procedure, namely, the SOM-GP procedure, in order to resolve
   problems inherent in stock price predictions. The SOM neural network is used to divide the sample data into several clusters, in such a manner that the objects within each
   cluster possess similar properties to each other, but differ from the objects in other clusters. The GP technique is applied to construct a mathematical prediction model
   that describes the functional relationship between technical indicators and the closing price of each cluster formed in the SOM neural network. The feasibility and
   effectiveness of the proposed hybrid SOM-GP prediction procedure are demonstrated through experiments aimed at predicting the finance and insurance sub-index of TAIEX
   (Taiwan stock exchange capitalisation weighted stock index). Experimental results show that the proposed SOM-GP prediction procedure can be considered a feasible and
   effective tool for stock price predictions, as based on the overall prediction performance indices. Furthermore, it is found that the frequent and alternating rise and
   fall, as well as the range of daily closing prices during the period, significantly increase the difficulties of predicting.

%A Hsun-Hsin Hsu
%A Li Chen
%A Chang-Huan Kou
%A Tai-Sheng Wang
%A Sing-Han Chen
%T Estimating Strength of Concrete Using a Grammatical Evolution
%B Fifth International Conference on Natural Computation, 2009. ICNC '09
%E Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun
%D 2009
%P 134--138
%I IEEE Computer Society
%C Tianjian, China
%K genetic algorithms, genetic programming, Grammatical Evolution
%X The main purpose of this paper is to propose an incorporating a grammatical evolution (GE) into the genetic algorithm (GA), called GEGA, and apply it to estimate the
   compressive strength of high-performance concrete (HPC). The GE, an evolutionary programming type system, automatically discovers complex relationships between significant
   factors and the strength of HPC in a more transparent way to enhance our understanding of the mechanisms. A GA was used afterward with GE to optimize the appropriate
   function type and associated coefficients using over 1,000 examples for which experimental data were available. The results show that this novel model, GEGA, can obtain a
   highly nonlinear mathematical equation which outperforms than the traditional multiple regression analysis (RA) with lower estimating errors for predicting the compressive
   strength of HPC.
%8 14-16  August

%A William H. Hsu
%A William M. Pottenger
%A Michael Welge
%A Jie Wu
%A Ting-Hao Yang
%T Genetic Algorithms for Attribute Synthesis in Large-Scale Data Mining
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1783
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-754.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) See also
   freitas:1999:AAGR Freitas "Data Mining with Evolutionary Algorithms" AAAI tech report WS-99-06
%@ 1-55860-611-4

%A William H. Hsu
%A Steven M. Gustafson
%T Wrappers for Automatic Parameter Tuning in Multi-Agent Optimization by Genetic Programming
%B IJCAI-2001 Workshop on Wrappers for Performance Enhancement in Knowledge Discovery in Databases (KDD)
%D 2001
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, robotic soccer
%X We present an adaptation of the standard genetic program (GP) to hierarchically decomposable, multi-agent learning problems. To break down a problem that requires
   cooperation of multiple agents, we use the team objective function to derive a simpler, intermediate objective function for pairs of cooperating agents. We apply GP to
   optimize first for the intermediate, then for the team objective function, using the final population from the earlier GP as the initial seed population for the next. This
   layered learning approach facilitates the discovery of primitive behaviors that can be reused and adapted towards complex objectives based on a shared team goal. We use
   this method to evolve agents to play a subproblem of robotic soccer (keep-away soccer). Finally, we show how layered learning GP evolves better agents than standard GP,
   including GP with automatically defined functions, and how the problem decomposition results in a significant learning-speed increase.
%8 4  August
%Z http://www.kddresearch.org/KDD/Workshops/IJCAI-2001/ Paper from author 19 Jul 2001. Also available as GECCO'2001 late breaking paper Coaching, seeding, LLGP, keep-away
   soccer (minimize number of turnovers), MAS, RoboCup, passing agents and keep-away soccer agents, ADF. Simple GP and ADFGP trained with one shot fitness function, ie not
   layered. Popsize 2000 "yeilded good results". Luke's ECJ. SoccerServer, TeamBots. See also \citegustafson:mastersthesis

%A William H. Hsu
%A Steven M. Gustafson
%T Genetic Programming for Layered Learning of Multi-Agent Tasks
%B 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Erik D. Goodman
%D 2001
%P 176--182
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, soccer, RoboCup
%U http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2001.pdf
%8 9-11  July
%Z GECCO-2001LB. Luke's ECJ, teambots. See also \citegustafson:mastersthesis

%A William H. Hsu
%A Steven M. Gustafson
%T Genetic Programming And Multi-agent Layered Learning By Reinforcements
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 764--771
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
   Nominated for best at GECCO award
%@ 1-55860-878-8

%A William H. Hsu
%A Scott J. Harmon
%A Edwin Rodriguez
%A Christopher Zhong
%T Empirical Comparison of Incremental Reuse Strategies in Genetic Programming for Keep-Away Soccer
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP010.pdf
%X Easy missions approaches to machine learning seek to synthesise solutions for complex tasks from those for simpler ones. In genetic programming, this has been achieved by
   identifying goals and fitness functions for subproblems of the overall problem. Solutions evolved for these subproblems are then reused to speed up learning, either as
   automatically defined functions (ADFs) or by seeding a new GP population. Previous positive results using both approaches for learning in multi-agent systems (MAS) showed
   that incremental reuse using easy missions achieves comparable or better overall fitness than monolithic simple GP. A key unresolved issue dealt with hybrid reuse using ADF
   plus easy missions. Results in the keep-away soccer domain (a test bed for MAS learning) were also inconclusive on whether compactness inducing reuse helped or hurt overall
   agent performance. In this paper, we compare monolithic (simple GP and GP with ADFs) and easy missions reuse to two types of GP learning systems with incremental reuse:
   GP/ADF hybrids with easy missions and single-mission incremental ADFs. As hypothesised, pure easy missions reuse achieves results competitive with the best hybrid
   approaches in this domain. We interpret this finding and suggest a theoretical approach to characterising incremental reuse and code growth.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Jianjun Hu
%A Erik D. Goodman
%T The Hierarchical Fair Competition (HFC) Model for Parallel Evolutionary Algorithms
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 49--54
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%U http://garage.cse.msu.edu/papers/GARAGe02-05-01.pdf
%X The HFC model for evolutionary computation is inspired by the stratified competition often seen in society and biology. Subpopulations are stratified by fitness.
   Individuals move from low-fitness subpopulations to higher-fitness subpopulations if and only if they exceed the fitness-based admission threshold of the receiving
   subpopulation, but not of a higher one. HFC's balanced exploration and exploitation, while avoiding premature convergence, is shown on a genetic programming example.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Jianjun Hu
%A Kisung Seo
%A Shaobo Li
%A Zhun Fan
%A Ronald C. Rosenberg
%A Erik D. Goodman
%T Structure Fitness Sharing (SFS) For Evolutionary Design By Genetic Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 780--787
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, evolutionary design, fitness sharing, mechatronic system, premature convergence, topology and parameter search
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Jianjun Hu
%A Erik D. Goodman
%A Kisung Seo
%A Min Pei
%T Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 772--779
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, adaptive evolutionary algorithm, fair competition principle, hierarchical topology, parallel evolutionary algorithms, premature
   convergence
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Jianjun Hu
%A Erik D. Goodman
%A Kisung Seo
%A Zhun Fan
%A Ronald C. Rosenberg
%T HFC: A Continuing EA Framework for Scalable Evolutionary Synthesis
%B Proceedings of the 2003 AAAI Spring Symposium - Computational Synthesis: From Basic Building Blocks to High Level Functionality
%D 2003
%P 106--113
%I AAAI press 445 Burgess Drive. Menlo park, CA, 94025, USA
%I AAAI
%C Stanford, California
%K genetic algorithms, genetic programming, scalability, sustainability, HFC
%U http://www-rcf.usc.edu/~jianjunh/paper/stanford_hfc.pdf
%X The scalability of evolutionary synthesis is impeded by its characteristic discrete landscape with high multimodality. It is also impaired by the convergent nature of
   conventional EAs. A generic framework, called Hierarchical Fair Competition (HFC), is proposed for formulation of continuing evolutionary algorithms. This framework
   features a hierarchical organisation of individuals by different fitness levels. By maintaining repositories of intermediate-fitness individuals and ensuring a continuous
   supply of raw genetic material into an environment in which it can be exploited, HFC is able to transform the convergent nature of current EAs into a sustainable
   evolutionary search framework. It is also well suited for the special demands of scalable evolutionary synthesis. An analog circuit synthesis problem, the eigenvalue
   placement problem, is used as an illustrative case study.
%8 24  March

%A Jianjun Hu
%A Erik D. Goodman
%A Kisung Seo
%T Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming
%B Genetic Programming Theory and Practice
%E Rick L. Riolo and Bill Worzel
%D 2003
%P 81--98
%I Kluwer
%K genetic algorithms, genetic programming, sustainable innovation, HFC, fair competition principle
%X Lack of sustainable search capability of genetic programming has severely constrained its application to more complex problems. A new evolutionary algorithm model named the
   continuous hierarchical fair competition (CHFC) model is proposed to improve the capability of sustainable innovation for single population genetic programming. It is
   devised by extracting the fundamental principles underlying sustainable biological and societal processes originally proposed in the multi-population HFC model. The
   hierarchical elitism, breeding probability distribution and individual distribution control over the whole fitness range enable CHFC to achieve sustainable evolution while
   enjoying flexible control of an evolutionary search process. Experimental results demonstrate its capability to do robust sustainable search and avoid the aging problem
   typical in genetic programming.
%O 6
%Z Part of \citeRioloWorzel:2003

%A Jianjun Hu
%A Erik Goodman
%T Topological Synthesis of Robust Dynamic Systems by Sustainable Genetic Programming
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 143--157
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, sustainable genetic programming, automated synthesis, dynamic systems, robust design, bond graphs, analog filter
%X Traditional robust design constitutes only one step in the detailed design stage, where parameters of a design solution are tuned to improve the robustness of the system.
   This chapter proposes that robust design should start from the conceptual design stage and genetic programming-based open-ended topology search can be used for automated
   synthesis of robust systems. Combined with a bond graph-based dynamic system synthesis methodology, an improved sustainable genetic programming technique - quick
   hierarchical fair competition (QHFC)- is used to evolve robust high-pass analog filters. It is shown that topological innovation by genetic programming can be used to
   improve the robustness of evolved design solutions with respect to both parameter perturbations and topology faults.
%O 9
%O pages missing
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A Jianjun Hu
%A Erik Goodman
%T Wireless Access Point Configuration by Genetic Programming
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 1178--1184
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Evolutionary design \& evolvable hardware, Real-world applications, Combinatorial \& numerical optimization, STGP
%U http://www-rcf.usc.edu/~jianjunh/paper/cec2004_wireless.pdf
%X Wireless access point configuration problem in wireless LAN deployment can be formulated as a non-linear optimization problem with variable number of parameters. In this
   paper, a strongly-typed genetic programming is applied to solve an abstract version of this problem successfully. It is argued that this problem can be used as a potential
   benchmark problem for evaluating techniques and investigating issues in strongly typed genetic programming, topologically open-ended synthesis by genetic programming, and
   simultaneous topological and parametric search
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE. \citecordella:evocop05 claims to outperform this
%@ 0-7803-8515-2

%A Jianjun Hu
%A Erik Goodman
%A Ronald Rosenberg
%T Topological search in automated mechatronic system synthesis using bond graphs and genetic programming
%B Proceedings of American Control Conference ACC 2004
%D 2004
%I 
%I American Control Conference
%K genetic algorithms, genetic programming
%8 July

%A Jianjun Hu
%T Sustainable Evolutionary Algorithms and Scalable Evolutionary Synthesis of Dynamic Systems
%R Ph.D. Thesis
%D 2004
%I 
%I Michigan State University
%C East Lancing, Michigan, 48823, USA
%K genetic algorithms, genetic programming, HFC
%U http://www-rcf.usc.edu/~jianjunh/paper/Hu_thesis_print.pdf
%X This dissertation concerns the principles and techniques for scalable evolutionary computation to achieve better solutions for larger problems with more computational
   resources. It suggests that many of the limitations of existent evolutionary algorithms, such as premature convergence, stagnation, loss of diversity, lack of reliability
   and efficiency, are derived from the fundamental convergent evolution model, the oversimplified "survival of the fittest" Darwinian evolution model. Within this model, the
   higher the fitness the population achieves, the more the search capability is lost. This is also the case for many other conventional search techniques. The main result of
   this dissertation is the introduction of a novel sustainable evolution model, the Hierarchical Fair Competition (HFC) model, and corresponding five sustainable evolutionary
   algorithms (EA) for evolutionary search. By maintaining individuals in hierarchically organized fitness levels and keeping evolution going at all fitness levels, HFC
   transforms the conventional convergent evolutionary computation model into a sustainable search framework by ensuring a continuous supply and incorporation of low-level
   building blocks and by culturing and maintaining building blocks of intermediate levels with its assembly-line structure. By reducing the selection pressure within each
   fitness level while maintaining the global selection pressure to help ensure exploitation of good building blocks found, HFC provides a good solution to the explore vs.
   exploitation dilemma, which implies its wide applications in other search, optimization, and machine learning problems and algorithms. The second theme of this dissertation
   is an examination of the fundamental principles and related techniques for achieving scalable evolutionary synthesis. It first presents a survey of related research on
   principles for handling complexity in artificially designed and naturally evolved systems, including modularity, reuse, development, and context evolution. Limitations of
   current genetic programming based evolutionary synthesis paradigm are discussed and future research directions are outlined. Within this context, this dissertation
   investigates two critical issues in topologically open-ended evolutionary synthesis, using bond-graph-based dynamic system synthesis as benchmark problems. For the issue of
   balanced topology and parameter search in evolutionary synthesis, an effective technique named Structure Fitness Sharing (SFS) is proposed to maintain topology search
   capability. For the representation issue in evolutionary synthesis, or more specifically the function set design problem of genetic programming, two modular set approaches
   are proposed to investigate the relationship between representation, evolvability, and scalability.
%8 18  August
%Z Related research http://www.egr.msu.edu/~hujianju/HFC http://www.egr.msu.edu/~hujianju/gpbg

%A Jianjun Hu
%A Ronald C. Rosenberg
%A Erik D. Goodman
%T Domain Specificity of Genetic Programming based Automated Synthesis: a Case Study with Synthesis of Mechanical Vibration Absorbers
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 275--290
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Automated synthesis, passive vibration absorber, bond graphs, mechatronic systems, domain knowledge
%X Genetic programming has proved its potential for automated synthesis of a variety of engineering systems such as electrical, control, and mechanical systems. Given any of
   these application domains, a set of generic GP functions can be developed for its synthesis. In this chapter, however, we illustrate that while a generic GP system can
   often be used to prove a concept, realistic or industrial automated synthesis often requires domain-specific GP configuration, especially of the GP function sets. As a case
   study, it is shown how the open-ended topology search capability of GP readily exploits _loopholes_ in a generic bond-graph-based GP function set and evolves
   high-performance but unrealistic mechanical vibration absorbers, even though the bond graphs would be readily implementable in, for example, the electrical domain. The
   preliminary attempt to constrain evolved topologies to only those that would be readily implementable in the mechanical domain was not sufficiently restrictive.
%O 18
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Jianjun Hu
%A Erik Goodman
%A Kisung Seo
%A Zhun Fan
%A Rondal Rosenberg
%T The Hierarchical Fair Competition Framework for Sustainable Evolutionary Algorithms
%J Evolutionary Computation
%V 13
%N 2
%D 2005
%P 241--277
%I MIT Press
%K genetic algorithms, genetic programming, sustainable evolutionary algorithms, building blocks, premature convergence, diversity, fair competition, hierarchical problem
   solving
%U http://www.ingentaconnect.com/content/mitpress/evco/2005/00000013/00000002/art00005
%X Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of
   or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's
   ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable
   evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the
   convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a
   continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals
   of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organised into different fitness levels, reducing the
   selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs
   based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on
   the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its
   tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult
   problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a local optimum, HFC allows the
   emergence of new optima continually in a bottom-up manner, maintaining low local selection pressure at all fitness levels, while fostering exploitation of high-fitness
   individuals through promotion to higher levels.
%8 Summer
%Z http://mitpress.mit.edu/catalog/item/default.asp?ttype=4&tid=25

%A Jianjun Hu
%A Xiwei Zhong
%A Erik D. Goodman
%T Open-ended robust design of analog filters using genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1619--1626
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, analog filter synthesis, automated design, bond graph, design, robust design
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1619.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Jianjun Hu
%A Shaobo Li
%A Erik D. Goodman
%T Evolutionary Robust Design of Analog Filters Using Genetic Programming
%B Evolutionary Computation in Dynamic and Uncertain Environments
%S Studies in Computational Intelligence
%E Shengxiang Yang and Yew-Soon Ong and Yaochu Jin
%V 51
%D 2007
%P 479--496
%I Springer
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/content/1w71041124712n78/
%X This chapter proposes a robust design approach that exploits the open ended topological synthesis capability of genetic programming (GP) to evolve robust low pass and high
   pass analog filters. Compared with a traditional robust design approach based on genetic algorithms (GAs), the open-ended topology search based on genetic programming and
   bond graph modeling (GPBG) is shown to be able to evolve more robust filters with respect to parameter perturbations than what was achieved through parameter tuning alone,
   for the test problems.
%O 21
%Z http://www.cse.sc.edu/~jianjunh/

%A Jianjun Hu
%A Erik D. Goodman
%A Shaobo Li
%A Ronald Rosenberg
%T Automated Synthesis of Mechanical Vibration Absorbers Using Genetic Programming
%J Artificial Intelligence for Engineering Design, Analysis and Manufacturing
%V 22
%N 3
%D 2008
%P 207--217
%I 
%K genetic algorithms, genetic programming, Automated Design, Bond Graphs, Conceptual Design, Evolutionary Design
%U http://journals.cambridge.org/action/displayAbstract;jsessionid=7665C0F109E52E12771D5DFCBD27C245.tomcat1?fromPage=online&aid=1903160
%X Conceptual innovation in mechanical engineering design has been extremely challenging compared to the wide applications of automated design systems in digital circuits.
   This paper presents an automated methodology for open-ended synthesis of mechanical vibration shock absorbers based on genetic programming and bond graphs. It is shown that
   our automated design system can automatically evolve passive vibration absorber that have performance equal to or better than the standard passive vibration absorbers
   invented in 1911. A variety of other vibration absorbers with competitive performance are also evolved automatically using a desktop PC in less than 10 h.
%Z AIEDAM also known as \citeCambridgeJournals:1903160 and \citeDBLP:journals/aiedam/HuGLR08

%A Jianjun Hu
%A Zhun Fan
%A Jiachuan Wang
%A Shaobo Li
%A Kisung Seo
%A Xiangdong Peng
%A Janis Terpenny
%A Ronald Rosenberg
%A Erik Goodman
%T GPBG: A Framework for Evolutionary Design of Multi-domain Engineering Systems Using Genetic Programming and Bond Graphs
%B Design by Evolution
%S Natural Computing Series
%E Philip F. Hingston and Luigi C. Barone and Zbigniew Michalewicz
%D 2008
%P 319--345
%I Springer
%C Berlin, Heidelberg
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/content/h373616x71445700/
%X Current engineering design is a multi-step process proceeding from conceptual design to detailed design and to evaluation and testing. It is estimated that 60percent of
   design decisions and most innovation occur in the conceptual design stage, which may include conceptual design of function, operating principles, layout, shape, and
   structure. However, few computational tools are available to help designers to explore the design space and stimulate the product innovation process. As a result, product
   innovation is strongly constrained by the designer's ingenuity and experience, and a systematic approach to product innovation is strongly needed.
%O 14

%A Jiaojiao Hu
%A Mei Xie
%T Fingerprint classification based on genetic programming
%B 2nd International Conference on Computer Engineering and Technology (ICCET), 2010
%V 6
%D 2010
%P V6--193--V6--196
%I 
%K genetic algorithms, genetic programming, BP network, FVC2004, SVM classifier, fingerprint classification, four-class problem, image classification, backpropagation,
   fingerprint identification, image classification, neural nets, support vector machines
%X In this paper, we present a novel algorithm for fingerprint classification. This algorithm classifies a fingerprint image into one of the five classes: Arch, Left loop,
   Right loop, Whorl, and Tented arch. Initially, preprocessing of fingerprint images is carried out to enhance the image. Then we use genetic programming (GP) to generate new
   features from the original dataset without prior knowledge. Finally we can classify the fingerprint through a combination of BP network and SVM classifiers, which can not
   only supplement their advantages, but also improve the computation efficiency. We experiment this algorithm on database from FVC2004. For the five-class problem, a
   classification accuracy of 93.6percent without any reject, and classification accuracy of 96.2percent with a 15percent reject rate. For the four-class problem (arch and
   tented arch combined into one class), classification error can be reduced to 3.6percent with only 7.2percent reject rate.
%8 16-18  April
%Z School of Electronic Engineering, University of Electronic Science and Technology of China Chengdu, China. Also known as \cite5486315

%A Ting Hu
%A Wolfgang Banzhaf
%T Evolvability and Acceleration in Evolutionary Computation
%R Technical Report 2008-04
%D 2008
%I 
%I Department of Computer Science, Memorial University of Newfoundland
%C St. John's, NL, Canada A1B 3X5
%K genetic algorithms, genetic programming
%U http://www.mun.ca/computerscience/research/MUN-CS-2008-04.pdf
%X Biological and artificial evolutionary systems can possess varying degrees of evolvability and different rates of evolution. Such quantities can be affected by various
   factors. Here, we review some evolutionary mechanisms and discuss new developments in biology that may improve evolvability or accelerate evolution in artificial systems.
   Biological notions are discussed to the degree they correspond to notions in evolutionary computation. We hope the findings put forward here can be used to design
   computational models of evolution that exhibit significant gains in evolvability and evolutionary speed.
%8 October
%Z cited by \citeWeise:2011:ieeeTEC

%A Ting Hu
%A Wolfgang Banzhaf
%T Measuring rate of evolution in genetic programming using amino acid to synonymous substitution ratio ka/ks
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1337--1338
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, ka/ks Ratio, Rate of evolution: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1337.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389352

%A Ting Hu
%A Wolfgang Banzhaf
%T Nonsynonymous to Synonymous Substitution Ratio ka/ks: Measurement for Rate of Evolution in Evolutionary Computation
%B Parallel Problem Solving from Nature - PPSN X
%S LNCS
%E Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume
%V 5199
%D 2008
%P 448--457
%I Springer
%C Dortmund
%K genetic algorithms, genetic programming
%X Measuring fitness progression using numeric quantification in an Evolutionary Computation (EC) system may not be sufficient to capture the rate of evolution precisely. In
   this paper, we define the rate of evolution R e in an EC system based on the rate of efficient genetic variations being accepted by the EC population. This definition is
   motivated by the measurement of amino acid to synonymous substitution ratio k a/k s in biology, which has been widely accepted to measure the rate of gene sequence
   evolution. Experimental applications to investigate the effects of four major configuration parameters on our rate of evolution measurement show that R e well reflects how
   evolution proceeds underneath fitness development and provides some insights into the effectiveness of EC parameters in evolution acceleration.
%8 13-17  September
%Z PPSN X
%@ 3-540-87699-5

%A Ting Hu
%A Wolfgang Banzhaf
%T The Role of Population Size in Rate of Evolution in Genetic Programming
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 85--96
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Ting Hu
%A Wolfgang Banzhaf
%T Neutrality and variability: two sides of evolvability in linear genetic programming
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 963--970
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X The notion of evolvability has been put forward to describe the "core mechanism" of natural and artificial evolution. Recently, studies have revealed the influence of the
   environment upon a system's evolvability. In this contribution, we study the evolvability of a system in various environmental situations. We consider neutrality and
   variability as two sides of evolvability. The former makes a system tolerant to mutations and provides a hidden staging ground for future phenotypic changes. The latter
   produces explorative variations yielding phenotypic improvements. Which of the two dominates is influenced by the environment. We adopt two tools for this study of
   evolvability: 1) the rate of adaptive evolution, which captures the observable adaptive variations driven by evolvability; and 2) the variability of individuals, which
   measures the potential of an individual to vary functionally. We apply these tools to a Linear Genetic Programming system and observe that evolvability is able to exploit
   its two sides in different environmental situations.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Ting Hu
%T Evolvability and Rate of Evolution in Evolutionary Computation
%R Ph.D. Thesis
%D 2010
%I 
%I Department of Computer Science, Memorial University of Newfoundland
%C ST. John's, Newfoundland, Canada
%K genetic algorithms, genetic programming
%U http://www.mun.ca/computerscience/graduate/thesis_TingHU.pdf
%X Evolvability has emerged as a research topic in both natural and computational evolution. It is a notion put forward to investigate the fundamental mechanisms that enable a
   system to evolve. A number of hypotheses have been proposed in modern biological research based on the examination of various mechanisms in the biosphere for their
   contribution to evolvability. Therefore, it is intriguing to try to transfer new discoveries from Biology to and test them in Evolutionary Computation (EC) systems, so that
   computational models would be improved and a better understanding of general evolutional mechanisms is achieved. Rate of evolution comes in different flavors in natural and
   computational evolution. Specifically, we distinguish the rate of fitness progression from that of genetic substitutions. The former is a common concept in EC since the
   ability to explicitly quantify the fitness of an evolutionary individual is one of the most important differences between computational systems and natural systems. Within
   the biological research community, the definition of rate of evolution varies, depending on the objects being examined such as gene sequences, proteins, tissues, etc. For
   instance, molecular biologists tend to use the rate of genetic substitutions to quantify how fast evolution proceeds at the genetic level. This concept of rate of evolution
   focuses on the evolutionary dynamics underlying fitness development, due to the inability to mathematically define fitness in a natural system. In EC, the rate of genetic
   substitutions suggests an unconventional and potentially powerful method to measure the rate of evolution by accessing lower levels of evolutionary dynamics. Central to
   this thesis is our new definition of rate of evolution in EC. We transfer the method of measurement of the rate of genetic substitutions from molecular biology to EC. The
   implementation in a Genetic Programming (GP) system shows that such measurements can indeed be performed and reflect well how evolution proceeds. Below the level of fitness
   development it provides observables at the genetic level of a GP population during evolution. We apply this measurement method to investigate the effects of four major
   configuration parameters in EC, i.e., mutation rate, crossover rate, tournament selection size, and population size, and show that some insights can be gained into the
   effectiveness of these parameters with respect to evolution acceleration. Further, we observe that population size plays an important role in determining the rate of
   evolution. We formulate a new indicator based on this rate of evolution measurement to adjust population size dynamically during evolution. Such a strategy can stabilise
   the rate of genetic substitutions and effectively improve the performance of a GP system over fixed-size populations. This rate of evolution measure also provides an avenue
   to study evolvability, since it captures how the two sides of evolvability, i.e., variability and neutrality, interact and cooperate with each other during evolution. We
   show that evolvability can be better understood in the light of this interplay and how this can be used to generate adaptive phenotypic variation via harnessing random
   genetic variation. The rate of evolution measure and the adaptive population size scheme are further transferred to a Genetic Algorithm (GA) to solve a real world
   application problem - the wireless network planning problem. Computer simulation of such an application proves that the adaptive population size scheme is able to improve a
   GA's performance against conventional fixed population size algorithms.
%8 May
%Z http://www.mun.ca/computerscience/graduate/grad_thesis.php

%A Ting Hu
%A Simon Harding
%A Wolfgang Banzhaf
%T Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm
%J Genetic Programming and Evolvable Machines
%V 11
%N 2
%D 2010
%P 205--225
%I 
%K genetic algorithms, genetic programming, Variable population size, Population bottleneck, Evolution acceleration, Parallel computing, GPU
%X With current developments of parallel and distributed computing, evolutionary algorithms have benefited considerably from parallelization techniques. Besides improved
   computation efficiency, parallelization may bring about innovation to many aspects of evolutionary algorithms. In this article, we focus on the effect of variable
   population size on accelerating evolution in the context of a parallel evolutionary algorithm. In nature it is observed that dramatic variations of population size have
   considerable impact on evolution. Interestingly, the property of variable population size here arises implicitly and naturally from the algorithm rather than through
   intentional design. To investigate the effect of variable population size in such a parallel algorithm, evolution dynamics, including fitness progression and population
   diversity variation, are analyzed. Further, this parallel algorithm is compared to a conventional fixed-population-size genetic algorithm. We observe that the dramatic
   changes in population size allow evolution to accelerate.
%8 June
%Z Not GP, preparation for it? 'a simulation of the APEA algorithm', compiled, CUDA, OneMax problem, Spears multi-model problem. 'Individuals are encoded as binary strings for
   both problems'

%A Ting Hu
%A Joshua Payne
%A Jason Moore
%A Wolfgang Banzhaf
%T Robustness, Evolvability, and Accessibility in Linear Genetic Programming
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 13--24
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X Whether neutrality has positive or negative effects on evolutionary search is a contentious topic, with reported experimental results supporting both sides of the debate.
   Most existing studies use performance statistics, e.g. success rate or search efficiency, to investigate if neutrality, either embedded or artificially added, can benefit
   an evolutionary algorithm. Here, we argue that understanding the influence of neutrality on evolutionary optimisation requires an understanding of the interplay between
   robustness and evolvability at the genotypic and phenotypic scales. As a concrete example, we consider a simple linear genetic programming system that is amenable to
   exhaustive enumeration, and allows for the full characterisation of these properties. We adopt statistical measurements from RNA systems to quantify robustness and
   evolvability at both genotypic and phenotypic levels. Using an ensemble of random walks, we demonstrate that the benefit of neutrality crucially depends upon its phenotypic
   distribution.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Yuh-Jyh Hu
%T A Genetic Programming Approach to Constructive Induction
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 146--151
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Yuh-Jyh Hu
%T Biopattern Discovery by Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 152--157
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98. Cited by \citeross:2001:gecco . PROSITE pattern language. GPPD. ambigious positions (Arikawa et al., 1992). greedy method to refine the patterns. Function
   set=(wildcard gap X(i) and X(i,j) ?). Gaps always separate terminals? Terminals=(all legal symbols, eg amino acids,nucleotides and symbol indexings).
%@ 1-55860-548-7

%A Yuh-Jyh Hu
%T Global Gene Expression Analysis with Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 753
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW010.ps
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Yuh-Jyh Hu
%T Prediction of consensus structural motifs in a family of coregulated RNA sequences
%J Nucleic Acids Research
%V 30
%N 17
%D 2002
%P 3886--3893
%I 
%K genetic algorithms, genetic programming
%U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=137409
%X Given a set of homologous or functionally related RNA sequences, the consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA
   motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us gain a deeper insight of the regulation activities. There have been
   various studies of RNA secondary structure prediction, but most of them are not focused on finding motifs from sets of functionally related sequences. Although recent
   research shows some new approaches to RNA motif finding, they are limited to finding relatively simple structures, e.g. stemloops. In this paper, we propose a novel genetic
   programming approach to RNA secondary structure prediction. It is capable of finding more complex structures than stem-loops. To demonstrate the performance of our new
   approach as well as to keep the consistency of our comparative study, we first tested it on the same data sets previously used to verify the current prediction systems. To
   show the flexibility of our new approach, we also tested it on a data set that contains pseudo knot motifs which most current systems cannot identify. A web-based user
   interface of the prediction system is set up at http://bioinfo. cis.nctu.edu.tw/service/gprm/.
%Z PMID: 12202774 p3887 negative examples randomly generated. fitness=F-score. pop=1000, 50gens. Tournament=2 (pop culled to 50percent???). virus 3'-UTR. Matthews correlation
   coefficient. GP fairly insensitive to crossover and mutation rates. GPRM

%A Yuh-Jyh Hu
%T GPRM: a genetic programming approach to finding common RNA secondary structure elements
%J Nucleic Acids Research
%V 31
%N 13
%D 2003
%P 3446--3449
%I 
%K genetic algorithms, genetic programming
%U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=168928
%X RNA molecules play an important role in many biological activities. Knowing its secondary structure can help us better understand the molecule's ability to function. The
   methods for RNA structure determination have traditionally been implemented through biochemical, biophysical and phylogenetic analyses. As the advance of computer
   technology, an increasing number of computational approaches have recently been developed. They have different goals and apply various algorithms. For example, some focus
   on secondary structure prediction for a single sequence; some aim at finding a global alignment of multiple sequences. Some predict the structure based on free energy
   minimisation; some make comparative sequence analyses to determine the structure. In this paper, we describe how to correctly use GPRM, a genetic programming approach to
   finding common secondary structure elements in a set of unaligned coregulated or homologous RNA sequences.
%8 1  July
%Z GPRM can be accessed at http://bioinfo.cis.nctu.edu.tw/service/gprm/ Computer and Information Science Department, National Chiao Tung University, 1001 Ta Hsueh Rd, Hsinchu,
   Taiwan *Tel: +886 35731795; Fax: +886 35721490; Email: yhu@cis.nctu.edu.tw PMID: 12824343 Cited by \citeKawaguchi:2005:NAR

%A Cheng-Lung Huang
%A Mu-Chen Chen
%A Chieh-Jen Wang
%T Credit scoring with a data mining approach based on support vector machines
%J Expert Systems with Applications
%V 33
%N 4
%D 2007
%P 847--856
%I 
%K genetic algorithms, genetic programming, Credit scoring, Support vector machine, Neural networks, Decision tree, Data mining, Classification
%X The credit card industry has been growing rapidly recently, and thus huge numbers of consumers' credit data are collected by the credit department of the bank. The credit
   scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately
   evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in
   many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant's credit score from the applicant's input
   features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks,
   genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally,
   combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimisation.
   Experimental results show that SVM is a promising addition to the existing data mining methods.
%8 November

%A Chia-Hui Huang
%A Han-Ying Kao
%T An effective linear approximation method for geometric programming problems
%B IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009
%D 2009
%P 1743--1747
%I 
%K geometric programming, constraint functions, effective linear approximation method, geometric programming problems, mathematical optimisation problem, objective functions,
   posynomial form, approximation theory
%X A geometric program (GP) is a type of mathematical optimisation problem characterised by objective and constraint functions, where
%8 Decemeber
%Z Not GP. Also known as \cite5373154

%A Chien-Feng Huang
%T Independent Sampling Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 367--374
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, independent sampling genetic algorithms, idealized genetic algorithms, building block detecting strategy, mate selection, Royal Road functions, bounded
   deception problem
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d03a.pdf
%X Premature convergence is the loss of diversity in the population that has long been recognised as one crucial factor that hinders the efficacy of crossover. We propose a
   strategy for independent sampling of building blocks in order to nicely implement implicit parallelism. Based on this methodology, we developed a modified version of GA:
   independent sampling genetic algorithms (ISGAs). Simply stated, each individual independently samples candidate schemata and creates population diversity in the first
   phase; subsequently we allow individuals to actively select their mates for reproduction. We will present experimental results on two benchmark problems, "Royal Road"
   functions of 64-bits and bounded deception of 30-bits, to show how the performance of GAs can be improved through the proposed approach.
%8 7-11  July
%Z A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Chien-Feng Huang
%T Using an Immune System Model to Explore Mate Selection in Genetic Algorithms
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2723
%D 2003
%P 1041--1052
%I Springer-Verlag Berlin
%C Chicago
%K Genetic Algorithms, AIS, immune system, mate selection
%U http://www.springerlink.com/app/home/contribution.asp?wasp=804ttvxwwp6x8clrrvv3&referrer=parent&backto=issue,114,130;journal,175,1398;linkingpublicationresults,id:105633,1
%X When Genetic Algorithms (GAs) are employed in multimodal function optimization, engineering and machine learning, identifying multiple peaks and maintaining subpopulations
   of the search space are two central themes. In this paper, an immune system model is adopted to develop a framework for exploring the role of mate selection in GAs with
   respect to these two issues. The experimental results reported in the paper will shed more light into how mate selection schemes compare to traditional selection schemes.
   In particular, we show that dissimilar mating is beneficial in identifying multiple peaks, yet harmful in maintaining subpopulations of the search space.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eights Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40602-6

%A Chien-Feng Huang
%A Luis M. Rocha
%T Exploration of RNA Editing and Design of Robust Genetic Algorithms
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 2799--2806
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms
%X This paper presents our computational methodology using Genetic Algorithms (GA) for exploring the nature of RNA editing. These models are constructed using several genetic
   editing characteristics that are gleaned from the RNA editing system as observed in several organisms. We have expanded the traditional Genetic Algorithm with artificial
   editing mechanisms as proposed by (Rocha, 1997). The incorporation of editing mechanisms provides a means for artificial agents with genetic descriptions to gain greater
   phenotypic plasticity, which may be environmentally regulated. Our first implementations of these ideas have shed some light into the evolutionary implications of RNA
   editing. Based on these understandings, we demonstrate how to select proper RNA editors for designing more robust GAs, and the results will show promising applications to
   real-world problems. We expect that the framework proposed will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state
   of research in Genetic Algorithms.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Chien-Feng Huang
%T The Role of Crossover in an Immunity Based Genetic Algorithm for Multimodal Function Optimization
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 2807--2814
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, mate selection, immune systems
%X When Genetic Algorithms are employed in multimodal function optimization, identifying multiple peaks and maintaining subpopulations of the search space are two central
   themes. In this paper, we use an immune system model to explore the role of crossover in GAs with respect to these two issues. The experimental results reported here will
   shed more light into how crossover affects the GA's search power in the context of multimodal function optimization. We will also show that an adaptive crossover strategy
   successfully achieves the two goals simultaneously. These results on the effects of crossover are a step toward a deeper understanding of how GAs work, and thus how to
   design more robust GAs for solving multimodal optimization problems.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Ching-Ya Huang
%A Shih-Yen Tsai
%A Te-Jen Su
%T FIR Equalizer using Genetic Programming
%B Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2008
%V II
%D 2008
%P 1440--1443
%I 
%C Hong Kong
%K genetic algorithms, genetic programming, Finite Impulse Response equalizer
%U http://www.iaeng.org/publication/IMECS2008/IMECS2008_pp1440-1443.pdf
%X The main duty of communication systems is to assure to provide adequate message interchange, through a certain channel, between a transmitter and a receiver. The distortion
   takes place in the process of transmitting message, and it usually leads to severe degradation. Consequently we need a device named equalizer filters to recover the desired
   information from the received signal. In this paper, a FIR equalizer based on the GP approach to recover the transmitted signal is proposed. In addition, the equalizer
   coefficient will be estimated by the GP algorithm.
%8 19-21  March

%A Haoming Huang
%A Michel Pasquier
%A Chai Quek
%T HiCEFS - A Hierarchical Coevolutionary Approach for the Dynamic Generation of Fuzzy System
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 3426--3433
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%X A novel hierarchical coevolutionary approach called HiCEFS for the dynamic generation of a fuzzy system from data is presented. This paper is focused on using the proposed
   hierarchical coevolutionary approach to generate a form of generic membership function (MF) called Irregular Shaped Membership Function (ISMF). This approach divides the
   ISMFs generation task into several subtasks of finding ISMFs for each input, which are co-evolved in separate genetic populations. The approach is able automatically
   allocate proper number of accurate ISMFs to fully represent the data distribution. Experimental results show that the fuzzy systems adopting the ISMFs generated by the
   proposed approach generally outperform those derived by the previous work both in accuracy and structure compactness and compare favourably against other well known
   systems.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Haoming Huang
%T Coevolutionary synthesis of fuzzy decision support systems.
%R Ph.D. Thesis
%D 2009
%I 
%I School of Computer Engineering, Nanyang Technological University
%C Singapore 639798
%U http://repository.ntu.edu.sg/bitstream/10356/19087/1/Haoming-Final%20Thesis%20v1.5.6-for%20print.pdf
%X Many essential applications in finance, medicine, engineering, and science require increasingly complex decision-making capabilities. There is accordingly a growing demand
   for decision support systems (DSSs) to assist humans in their tasks. To provide accurate and reliable decision support, a DSS needs not only to be robust in the face of the
   uncertainty but also to model the decision-making logic in a form that is understandable. Compared with other machine learning methods, fuzzy rule-based systems possess the
   merits of providing strong approximate reasoning in the presence of imprecise data while representing domain knowledge as a set of interpretable semantic rules. Using them
   to realise DSSs is thus a most suitable approach yielding powerful fuzzy decision support systems (FDSSs). However, the synthesis of an optimal FDSS with well-balanced
   accuracy and interpretability is an arduous task. Experience shows that it is very difficult for human experts to manually design its two most important components, the
   fuzzy membership functions and fuzzy rule base, which directly affect system performance. Ad-hoc architectures, which must be redesigned anew for every application, and
   improperly chosen parameters typically introduce unwanted biases and unavoidably result in suboptimal systems. Ideally, the decision-making logic should therefore be
   induced automatically from example and further optimised for the problem at hand. To achieve this goal, a generic approach is needed that can automatically synthesise an
   accurate and interpretable FDSS, while requiring minimal or no human effort.
%Z Not GP? Centre for Computational Intelligence, Supervisor: Michel B Pasquier (SCE), URLs broken June 2010

%A Hsien-Da Huang
%A Jih Tsung Yang
%A Shu Fong Shen
%A Jorng-Tzong Horng
%T An Evolution Strategy to Solve Sports Scheduling Problems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 943
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming, poster papers
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jih-Jeng Huang
%A Gwo-Hshiung Tzeng
%A Chorng-Shyong Ong
%T Two-stage genetic programming (2SGP) for the credit scoring model
%J Applied Mathematics and Computation
%V 174
%N 2
%D 2006
%P 1039--1053
%I 
%K genetic algorithms, genetic programming, Credit scoring model, Artificial neural network (ANN), Decision trees, Rough sets, Two-stage genetic programming (2SGP)
%U http://www.scorto.ru/downloads/Two-stage%20genetic%20programming%20(2SGP)%20for%20the%20credit%20scoring%20model.pdf
%X Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial
   neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction
   of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models.
   In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF-THEN rules and the
   discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models.
%8 15  March

%A Jiangtao Huang
%A Chuang Deng
%T A Novel Multiclass Classification Method with Gene Expression Programming
%B International Conference on Web Information Systems and Mining, WISM 2009
%D 2009
%P 139--143
%I 
%K genetic algorithms, genetic programming, computer programs, data mining, eigenvalue centroid, eigenvalue power function, gene expression programming, genotype-phenotype
   genetic algorithm, linear chromosomes, machine learning algorithms, multiclass classification method, data mining, eigenvalues and eigenfunctions, learning (artificial
   intelligence)
%X Classification is one of the fundamental tasks of data mining, and many machine learning algorithms are inherently designed for binary (two-class) decision problems. Gene
   expression programming (GEP) is a genotype/phenotype genetic algorithm that evolves computer programs of different sizes and shapes (expression trees) encoded in linear
   chromosomes of fixed length. In this paper, we propose a novel method for multiclass classification by using GEP, a new hybrid of genetic algorithms (GAs) and genetic
   programming (GP). Different to the common method of formulating a multiclass classification problem as multiple two-class problems, we construct a novel multiclass
   classification by using eigenvalue centroid of each class and eigenvalue-power function. Experimental results on two real data sets demonstrate that method is able to
   achieve a preferable solution.
%8 November
%Z Also known as \cite5369449

%A Lorenz Huelsbergen
%T Toward Simulated Evolution of Machine Language Iteration
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 315--320
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp96.ps
%8 28--31  July
%Z GP-96 Cites \citekoza:book, \citeicnn93:kinnear, \citebrave:1996:aigp2. Virtual register machine (VRM) Finnegan SML/NJ. p 316 "GP can automatically synthesis multiplication
   routines." "All (GP) solutions discovered to date are general". 12 instructions. Pop=1024, 2000 GP runs versus 1000000000 random programs.

%A Lorenz Huelsbergen
%T Learning Recursive Sequences via Evolution of Machine-Language Programs
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 186--194
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp97.ps
%8 13-16  July
%Z GP-97. Comparison with random search

%A Lorenz Huelsbergen
%T Finding General Solutions to the Parity Problem by Evolving Machine-Language Representations
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 158--166
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp98.ps
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Lorenz Huelsbergen
%T Fast Evolution of Custom Machine Representations
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 1
%D 2005
%P 97--104
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%U http://netlib.bell-labs.com/who/lorenz/papers/huelsbergen-cec2005.pdf
%X Described are new approaches for evaluating computer program representations for use in automated search methodologies such as the evolutionary design of software.
   Previously, program representations have been either evaluated directly on raw hardware, providing high speed but little control and flexibility; or, programs were
   interpreted by a software interpreter which can incorporate much flexibility into a program's evaluation, but does so at a large cost in time due to interpretation
   overheads. Here we bridge this gap by providing intermediate compilation techniques for machine representations that approach the speed of running raw bits directly on
   hardware, but that have all the flexibility and control of custom instruction sets. In particular, we describe two compilation techniques: the first uses just-in-time
   compilation to convert a custom instruction sequence to machine code; the second compiles an instruction set specification into a specialised interpreter which incurs only
   small overheads for instruction decoding. We show that both techniques can provide manyfold speedups over direct interpretation while retaining the expressiveness of custom
   representations.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. 'complex control structures such as loops and recursion'. 'branches...are always realtive to the program
   counter' (target address fix_offset macro used after crossover). JIT. 'MIPS instruction set architecture as the target native machine'. Exact number of instrauctions
   interprestted can be controlled (ie limited) via termchk_macro.
%@ 0-7803-9363-5

%A Jonatan Hugosson
%A Erik Hemberg
%A Anthony Brabazon
%A Michael O'Neill
%T An investigation of the mutation operator using different representations in Grammatical Evolution
%B 2nd International Symposium "Advances in Artificial Intelligence and Applications"
%V 2
%D 2007
%P 409--419
%I 
%C Wisla, Poland
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.proceedings2007.imcsit.org/pliks/45.pdf
%X Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype
   similar to that which exists in nature by using a grammar to map between the genotype and phenotype. This study seeks to extend our understanding of GE by examining the
   impact of different genotypic representations in order to determine whether certain representations, and associated diversity-generation operators, improve GE's efficiency
   and effectiveness. Four mutation operators using two different representations, binary and gray code representation respectively, are investigated. The differing
   combinations of representation and mutation operator are tested on three benchmark problems. The results provides support for the continued use of the standard genotypic
   integer representation as the alternative representations do not exhibit higher locality nor better GE performance. The results raise the question as to whether higher
   locality in GE actually improves GE performance.
%8 October  15-17

%A Jonatan Hugosson
%A Erik Hemberg
%A Anthony Brabazon
%A Michael O'Neill
%T Genotype representations in grammatical evolution
%J Applied Soft Computing
%V 10
%N 1
%D 2010
%P 36--43
%I 
%K genetic algorithms, genetic programming, Grammatical evolution, Representation
%U http://www.sciencedirect.com/science/article/B6W86-4WGK6J4-1/2/69a04787be7085909d54edcef2d4d45a
%X Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype
   similar to that which exists in nature by using a grammar to map between the genotype and phenotype. Two variants of genotype representation are found in the literature,
   namely, binary and integer forms. For the first time we analyse and compare these two representations to determine if one has a performance advantage over the other. As
   such this study seeks to extend our understanding of GE by examining the impact of different genotypic representations in order to determine whether certain
   representations, and associated diversity-generation operators, improve GE's efficiency and effectiveness. Four mutation operators using two different representations,
   binary and gray code representation, are investigated. The differing combinations of representation and mutation operator are tested on three benchmark problems. The
   results provide support for the use of an integer-based genotypic representation as the alternative representations do not exhibit better performance, and the integer
   representation provides a statistically significant advantage on one of the three benchmarks. In addition, a novel wrapping operator for the binary and gray code
   representations is examined, and it is found that across the three problems examined there is no general trend to recommend the adoption of an alternative wrapping
   operator. The results also back up earlier findings which support the adoption of wrapping.
%8 January

%A Anthony Hui
%T Using Genetic Programming to Perform Time-Series Forecasting of Stock Prices
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 83--90
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/Hui.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Paul Hulse
%A Richard Gerber
%A Jenanne Price
%T Distributed Genetic Programming In Java
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 81--86
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 TARA
%@ 0-18-206995-8

%A Chun-Min Hung
%A Yueh-Min Huang
%A Ming-Shi Chang
%T Alignment using genetic programming with causal trees for identification of protein functions
%J Nonlinear Analysis
%V 65
%N 5
%D 2006
%P 1070--1093
%I 
%K genetic algorithms, genetic programming
%X A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers
   considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful
   conserved domains, maximising the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can
   minimise the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a
   hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it
   incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical
   homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the
   comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model.
%8 1  September
%Z Hybrid Systems and Applications

%A Ching-Tsung Hung
%A Shih-Huang Chen
%T A comparison of three forecasting methods to establish a flexible pavement serviceability index
%B 2010 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
%D 2010
%P 926--929
%I 
%K genetic algorithms, genetic programming, flexible pavement serviceability index, forecasting method, fuzzy regression model, hyperplane transform, linear regression,
   maintenance strategy, normal distribution, pavement surfaces data, regression modeling, support vector machine, fuzzy set theory, maintenance engineering, normal
   distribution, regression analysis, roads, structural engineering, support vector machines
%X Since 1960, the pavement serviceability index has supported the efforts of engineers who make decisions concerning maintenance strategies. The data of pavement surfaces do
   not belong to a normal distribution. Because the data violate the basic assumptions of linear regression, the pavement serviceability index is not suitable for regression
   modelling. Many kinds of prediction models with non-statistical foundations have been developed in recent years. To establish a flexible pavement serviceability index, this
   paper considers a fuzzy regression model, a support vector machine and a genetic programming. Our support vector machine has the highest predictive accuracy of the three
   methods in this study. The support vector machine uses a hyperplane transform to process interactions among pavement variables.
%8 Decemeber
%Z Also known as \cite5674216

%A Rachel Hunt
%A Mark Johnston
%A Will N. Browne
%A Mengjie Zhang
%T Sampling Methods in Genetic Programming for Classification with Unbalanced Data
%B Australasian Conference on Artificial Intelligence
%S Lecture Notes in Computer Science
%E Jiuyong Li
%V 6464
%D 2010
%P 273--282
%I Springer
%K genetic algorithms, genetic programming
%X This work investigates the use of sampling methods in Genetic Programming (GP) to improve the classification accuracy in binary classification problems in which the
   datasets have a class imbalance. Class imbalance occurs when there are more data instances in one class than the other. As a consequence of this imbalance, when overall
   classification rate is used as the fitness function, as in standard GP approaches, the result is often biased towards the majority class, at the expense of poor minority
   class accuracy. We establish that the variation in training performance introduced by sampling examples from the training set is no worse than the variation between GP runs
   already accepted. Results also show that the use of sampling methods during training can improve minority class classification accuracy and the robustness of classifiers
   evolved, giving performance on the test set better than that of those classifiers which made up the training set Pareto front.

%A Andrew Hunter
%T Using multiobjective genetic programming to infer logistic polynomial regression models
%B 15th European Conference on Artificial Intelligence
%E Frank Van Harmelen
%D 2002
%P 193--197
%I IOS Press
%C Lyon, France
%K genetic algorithms, genetic programming
%8 21-26  July
%Z ECAI 2002 http://ecai2002.univ-lyon1.fr/show_en.pl?page=en/program/ecai.html

%A Limin Huo
%A Xinqiao Fan
%A Yunfang Xie
%A Jinliang Yin
%T Short-Term Load Forecasting Based on the Method of Genetic Programming
%B International Conference on Mechatronics and Automation, ICMA 2007
%D 2007
%P 839--843
%I IEEE
%C Harbin, China
%K genetic algorithms, genetic programming
%X The algorithm of Genetic Programming is described and applied to short-term load forecasting. For the fault in history load data, the load samples are filtered and
   processed generally before using, and then the load series of the same time point but different days are chosen as the training sets. According to the complex expressive
   capacity of Genetic Programming, the future short-term load model of different time point is forecasted by time-sharing. This method of Genetic Programming can find out
   relevant elements to electric load data automatically, so the artificial errors in forecasting can be avoided effectively. And the future load value of each time point can
   be calculated with the corresponding model created. Finally, it proves that the method of Genetic Programming in short-term load forecasting is better through out
   comparison between the results forecasted by Genetic Programming and time series.
%8 5-8  August
%Z Department of Mechanical and Electronic Engineering, Agricultural University of Hebei, Baoding 071001, China.

%A Limin Huo
%A Jinliang Yin
%A Yao Yu
%A Liguo Zhang
%T Distribution Network Reconfiguration Based on Load Forecasting
%B International Conference on Intelligent Computation Technology and Automation, ICICTA 2008
%V 1
%D 2008
%P 1039--1043
%I 
%K genetic algorithms, genetic programming, decision making, distribution network reconfiguration, line loss calculation data, load forecasting, partheno-genetic programming
   algorithm, distribution networks, load forecasting
%X Line loss calculation data adopted in the previous distribution network reconfiguration was historical load data or real-time data. And that reduced the realistic
   significance of distribution network reconfiguration. A new method is presented. At first forecast the load, then apply the load data forecasted to the line loss
   calculation. By do so the decision can be made in advance that if the distribution network reconfiguration is needed at some time of the future. Load forecasting adopted
   genetic programming algorithm (GP). Distribution network reconfiguration adopted partheno-genetic algorithm (PGA). And the partheno-genetic algorithm was improved according
   to the features of the distribution network reconfiguration.
%8 October
%Z Also known as \cite4659648

%A Daniar Hussain
%A Steven Malliaris
%T Evolutionary Techniques Applied to Hashing: An efficient data retrieval method
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 760
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, hashing, poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW054.ps
%X Hashing is an efficient method for storage and retrieval of large amounts of data. Presented here is an evolutionary algorithm to locate efficient hashing functions for
   specific data sets by sampling and evolving from the set of polynomials. Functions derived in this way show consistently better performance than other common hashing
   methods, and indicate the power of evolutionary algorithms in search and retrieval.
%8 10-12  July
%Z Evolves better hash function. A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference
   (GP-2000) Part of \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Talib S. Hussain
%A Roger A. Browse
%T Basic Properties of Attribute Grammar Encoding
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://openmap.bbn.com/~thussain/publications/1998_gp98paper.pdf
%8 22-25  July
%Z GP-98LB, GP-98PhD Student Workshop

%A Talib S. Hussain
%A Roger A. Browse
%T Genetic Encoding of Neural Networks using Attribute Grammars
%B CITO Researcher Retreat
%D 1998
%I 
%C Hamilton, Ontario, Canada
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/397503.html
%X The discovery of good neural network solutions to complex problems may be facilitated through the use of evolutionary computation techniques, such as genetic algorithms or
   genetic programming. One key issue in the development of any system which will evolve neural networks is how and what information about a neural network will be encoded in
   the genetic description that will be manipulated by the evolutionary processes. Several approaches have been taken to this encoding problem, including direct, structural,
   parametric, and grammatical encoding. We present a new grammatical encoding technique in which an attribute grammar is used to represent a class of neural networks. We
   propose that the resulting encoding offers several improvements over existing approaches.
%O The Pennsylvania State University CiteSeer Archives
%8 May  12-14

%A Talib S. Hussain
%A Roger A. Browse
%T Attribute Grammars for Genetic Representations of Neural Networks and Syntactic Constraints of Genetic Programming
%B Workshop on Evolutionary Computation. Held at the 12 Canadian Conference on Artificial Intelligence
%D 1998
%I 
%C Vancouver, Canada
%K genetic algorithms, genetic programming, grammar
%U http://citeseer.ist.psu.edu/393107.html
%X this paper, we give a broad overview of our research into attribute grammar representations, from the basic and known capabilities, to the current ideas being addressed, to
   the future directions of our research.
%O The Pennsylvania State University CiteSeer Archives
%8 17  June

%A Talib S. Hussain
%T Workshop on advanced grammar techniques within genetic programming and evolutionary computation
%B Advanced Grammar Techniques Within Genetic Programming and Evolutionary Computation
%E Talib S. Hussain
%D 1999
%P 72
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, grammar, ANN
%U http://openmap.bbn.com/~thussain/publications/1999_gecco99bofworkshop.pdf
%8 13  July
%Z GECCO-99WKS Part of wu:1999:GECCOWKS

%A Talib S. Hussain
%A Roger A. Browse
%T Genetic operators with dynamic biases that operate on attribute grammar representations of neural networks
%B Advanced Grammar Techniques Within Genetic Programming and Evolutionary Computation
%E Talib S. Hussain
%D 1999
%P 83--86
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, grammar, ANN
%8 13  July
%Z GECCO-99WKS Part of wu:1999:GECCOWKS

%A Abo El-Abbass Hussian
%A Alaa Sheta
%A Mahmoud Kamel
%A Mohamed Telbaney
%A Ashraf Abdelwahab
%T Modeling of a Winding Machine Using Genetic Programming
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 398--402
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, control system design
%X In this paper, we present a new method for modeling the dynamics of a winding process using genetic programming and compare it with traditional modeling approaches. Data
   sets collected from an actual industrial process was used throughout the experiments. Three models were developed to describe the dynamics of the winding process.
   Experimental results are presented and discussed.
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Wen-Jyi Hwang
%A Chien-Min Ou
%A Rui-Chuan Lin
%A Wen-Wei Hu
%T Genetic Programming for Robust Video Transmission
%B International Conference on Informatics, Cybernetics, and Systems, ICICS 2003
%E Xuemin Chen
%D 2003
%I 
%I I-Shou University, IEEE Taipei Section
%C I-Shou University, Kaohsiung, Taiwan
%K genetic algorithms, genetic programming
%8 Decemeber  14-16
%Z http://www.isu.edu.tw/icics2003/ 15-Dec-03 Session C(2):Multimedia Processing National Taiwan Normal University, Ching-Yun University, Chung Yuan Christian University

%A Wen-Jyi Hwang
%A Chien-Min Ou
%A Rui-Chuan Lin
%A Wen-Wei Hu
%T Layered video transmission based on genetic programming for lossy channels
%J Neurocomputing
%V 57
%D 2004
%P 361--372
%I 
%K genetic algorithms, genetic programming, Genetic algorithm, Video transmission, Wavelet transform
%U http://www.sciencedirect.com/science/article/B6V10-4BJ23B3-1/2/4d871f85b5d703962a9dd8745bac3672
%X This paper presents a novel robust layered video transmission design algorithm for noisy channels. In the algorithm, the 3D SPIHT coding technique is used to encode the
   video sequences for the transmission of each layer. A new error protection allocation scheme based on genetic programming is then employed to determine the degree of
   protection for each layer so that the average distortion of the reconstructed images after transmission can be minimised. Simulation results show that, subject to the same
   amount of redundancy bits for error protection, the new algorithm outperforms other existing algorithms where equal-protection schemes are adopted.
%O New Aspects in Neurocomputing: 10th European Symposium on Artificial Neural Networks 2002

%A Matthew R. Hyde
%A Edmund K. Burke
%A Graham Kendall
%T Evolving human-competitive reusable 2D strip packing heuristics
%B GECCO-2009 Workshop on Automated heuristic design: crossing the chasm for search methods
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 2189--2192
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X This extended abstract presents preliminary work on reusable automatically generated heuristics for the 2D strip packing problem. It builds on our previous work, where the
   heuristics were not shown to be reusable. The best constructive heuristic for this problem in the literature is 'best-fit', and the motivation of this work is to obtain
   heuristics which are comparable to the performance of this heuristic.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Edmund K. Burke
%A Matthew R. Hyde
%A Graham Kendall
%T Providing a memory mechanism to enhance the evolutionary design of heuristics
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Genetic programming approaches have previously been employed in the literature to evolve heuristics for various combinatorial optimisation problems. This paper presents a
   hyper-heuristic genetic programming methodology to evolve more sophisticated one dimensional bin packing heuristics than have been evolved previously. The heuristics have
   access to a memory, which allows them to make decisions with some knowledge of their potential future impact. In contrast to previously evolved heuristics for this problem,
   we show that these heuristics evolve to draw upon this memory in order to facilitate better planning, and improved packings. This fundamental difference enables an evolved
   heuristic to represent a dynamic packing strategy rather than a fixed packing strategy. A heuristic can change its behaviour depending on the characteristics of the pieces
   it has seen before, because it has evolved to draw upon its experience.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586388

%A Edmund K. Burke
%A Matthew Hyde
%A Graham Kendall
%A John Woodward
%T A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics
%J IEEE Transactions on Evolutionary Computation
%V 14
%N 6
%D 2010
%P 942--958
%I 
%K genetic algorithms, genetic programming, volutionary computation, evolving 2D strip packing heuristics, genetic programming hyper heuristic approach, search methodologies,
   computational complexity, search problems
%X We present a genetic programming (GP) system to evolve reusable heuristics for the 2-D strip packing problem. The evolved heuristics are constructive, and decide both which
   piece to pack next and where to place that piece, given the current partial solution. This paper contributes to a growing research area that represents a paradigm shift in
   search methodologies. Instead of using evolutionary computation to search a space of solutions, we employ it to search a space of heuristics for the problem. A key
   motivation is to investigate methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building
   blocks for solution methods. However, the task of intelligently searching through all of the potential combinations of these components is better suited to a computer. With
   such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer
   takes on some of the design process by intelligently combining these components. This paper shows that a GP hyper-heuristic can be employed to automatically generate human
   competitive heuristics in a very-well studied problem domain.
%8 Decemeber
%Z also known as \cite5491153

%A Matthew Hyde
%T A genetic programming hyper-heuristic approach to automated packing
%R Ph.D. Thesis
%D 2010
%I 
%I School of Computer Science, University of Nottingham
%C UK
%K genetic algorithms, genetic programming
%U http://etheses.nottingham.ac.uk/1625/
%X This thesis presents a programme of research which investigated a genetic programming hyper-heuristic methodology to automate the heuristic design process for one, two and
   three dimensional packing problems. Traditionally, heuristic search methodologies operate on a space of potential solutions to a problem. In contrast, a hyper-heuristic is
   a heuristic which searches a space of heuristics, rather than a solution space directly. The majority of hyper-heuristic research papers, so far, have involved selecting a
   heuristic, or sequence of heuristics, from a set predefined by the practitioner. Less well studied are hyper-heuristics which can create new heuristics, from a set of
   potential components. This thesis presents a genetic programming hyper-heuristic which makes it possible to automatically generate heuristics for a wide variety of packing
   problems. The genetic programming algorithm creates heuristics by intelligently combining components. The evolved heuristics are shown to be highly competitive with human
   created heuristics. The methodology is first applied to one dimensional bin packing, where the evolved heuristics are analysed to determine their quality, specialisation,
   robustness, and scalability. Importantly, it is shown that these heuristics are able to be reused on unseen problems. The methodology is then applied to the two dimensional
   packing problem to determine if automatic heuristic generation is possible for this domain. The three dimensional bin packing and knapsack problems are then addressed. It
   is shown that the genetic programming hyper-heuristic methodology can evolve human competitive heuristics, for the one, two, and three dimensional cases of both of these
   problems. No change of parameters or code is required between runs. This represents the first packing algorithm in the literature able to claim human competitive results in
   such a wide variety of packing domains.
%8 March

%A Edmund K. Burke
%A Matthew R. Hyde
%A Graham Kendall
%A John Woodward
%T Automating the Packing Heuristic Design Process with Genetic Programming
%J Evolutionary Computation
%V 20
%N 1
%D 2012
%P 63--89
%I 
%K genetic algorithms, genetic programming, evolutionary design, cutting and packing, hyper-heuristicsn
%X The literature shows that one, two and three dimensional bin packing and knapsack packing are difficult problems in Operational Research. Many techniques, including exact,
   heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance.
   This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the
   possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It
   is not necessary to change the methodology depending on the problem type (one, two or three dimensional knapsack and bin packing problems), and it therefore has a level of
   generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the
   literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing
   methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive
   with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide
   variety of packing domains.
%8 Spring

%A Heikki Hy{\"o}tyniemi
%A Heikki Koivo
%T Genes, codes, and dynamic systems
%B Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA)
%S Proceedings of the University of Vaasa, Nro. 13
%E Jarmo T. Alander
%D 1996
%P 225--232
%I University of Vaasa
%I Finnish Artificial Intelligence Society
%C Vaasa (Finland)
%U ftp://ftp.uwasa.fi/cs/2NWGA/Hyotyniemi.ps.Z
%8 19.-23.~  August
%Z Hyotyniemi.ps.Z contains only the first few pages. About expressing Turing Machines as recurrent neural networks. However these do not appear to be evolve. Apparently an
   abstract of \citehyotyniemi:1996:STeP

%A Heikki Hy{\"o}tyniemi
%T Turing Machines are Recurrent Neural Networks
%B Proceedings of STeP'96
%E Jarmo Alander and Timo Honkela and Matti Jakobsson
%D 1996
%P 13--24
%I Finnish Artificial Intelligence Society
%U http://www.hut.fi/~hhyotyni/HH1/HH1.ps
%X Any algebraically computable function can be expressed as a recurrent neural network structure consisting of identical computing elements (or, equivalently, as a nonlinear
   discrete-time system of the form , where is a simple `cut' function). A constructive proof is presented in this paper.

%A Hitoshi Iba
%A Hugo {de Garis}
%A Tetsuya Higuchi
%T Evolutionary learning of predatory behaviors based on structured classifiers
%B From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior
%E Jean-Arcady Meyer and Herbert L. Roitblat and Stewart W. Wilson
%D 1993
%P 356--363
%I MIT Press
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/showciting?cid=38619
%Z SAB'92 http://www.isab.org/confs/sab92.php
%@ 0-262-63149-0

%A Hitoshi Iba
%A Hugo {de Garis}
%A Taisuke Sato
%T Solving identification problems by structured genetic algorithms
%R Technical report ETL-TR-93-17
%D 1993
%I 
%I Electrotechnical Laboratory
%C 1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1993_sipsGA.pdf
%8 10  August
%Z This paper is based on our earlier results presented at ICGA93 \citeicga93:iba

%A Hitoshi Iba
%A Tatsuya Niwa
%A Taisuke Sato
%T Evolutionary Learning of Boolean Concepts: An empirical Study
%R Technical Report ETL-TR-93-25
%D 1993
%I 
%I Electrotechnical Laboratory
%C 1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/etl-tr-93-25.pdf
%8 18  October

%A Hitoshi Iba
%A Takio Karita
%A Hugo {de Garis}
%A Taisuke Sato
%T System Identification Using Structured Genetic Algorithms
%B Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93
%E Stephanie Forrest
%D 1993
%P 279--286
%I Morgan Kaufmann
%C University of Illinois at Urbana-Champaign
%K genetic algorithms, genetic programming
%8 17-21  July
%Z Hierarchical tree GA, used for learning sequence of multiple variables and then predicting, STOGANOFF. See also \citeIba:1993:sipsGA

%A Hitoshi Iba
%A Hugo {de Garis}
%A Taisuke Sato
%T Genetic Programming Using a Minimum Description Length Principle
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 265--284
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%X This paper introduces a Minimum Description Length (MDL) principle to define fitness functions in Genetic Programming (GP). In traditional (Koza-style) GP, the size of
   trees was usually controlled by user-defined parameters, such as the maximum number of nodes and maximum tree depth. Large tree sizes meant that the time necessary to
   measure their fitnesses often dominated total processing time. To overcome this difficulty, we introduce a method for controlling tree growth, which uses an MDL principle.
   Initially we choose a "decision tree" representation for the GP chromosomes, and then show how an MDL principle can be used to define GP fitness functions. Thereafter we
   apply the MDL-based fitness functions to some practical problems. Using our implemented system "STROGANOFF", we show how MDL-based fitness functions can be applied
   successfully to problems of pattern recognitions. The results demonstrate that our approach is superior to usual neural networks in terms of general...
%O 12
%Z Describes MDL; Work on both decision trees and GMDH symbolic regression trees (STROGANOFF). Nature of trees (ie never worse than component trees) more important than MDL?

%A Hitoshi Iba
%A Taisuke Sato
%T Meta-level strategy learning for GA based on structured representation
%B Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence
%E Jin-Hyung Kim
%D 1992
%P 548--554
%I 
%I Center for Artificial Intelligence Research, Kaist
%C Seoul, Korea
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1992_mlslsGA.pdf
%8 15-18  September
%Z ETL-TR92-12 http://www.pricai.org/pricai-92.html

%A H. Iba
%A T. Sato
%T Extension of STROGANOFF for symbolic problems
%R Technical report ETL-TR-94-1
%D 1992
%I 
%I Electrotechnical Laboratory
%C 1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan
%K genetic algorithms, genetic programming

%A Hitoshi Iba
%A Tetsuya Higuchi
%A Hugo {de Garis}
%A Taisuke Sato
%T Evolutionary Learning Strategy using Bug-Based Search
%B Proceedings of the 13th International Joint Conference on Artificial Intelligence
%E Ruzena Bajcsy
%V 1
%D 1993
%P 960--966
%I Morgan Kaufmann
%C Chambery, France
%K genetic algorithms
%U http://ijcai.org/Past%20Proceedings/IJCAI-93-VOL2/PDF/018.pdf
%8 August  28 -  September  3
%Z IJCAI
%@ 1-55860-300-X

%A Hitoshi Iba
%A Taisuke Sato
%A Hugo {de Garis}
%T System identification approach to genetic programming
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 401--406
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, Boolean concept formation, STROGANOFF, adaptive program, adaptive search, local parameter tuning mechanism, minimum description
   length-based selection criterion, multiple node types, multiple regression analysis, nonlinear function fitting, nonnumerical reasoning, numerical problems, statistical
   search, structured representation, symbolic reasoning, symbolic regression problems, system identification, tree pruning, tree structures, Boolean functions,
   identification, search problems, statistical analysis, symbol manipulation, trees (mathematics), tuning
%X Introduces a new approach to genetic programming (GP), based on a system identification technique, which integrates a GP-based adaptive search of tree structures and a
   local parameter tuning mechanism employing a statistical search. In Proc. 5th Int. Joint Conf. on Genetic Algorithms (1993), we introduced our adaptive program called
   STROGANOFF (STructured Representation On Genetic Algorithms for NOnlinear Function Fitting), which integrated a multiple regression analysis method and a GA-based search
   strategy. The effectiveness of STROGANOFF was demonstrated by solving several system identification (numerical) problems. This paper extends STROGANOFF to symbolic
   (non-numerical) reasoning, by introducing multiple types of nodes, using a modified minimum description length (MDL) based selection criterion, and a pruning of the
   resultant trees. The effectiveness of this system-identification approach to GP is demonstrated by successful application to Boolean concept formation and to symbolic
   regression problems
%8 27-29  June

%A Hitoshi Iba
%A Taisuke Sato
%T Genetic Programming with Local Hill-Climbing
%R Technical Report ETL-TR-94-4
%D 1994
%I 
%I Electrotechnical Laboratory
%C 1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1994_GPlHC.pdf
%Z Also published in PPSN-94, see \citeiba:1994:GPlHCppsn3

%A Hitoshi Iba
%A Hugo {de Garis}
%A Taisuke Sato
%T Genetic Programming with Local Hill-Climbing
%B Parallel Problem Solving from Nature III
%S LNCS
%E Yuval Davidor and Hans-Paul Schwefel and Reinhard M\"anner
%V 866
%D 1994
%P 334--343
%I Springer-Verlag Berlin, Germany
%C Jerusalem
%K genetic algorithms, genetic programming
%U http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6
%X This paper proposes a new approach to Genetic Programming (GP). In traditional GP, recombination can cause frequent disruption of building-blocks or mutation can cause
   abrupt changes in the semantics. To overcome these difficulties, we supplement traditional GP with a recovery mechanism of disrupted building-blocks. More precisely, we
   integrate the structural search of traditional GP with a local hill-climbing search, using a relabeling procedure. This integration allows us to extend GP for Boolean and
   numerical problems. We demonstrate the superior effectiveness of our approach with experiments in Boolean concept formation and symbolic regression.
%8 9-14  October
%Z 'We demonstrate the superior effectiveness of GP+local Hill Climbing with experiments in Boolean concept formation and symbolic regression'. Boolean GP combines GP with
   Adaptive Logic Network trees. Combination can evove to cope with time varying fitness functions. Numerical GP combines GP with GMDH (Group Method of Data Handling,
   Ivakhnenko) PPSN3 see also technical note \citeIba:1994:GPlHC
%@ 3-540-58484-6

%A Hitoshi Iba
%T Introduction to Genetic Algorithms
%D 1994
%I Ohm-sha
%K genetic algorithms
%Z in Japanese

%A Hitoshi Iba
%A Taisuke Sato
%A Hugo {de Garis}
%T Numerical Genetic Programming for System Identification
%B Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications
%E Justinian P. Rosca
%D 1995
%P 64--75
%I 
%C Tahoe City, California, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_nGPsi.pdf
%8 9  July
%Z This paper based on earlier results (\citeicga93:iba \citeIba:1994:siGP and ETL-TR-94-20 1994 (submitted to ICEC-95, see \citeiba:1885:rgn)). part of \citerosca:1995:ml

%A Hitoshi Iba
%A Hugo {de Garis}
%A Taisuke Sato
%T Temporal Data Processing Using Genetic Programming
%B Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)
%E Larry J. Eshelman
%D 1995
%P 279--286
%I Morgan Kaufmann San Francisco, CA, USA
%C Pittsburgh, PA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_tdpgp.pdf
%X This paper reports an extension of STROGANOFF called R-STROGANOFF which uses special memory terminal nodes to provide a form of recurrancy to process time ordered events.
   All functions are polynomials (quadratics in the examples), terminals are either inputs or memories. Each memory terminals hold the value of a function node on the previous
   time step. The coeffients of the polynomials are learnt by trying to match the training data using a 'Generalised Error Proporgation Algorithm'. This is determinstic. Seems
   like STROGANOFF's (but different?), time sequence based, based on back-propagation. The coefficients are recalculated each generation (assuming tree has changed). Fitness
   function used 'minimum description length' (MDL). Quadratic coefficients mya be limited to 0<=x<=1 to avoid divergence. Examples: 2 step 0-1 oscilator, 4 Tomita languages
   (on binary alphabet). Tree could be converted to finite state automata, which was more general than tree, ie works in all cases including those not in the training set. On
   the tomita languages problems 'R-STROGANOFF works almost as well as (the best) best recurrent networks'
%8 15-19  July
%@ 1-55860-370-0

%A Hitoshi Iba
%A Taisuke Sato
%A Hugo {de Garis}
%T Recombination Guidance for Numerical Genetic Programming
%B 1995 IEEE Conference on Evolutionary Computation
%V 1
%D 1995
%P 97--102
%I IEEE Press Piscataway, NJ, USA
%C Perth, Australia
%K genetic algorithms, genetic programming, adaptive estimation, computer vision, numerical analysis, search problems, statistical analysis, time series, STROGANOFF, adaptive
   program, adaptive recombination mechanism, chaotic time series prediction, genetic algorithm-based search strategy, genetic program recombination, multiple regression
   analysis method, nonlinear function fitting, numerical genetic programming, structured representation, system identification problems
%X In our earlier papers, we introduced our adaptive program called STROGANOFF (i.e. STructured Representation On Genetic Algorithms for Non-linear Function Fitting), which
   integrated a multiple regression analysis method and a GA-based search strategy. The effectiveness of STROGANOFF was demonstrated by solving several system identification
   problems. This paper proposes an "adaptive recombination" mechanism for STROGANOFF. Our intention is to exploit already built structures by 'adaptive recombination', in
   which GP recombination is guided by a certain measure. The effectiveness of our approach is shown by the experiment in predicting a chaotic time series. Thereafter we
   describe real-world applications of STROGANOFF to computer vision.
%8 29  November  - 1  Decemeber
%Z ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html Female face outline.
   Stately home Hursley house windows.
%@ 0-7803-2759-4

%A Hitoshi Iba
%A Hugo {de Garis}
%T Extending Genetic Programming with Recombinative Guidance
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 69--88
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/92
%X This chapter introduces a recombinative guidance mechanism for GP (Genetic Programming), and shows the effectiveness of our approach using various experiments. Traditional
   GP blindly combines subtrees, by applying crossover operations. This blind replacement, in general, can often disrupt beneficial building-blocks in tree structures.
   Randomly chosen crossover points ignore the semantics of the parent trees. Our goal is to exploit already built structures by adaptive recombination, in which GP
   recombination is guided by ``S-value'' measures. We present various S-value definitions, and show that the performance depends upon the definition.
%O 4
%@ 0-262-01158-1

%A Hitoshi Iba
%T Emergent Cooperation for Multiple Agents using Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 66--74
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 see also \citeiba:1996:ecmaPPSN
%@ 0-18-201031-7

%A Hitoshi Iba
%T Random Tree Generation for Genetic Programming
%R Technical Report ETL-TR-95-35
%D 1995
%I 
%I ElectroTechnical Laboratory (ETL)
%C 1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_rtgTR.pdf
%8 14  November

%A Hitoshi Iba
%T Random Tree Generation for Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 75--82
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-201031-7

%A Hitoshi Iba
%T Random Tree Generation for Genetic Programming
%B Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation
%S LNCS
%E Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel
%V 1141
%D 1996
%P 144--153
%I Springer Verlag Heidelberg, Germany
%C Berlin, Germany
%K genetic algorithms, genetic programming
%X This paper introduces a random tree generation algorithm for GP (Genetic Programming). Generating random trees is an essential part of GP. However, the recursive method
   commonly used in GP does not necessarily generate random trees, i.e the standard GP initialisation procedure does not sample the space of possible initial trees uniformly.
   This paper proposes a truly random tree generation procedure for GP. Our approach is grounded upon a bijection method, i.e., a 1-1 correspondence between a tree with n
   nodes and some simple word composed by letters x and y. We show how to use this correspondence to generate a GP tree and how GP search is improved by using this randomness
%8 22-26  September
%Z http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 bijection, tree_by_dyck Demonstrated on Mackey-Glass compared to 'grow' method (not ramped half-and-half)
%@ 3-540-61723-X

%A Hitoshi Iba
%T Emergent Cooperation for Multiple Agents Using Genetic Programming
%B Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation
%S LNCS
%E Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel
%V 1141
%D 1996
%P 32--41
%I Springer Verlag Heidelberg, Germany
%C Berlin, Germany
%K genetic algorithms, genetic programming
%X This paper presents the emergence of the cooperative behaviour for the multiple agents by means of Genetic Programming (GP). Our experimental domain is the Tile World, a
   multi-agent test bed [Pollack90]. The world consists of a simulated robot agent and a simulated environment which is both dynamic and unpredictable. For the purpose of
   evolving the cooperative behavior, we propose three types of strategies, i.e, homogeneous breeding, heterogeneous breeding, and co-evolutionary breeding. The effectiveness
   of these three types of GP-based multi-agent learning is discussed with comparative experiments.
%8 22-26  September
%Z http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 Comparison of homogeneous, heterogeneous and co-evolutionary breeding on 'Tile world' simulated environment problem.
%@ 3-540-61723-X

%A Hitoshi Iba
%T Genetic Programming
%D 1996
%I Tokyo Denki University Press
%K genetic algorithms, genetic programming
%Z in Japanese

%A Hitoshi Iba
%A Tishihide Nozoe
%A Kanji Ueda
%T Evolving Communicating Agents based on Genetic Programming
%B Proceedings of the 1997 IEEE International Conference on Evolutionary Computation
%D 1997
%P 297--302
%I IEEE Press Piscataway, NJ, USA
%C Indianapolis, IN, USA
%K genetic algorithms, genetic programming, Artificial intelligence, Cloning, Intelligent agent, Laboratories, Learning, Multiagent systems, Robot kinematics, Robustness,
   Testing, cooperative systems, digital simulation, games of skill, intelligent control, learning (artificial intelligence), linear programming, software agents, GP based
   multi agent learning, co-evolutionary breeding strategy, communicating agents, comparative experiments, cooperative behaviour, evolving communicating agents, multi agent
   test bed, pursuit game, simulated environment, simulated robot agents
%X The paper presents the emergence of the cooperative behavior for communicating agents by means of genetic programming (GP). Our experimental domain is the pursuit game, a
   multi agent test bed. The world consists of simulated robot agents and a simulated environment which is both dynamic and unpredictable. For the purpose of evolving the
   cooperative behavior, we use the co-evolutionary breeding strategy. We confirm the emergence of cooperation via communication. The effectiveness of GP based multi agent
   learning is discussed with comparative experiments
%8 13-16  April
%Z ICEC-97
%@ 0-7803-3949-5

%A Hitoshi Iba
%T Multiple-Agent Learning for a Robot Navigation Task by Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 195--200
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/iba_1997_malrntGP.pdf
%8 13-16  July
%Z GP-97

%A Hitoshi Iba
%T Complexity-based Fitness Evaluation for Variable Length Representation
%D 1997
%I 
%C East Lansing, MI, USA
%K genetic algorithms, genetic programming, bloat, variable size representation
%U http://citeseer.ist.psu.edu/327857.html
%X This paper introduces a Minimum Description Length (MDL) principle to define fitness functions in Genetic Programming (GP). In traditional (Koza-style) GP, the size of
   trees was usually controlled by user-defined parameters, such as the maximum number of nodes and maximum tree depth. Large tree sizes meant that the time necessary to
   measure their fitnesses often dominated total processing time. To overcome this difficulty, we introduce a method for controlling tree growth, which uses an...
%O Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97
%8 20  July

%A Hitoshi Iba
%T Complexity-based fitness evaluation
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%I Oxford University Press
%K genetic algorithms, genetic programming
%O section C4.4
%@ 0-7503-0392-1

%A Hitoshi Iba
%T Identification
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%I Oxford University Press
%K genetic algorithms, genetic programming, stroganoff, gmdh
%U http://www.crcnetbase.com/isbn/9780750308953
%O section F1.4
%@ 0-7503-0392-1

%A Hitoshi Iba
%T System identification using structured genetic algorithms
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%I Oxford University Press
%K genetic algorithms, genetic programming, stroganoff, gmdh, sgpc version 1.1
%O section G1.4
%@ 0-7503-0392-1

%A Hitoshi Iba
%T Multi-Agent Reinforcement Learning with Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 167--172
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Hitoshi Iba
%T Evolutionary learning of communicating agents
%J Information Sciences
%V 108
%N 1-4
%D 1998
%P 181--205
%I 
%K genetic algorithms, genetic programming, Multi-agent system, Distributed artificial intelligence
%U http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-F/2/ecac160ea272b4818c97d3aab09527d4
%X This paper presents the emergence of the cooperative behavior for communicating agents by means of Genetic Programming (GP). Our experimental domains are the pursuit game
   and the robot navigation task. We conduct experiments with the evolution of the communicating agents and show the effectiveness of the emergent communication in terms of
   the robustness of generated GP programs. The performance of GP-based multi-agent learning is discussed with comparative experiments by using different breeding strategies,
   i.e., homogenous breeding and heterogeneous breeding.
%8 July
%Z Information Sciences http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt

%A Hitoshi Iba
%T Evolving Multiple Agents by Genetic Programming
%B Advances in Genetic Programming 3
%E Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline
%D 1999
%P 447--466
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming, QGP
%U http://www.cs.bham.ac.uk/~wbl/aigp3/ch19.pdf
%X On the emergence of the cooperative behaviour for multiple agents by means of Genetic Programming (GP). Our experimental domains are multi-agent test beds, i.e., the robot
   navigation task and the Tile World. The world consists of a simulated robot agent and a simulated environment which is both dynamic and unpredictable. In our previous
   paper, we proposed three types of strategies, i.e, homogeneous breeding, heterogeneous breeding, and co-evolutionary breeding, for the purpose of evolving the cooperative
   behavior. We use the heterogeneous breeding in this paper. The previous Q-learning approach commonly used for the multi-agent task has the difficulty with the combinatorial
   explosion for many agents. This is because the state space for Q-table is so huge for the practical computer resources. We show how successfully GP-based multi-agent
   learning is applied to multi-agent tasks and compare the performance with Q-learning by experiments. Thereafter, we conduct experiments with the evolution of the
   communicating agents. The communication is an essential factor for the emergence of cooperation. This is because a collaborative agent must be able to handle situations in
   which conflicts arise and must be capable of negotiating with other agents to reach an agreement. The effectiveness of the emergent communication is empirically shown in
   terms of the robustness of generated GP programs.
%O 19
%8 June
%Z AiGP3
%@ 0-262-19423-6

%A Hitoshi Iba
%T Evolutionary Computing
%D 1999
%I Tokyo University Press
%K genetic algorithms, genetic programming
%Z in Japanese

%A Hitoshi Iba
%T Bagging, Boosting, and Bloating in Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1053--1060
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, classifier ensembles
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-407.ps
%X subpopulations
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) 10
   Subpopulations each has its own training data (produced using the boosting or bagging methods. Best of each subpopulation has vote in final result. Do we actually need
   subpopulations, could not the whole algorithm be split into T entirely separate GP runs? SGPC1.1 p1054 "controlling the bloating effect is closely related to the
   performance improvement..." noisy cos(2x)=1-sin(x)**2, Mackey-Glass chaotic time series, 6MUX, symbolic regression, nikkei225 Description of boosting weight adjustment
   algorithm (p1054) seems to be wrong? p1056 BagGP, BoostGP > GP, BagGP=BoostGP But only in the case of noisy cos(2x) does difference (table 2) seem big. Mention of DSS and
   PADO. p1059 Says Bagging and Boosting yield lower bloat. (does not explain why) Little supporting data (Fig 5). Boosting v co-evolution
%@ 1-55860-611-4

%A Hitoshi Iba
%A Takashi Sasaki
%T Using Genetic Programming to Predict Financial Data
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 1
%D 1999
%P 244--251
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, time series
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143
%@ 0-7803-5537-7 (Microfiche)

%A Hitoshi Iba
%A Nikolay Nikolaev
%T Financial data prediction by means of genetic programming
%B Computing in Economics and Finance
%D 2000
%I 
%C Universitat Pompeu Fabra, Barcelona, Spain
%K genetic algorithms, genetic programming
%U http://enginy.upf.es/SCE/papers/paper330.ps.gz broken
%8 6-8  July
%Z http://enginy.upf.es/SCE/index2.html http://ideas.repec.org/p/sce/scecf0/z101.html number Z101

%A Hitoshi Iba
%A Makoto Terao
%T Controlling Effective Introns for Multi-Agent Learning by Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 419--426
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/478951.html
%X This paper presents the emergence of the cooperative behavior for multiple agents by means of Genetic Programming (GP). For the purpose of evolving the e#ective cooperative
   behavior, we propose a controlling strategy of introns, which are non-executed code segments dependent upon the situation. The traditional approach to removing introns was
   able to cope with only a part of syntactically defined introns, which excluded other frequent types of introns. The validness of our approach is discussed with comparative
   experiments with robot simulation tasks, i.e., a navigation problem and an escape problem.
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Hitoshi Iba
%A Nikolay Nikolaev
%T Genetic Programming Polynomial Models of Financial Data Series
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 1459--1466
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, time series, stroganoff
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Hitoshi Iba
%A Erina Sakamoto
%T Inference Of Differential Equation Models By Genetic Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 788--795
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, bioinformatics, differential equation, E-cell, genome informatics, Lotka-Volterra model, S-systems
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%X An evolutionary method for identifying a causal model from the observed time series data. We use a system of ordinary differential equations (ODEs) as the causal model.
   This approach is well known to be useful for the practical application, e.g., bioinformatics, chemical reaction models, controlling theory etc. To explore the search space
   more effectively in the course of evolution, the right-hand sides of ODEs are inferred by Genetic Programming (GP) and the least mean square (LMS) method is used along with
   the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs.
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Hitoshi Iba
%T Inference of differential equation models by genetic programming
%J Information Sciences
%V 178
%N 23
%D 2008
%P 4453--4468
%I 
%K genetic algorithms, genetic programming, Ordinary differential equations, Genome informatics
%X This paper describes an evolutionary method for identifying a causal model from the observed time-series data. We use a system of ordinary differential equations (ODEs) as
   the causal model. This approach is known to be useful for practical applications, e.g., bioinformatics, chemical reaction models, control theory, etc. To explore the search
   space more effectively in the course of evolution, the right-hand sides of ODEs are inferred by genetic programming (GP) and the least mean square (LMS) method is used
   along with the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs. We also describe an extension
   of the approach to the inference of differential equation systems with transcendental functions.
%O Special Section: Genetic and Evolutionary Computing
%8 1  Decemeber
%Z The reaction between formaldehyde and carbamide in the aqueous solution gives methylol urea which continues to react with carbamide and form methylene urea. GP with LMS.
   Forced vibration with damping. ODE. Penalty against bloat. S-expression: power-law exponents for terminal set. MDL. Fourth order Runge-Kutta. Numerical overflow -> poor
   fitness -> weeded out. Synthetic data. E-CELL SE, Michaelis-Menten law. Levenberg-Marquardt Is genotype "repaired" or just phenotype? p4467 considers possibility that there
   is more than one solution.

%A Hitoshi Iba
%A Yoshihiko Hasegawa
%A Topon Kumar Paul
%T Applied Genetic Programming and Machine Learning
%S CRC Complex and Enterprise Systems Engineering
%D 2009
%I CRC
%K genetic algorithms, genetic programming
%X Reflecting rapidly developing concepts and newly emerging paradigms in intelligent machines, this text is the first to integrate genetic programming and machine learning
   techniques to solve diverse real-world tasks.These tasks include financial data prediction, day-trading rule development; and bio-marker selection. Written by a leading
   authority, this text will teach readers how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process
   through the design of objective fitness functions, and examine the search performance of the evolutionary system. All source codes and GUIs are available for download from
   the author's website.
%Z Book review in \citeVeeramachaneni:2011:GPEM. http://www.crcpress.com/product/isbn/9781439803691;jsessionid=FIWH8j32tAoXjObJ2dSjAA** Introduction. Evolutionary computation.
   Genetic Programming. Hybrid Genetic Programming and GMDH System. Principles of STROGANOFF. Classification by Ensemble of Genetic Programming Rules. Probabilistic Program
   Evolution with Estimation of Distribution. Other Related Methods. Discussion. Conclusion. Appendix A: STROGANOFF system overviews. Appendix B: MVGPC system overviews.
%@ 1439803692

%A Hitoshi Iba
%A Claus Aranha
%T Composition of Music and Financial Strategies via Genetic Programming
%B Genetic Programming Theory and Practice VIII
%S Genetic and Evolutionary Computation
%E Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva
%V 8
%D 2010
%P 211--226
%I Springer
%C Ann Arbor, USA
%K genetic algorithms, genetic programming
%U http://www.springer.com/computer/ai/book/978-1-4419-7746-5
%O 13
%8 20-22  May
%Z part of \citeRiolo:2010:GPTP

%A Aitor Ibarra
%A J. Lanchares
%A J. Mendias
%A J. I. Hidalgo
%A R. Hermida
%T Transformation of Equational Specification by Means of Genetic Programming
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 248--257
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming, FRESH
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780248.pdf
%X High Level Synthesis (HLS) is a designing methodology aimed to the synthesis of hardware devices from behavioural specifications. One of the techniques used in HLS is
   formal verification. In this work we present an evolutionary algorithm in order to optimize circuit equational specifications by means of a special type of genetic
   operator. We have named this operator algebraic mutation, carried out with the help of the equations that Formal Verification Synthesis offers. This work can be classified
   within the development of an automatic tool of Formal Verification Synthesis by using genetic techniques. We have applied this technique to a simple circuit equational
   specification and to a much more complex algebraic equation. In the first case our algorithm simplifies the equation until the best specification is found and in the second
   a solution improving the former is always obtained.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP Algebraic mutation. No crossover. gpcc++ 0.5.2 Two example equations simplified. Pop size 4. 60 percent improvement.
%@ 3-540-43378-3

%A Takumi Ichimura
%A Kazuya Mera
%A Akira Hara
%T A Knowledge Acquisition Method of Judgment Rules for Spam E-mail by using Self Organizing Map and Automatically Defined Groups by Genetic Programming
%B Self-Organizing Maps
%E George K Matsopoulos
%D 2010
%I InTech
%K genetic algorithms, genetic programming
%U http://www.intechopen.com/articles/show/title/a-knowledge-acquisition-method-of-judgment-rules-for-spam-e-mail-by-using-self-organizing-map-and-au
%X In this paper, we propose a classification method for Spam E-mail based on the results of SpamAssassin. This method can learn patterns of Ham and Spam E-mails. First, SOM
   can classify many E-mails into the some categories. In this phase, we can see the characters of current received Spam E-mails. Second, ADG can extract the correct judgement
   rules of Hams misjudged as Spams. However, there are a few cases of Spam misjudged as Ham. In this experiment, ADG makes an over fitting to the characters of Hams. We have
   met the problems according to the limitation of classification capability by SOM and explosive search in GP using many nodes as shown in T1 Therefore, we improve the
   proposed method
%O 24
%8 April
%Z http://www.intechopen.com/books/show/title/self-organizing-maps

%A R. Ichise
%T Inductive Logic Programming and Genetic Programming
%B European Conference on Artificial Intelligence
%E Henri Prade
%D 1998
%I 
%C Brighton
%K genetic algorithms, genetic programming
%8 23-28  August
%Z ECAI-98 young researcher paper

%A Ilknur Icke
%A Andrew Rosenberg
%T Dimensionality reduction using symbolic regression
%B GECCO 2010 Late breaking abstracts
%E Daniel Tauritz
%D 2010
%P 2085--2086
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%X In this paper, we propose a symbolic regression approach for data visualisation that is suited for classification tasks. Our algorithm seeks a visually and semantically
   interpretable lower dimensional representation of the given dataset that would increase classifier accuracy as well. This simultaneous identification of easily
   interpretable dimensionality reduction and improved classification accuracy relieves the user of the burden of experimenting with the many combinations of classification
   and dimensionality reduction techniques
%8 7-11  July
%Z Flubber, ECJ, WEKA, UCI wisconsin breast, leptographsus crabs. Compare with PCA, MDS and random projections. no significant improvement. Also known as \cite1830874
   Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.

%A Ilknur Icke
%A Andrew Rosenberg
%T Multi-Objective Genetic Programming Projection Pursuit for Exploratory Data Modeling
%B Workshop for Women in Machine Learning
%E Diane Oyen
%D 2010
%I 
%C Canada
%K genetic algorithms, genetic programming, MOG3P
%U http://pami.uwaterloo.ca/~ealee/wiml/2010/program/WiML2010_IlknurIcke.pdf
%X For classification problems, feature extraction is a crucial process which aims to find a suitable data representation that increases the performance of the machine
   learning algorithm. According to the curse of dimensionality theorem, the number of samples needed for a classification task increases exponentially as the number of
   dimensions (variables, features) increases. On the other hand, it is costly to collect, store and process data. Moreover, irrelevant and redundant features might hinder
   classifier performance. In exploratory analysis settings, high dimensionality prevents the users from exploring the data visually. Feature extraction is a two-step process:
   feature construction and feature selection. Feature construction creates new features based on the original features and feature selection is the process of selecting the
   best features as in filter, wrapper and embedded methods. In this work, we focus on feature construction methods that aim to decrease data dimensionality for visualisation
   tasks. Various linear (such as principal components analysis (PCA), multiple discriminants analysis (MDA), exploratory projection pursuit) and non-linear (such as
   multidimensional scaling (MDS), manifold learning, kernel PCA/LDA, evolutionary constructive induction) techniques have been proposed for dimensionality reduction. Our
   algorithm is an adaptive feature extraction method which consists of evolutionary constructive induction for feature construction and a hybrid filter/wrapper method for
   feature selection.
%8 6  Decemeber
%Z WiML 2010 http://pami.uwaterloo.ca/~ealee/wiml/2010/index.php

%A Ilknur Icke
%A Andrew Rosenberg
%T Multi-Objective Genetic Programming for Visual Analytics
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 322--334
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming: poster
%X Visual analytics is a human-machine collaboration to data modelling where extraction of the most informative features plays an important role. Although feature extraction
   is a multi-objective task, the traditional algorithms either only consider one objective or aggregate the objectives into one scalar criterion to optimise. In this paper,
   we propose a Pareto-based multi-objective approach to feature extraction for visual analytics applied to data classification problems. We identify classifiability, visual
   interpretability and semantic interpretability as the three equally important objectives for feature extraction in classification problems and define various measures to
   quantify these objectives. Our results on a number of benchmark datasets show consistent improvement compared to three standard dimensionality reduction techniques. We also
   argue that exploration of the multiple Pareto-optimal models provide more insight about the classification problem as opposed to a single optimal solution.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Christian Igel
%T Causality of Hierarchical Variable Length Representations
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%P 324--329
%I IEEE Press
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming
%U http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/igel/CoHVLR.ps.gz
%X n this paper the strong causality of program tree representations is considered. A quantitative, probabilistic causality measure is used in contrast to statistical fitness
   landscape analysis methods. Although it fails to rank different problems according to their difficulty, it is helpful to choose the right coding for a given task. The
   investigation uses a metric on the search space called tree edit distance. Different ways to define such a measure are discussed.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
%@ 0-7803-4869-9

%A Christian Igel
%A Kumar Chellapilla
%T Fitness Distributions: Tools for Designing Efficient Evolutionary Computations
%B Advances in Genetic Programming 3
%E Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline
%D 1999
%P 191--216
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.867
%X Fitness distributions are employed as tools for understanding the effects of variation operators in Genetic Programming. Eleven operators are analysed on four common
   benchmark problems by estimating generation dependent features of the fitness distributions, e.g. the probability of improvement and the expected average fitness change.
%O 9
%8 June
%Z AiGP3
%@ 0-262-19423-6

%A Christian Igel
%A Martin Kreutz
%T Using Fitness Distributions to Improve the Evolution of Learning Structures
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 3
%D 1999
%P 1902--1909
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, fitness distributions, density estimation, gradient-based operators
%U http://citeseer.ist.psu.edu/294668.html
%X the absolute benefit, a measure of improvement in the fitness space, is derived from the viewpoint of fitness distribution and fitness trajectory analysis. It is used for
   online operator-adaptation, where the optimisation of density estimation models serves as an example. A new information theory based measure is proposed to judge the
   accuracy of the evolved models. Further, the absolute benefit is applied to offline analysis of new gradient based operators used for coefficient adaptation in genetic
   programming. An efficient method to calculate the gradient information is presented.
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143
%@ 0-7803-5537-7 (Microfiche)

%A Christian Igel
%A Kumar Chellapilla
%T Investigating the Influence of Depth and Degree of Genotypic Change on Fitness in Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1061--1068
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-422.ps
%X In this paper we investigate the influence of (a) the amount of variation generated in the genotype and (b) the depth of application of variation operators on the offspring
   fitness in genetic programming. Simulation results on three common test problems indicate that for certain features of the fitness distribution the location of the
   variation may play as important a role as the choice of the applied operators.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) Errata: We
   thought of binary trees when the second paragraph on the second page (i.e. 1062) was written...
%@ 1-55860-611-4

%A Christian Igel
%A Marc Toussaint
%T Neutrality and Self-Adaptation
%J Natural Computing
%V 2
%N 2
%D 2003
%P 117--132
%I 
%K genetic algorithms, genetic programming, evolutionary computation, genotype-phenotype mapping, neutrality, No-Free-Lunch theorem, redundancy, self-adaptation
%U http://ipsapp009.kluweronline.com/content/getfile/5030/5/1/abstract.htm
%X Neutral genotype-phenotype mappings can be observed in natural evolution and are often used in evolutionary computation. In this article, important aspects of such
   encodings are analysed. First, it is shown that in the absence of external control neutrality allows a variation of the search distribution independent of phenotypic
   changes. In particular, neutrality is necessary for self-adaptation, which is used in a variety of algorithms from all main paradigms of evolutionary computation to
   increase efficiency. Second, the average number of fitness evaluations needed to find a desirable (e.g., optimally adapted) genotype depending on the number of desirable
   genotypes and the cardinality of the genotype space is derived. It turns out that this number increases only marginally when neutrality is added to an encoding presuming
   that the fraction of desirable genotypes stays constant and that the number of these genotypes is not too small.
%Z Article ID: 5126729

%A Hitoshi Iima
%A Nobuo Sannomiya
%T Genetic Algorithm for a Large-Scale Scheduling Problem in an Electric Wire Production Process
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1784
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-707.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Takamasa Iio
%A Ivan Tanev
%A Katsunori Shimohara
%T Evolutionary adaptive behavior in noisy multi-agent system
%B SICE Annual Conference
%D 2008
%P 1506--1509
%I 
%C Japan
%K genetic algorithms, genetic programming, environmental information, evolutionary adaptive behavior, multi-agent system, perceptual noise, multi-agent systems
%X In this paper, we discuss a relationship between perceptual noise and fitness of agents in a multi-agent system. In multi-agent system, agents perceive environmental
   information and act based on this information. Therefore, in case that the perceptual information contains some noise, a cooperative behavior of agents is more challenging
   and the resulting fitness of the agents is inferior. In order to develop a behavior of the agents that is robust to the perception noise, we evolved the behavior of the
   agents in noisy environment. As a result, the evolved behavior, obtained in a noisy environment is superior (in terms of robustness) than that evolved in noiseless
   environment.
%8 20-22  August
%Z Also known as \cite4654898

%A Auke Jan Ijspeert
%T Design of artificial neural oscillatory circuits for the control of lamprey- and salamander-like locomotion using evolutionary algorithms
%R Ph.D. Thesis
%D 1998
%I 
%I Department of Artificial Intelligence, University of Edinburgh
%C UK
%K genetic algorithms, artificial life, CPG
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/ijspeert
%X This dissertation investigates the evolutionary design of oscillatory artificial neural networks for the control of animal-like locomotion. It is inspired by the neural
   organisation of locomotor circuitries in vertebrates, and explores in particular the control of undulatory swimming and walking. The difficulty with designing such
   controllers is to find mechanisms which can transform commands concerning the direction and the speed of motion into the multiple rhythmic signals sent to the multiple
   actuators typically involved in animal-like locomotion. In vertebrates, such control mechanisms are provided by central pattern generators which are neural circuits capable
   of producing the patterns of oscillations necessary for locomotion without oscillatory input from higher control centres or from sensory feedback. This thesis explores the
   space of possible neural configurations for the control of undulatory locomotion, and addresses the problem of how biologically plausible neural controllers can be
   automatically generated. Evolutionary algorithms are used to design connectionist models of central pattern generators for the motion of simulated lampreys and salamanders.
   This work is inspired by Ekeberg's neuronal and mechanical simulation of the lamprey [Ekeberg 93]. The first part of the thesis consists of developing alternative neural
   controllers for a similar mechanical simulation. Using a genetic algorithm and an incremental approach, a variety of controllers other than the biological configuration are
   successfully developed which can control swimming with at least the same efficiency. The same method is then used to generate synaptic weights for a controller which has
   the observed biological connectivity in order to illustrate how the genetic algorithm could be used for developing neurobiological models. Biologically plausible
   controllers are evolved which better fit physiological observations than Ekeberg's hand-crafted model. Finally, in collaboration with Jerome Kodjabachian, swimming
   controllers are designed using a developmental encoding scheme, in which developmental programs are evolved which determine how neurons divide and get connected to each
   other on a two-dimensional substrate. The second part of this dissertation examines the control of salamander-like swimming and trotting. Salamanders swim like lampreys
   but, on the ground, they switch to a trotting gait in which the trunk performs a standing wave with the nodes at the girdles. Little is known about the locomotion circuitry
   of the salamander, but neurobiologists have hypothesised that it is based on a lamprey-like organisation. A mechanical simulation of a salamander-like animat is developed,
   and neural controllers capable of exhibiting the two types of gaits are evolved. The controllers are made of two neural oscillators projecting to the limb motoneurons and
   to lamprey-like trunk circuitry. By modulating the tonic input applied to the networks, the type of gait, the speed and the direction of motion can be varied. By developing
   neural controllers for lamprey- and salamander-like locomotion, this thesis provides insights into the biological control of undulatory swimming and walking, and shows how
   evolutionary algorithms can be used for developing neurobiological models and for generating neural controllers for locomotion. Such a method could potentially be used for
   designing controllers for swimming or walking robots, for instance.

%A Auke Jan Ijspeert
%A Jerome Kodjabachian
%T Evolution and Development of a Central Pattern Generator for the Swimming of a Lamprey
%J Artificial Life
%V 5
%N 3
%D 1999
%P 247--269
%I 
%K genetic algorithms, genetic programming, neural control, developmental encoding, SGOCE, simulation, central pattern generator, CPG, swimming, lamprey
%X This article describes the design of neural control architectures for locomotion using an evolutionary approach. Inspired by the central pattern generators found in
   animals, we develop neural controllers that can produce the patterns of oscillations necessary for the swimming of a simulated lamprey. This work is inspired by Ekeberg's
   neuronal and mechanical model of a lamprey [11] and follows experiments in which swimming controllers were evolved using a simple encoding scheme [25, 26]. Here,
   controllers are developed using an evolutionary algorithm based on the SGOCE encoding [31, 32] in which a genetic programming approach is used to evolve developmental
   programs that encode the growing of a dynamical neural network. The developmental programs determine how neurons located on a two-dimensional substrate produce new cells
   through cellular division and how they form efferent or afferent interconnections. Swimming controllers are generated when the growing networks eventually create
   connections to the muscles located on both sides of the rectangular substrate. These muscles are part of a two-dimensional mechanical simulation of the body of the lamprey
   in interaction with water. The motivation of this article is to develop a method for the design of control mechanisms for animal-like locomotion. Such a locomotion is
   characterized by a large number of actuators, a rhythmic activity, and the fact that efficient motion is only obtained when the actuators are well coordinated. The task of
   the control mechanism is therefore to transform commands concerning the speed and direction of motion into the signals sent to the multiple actuators. We define a fitness
   function, based on several simulations of the controller with different commands settings, that rewards the capacity of modulating the speed and the direction of swimming
   in response to simple, varying input signals. Central pattern generators are thus evolved capable of producing the relatively complex patterns of oscillations necessary for
   swimming. The best solutions generate traveling waves of neural activity, and propagate, similarly to the swimming of a real lamprey, undulations of the body from head to
   tail propelling the lamprey forward through water. By simply varying the amplitude of two input signals, the speed and the direction of swimming can be modulated.
%8 Summer
%Z http://alife.tuke.sk/projekty/abstract/abstract99.html#a34 Also available as University of Edinburgh Technical report IngentaPDF version crashes my acrobat reader

%A Yoshikazu Ikeda
%A Shozo Tokinaga
%T Approximation of Chaotic Dynamics by Using Smaller Number of Data Based upon the Genetic Programming and Its Applications
%J IEICE Transactions on fundamentals of electronics, communications and computer sciences
%V E83A
%N 8
%D 2000
%P 1599--1607
%I Oxford University Press
%I The Institute of Electronics, Information and Communication Engineers. JAPAN
%K genetic algorithms, genetic programming, nonlinear dynamics, system identification, Nonlinear Signal Processing, chaotic dynamics, economics,identification,prediction
%U http://www.ee.psu.ac.th/ieice/2000/pdf/e83-a_8_1599.pdf
%X This paper deals with the identification of system equation of the chaotic dynamics by using smaller number of data based upon the genetic programming (GP). The problem to
   estimate the system equation from the chaotic data is important to analyze the structure of dynamics in the fields such as the business and economics. Especially, for the
   prediction of chaotic dynamics, if the number of data is restricted, we can not use conventional numerical method such as the linear-reconstruction of attractors and the
   prediction by using the neural networks. In this paper we use an efficient method to identify the system equation by using the GP. In the GP, the performance (fitness) of
   each individual is defined as the inversion of the root mean square error of the spectrum obtained by the original and predicted time series to suppress the effect of the
   initial value of variables. Conventional GA (Genetic Algorithm) is combined to optimize the constants in equations and to select the primitives in the GP representation. By
   selecting a pair of individuals having higher fitness, the crossover operation is applied to generate new individuals. The crossover operation used here means the
   replacement of a part of tree in individual A by a part of tree in individual B. To avoid the meaningless genetic operation, the validity of prefix representation of the
   subtree to be embedded to the other tree is probed by using the stack count. These newly generated individuals replace old individuals with lower fitness. The mutation
   operation is also used to avoid the convergence to the local minimum. In the simulation study, the identification method is applied at first to the well known chaotic
   dynamics such as the Logistic map and the Henon map. Then, the method is applied to the identification of the chaotic data of various time series by using one dimensional
   and higher dimensional system. The result shows better prediction than conventional ones in cases where the number of data is small.

%A Yoshikazu Ikeda
%A Shozo Tokinaga
%T Analysis of Price Changes in Artificial Double Auction Markets Consisting of Multi-Agents Using Genetic Programming for Learning and Its Applications
%J IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
%V 90-A
%N 10
%D 2007
%P 2203--2211
%I 
%K genetic algorithms, genetic programming, artificial double auction market, multi-agents, electricity market, control of chaos
%X In this paper, we show the analysis of price changes in artificial double auction markets consisting of multi-agents who learn from past experiences based on the Genetic
   Programming (GP) and its applications. For simplicity, we focus on the double auction in an electricity market. Agents in the market are allowed to buy or sell items
   (electricity) depending on the prediction of situations. Each agent has a pool of individuals (decision functions) represented in tree structures to decide bid price by
   using the past result of auctions. A fitness of each individual is defined by using successful bids and a capacity usage of production units for a production of items, and
   agents improve their individuals based on the GP to get higher return in coming auctions. In simulation studies, changes of bid prices and returns of bidders are discussed
   depending on demand curves of customers and the weight between an average profit obtained by successful bids and the capacity usage rate of production units. The validation
   of simulation studies is examined by comparing results with classical models and price changes in real double auction markets. Since bid prices bear relatively large
   changes, we apply an approximate method for a control by forcing agents stabilize the changes in bid prices. As a result, we see the stabilization scheme of bid prices in
   double auction markets is not realistic, then it is concluded that the market contains substantial instability.

%A Yoshikazu Ikeda
%A Shozo Tokinaga
%T Multi-Fractality Analysis of Time Series in Artificial Stock Market Generated by Multi-Agent Systems Based on the Genetic Programming and Its Applications
%J IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
%V 90-A
%N 10
%D 2007
%P 2212--2222
%I 
%K genetic algorithms, genetic programming, multi-fractal, artificial stock market, multi-agent-based modeling
%X There are several methods for generating multi-fractal time series, but the origin of the multi-fractality is not discussed so far. This paper deals with the
   multi-fractality analysis of time series in an artificial stock market generated by multi-agent systems based on the Genetic Programming (GP) and its applications to
   feature extractions. Cognitive behaviors of agents are modeled by using the GP to introduce the co-evolutionary (social) learning as well as the individual learning. We
   assume five types of agents, in which a part of the agents prefer forecast equations or forecast rules to support their decision making, and another type of the agents
   select decisions at random like a speculator. The agents using forecast equations and rules usually use their own knowledge base, but some of them use their public (common)
   knowledge base to improve trading decisions. For checking the multi-fractality we use an extended method based on the continuous time wavelet transform. Then, it is shown
   that the time series of the artificial stock price reveals as a multi-fractal signal. We mainly focus on the proportion of the agents of each type. To examine the role of
   agents of each type, we classify six cases by changing the composition of agents of types. As a result, in several cases we find strict multi-fractality in artificial stock
   prices, and we see the relationship between the realizability (reproducibility) of multi-fractality and the system parameters. By applying a prediction method for
   mono-fractal time series as counterparts, features of the multi-fractal time series are extracted. As a result, we examine and find the origin of multi-fractal processes in
   artificial stock prices.

%A I. M. Ikram
%T An occam Library for Genetic Programming on Transputer Networks
%B Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications
%E Hamid R. Arabnia
%D 1996
%P 1186--1189
%I CSREA
%C Sunnyvale, California
%K genetic algorithms, genetic programming, occam, Transputers
%X This paper describes the contents of a library of occam procedures used to implement parallel versions of the Genetic Programming (GP) machine learning paradigm. GP
   attempts to evolve solutions to machine learning problems, in the form of trees encoding programs or expressions. As occam lacks recursion and both higher order functions
   and function pointers, the implementation of a generic tree evaluation procedure for trees containing arbitrary functions is not trivial. We present a concurrent algorithm
   used to alleviate this problem.
%8 9-11  August
%Z Ismail Ikram http://cs.ru.ac.za/homes/g93i0527/
%@ 0-9648666-4-1

%A Tin Ilakovac
%A Zeljka Perkovic
%A Strahil Ristov
%T The Use of Genetic Algorithms in the Optimization of Competitive Neural Networks which Resolve the Stuck Vectors Problem
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 499
%I MIT Press
%C Stanford University, CA, USA
%K Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 GA paper

%A Michael Iles
%A Dwight Deugo
%T A search for routing strategies in a peer-to-peer network using genetic programming
%B Proceedings 21st IEEE Symposium on Reliable Distributed Systems
%D 2002
%P 341--346
%I 
%K genetic algorithms, genetic programming, computer networks, discrete event simulation, learning (artificial intelligence), protocols, telecommunication network routing,
   Gnutella protocol, machine learning techniques, resource location optimization, routing strategies, simulated peer-to-peer network, traffic flow scenarios
%X Results taken from a simulated peer-to-peer network are described, in which genetic programming is used to evolve routing strategies that optimise resource location in
   various traffic flow scenarios. In all cases the evolved strategies result in more numerous resource locations than a pure, non-adaptive peer-to-peer protocol such as the
   Gnutella protocol. The resulting evolved strategies are described, and empirical validation of the Gnutella protocol is given via both its creation through machine-learning
   techniques, and through the analysis of real-world constants used in the protocol.
%8 13-16  October
%Z Inspec Accession Number: 7516795. Carleton Univ., Ottawa, Ont., Canada

%A Mehmet Ali Ilgin
%A Surendra M. Gupta
%T Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art
%J Journal of Environmental Management
%V 91
%N 3
%D 2010
%P 563--591
%I 
%K genetic algorithms, genetic programming, Closed-loop supply chains, Disassembly, Environmentally conscious manufacturing, Environmentally conscious product design, Product
   recovery, Remanufacturing, Reverse logistics
%U http://www.sciencedirect.com/science/article/B6WJ7-4XHC6JT-5/2/d21573d2beec024e5b27fd2fdb11b653
%X Gungor and Gupta [1999, Issues in environmentally conscious manufacturing and product recovery: a survey. Computers and Industrial Engineering, 36(4), 811-853] presented an
   important review of the development of research in Environmentally Conscious Manufacturing and Product Recovery (ECMPRO) and provided a state of the art survey of published
   work. However, that survey covered most papers published through 1998. Since then, a lot of activity has taken place in EMCPRO and several areas have become richer. Many
   new areas also have emerged. In this paper we primarily discuss the evolution of ECMPRO that has taken place in the last decade and discuss the new areas that have come
   into focus during this time. After presenting some background information, the paper systematically investigates the literature by classifying over 540 published references
   into four major categories, viz., environmentally conscious product design, reverse and closed-loop supply chains, remanufacturing, and disassembly. Finally, we conclude by
   summarising the evolution of ECMPRO over the past decade together with the avenues for future research.
%Z survey

%A Nesa Ilich
%T A Strongly Feasible Evolution Program for non-linear optimization of Network Flows
%R Ph.D. Thesis
%D 2000
%I 
%I Department of Civil and Geological Sciences, University of Manitoba
%C Winnipeg, Canada
%K genetic algorithms, genetic programming, Evolution Programs, Network Flows, Non-Linear Constraints
%X This thesis describes the main features of a Strongly Feasible Evolution Program (SFEP) for solving network flow programs that can be non-linear both in the constraints and
   in the objective function. The approach is a hybrid of a network flow algorithm and an evolution program. Network flow theory is used to help conduct the search exclusively
   within the feasible region, while progress towards optimal points in the search space is achieved using evolution programming mechanisms such as recombination and mutation.
   The solution procedure is based on a recombination operator in which all parents in a small mating pool have equal chance of contributing their genetic material to an
   offspring. When an offspring is created with better fitness value than that of the worst parent, the worst parent is discarded from the mating pool while the offspring is
   placed in it. The main contributions are in the massive parallel initialization procedure which creates only feasible solutions with simple heuristic rules that increase
   chances of creating solutions with good fitness values for the initial mating pool, and the gene therapy procedure which fixes "defective genes" ensuring that the offspring
   resulting from recombination is always feasible. Both procedures use the properties of network flows. Tests were conducted on a number of previously published
   transportation problems with 49 and 100 decision variables, and on two problems involving water resources networks with complex non-linear constraints with up to 1500
   variables. Convergence to equal or better solutions was achieved with often less than one tenth of the previous computational efforts.
%8 October

%A Janine H. Imada
%A Brian J. Ross
%T Using feature-based fitness evaluation in symbolic regression with added noise
%B GECCO-2008 Late-Breaking Papers
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 2153--2158
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, noisy signals, symbolic regression
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2153.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1389039

%A Janine Imada
%T Evolutionary synthesis of stochastic gene network models using feature-based search spaces
%R M.S. Thesis M.Sc. Computer Science
%D 2009
%I 
%I Department of Computer Science, Brock University
%C St. Catharines, Ontario, Canada
%K genetic algorithms, genetic programming
%U http://hdl.handle.net/10464/2853
%X A feature-based fitness function is applied in a genetic programming system to synthesise stochastic gene regulatory network models whose behaviour is defined by a time
   course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating
   signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined
   from a set of statistical features characterising the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving
   symbolic regression with added noise and gene regulatory network models based on the stochastic pi-calculus, it is shown to successfully target oscillating and
   non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or
   stochastic behaviour.
%8 28  January
%Z cited by \citeRoss:2011:GPEM

%A Janine Imada
%A Brian J. Ross
%T Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces
%J New Generation Computing
%V 29
%D 2011
%P 365--390
%I Ohmsha, Ltd. and Springer
%K genetic algorithms, genetic programming, Stochastic, Statistical Features, Gene Regulatory Networks, Time Series
%X A feature-based fitness function is applied in a genetic programming system to synthesise stochastic gene regulatory network models whose behaviour is defined by a time
   course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating
   signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise and/or stochastic behaviour. This paper explores a
   fitness measure determined from a set of statistical features characterising the time series' sequence of values, rather than the actual values themselves. Through a series
   of experiments involving modular gene regulatory network models based on the stochastic pi-calculus, it is shown to successfully target oscillating and non-oscillating
   signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic
   behaviour.
%8 October

%A Joe Imae
%A Nobuyuki Ohtsuki
%A Yoshiteru Kikuchi
%A Tomoaki Kobayashi
%T A minimax control design for nonlinear systems based on genetic programming: Jung's collective unconscious approach
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 1702--1707
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%X When it comes to the minimax controller design, it would be extremely difficult to obtain such controllers in the nonlinear situations. One of the reasons is that the
   minimax controller should be robust against any kind of disturbances in the nonlinear situations. In this paper, we propose a difficulty-free design method of minimax
   control problems. First, based on the genetic programming and Jung's collective unconscious, this paper presents a very simple design technique to solve the minimax control
   problems, where the minimax controller may be constructed only paying attention to the minimisation process. It would be surprising that the maximization process is not
   needed in the construction of minimax controllers. Then, some simulations are given to demonstrate the usefulness of the proposed design technique with the identification
   problem, and minimax control problems.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Joe Imae
%A Yoshiteru Kikuchi
%A Nobuyuki Ohtsuki
%A Tomoaki Kobayashi
%T A nonlinear control system design based on HJB/HJI/FBI equations via a differential genetic programming approach
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 763--769
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%X Based on the differential genetic programming, a new design method is proposed for optimal and/or robust controllers of nonlinear systems. First we introduce a new type of
   the genetic programming (GP), so-called differential GP (DGP), combining GP with an automatic differentiation scheme, which could solve Hamilton-Jacobi-Bellman (HJB) /
   Hamilton-Jacobi-Isaacs (HJI) / Francis-Byrnes-Isidori (FBI) equations. Lastly, the effectiveness of a DGP based design method is demonstrated through some design examples
   of nonlinear systems.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Joe Imae
%A Yasuhiko Morita
%A Guisheng Zhai
%A Tomoaki Kobayashi
%T An evolutionary approach to identification problems with incomplete output data
%B SICE Annual Conference
%D 2008
%P 2262--2265
%I 
%C Japan
%K genetic algorithms, genetic programming, evolutionary algorithm, nonlinear system identification problems, identification, nonlinear control systems
%X In this paper, we consider nonlinear system identification problems in the case where output data is incomplete. We propose an identification method based on an
   evolutionary algorithm, which is a fusion of a genetic algorithm (GA) and genetic programming (GP), and illustrate the effectiveness of the proposed method through a
   simulation and an experiment with a cart.
%8 20-22  August
%Z Also known as \cite4655041

%A Joe Imae
%A Yasuhiko Morita
%A Guisheng Zhai
%A Tomoaki Kobayashi
%T A GP-based Design Method for Nonlinear Control Systems using Differential Flatness
%B World Automation Congress (WAC), 2010
%D 2010
%I TSI Press
%C Kobe, Japan
%K genetic algorithms, genetic programming, GP-based design method, MIMO systems, decoupling process, differential flatness theory, nonlinear control systems, MIMO systems,
   control system synthesis, nonlinear control systems
%U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5665445
%X In this paper, we propose a practical and systematic approach to the control design method for MIMO systems based on flatness theory. The proposed approach focuses on the
   emergent ability of genetic programming and the decoupling ability of Descusse and Moog's algorithm. The former could generate nonlinear functions as the flat outputs, and
   the latter could construct dynamic controllers through the decoupling process. Some simulations are carried out to show the effectiveness of the proposed approach.
%8 19-23  September
%Z Submarine. http://confconnect.info/confconnect/ocs/index.php?conference=wac&schedConf=WAC2010&page=schedConf&op=presentations Osaka Prefecture Univ., Sakai, Japan. Also
   known as \cite5665445

%A Kosuke Imamura
%A James A. Foster
%A Axel W. Krings
%T The Test Vector Problem and Limitations to Evolving Digital Circuits
%B The Second NASA/DoD workshop on Evolvable Hardware
%E Jason Lohn and Adrian Stoica and Didier Keymeulen
%D 2000
%P 75--80
%I IEEE Computer Society 1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA
%I Jet Propulsion Laboratory, California Institute of Technology
%C Palo Alto, California
%K genetic algorithms, logic design, logic testing, VLSI, evolutionary techniques, evolving digital circuits, test vector generation problem, test vector problem, truth table
%X Evolvable Hardware (EHW) has been proposed as a new technique to design complex systems. Often, complex systems turn out to be very difficult to evolve. The problem is that
   a general strategy is too difficult for the evolution process to discover directly. This paper proposes a new approach that performs incremental evolution in two
   directions: from complex system to sub-systems and from subsystems back to complex system. In this approach, incremental evolution gradually decomposes a complex problem
   into some sub-tasks. In a second step, we gradually make the tasks more challenging and general. Our approach automatically discovers the sub-tasks, their sequence as well
   as circuit layout dimensions. Our method is tested in a digital circuit domain and compared to direct evolution. We show that our bidirectional incremental approach can
   handle more complex, harder tasks and evolve them more effectively, then direct evolution.
%8 13-15  July
%Z EH2000 http://ic.arc.nasa.gov/projects/eh2000/
%@ 0-7695-0762-X

%A Kosuke Imamura
%A James A. Foster
%T Fault-Tolerant Computing with N-Version Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 178
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster, Fault-Tolerant N-Version Genetic Programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Kosuke Imamura
%A Robert B. Heckendorn
%A Terence Soule
%A James A. Foster
%T $N$-version Genetic Programming via Fault Masking
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 172--181
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780172.pdf
%X We introduce a new method, N-Version Genetic Programming (NVGP), for building fault tolerant software by building an ensemble of automatically generated modules in such a
   way as to maximize their collective fault masking ability. The ensemble itself is an example of n-version modular redundancy for fault tolerance, where the output of the
   ensemble is the most frequent output of n independent modules. By maximising collective fault masking, NVGP approaches the fault tolerance expected from n version modular
   redundancy with independent faults in component modules. The ensemble comprises individual modules from a large pool generated with genetic programming, using operators
   that increase the diversity of the population. Our experimental test problem classified promoter regions in Escherichia coli DNA sequences. For this problem, NVGP reduced
   the number and variance of errors over single modules produced by GP, with statistical significance.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP, UCI ML e.coli benchmark (balanced training 35 positives, 35 negatives). beowulf. 2-gram (16 possible). linear gp (MIPS like). max
   length 80. 4 read/write registers (memory). 5 crossover types. Inversion (!). 2 mutation operators, tournament fitness=correlation coefficient. 40 isolated islands (demes)
   each 100 individuals. ensemble = composition from (randomly chosen) island. ensemble is qualified if number of its errors <= number of errors expected if its components
   were _independent_ 14% to 58% improvement in error rate for ensemble (of 30) compared to single GP (pop 100).
%@ 3-540-43378-3

%A Kosuke Imamura
%A Robert B. Heckendorn
%A Terence Soule
%A James A. Foster
%T Abstention Reduces Errors--decision Abstaining N-version Genetic Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 796--803
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Kosuke Imamura
%T Abstention Reduces Errors - Decision Abstaining N-version Genetic Programming
%B Graduate Student Workshop
%E Sean Luke and Conor Ryan and Una-May O'Reilly
%D 2002
%P 284--287
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York
%K genetic algorithms, genetic programming
%8 8  July
%Z Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic
   Programming Conference (GP-2002) part of barry:2002:GECCO:workshop

%A Kosuke Imamura
%A Terence Soule
%A Robert B. Heckendorn
%A James A. Foster
%T Behavioral Diversity and a Probabilistically Optimal GP Ensemble
%J Genetic Programming and Evolvable Machines
%V 4
%N 3
%D 2003
%P 235--253
%I 
%K genetic algorithms, genetic programming, N-version programming, classification, ensemble, diversity
%X We propose N-version Genetic Programming (NVGP) as an ensemble method to enhance accuracy and reduce performance fluctuation of programs produced by genetic programming.
   Diversity is essential for forming successful ensembles. NVGP quantifies behavioural diversity of ensemble members and defines NVGP optimal as an ensemble that has
   independent fault occurrences among its members. We observed significant accuracy improvement by NVGP optimal ensembles when applied to a DNA segment classification
   problem.
%8 September
%Z Article ID: 5141123 CJMP CJMPI p243 five different types of crossover NVGP

%A Yoshiyuki Inagaki
%T On synchronized evolution of the network of automata
%R Ph.D. Thesis
%D 1999
%I 
%I University of California, Irvine
%K genetic algorithms, genetic programming, Computer science, Sequential machine theory, Artificial intelligence
%Z see also \citeinagaki:2002:TEC

%A Yoshiyuki Inagaki
%T On Synchronized Evolution of the Network of Automata
%J IEEE Transactions on Evolutionary Computation
%V 6
%N 2
%D 2002
%P 147--158
%I 
%K genetic algorithms, genetic programming, Evolutionary programming, finite automaton, sequence prediction problem, DFA, FSM, DT, music
%U http://ieeexplore.ieee.org/iel5/4235/21497/00996014.pdf?tp=&arnumber=996014&isnumber=21497&arSt=147&ared=158&arAuthor=Inagaki%2C+Y.%3B
%X One of the tasks in machine learning is to build a device that predicts each next input symbol of a sequence as it takes one input symbol from the sequence. We studied new
   approaches to this task. We suggest that deterministic finite automata (DFA) are good building blocks for this device together with genetic algorithms (GAs), which let
   these automata evolve to predict each next input symbol of the sequence. Moreover, we studied how to combine these highly fit automata so that a network of them would
   compensate for each others weaknesses and predict better than any single automaton.We studied the simplest approaches to combine automata: building trees of automata with
   special-purpose automata, which may be called switchboards. These switchboard automata are located on the internal nodes of the tree, take an input symbol from the input
   sequence just as do other automata, and predict which subtree will make a correct prediction on each next input symbol. GAs again play a crucial role in searching for
   switchboard automata. We studied various ways of growing trees of automata and tested them on sample input sequences, mainly note pitches, note duration, and up/down notes
   of Bach s Fugue IX. The test results show that DFAs together with GAs seem to be very effective for this type of pattern learning task.
%8 April
%Z 1089-778X(02)04103-6 crossover combining 2 tree roots with switchboard. p150 bit vector scores. Clock DFA (overfitting?????). Time series prediction (stock market).

%A Andrew Innes
%T Genetic Programing for Cephalometric Landmark Detection
%R Ph.D. Thesis
%D 2007
%I 
%I School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University
%C Victoria, Australia
%K genetic algorithms, genetic programming
%U http://adt.lib.rmit.edu.au/adt/uploads/approved/adt-VIT20080221.123310/public/02whole.pdf
%X The domain of medical imaging...
%8 29  August

%A Y. Inoue
%A T. Tohge
%A H. Iba
%T Cooperative Transportation by Humanoid Robots: Learning to Correct Positioning
%B Design and Application of Hybrid Intelligent Systems
%S Frontiers in Artificial Intelligence and Applications Vol. 104
%E Ajith Abraham and Mario K\"oppen and Katrin Franke
%D 2003
%P 1124--1134
%I IOS Press Amsterdam, Berlin, Oxford, Tokyo, Washington D.C.
%C Melbourne
%K genetic algorithms, genetic programming
%U http://www.iba.k.u-tokyo.ac.jp/papers/2003/inoueHIS2003.pdf
%X In this paper, we describe a cooperative transportation problem with two humanoid robots and introduce a machine learning approach to solving the problem. The difficulty of
   the task lies on the fact that each position shifts with the other's while they are moving. Therefore, it is necessary to correct the position in a realtime manner.
   However, it is difficult to generate such an action in consideration of the physical formula.We empirically show how successful the humanoid robot HOAP-1's cooperate with
   each other for the sake of the transportation as a result of Q-learning.
%O Real-World Applications
%8 Decemeber
%Z part of his03:book
%@ 1-58603-394-8

%A Yutaka Inoue
%A Takahiro Tohge
%A Hitoshi Iba
%T Learning for Cooperative Transportation by Autonomous Humanoid Robots
%B Evolvable Machines: Theory \& Practice
%S Studies in Fuzziness and Soft Computing
%E Nadia Nedjah and Luiza de Macedo Mourelle
%V 161
%D 2004
%P 3--20
%I Springer
%C Berlin
%K genetic algorithms, genetic programming
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html
%O 1
%Z Springer says published in 2005 but available Nov 2004
%@ 3-540-22905-1

%A Muhammad Iqbal
%A Mengjie Zhang
%A Will Browne
%T Automatically defined functions for learning classifier systems
%B Fourteenth international workshop on learning classifier systems
%E Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz
%D 2011
%P 375--382
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, 20mux
%X This work introduces automatically defined functions (ADFs) for learning classifier systems (LCS). ADFs had been successfully implemented in genetic programming (GP)for
   various domain problems such as multiplexer and even-odd parity, but they have never been attempted in LCS research field before. ADFs in GP contract program trees and
   shorten training times whilst providing resilience to destructive genetic operators. We have implemented ADFs in Wilson's accuracy based LCS, known as XCS [14]. This
   initial investigation of ADFs in LCS shows that the multiple genotypes to a phenotype issue in feature rich encodings disables the subsumption deletion function. The
   additional methods and increased search space also leads to much longer training times. This is compensated by the ADFs containing useful knowledge, such as the importance
   of the address bits in the multiplexer problem. The ADFs also create masks that autonomously subdivide the search space into areas of interest and uniquely, areas of not
   interest. The next stage of this work is to implement simplification methods and then determine methods by which ADFs can facilitate scaling for more complex problems
   within the same problem domain.
%8 12-16  July
%Z Also known as \cite2002022 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Zahir Irani
%A Amir Shari
%T Genetic Algorithm Optimization of Investment Justification Theory
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 87--92
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Zahir Irani
%A Amir M. Sharif
%T A Revised Perspective on the Evaluation of IT/IS Investments using an Evolutionary Approach
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Muhammad Irfan
%A Qaiser Habib
%A Ghulam M. Hassan
%A Khawaja M. Yahya
%A Samira Hayat
%T Combinational digital circuit synthesis using Cartesian Genetic Programming from a NAND gate template
%B 6th International Conference on Emerging Technologies (ICET 2010)
%D 2010
%P 343--347
%I 
%K genetic algorithms, genetic programming, Cartesian genetic programming, NAND gate template, combinational digital circuit synthesis, evolutionary synthesis, mutation
   operator, NAND circuits, combinational circuits, network synthesis
%X Evolutionary synthesis of combinational digital circuits is a promising research area and many a success has been achieved in this field. This paper presents a new
   technique for the synthesis of combinational circuits by using Cartesian Genetic Programming (CGP) and uniform NAND gate based templates. Using a uniform gate template
   implies an ease in the fabrication process but in some instances, the number of gates required may increase which can be optimised by CGP. The mutation operator has been
   used for achieving convergence. A 2-bit multiplier and 4-bit odd parity generator circuits have been evolved for experimentation and comparison to previous results. The
   results obtained are compared to earlier work done in the same field. Moreover, the relationship of evolution time (in terms of number of generations) to the population
   size has been established and analysed.
%8 October
%Z University of Engineering & Technology Peshawar, Pakistan. Also known as \cite5638462

%A Satoru Isaka
%T An Empirical Study of Facial Image Feature Extraction by Genetic Programming
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 93--99
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Celso Yoshikazu Ishida
%A Aurora Trinidad Ramirez Pozo
%T GPSQL Miner: SQL-Grammar Genetic Programming in Data Mining
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 1226--1231
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming, SQL
%X The present work describes GPSQL Miner, a Genetic Programming system for mining relational databases. This system uses Grammar Genetic Programming for classification task
   and one of its main features is the representation of the classifiers. The system uses SQL grammar, which facilitates the evaluation process, once the data are in
   relational databases. The tool was tested with some databases and the results were compared with other algorithms. These first experiments had shown promising results for
   the classification task.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Yoko Ishino
%A Yan Jin
%T Estimate design intent: a multiple genetic programming and multivariate analysis based approach
%J Advanced Engineering Informatics
%V 16
%N 2
%D 2002
%P 107--125
%I 
%K genetic algorithms, genetic programming, Design process, Design intent, Multivariate analysis
%U http://www.sciencedirect.com/science/article/B6X1X-45XR6TT-3/2/d9b1ec675457ba42091348338705293d
%X Understanding design intent of designers is important for managing design quality, achieving coherent integration of design solutions, and transferring design knowledge.
   This paper focuses on automatically estimating design intent, represented as a summation of weighted functions, based on the operational and product-specific information
   monitored through design processes. This estimated design intent provides a basis for us to identify the evaluation tendency of designers' ways of doing design. To
   represent and estimate the design intent, we introduced a staged design evaluation model as a general yet powerful model of design decision-making process, and developed a
   methodology for estimation of design intent (MEDI) as a reasoning method. MEDI is composed of two basic algorithms. One is our newly introduced multiple genetic programming
   (MGP) and the other is statistical multivariate analysis including principal component analysis and multivariate regression. The characteristics of MEDI are; (1) principal
   component analysis provides approximate evaluation of how much preferable a specific product model is, assuming the final product model (or design) is the most preferable
   one; (2) MGP enables us to simultaneously estimate both structure of target performance functions and the approximate values of their weights for a domain of design
   problems; and (3) multivariate regression readjusts the approximate weights obtained by MGP into more accurate ones for specific design problems within the domain. Our
   framework and methods have been successfully tested in a case study of designing a double-reduction gear system.

%A Ismail A. Ismail
%A Nabawia A. ElRamly
%A Mohammed A. Abd-ElWahid
%A Passent M. ElKafrawy
%A Mohammed M. Nasef
%T Genetic Programming Framework for Fingerprint Matching
%J International Journal of Computer Science and Information Security
%D 2009
%I LJS Publisher and IJCSIS Press
%K genetic algorithms, genetic programming, Fingerprint matching, minutiae points
%U http://arxiv.org/abs/0912.1017
%X A fingerprint matching is a very difficult problem. Minutiae-based-matching is the most popular and widely used technique for fingerprint matching. The minutiae points
   considered in automatic identification systems are based normally on termination and bifurcation points. In this paper we propose a new technique for fingerprint matching
   using minutiae points and genetic programming. The goal of this paper is extracting the mathematical formula that defines the minutiae points. .

%A Akira Ito
%A Hiroyuki Yano
%T The Emergence of Cooperation in a Society of Autonomous Agents -- The Prisoner's Dilemma Game Under the Disclosure of Contract Histories --
%B ICMAS-95 Proceedings First International Conference on Multi-Agent Systems
%E Victor Lesser
%D 1995
%P 201--208
%I AAAI Press/MIT Press
%C San Francisco, California, USA
%K multi-agent
%8 12--14  June
%Z Society of agents play PD against each other according to inherited strategy. Strategies are specified by (possibly recursive) programs written in a small language.
   Programs are mutated but no crossover.
%@ 0-262-62102-9

%A Takuya Ito
%A Hitoshi Iba
%A Masayuki Kimura
%T Robustness of Robot Programs Generated by Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%O 321--326
%8 28--31  July
%Z GP-96. Moderatly complex robot simulation
%@ 0-262-61127-9

%A Takuya Ito
%A Hitoshi Iba
%A Satoshi Sato
%T Non-Destructive Depth-Dependent Crossover for Genetic Programming
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 71--82
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%X In our previous paper [Ito et al., 1998], a depth-dependent crossover was proposed for GP. The purpose was to solve the difficulty of the blind application of the normal
   crossover, i.e., building blocks are broken unexpectedly. In the depth-dependent crossover, the depth selection ratio was varied according to the depth of a node. However,
   the depth-dependent crossover did not work very effectively as generated programs became larger. To overcome this, we introduce a non-destructive depth-dependent crossover,
   in which each offspring is kept only if its fitness is better than that of its parent. We compare GP performance with the depth-dependent crossover and that with the
   non-destructive depth-dependent crossover to show the effectiveness of our approach. Our experimental results clarify that the non-destructive depth-dependent crossover
   produces smaller programs than the depth-dependent crossover.
%8 14-15  April
%Z EuroGP'98. Santa Fe artificial ant
%@ 3-540-64360-5

%A Takuya Ito
%A Hitoshi Iba
%A Satoshi Sato
%T Depth-Dependent Crossover for Genetic Programming
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%P 775--780
%I IEEE Press
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming
%X It is known that selection and crossover operators contribute to generate solutions in GP. Traditionally, crossover points are selected randomly by a normal (canonical)
   crossover. However, the traditional method has several difficulties that building blocks (i.e. effective partial programs) are broken because of blind application of the
   normal crossover. This paper proposes a depth-dependent crossover for GP, in which the depth selection ratio is varied according to the depth of a node. This proposed
   method is to accumulate building blocks via the encapsulation of the depth-dependent crossover. We compare GP performance with the depth-dependent crossover and that with
   the normal crossover. Our experimental results clarify that the superiority of the proposed crossover to the normal.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
%@ 0-7803-4869-9

%A Takuya Ito
%A Hitoshi Iba
%A Satoshi Sato
%T A Self-Tuning Mechanism for Depth-Dependent Crossover
%B Advances in Genetic Programming 3
%E Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline
%D 1999
%P 377--399
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/aigp3/ch16.pdf
%X There are three genetic operators: crossover, mutation and reproduction in Genetic Programming (GP). Among these genetic operators, the crossover operator mainly
   contributes to searching for a solution program. Therefore, we aim at improving the program generation by extending the crossover operator. The normal crossover selects
   crossover points randomly and destroys building blocks. We think that building blocks can be protected by swapping larger substructures. In our former work, we proposed a
   depth-dependent crossover. The depth-dependent crossover protected building blocks and constructed larger building blocks easily by swapping shallower nodes. However, there
   was problem-dependent characteristics on the depth-dependent crossover, because the depth selection probability was fixed for all nodes in a tree. To solve this difficulty,
   we propose a self-tuning mechanism for the depth selection probability. We call this type of crossover a "self-tuning depth-dependent crossover". We compare GP performances
   of the selftuning depthdependent crossover with performances of the original depth-dependent crossover. Our experimental results clarify the superiority of the self tuning
   depth dependent crossover.
%O 16
%8 June
%Z AiGP3 11 mux, santa fe ant, 4-even parity, simulated robot
%@ 0-262-19423-6

%A Takuya Ito
%T Efficient program generation by genetic programming
%R Ph.D. Thesis
%D 1999
%I 
%I Japan Advanced Instutute of Science and Technology
%C Ishikawa
%K genetic algorithms, genetic programming

%A Choshu Ito
%T RF-LDMOSFET Modeling Using Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 221--227
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Keith Ito
%T Simple Robots in a Complex World: Collaborative Exploration Behavior using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 91--99
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/Ito.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Laur Ivan
%T Automatic Parallelization of Loops in Sequential Programs using Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Makoto Iwashita
%A Hitoshi Iba
%T Island Model GP with Immigrants Aging and Depth-Dependent Crossover
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 267--272
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming, Deme, Migration
%X This paper proposes a new method for island model GP. The proposed method applies a traditional genetic operator to an aborigine and a depth-dependent crossover to the
   immigrants according to their ages, which show how long they survive in the island.This method can provide both local and global search strategies. The experimental results
   have shown that our approach works effectively.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Ashkan Izadi
%A Vic Ciesielski
%T An exploration of genetic programming for non-photorealistic animations
%B 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010)
%V 9
%D 2010
%P 255--259
%I 
%K genetic algorithms, genetic programming, aesthetic characteristics, aesthetically pleasing animations, non-photorealistic animation, non-photorealistic rendering,
   triangular brushstrokes, computer animation, rendering (computer graphics)
%X In this paper we present a new technique for non photo-realistic rendering by using genetic programming. Our technique produces aesthetically pleasing animations in which a
   subject gradually emerges from a random collection of brushstrokes. We employ triangular brushstrokes with three different possibilities of strokes drawing on the canvas.
   The animations are evaluated by using a numerical measure of similarity to a target image and a qualitative evaluation of aesthetic characteristics by an artist. We provide
   many facilities to the artists to control the rendered images and create desirable animations.
%8 9-11  July
%Z Also known as \cite5563645

%A Yoshihiro Izumi
%A Tokiyo Yamaguchi
%A Shingo Mabu
%A Kotaro Hirasawa
%A Jingle Hu
%T Trading Rules on the Stock Markets using Genetic Network Programming with Candlestick Chart
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 8531--8536
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, GNP
%X A new evolutionary method named Genetic Network Programming, GNP has been proposed. GNP represents its solutions as directed graph structures which have some useful
   features inherently. For example, GNP has the implicit memory function which memorises the past action sequences of agents, and GNP can re-use nodes repeatedly in the
   network flow, so very compact graph structures can be made. In this paper, buying /selling model for stock market using GNP with Candlestick Chart has been proposed and its
   effectiveness is confirmed by simulations.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Hajira Jabeen
%A Abdul Rauf Baig
%T Review of Classification Using Genetic Programming
%J International Journal of Engineering Science and Technology
%V 2
%N 2
%D 2010
%P 94--103
%I 
%K genetic algorithms, genetic programming, Data Classification, Survey, Taxonomy
%U http://www.ijest.info/docs/IJEST10-02-02-06.pdf
%X Genetic programming (GP) is a powerful evolutionary algorithm introduced to evolve computer programs automatically. It is a domain independent, stochastic method with an
   important ability to represent programs of arbitrary size and shape. Its flexible nature has attracted numerous researchers in data mining community to use GP for
   classification. In this paper we have reviewed and analyzed tree based GP classification methods and propose taxonomy of these methods. We have also discussed various
   strengths and weaknesses of the technique and provide a framework to optimize the task of GP based classification.
%8 February
%Z National University of Computer and Emerging Sciences, Islamabad, Pakistan

%A Hajira Jabeen
%A Abdul Rauf Baig
%T Particle Swarm Optimization Based Tuning of Genetic Programming Evolved Classifier Expressions
%B Nature Inspired Cooperative Strategies for Optimization, NICSO 2010
%S Studies in Computational Intelligence
%E Juan Ram\'on Gonz\'alez and David A. Pelta and Carlos Cruz and Germ\'an Terrazas and Natalio Krasnogor
%V 284
%D 2010
%P 385--397
%I Springer
%C Granada, Spain
%K genetic algorithms, genetic programming, PSO
%X Genetic Programming (GP) has recently emerged as an effective technique for classifier evolution. One specific type of GP classifiers is arithmetic classifier expression
   trees. In this paper we propose a novel method of tuning these arithmetic classifiers using Particle Swarm Optimization (PSO) technique. A set of weights are introduced
   into the bottom layer of evolved GP classifier expression tree, associated with each terminal node. These weights are initialized with random values and optimized using
   PSO. The proposed tuning method is found efficient in increasing performance of GP classifiers with lesser computational cost as compared to GP evolution for longer number
   of generations. We have conducted a series of experiments over datasets taken from UCI ML repository. Our proposed technique has been found successful in increasing the
   accuracy of classifiers in much lesser number of function evaluations.
%8 May  12-14
%Z NICSO

%A Hajira Jabeen
%A Abdul Rauf Baig
%T CLONAL-GP Framework for Artificial Immune System Inspired Genetic Programming for Classification
%B 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2010, Part I
%S Lecture Notes in Computer Science
%E Rossitza Setchi and Ivan Jordanov and Robert J. Howlett and Lakhmi C. Jain
%V 6276
%D 2010
%P 61--68
%I Springer
%C Cardiff
%K genetic algorithms, genetic programming
%X This paper presents a novel framework for artificial immune system (AIS) inspired evolution in Genetic Programming (GP). A typical GP system uses the reproduction operators
   mimicking the phenomena of natural evolution to search for efficient classifiers. The proposed framework uses AIS inspired clonal selection algorithm to evolve classifiers
   using GP. The clonal selection principle states that, in human immune system, high affinity cells that recognise the invading antigens are selected to proliferate.
   Furthermore, these cells undergo hyper mutation and receptor editing for maturation. In this paper, we propose a computational implementation of the clonal selection
   principle. The motivation for using non-Darwinian evolution includes avoidance of bloat, training time reduction and simpler classifiers. We have performed empirical
   analysis of proposed framework over a benchmark dataset from UCI repository. The CLONAL-GP is contrasted with two variants of GP based classification mechanisms and results
   are found encouraging.
%8 September  8-10
%Z KES (1)

%A Hajira Jabeen
%A Abdul Rauf Baig
%T A Framework for Optimization of Genetic Programming Evolved Classifier Expressions Using Particle Swarm Optimization
%B Hybrid Artificial Intelligence Systems, 5th International Conference, HAIS 2010, San Sebasti\'an, Spain, June 23-25, 2010. Proceedings, Part I
%S Lecture Notes in Computer Science
%E Manuel Gra\~na Romay and Emilio Corchado and M. Teresa Garc\'ia-Sebast\'ian
%V 6076
%D 2010
%P 56--63
%I Springer
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.1007/978-3-642-13769-3_7

%A Hajira Jabeen
%A Abdul Rauf Baig
%T DepthLimited crossover in GP for classifier evolution
%J Computers in Human Behavior
%V 27
%N 5
%D 2011
%P 1475--1481
%I 
%K genetic algorithms, genetic programming, Crossover, Depth Limited, Bloat, Classification, Data mining
%U http://www.sciencedirect.com/science/article/B6VDC-51FWRJY-1/2/813b60cff35fd1e0399e95fb3fa246be
%X Genetic Programming (GP) provides a novel way of classification with key features like transparency, flexibility and versatility. Presence of these properties makes GP a
   powerful tool for classifier evolution. However, GP suffers from code bloat, which is highly undesirable in case of classifier evolution. In this paper, we have proposed an
   operator named DepthLimited crossover. The proposed crossover does not let trees increase in complexity while maintaining diversity and efficient search during evolution.
   We have compared performance of traditional GP with DepthLimited crossover GP, on data classification problems and found that DepthLimited crossover technique provides
   compatible results without expanding the search space beyond initial limits. The proposed technique is found efficient in terms of classification accuracy, reduced
   complexity of population and simplicity of evolved classifiers.
%8 September

%A David Jackson
%T Automatic Synthesis of Instruction Decode Logic by Genetic Programming
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 318--327
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=318
%X On many modern computers, the processor control unit is microprogrammed rather than built directly in hardware. One of the tasks of the microcode is to decode machine-level
   instructions: for each such instruction, it must be ensured that control-flow is directed to the appropriate microprogram for emulating it. We have investigated the use of
   genetic programming for evolving this instruction decode logic. Success is highly dependent on the number of opcodes in the instruction set and their relationship to the
   conditional branch and shift instructions offered on the micro architecture, but experimental results are promising.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A David Jackson
%T Evolving Defence Strategies by Genetic Programming
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 281--290
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=281
%X Computer games and simulations are commonly used as a basis for analysing and developing battlefield strategies. Such strategies are usually programmed explicitly, but it
   is also possible to generate them automatically via the use of evolutionary programming techniques. We focus in particular on the use of genetic programming to evolve
   strategies for a single defender facing multiple simultaneous attacks. By expressing the problem domain in the form of a "Space Invaders" game, we show that it is possible
   to evolve winning strategies for an increasingly complex sequence of scenarios.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A David Jackson
%T Parsing and translation of expressions by genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1681--1688
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, application, experimentation, software tools
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1681.pdf
%X We have investigated the potential for using genetic programming to evolve compiler parsing and translation routines for processing arithmetic and logical expressions as
   they are used in a typical programming language. Parsing and translation are important and complex real-world problems for which evolved solutions must make use of a range
   of programming constructs. The exercise also tests the ability of genetic programming to evolve extensive and appropriate use of abstract data types namely, stacks.
   Experimentation suggests that the evolution of such code is achievable, provided that program function and terminal sets are judiciously chosen.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A David Jackson
%T Dormant program nodes and the efficiency of genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1745--1751
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, dormant node, efficiency, experimentation, fitness preserving crossover, intron, performance
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1745.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A David Jackson
%T Fitness Evaluation Avoidance in Boolean GP Problems
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2530--2536
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X A technique has been devised which, via consideration of the program nodes executed during fitness evaluation, allows a genetic programming system to determine many
   instances in which invocation of the fitness function can be avoided. The nature of Boolean logic problems renders them of particular interest as a focus of study for the
   application of this technique, and experimental evidence shows that significant speed-ups in execution time can be achieved when evolving solutions to these problems.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. Santa Fe Ant, 6-mux, 5-parity. Visit tree. ~2 fold speed up. 2.8GHz pentium 500 6-mux 50-gens runs 6-10mins in
   total. AND, OR, NOT, IF
%@ 0-7803-9363-5

%A David Jackson
%T Evolution of Processor microcode
%J IEEE Transactions on Evolutionary Computation
%V 9
%N 1
%D 2005
%P 44--54
%I 
%K genetic algorithms, genetic programming, firmware, microcomputers, microprogramming computer processor, evolutionary computing technique, genetic programming system,
   machine code, microprogrammed system, processor microcode
%X The control unit of many modern computer processors is implemented using microcode. Because of its low level and high complexity, writing microcode that is not only correct
   but efficient is extremely challenging. An interesting question is whether evolutionary computing techniques could be used to generate microprograms that are of the
   necessary quality. To answer this, a genetic programming system has been built that evolves microprograms for an architecture that incorporates many of the features common
   to real microprogrammed systems. Fitness is assessed via simulated execution to determine whether candidate solutions effect the correct machine state changes. The system
   has been used to evolve microprograms that emulate a range of machine code instructions, of varying complexity. It has been found that, provided appropriate evolutionary
   guidance is extracted from operational specifications of those instructions, the approach is largely successful in generating solutions that are both correct and optimal.
%8 February

%A David Jackson
%A Adrian P. Gibbons
%T Layered Learning in Boolean GP Problems
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 148--159
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X Layered learning is a decomposition and reuse technique that has proved to be effective in the evolutionary solution of difficult problems. Although previous work has
   integrated it with genetic programming (GP), much of the application of that research has been in relation to multi-agent systems. In extending this work, we have applied
   it to more conventional GP problems, specifically those involving Boolean logic. We have identified two approaches which, unlike previous methods, do not require prior
   understanding of a problem's functional decomposition into sub-goals. Experimentation indicates that although one of the two approaches offers little advantage, the other
   leads to solution-finding performance significantly surpassing that of both conventional GP systems and those which incorporate automatically defined functions.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A David Jackson
%T Hierarchical genetic programming based on test input subsets
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1612--1619
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, decomposition, hierarchical GP, program architecture
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1612.pdf
%X Crucial to the more widespread use of evolutionary computation techniques is the ability to scale up to handle complex problems. In the field of genetic programming, a
   number of decomposition and reuse techniques have been devised to address this. As an alternative to the more commonly employed encapsulation methods, we propose an
   approach based on the division of test input cases into subsets, each dealt with by an independently evolved code segment. Two program architectures are suggested for this
   hierarchical approach, and experimentation demonstrates that they offer substantial performance improvements over more established methods. Difficult problems such as
   even-10 parity are readily solved with small population sizes.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A David Jackson
%T The Performance of a Selection Architecture for Genetic Programming
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 170--181
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A David Jackson
%T Partitioned Incremental Evolution of Hardware Using Genetic Programming
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 86--97
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A David Jackson
%T The Generalisation Ability of a Selection Architecture for Genetic Programming
%B Parallel Problem Solving from Nature - PPSN X
%S LNCS
%E Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume
%V 5199
%D 2008
%P 468--477
%I Springer
%C Dortmund
%K genetic algorithms, genetic programming
%X As an alternative to various existing approaches to incorporating modular decomposition and reuse in genetic programming (GP), we have proposed a new method for
   hierarchical evolution. Based on a division of the problem's test case inputs into subsets, it employs a program structure that we refer to as a selection architecture.
   Although the performance of GP systems based on this architecture has been shown to be superior to that of conventional systems, the nature of evolved programs is radically
   different, leading to speculation as to how well such programs may generalise to deal with previously unseen inputs. We have therefore performed additional experimentation
   to evaluate the approach's generalisation ability, and have found that it seems to stand up well against standard GP in this regard.
%8 13-17  September
%Z PPSN X
%@ 3-540-87699-5

%A David Jackson
%T Behavioural Diversity and Filtering in GP Navigation Problems
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 256--267
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming, poster
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A David Jackson
%T Self-Adaptive Focusing of Evolutionary Effort in Hierarchical Genetic Programming
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 1821--1828
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X In an attempt to address the scaling up of genetic programming to handle complex problems, we have proposed a hierarchical approach in which programs are formed from
   independently evolved code fragments, each of which is responsible for handling a subset of the test input cases. Although this approach offers substantial performance
   advantages in comparison to more conventional systems, the programs it evolves exhibit some undesirable properties for certain problem domains. We therefore propose the
   introduction of a self adaptive mechanism that allows the system dynamically to focus evolutionary effort on the program components most in need. Experimentation reveals
   that not only does this technique lead to better-behaved programs, it also gives rise to further significant performance improvements.
%8 18-21  May
%Z even 4-parity, 5-parity, majority on, symbolic regression 4x**4 -3x**3 +2x**2 -x. Refers to \citeroberts:2001:EuroGP. CEC 2009 - A joint meeting of the IEEE, the EPS and
   the IET. IEEE Catalog Number: CFP09ICE-CDR

%A David Jackson
%T The identification and exploitation of dormancy in genetic programming
%J Genetic Programming and Evolvable Machines
%V 11
%N 1
%D 2010
%P 89--121
%I 
%K genetic algorithms, genetic programming, Introns, Efficiency, Performance, Simplification
%X In genetic programming, introns, fragments of code which do not contribute to the fitness of individuals, are usually viewed negatively, and much research has been
   undertaken into ways of minimising their occurrence or effects. However, identification and removal of introns is often computationally expensive and sometimes intractable.
   We have therefore focused our attention on one particular class of intron, which we refer to as dormant nodes. Mechanisms for locating such nodes are cheap to implement,
   and reveal that the presence of dormancy can be extensive. Once identified, dormancy can be exploited in at least three ways: improving execution efficiency, improving
   solution-finding performance, and simplifying program code. Experimentation shows that the gains to be had in all three cases can be significant.
%8 March
%Z artificial ant Santa Fe trail, Maze navigation, Space Invaders arcade game, parsing arithmetic and logical expressions into postfix (Reverse Polish, RPN) 6-multiplexer,
   Even-4, parity

%A David Jackson
%T Phenotypic Diversity in Initial Genetic Programming Populations
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 98--109
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X A key factor in the success or otherwise of a genetic programming population in evolving towards a solution is the extent of diversity amongst its members. Diversity may be
   viewed in genotypic (structural) or in phenotypic (behavioural) terms, but the latter has received less attention. We propose a method for measuring phenotypic diversity in
   terms of the run-time behaviour of programs. We describe how this is applicable to a range of problem domains and show how the promotion of such diversity in initial
   genetic programming populations can have a substantial impact on solution-finding performance.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A David Jackson
%T Promoting Phenotypic Diversity in Genetic Programming
%B PPSN 2010 11th International Conference on Parallel Problem Solving From Nature
%S Lecture Notes in Computer Science
%E Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph
%V 6239
%D 2010
%P 472--481
%I Springer
%C Krakow, Poland
%K genetic algorithms, genetic programming
%X Population diversity is generally seen as playing a crucial role in the ability of evolutionary computation techniques to discover solutions. In genetic programming,
   diversity metrics are usually based on structural properties of individual program trees, but are also sometimes based on the spread of fitness values in the population. We
   explore the use of a further interpretation of diversity, in which differences are measured in terms of the behaviour of programs when executed. Although earlier work has
   shown that improving behavioural diversity in initial GP populations can have a marked beneficial effect on performance, further analysis reveals that lack of behavioural
   diversity is a problem throughout whole runs, even when other diversity levels are high. To address this, we enhance phenotypic diversity via modifications to the crossover
   operator, and show that this can lead to additional performance improvements.
%8 11-15  September

%A David Jackson
%T Mutation as a diversity enhancing mechanism in genetic programming
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1371--1378
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X In various evolutionary computing algorithms, mutation operators are employed as a means of preserving diversity of populations. In genetic programming (GP), by contrast,
   mutation tends to be viewed as offering little benefit, to the extent that it is often not implemented in GP systems. We investigate the role of mutation in GP, and attempt
   to answer questions regarding its effectiveness as a means for enhancing diversity, and the consequent effects of any such diversity promotion on the solution finding
   performance of the algorithm. We find that mutation can be beneficial for GP, but subject to the proviso that it be tailored to enhance particular forms of diversity.
%8 12-16  July
%Z Santa Fe Ant, 600 steps, diversity of path. Mux, 4-even-parity, polynomial (diversity = 32 floats). Also known as \cite2001761 GECCO-2011 A joint meeting of the twentieth
   international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)

%A David Jackson
%T A New, Node-Focused Model for Genetic Programming
%B Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012
%S LNCS
%E Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta
%V 7244
%D 2012
%P 49--60
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Graph-based representation
%X We introduce Single Node Genetic Programming (SNGP), a new graph-based model for genetic programming in which every individual in the population consists of a single
   program node. Function operands are other individuals, meaning that the graph structure is imposed externally on the population as a whole, rather than existing within its
   members. Evolution is via a hill-climbing mechanism using a single reversible operator. Experimental results indicate substantial improvements over conventional GP in terms
   of solution rates, efficiency and program sizes.
%8 11-13  April
%Z SNGP, 6-mux, parity, symbolic regression. Part of \citeMoraglio:2012:GP EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012

%A Helen Jackson
%T Toward a Symbiotic Coevolutionary Approach to Architecture
%B Creative Evolutionary Systems
%E Peter J. Bentley and David W. Corne
%D 2001
%P 299--313
%I Morgan Kaufmann
%K genetic algorithms, genetic programming, coevolution, lindenmayer systems
%U http://www.sciencedirect.com/science/article/B85XH-4P615HB-Y/2/e89b8cfc99c3d25e0cb0177455fa539c
%X This chapter builds on earlier work using genetic programming (GP) and a Lindenmayer system (L-system) representation within the sphere of generative architectural design.
   L-systems are explained briefly and two contrasting embryology strategies are outlined. Artificial selection is discussed, and the wide divergence of opinion as to what
   might constitute an architectural configuration illustrated. Examples of successful single-goal evolution are presented, with the space syntax measure of integration
   investigated as a generic identifier of architectural form. Dual-and multigoal evolution are considered within the context of the architectural design discipline. It is
   suggested that an appropriate response to the complex nature of architectural organisms is the development of a symbiotic coevolutionary metaphor where interwoven systems
   within architecture are viewed as mutual species. The classification of these species leads toward a more architecture-specific genetic code. An outline of future work
   intended to develop such a representation begins with the identification of a naive architectural form representation and summarizes a gradual process for the refinement of
   this representation into a genuinely useful encoding of architectural form.
%O 11
%8 July
%Z generic fitness function. spatial embryology Part of \citeBentley:2002:bookCES
%@ 1-55860-673-4

%A Christian Jacob
%T Genetic L-System Programming
%B Parallel Problem Solving from Nature III
%S LNCS
%E Yuval Davidor and Hans-Paul Schwefel and Reinhard M\"anner
%V 866
%D 1994
%P 334--343
%I Springer-Verlag Berlin, Germany
%C Jerusalem
%K genetic algorithms, genetic programming
%U http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6
%X We present the Genetic L-System Programming (GLP) paradigm for evolutionary creation and development of parallel rewrite systems (L-systems, Lindenmayer-systems) which
   provide a commonly used formalism to describe developmental processes of natural organisms. The L-system paradigm will be extended for the purpose of describing time- and
   context-dependent formation of formal data structures representing rewrite rules or computer programs (expressions). With GLP two methods gleaned from nature are combined:
   simulated evolution and simulated structure formation. A prototypical GLP system implementation is described. Controlled evolution of complex structures is exemplified by
   the development of tree structures generated by the movement of a 3D-turtle.
%8 9-14  October
%Z GLP combines simulated evolution and simulated structure formation (based on Lindenmayer systems) PPSN3 L-systems difficult for human programmers to use, presents simple
   example where L-system is evolved using a GP. Initial population created from pool of pre-defined patterns (subtrees, building blocks?) rather than GP functions or
   terminals. Such patterns and genetic operators have a rank (like a fitness) which is used to bias the choice of pattern. A pattern is a gramtical rule and specifies (a
   number of possible) types for each of its arguments. Genetic operators include copying templates (sub trees?) into the pattern pool (or genetic library).
%@ 3-540-58484-6

%A Christian Jacob
%T Evolving Evolution Programs: Genetic Programming and L-Systems
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 107--115
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X Parallel rewrite systems in the form of string based L-systems are used for modelling and visualising growth processes of artificial plants. It is demonstrated how to use
   evolutionary algorithms for inferring L-systems encoding structures with characteristic properties. We describe our Mathematica based genetic programming system Evolvica ,
   present an L-system encoding via expressions, and explain how to generate, modify and breed L-systems through simulated evolution techniques. Extensions of genetic
   programming operators and expression generation methods strongly relying on templates and pattern matching are shown by example.
%8 28--31  July
%Z GP-96

%A Christian Jacob
%T Evolution Programs Evolved
%B Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation
%S LNCS
%E Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel
%V 1141
%D 1996
%P 42--51
%I Springer Verlag Heidelberg, Germany
%C Berlin, Germany
%K genetic algorithms, genetic programming, L-Systems, Growth Grammars, morphogenesis
%U http://pages.cpsc.ucalgary.ca/~jacob/Publications/PPSN-96-EvolutionPrograms.pdf
%X Growth grammars in the form of parallel rewrite systems (L-systems) are used to model morphogenetic processes of plant structures. With the help of evolutionary programming
   techniques developmental programs are bred which encode plants that exhibit characteristic growth patterns advantageous in competitive environments. Program evolution is
   demonstrated on the basis of extended genetic programming on symbolic expressions with genetic operators and expression generation strongly relying on templates and pattern
   matching.
%8 22-26  September
%Z http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 '3.2 Stochastic generation of L-system encodings by template' Lindenmayer rewrite grammars
%@ 3-540-61723-X

%A Christian Jacob
%T MathEvolvica - Simulated Evolution of Development Programs in Nature
%R Ph.D. Thesis
%D 1995
%I 
%I Arbeitsberichte des Instituts fur Mathematische Maschinen und Datenverarbeitung (IMMD), Informatik, Band 28(10), Erlangen
%C Germany
%K genetic algorithms, genetic programming
%U http://katalog.tub.tu-harburg.de/Record/191683663
%Z In German. MathEvolvica: simulierte Evolution von Entwicklungsprogrammen der Natur

%A Christian Jacob
%T Principia Evolvica -- Simulierte Evolution mit Mathematica
%D 1997
%I dpunkt.verlag
%C Heidelberg, Germany
%K genetic algorithms, genetic programming
%U http://library.wolfram.com/infocenter/TechNotes/282/
%O In German
%8 August
%Z The book has 712 pages and comes with a CD that contains a lot of Mathematica notebooks with explanatory text, graphics, and animations. I just started some web pages to
   make part of this material available: http://www2.informatik.uni-erlangen.de/~jacob/Evolvica/EA-Mathematica.html The publishers web site is: http://www.dpunkt.de For
   English translation see \citejacob:2001:iecm
%@ 3-920993-48-9

%A Christian Jacob
%T Lindenmayer systems and growth program evolution
%B Advanced Grammar Techniques Within Genetic Programming and Evolutionary Computation
%E Talib S. Hussain
%D 1999
%P 76--79
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%8 13  July
%Z GECCO-99WKS Part of wu:1999:GECCOWKS

%A Christian Jacob
%T Computer Physics Communications
%J Evolution and coevolution of developmental programs
%V 121-122
%D 1999
%P 46--50
%I 
%K genetic algorithms, genetic programming
%X The developmental processes of single organisms, such as growth and structure formation, can be described by parallel rewrite systems in the form of Lindenmayer systems,
   which also allow one to generate geometrical structures in 3D space using turtle interpretation. We present examples of L-systems for growth programs of plant-like
   structures. Evolution-based programming techniques are applied to design L-systems by Genetic L-system Programming (GLP), demonstrating how developmental programs for
   plants, exhibiting specific morphogenetic properties can be interactively bred or automatically evolved. Finally, we demonstrate coevolutionary effects among plant
   populations consisting of different species, interacting with each other, competing for resources like sunlight and nutrients, and evolving successful reproduction
   strategies in their specific environments.
%8 September -  October
%Z Proceedings of the Europhysics Conference on Computational Physics CCP 1998

%A Christian Jacob
%T Illustrating Evolutionary Computation with Mathematica
%D 2001
%I Morgan Kaufmann
%K genetic algorithms, genetic programming
%U http://www.amazon.com/Illustrating-Evolutionary-Computation-Mathematica-Intelligence/dp/1558606378/ref=sr_1_1?ie=UTF8&s=books&qid=1266160160&sr=1-1
%X An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it
   could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary
   computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the
   book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the
   evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide
   you with a visually rich and engaging account of this complex subject. Features: Introduces the major mechanisms of biological evolution. Demonstrates many fascinating
   aspects of evolution in nature with simple, yet illustrative examples. Explains each of the major branches of evolutionary computation: genetic algorithms, genetic
   programming, evolutionary programming, and evolution strategies. Demonstrates the programming of computers by evolutionary principles using Evolvica, a genetic programming
   system designed by the author. Shows in detail how to evolve developmental programs modeled by cellular automata and Lindenmayer systems. Provides Mathematica notebooks on
   the Web that include all the programs in the book and supporting animations, movies, and graphics. Christian Jacob is assistant professor in the Department of Computer
   Science at the University of Calgary. His areas of interest include evolutionary algorithms, Lindenmayer systems, ecosystems modeling, distributed computing, alternative
   programming paradigms, biocomputing, and bioinformatics. He is the author of the German edition of this book, Principia Evolvica Simulierte Evolution mit Mathematica
   \citejacob:1997:deutsch Part 1: Fascinating Evolution Part 2: Evolutionary Computation Part 3: If Darwin was a Programmer Part 4: Evolution of Developmental Programs
%Z English version of \citejacob:1997:deutsch
%@ 1-55860-637-8

%A Christian Jacob
%T The art of genetic programming
%J IEEE Intelligent Systems
%V 15
%N 3
%D 2000
%P 83--84
%I 
%K genetic algorithms, genetic programming
%U http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf
%8 May -  June
%Z part of \citehirsh:2000:GP

%A Christian Jacob
%A Ian Burleigh
%T Genetic Programming inside a Cell
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 191--206
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Agent-based Biological Modelling, agent, Gene Regulatory System, gene regulation, Lactose Operon, Bioinformatics, Simulation, Swarm
   Intelligence, Self-Organisation
%X Gene Regulation and Self-Organization: Inspirations from Genetic Programming in vivo We present an agent-based, 3D model of the lactose (lac) operon, a gene regulatory
   system in the bacterium E. coli. The lac operon is a prime example of a _real genetic programming_ system, which has been studied extensively and lends itself to rigorous
   mathematical analysis and computational simulations. We suggest natural gene regulatory systems, as observed within E. coli, to serve as testbeds for future in silico
   genetic programming systems.
%O 13
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%T 4th International Conference on Artificial Immune Systems: ICARIS 2005
%S Lecture Notes in Computer Science
%E Christian Jacob and Marcin L. Pilat and Peter J. Bentley and Jonathan Timmis
%V 3627
%D 2005
%I Springer
%C Banff, Alberta, Canada
%8 August  14-17
%@ 3-540-28175-4

%A Yaser Jafarian
%A Elnaz Kermani
%A Mohammad H. Baziar
%T Empirical predictive model for the vmax/amax ratio of strong ground motions using genetic programming
%J Computer \& Geosciences
%V 36
%N 12
%D 2010
%P 1523--1531
%I 
%K genetic algorithms, genetic programming, Earthquake, Predictive model, vmax/amax ratio, Frequency content
%U http://www.sciencedirect.com/science/article/B6V7D-517YN79-1/2/f812ef6b3ddb0cdd20c12efbec9c4b09
%X Earthquake-induced deformation of structures is strongly influenced by the frequency content of input motion. Nevertheless, state-of-the-practice studies commonly use the
   intensity measures such as peak ground acceleration (PGA), which are not frequency dependent. The vmax/amax ratio of strong ground motions can be used in seismic hazard
   studies as a parameter that captures the influence of frequency content. In the present study, genetic programming (GP) is employed to develop a new empirical predictive
   equation for the vmax/amax ratio of the shallow crustal strong ground motions recorded at free field sites. The proposed model is a function of earthquake magnitude,
   closest distance from source to site (Rclstd), faulting mechanism, and average shear wave velocity over the top 30 m of site (Vs30). A wide-ranging database of strong
   ground motion released by Pacific Earthquake Engineering Research Center (PEER) was used. It is demonstrated that residuals of the final equation show insignificant bias
   against the variations of the predictive parameters. The results indicate that vmax/amax increases through increasing earthquake magnitude and source-to-site distance while
   magnitude dependency is considerably more than distance dependency. In addition, the proposed model predicts higher vmax/amax ratio at softer sites that possess higher
   fundamental periods. Consequently, as an instance for the application of the proposed model, its reasonable performance in liquefaction potential assessment of sands and
   silty sands is presented.

%A Romuald Jagielski
%A John S. Gero
%T A Genetic Programming Approach To The Space Layout Planning Problem
%B CAAD Futures 97
%E Richard Junge
%D 1997
%I Kluwer Academic Publishers
%C Technical University Munich, Germany
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/315959.html
%X The space layout planning problem belongs to the class of NP-hard problems with a wide range of practical applications. Many algorithms have been developed in the past,
   however recently evolutionary techniques have emerged as an alternative approach to their solution. In this paper, a genetic programming approach, one variation of
   evolutionary computation, is discussed. A representation of the space layout planning problem suitable for genetic programming is presented along with some implementation
   details and results.
%O The Pennsylvania State University CiteSeer Archives
%8 4-6  August
%Z http://www.caadfutures.arch.tue.nl/proceedings_97.htm
%@ 0-7923-4726-9

%A Romuald Jagielski
%T Genetic Programming Prediction of Solar Activity
%B Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents
%S Lecture Notes in Computer Science
%E Kwong Sak Leung and Lai-Wan Chan and Helen Meng
%V 1983
%D 2000
%P 199--205
%I Springer-Verlag
%C Shatin, N.T., Hong Kong, China
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/1983/19830199.pdf", acknowledgement = ack-nhfb
%X For many practical applications, such as planning for satellite orbits and space missions, it is important to estimate the future values of the sunspot numbers. There have
   been numerous methods used for this particular case of time series prediction, including recently neural networks. In this paper we present genetic programming technique
   employed to sunspot series prediction. The paper investigates practical solutions and heuristics for an effective choice of parameters and functions of genetic programming.
   The results obtained expect the maximum in the current cycle of the smoothed series monthly sunspot numbers is $164 \pm 20$, and $162 \pm 20$ for the next cycle maximum, at
   the 95% level of confidence. These results are discussed and compared with other predictions.
%8 13-15  Decemeber
%@ 3-540-41450-9

%A Pooja Jain
%A M. C. Deo
%T Artificial Intelligence Tools to Forecast Ocean Waves in Real Time
%J The Open Ocean Engineering Journal
%V 1
%D 2008
%P 13--20
%I 
%K genetic algorithms, genetic programming
%U http://www.bentham-open.org/pages/b_viewarticIe.php
%X Prediction of wind generated ocean waves over short lead times of the order of some hours or days is helpful in carrying out any operation in the sea such as repairs of
   structures or laying of submarine pipelines. This paper discusses an application of different artificial intelligent tools for this purpose. The physical domain where the
   wave forecasting is made belongs to the western part of the Indian coastline in Arabian Sea. The tools used are artificial neural networks, genetic programming and model
   trees. Station specific forecasts are made at those locations where wave data are continuously observed. A time series forecasting scheme is employed. Based on a sequence
   of preceding observations forecasts are made over lead times of 3 hr to 72 hr. Large differences in the accuracy of the forecasts were not seen when alternative forecasting
   tools were employed and hence the user is free to use any one of them as per her convenience and confidence. A graphical user interface has been developed that operates on
   the received wave height data from the field and produces the forecasts and further makes them accessible to any user located anywhere in the world.
%Z Department of Civil Engineering, IIT Bombay

%A Pooja Jain
%A M. C. Deo
%A G. Latha
%A V. Rajendran
%T Real time wave forecasting using wind time history and numerical model
%J Ocean Modelling
%V 36
%N 1-2
%D 2011
%P 26--39
%I 
%K genetic algorithms, genetic programming, Artificial neural networks, Model trees, Wave prediction, Numerical wave prediction
%U http://www.sciencedirect.com/science/article/B6VPS-50XCY8V-1/2/535abd8afbb53832e8278b7eaf4d3932
%X Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a
   few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so
   and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for
   those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial
   intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modelling of wind to obtain waves.
   Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted
   waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria.
   Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were
   better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.

%A Nick Jakobi
%A Phil Husbands
%A Tom Smith
%T Robot Space Exploration by Trial and Error
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 807--815
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K Evolutionary Robotics
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Domagoj Jakobovi\'c
%A Leo Budin
%T Dynamic Scheduling with Genetic Programming
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 73--84
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050073.pdf
%X This paper investigates the use of genetic programming in automatic synthesis of scheduling heuristics. The applied scheduling technique is priority scheduling, where the
   next state of the system is determined based on priority values of certain system elements. The evolved solutions are compared with existing scheduling heuristics for
   single machine dynamic problem and job shop scheduling with bottleneck estimation.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Domagoj Jakobovi\'c
%A Leonardo Jelenkovi\'c
%A Leo Budin
%T Genetic Programming Heuristics for Multiple Machine Scheduling
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 321--330
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X In this paper we present a method for creating scheduling heuristics for parallel proportional machine scheduling environment and arbitrary performance criteria. Genetic
   programming is used to synthesise the priority function which, coupled with an appropriate meta-algorithm for a given environment, forms the priority scheduling heuristic.
   We show that the procedures derived in this way can perform similarly or better than existing algorithms. Additionally, this approach may be particularly useful for those
   combinations of scheduling environment and criteria for which there are no adequate scheduling algorithms.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Mohammad Jamshidi
%T Autonomous control of complex systems: robotic applications
%J Applied Mathematics and Computation
%V 120
%N 1-3
%D 2001
%P 15--29
%I 
%K genetic algorithms, genetic programming, Autonomy, Control systems, Complex systems, Robotics, Behavior control
%U http://www.sciencedirect.com/science/article/B6TY8-42RVSF8-3/1/d9087f02589b85a2c6ef556307f7c0a8
%X One of the biggest challenges of any control paradigm is being able to handle large complex systems under unforeseen uncertainties. A system may be called complex here if
   its dimension (order) is too high and its model (if available) is nonlinear, interconnected, and information on the system is uncertain such that classical techniques
   cannot easily handle the problem. Soft computing, a collection of fuzzy logic, neuro-computing, genetic algorithms and genetic programming, has proven to be a powerful tool
   for adding autonomy to many complex systems. For such systems the size soft computing control architecture will be nearly infinite. Examples of complex systems are power
   networks, national air traffic control system, an integrated manufacturing plant, etc. In this paper a new rule base reduction approach is suggested to manage large
   inference engines. Notions of rule hierarchy and sensor data fusion are introduced and combined to achieve desirable goals. New paradigms using soft computing approaches
   are used to design autonomous controllers for a number of robotic applications at the ACE Center are also presented briefly.
%8 10  May

%A Zahoor Jan
%A Arfan Jaffar
%A Fauzia Jabeen
%A Azhar Rauf
%T Watermarking scheme based on wavelet transform, genetic programming and Watson perceptual distortion control model for JPEG2000
%B 6th International Conference on Emerging Technologies (ICET 2010)
%D 2010
%P 128--133
%I 
%K genetic algorithms, genetic programming, JPEG2000 image, Watson perceptual distortion control, authentication mechanism, copyright protection, digital watermarking scheme,
   discrete wavelet transform, electronic document, just noticeable difference, copyright, discrete wavelet transforms, image coding, message authentication, watermarking
%X Embedding of the digital watermark in an electronic document proves to be a viable solution for the protection of copyright and for authentication. In this paper we
   proposed a watermarking scheme based on wavelet transform, genetic programming (GP) and Watson distortion control model for JPEG2000. To select the coefficients for
   watermark embedding image is first divided into 32x32 blocks. Discrete Wavelet Transform DWT of each block is obtained. Coefficients in LH, HL and HH subbands of each 32 by
   32 block are selected based on the Just Noticeable Difference (JND). Watermark is embedded by carefully chosen watermarking level. Choice of watermarking level is very
   important. The two important properties robustness and imperceptibility depends on good choice of watermarking level. GP is used to obtain mathematical function
   representing optimum watermarking level. The proposed scheme is tested and gives a good compromise between the robustness and imperceptibly.
%8 18-19  October
%Z Dept. of Comput. Sci., Nat. Univ. of Comput. & Emerging Sci., Islamabad, Pakistan Also known as \cite5638368

%A Cesar Janeczko
%A Heitor S. Lopes
%T A genetic approach to ARMA filter synthesis for EEG signal simulation
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 373--378
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, ARMA, filter, EEG, image/ signal processing
%U http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/cec2000b.zip
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Ha-Young Jang
%A Byoung-Tak Zhang
%T Automated construction of diagnosis rules from DNA samples
%B Proceedings of the Third Asian-Pacific workshop on Genetic Programming
%E The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen
%D 2006
%P 47--56
%I 
%C Military Technical Academy, Hanoi, VietNam
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/ASPGP06_Jang_revised.pdf
%X We propose a molecular computing algorithm for constructing diagnosis rules from blood sample automatically. Different to disease diagnosis based on microarray, proposed
   method can make a diagnosis without statistical analysis of sample. Every operator in the proposed method can be implemented with conventional wet-lab techniques such as
   Polymerase Chain Reaction (PCR), hybridisation and affinity separation. Tested on a real disease data, simulation results show not only the feasibility of proposed method
   but also the possibility of biological information processing. The use of huge population in molecular evolutionary algorithm also can give various insights to evolutionary
   computation.
%Z http://www.aspgp.org

%A Cezary Z. Janikow
%T A Methodology for Processing Problem Constraints in Genetic Programming
%J Computers and Mathematics with Applications
%V 32
%N 8
%D 1996
%P 97--113
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/326996.html
%X Search mechanisms of artificial intelligence combine two elements: representation, which determines the search space, and a search mechanism, which actually explores the
   space. Unfortunately, many searches may explore redundant and/or invalid solutions. Genetic programming refers to a class of evolutionary algorithms based on genetic
   algorithms, but using a parameterized representation in the form of trees. These algorithms perform searches based on simulation of nature. They face the same problems of
   redundant/invalid subspaces. These problems have just recently been addressed in a systematic manner. This paper presents a methodology devised for the public domain
   genetic programming tool lil-gp. This methodology uses data typing and semantic information to constrain the representation space so that only valid, and possibly unique,
   solutions will be explored. The user enters problem-specific constraints, which are transformed into a normal set. This set is checked for feasibility, and subsequently, it
   is used to limit the space being explored. The constraints can determine valid, possibly unique spaces. Moreover, they can also be used to exclude subspaces the user
   considers uninteresting, using some problem-specific knowledge. A simple example is followed thoroughly to illustrate the constraint language, transformations, and the
   normal set. Experiments with Boolean 11-multiplexer illustrate practical applications of the method to limit redundant space exploration by using problem-specific
   knowledge.
%Z http://laplace.cs.umsl.edu/~janikow/cgp-lilgp/ CGP uses GP [Koza] to evolve programs (or trees in general). It extends GP by allowing syntactic and sematical constraints on
   function calls (the constraints can be weighted rather than strict), plus function overloading. In future releases, evolution of representation (i.e., constraints), ADFs,
   and recursive functions are planned. lil-gp comparison of solving 11-multiplexor problem nine different ways with different type systems. Some tighter (than Koza) type
   systems (eg different address and data bits, different function sets) are worse than Koza GP and some are better. Problem dependant reasons for this suggested. Comparison
   with GIL. STGP

%A Cezary Z. Janikow
%A Scott DeWeese
%T Processing Constraints in Genetic Programming with CGP2.1
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 173--180
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Cezary Z. Janikow
%T Constrained genetic programming
%B Advanced Grammar Techniques Within Genetic Programming and Evolutionary Computation
%E Talib S. Hussain
%D 1999
%P 80--82
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%8 13  July
%Z GECCO-99WKS Part of wu:1999:GECCOWKS

%A Cezary Z. Janikow
%A Rahul A Deshpande
%T Adaptation of Representation in Genetic Programming
%B Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems, and Artificial Life (ANNIE'2003)
%E Cihan H. Dagli and Anna L. Buczak and Joydeep Ghosh and Mark J. Embrechts and Okan Ersoy
%D 2003
%P 45--50
%I ASME Press
%K genetic algorithms, genetic programming
%X This paper discusses our initial work on automatically adapting Genetic Programming (GP) representation. We present here two independent techniques: AMS and ACE. Both
   techniques are based on Constrained GP (CGP), which uses mutation set methodology to prune the representation space according to some context-specific constraints. The ASM
   technique monitors the performance of local context heuristics when used in mutation/crossover, during GP evolution, and dynamically modifies the heuristics. The ACE
   technique iterates complete CGP runs and then uses the distribution information from the best solutions to adjust the heuristics for the next iteration. As the results
   indicate, GP is able to gain substantial performance improvements as well as learn qualitative heuristics.
%8 2-5  November

%A Cezary Z. Janikow
%T ACGP: Adaptable Constrained Genetic Programming
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 191--206
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, representation, learning, adaptation, heuristics
%X Genetic Programming requires that all functions/terminals (tree labels) be given a priori. In the absence of specific information about the solution, the user is often
   forced to provide a large set, thus enlarging the search space often resulting in reducing the search efficiency. Moreover, based on heuristics, syntactic constraints, or
   data typing, a given subtree may be undesired or invalid in a given context. Typed Genetic Programming methods give users the power to specify some rules for valid tree
   construction, and thus to prune the otherwise unconstrained representation in which Genetic Programming operates. However, in general, the user may not be aware of the best
   representation space to solve a particular problem. Moreover, some information may be in the form of weak heuristics. In this work, we present a methodology, which
   automatically adapts the representation for solving a particular problem, by extracting and using such heuristics. Even though many specific techniques can be implemented
   in the methodology, in this paper we use information on local first-order (parent-child) distributions of the functions and terminals. The heuristics are extracted from the
   population by observing their distribution in better individuals. The methodology is illustrated and validated using a number of experiments with the 11-multiplexer.
   Moreover, some preliminary empirical results linking population size and the sampling rate are also given.
%O 12
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A Cezary Z. Janikow
%T Adapting Representation in Genetic Programming
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 507--518
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030507.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Cezary Z. Janikow
%T ACGP is a new method to explore regularity
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WMOD006.pdf
%8 26-30  June
%Z GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A Cezary Z. Janikow
%T Adaptable Constrained Genetic Programming: Extensions and Applications
%R Summer Faculty Fellowship Program 2004 Volumes 1 and 2, Page: 11-1 - 11-7
%D 2005
%I 
%I NASA
%K genetic algorithms, genetic programming
%X An evolutionary algorithm applies evolution-based principles to problem solving. To solve a problem, the user defines the space of potential solutions, the representation
   space. Sample solutions are encoded in a chromosome-like structure. The algorithm maintains a population of such samples, which undergo simulated evolution by means of
   mutation, crossover, and survival of the fittest principles. Genetic Programming (GP) uses tree-like chromosomes, providing very rich representation suitable for many
   problems of interest. GP has been successfully applied to a number of practical problems such as learning Boolean functions and designing hardware circuits. To apply GP to
   a problem, the user needs to define the actual representation space, by defining the atomic functions and terminals labeling the actual trees. The sufficiency principle
   requires that the label set be sufficient to build the desired solution trees. The closure principle allows the labels to mix in any arity-consistent manner. To satisfy
   both principles, the user is often forced to provide a large label set, with ad hoc interpretations or penalties to deal with undesired local contexts. This unfortunately
   enlarges the actual representation space, and thus usually slows down the search. In the past few years, three different methodologies have been proposed to allow the user
   to alleviate the closure principle by providing means to define, and to process, constraints on mixing the labels in the trees. Last summer we proposed a new methodology to
   further alleviate the problem by discovering local heuristics for building quality solution trees. A pilot system was implemented last summer and tested throughout the
   year. This summer we have implemented a new revision, and produced a User's Manual so that the pilot system can be made available to other practitioners and researchers. We
   have also designed, and partly implemented, a larger system capable of dealing with much more powerful heuristics.
%8 1  August
%Z http://www.sti.nasa.gov/scan/rss99-01.html Document ID: 20050202032 Report #: None Sales Agency: CASI Hardcopy A02 No Copyright Source: Missouri Univ. (Saint Louis, MO,
   United States)

%A Cezary Z. Janikow
%A Christopher J. Mann
%T CGP visits the Santa Fe trail: effects of heuristics on GP
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1697--1704
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, evolutionary computation, design, experimentation, heuristics
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1697.pdf
%X GP uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the trees, and GP
   searches the space. Previous research and experimentation show that the choice of the function/terminal set, choice of the initial population, and some other explicit and
   implicit "design" factors have great influence on both the quality and the speed of the evolution. Such heuristics are valuable simply because they improve GP's
   performance, or because they enforce some desired properties on the solutions. In this paper, we evaluate the effect of heuristics on GP solving the Santa Fe trail. We
   concentrate on improving the solution quality, but we also look at efficiency. Various heuristics are tried and mixed by hand, while evaluated with the help of the CGP
   system. Results show that some heuristics result in very substantial performance improvements, that complex heuristics are usually not decomposable, and that the heuristics
   generalize to apply to other similar problems, but the applicability reduces with the complexity of the heuristics and the dissimilarity of the new problem to the old one.
   We also compare such user-mixed heuristics with those generated by the ACGP system which automatically extracts heuristics improving GP performance.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Cezary Z. Janikow
%T Adaptable Representation in GP
%B Genetic and Evolutionary Computation Conference (GECCO2005) workshop program
%E Franz Rothlauf and Misty Blowers and J\"urgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J\"orn Grahl and Gregory Hornby and Edwin D. de Jong and
   Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor\`a and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and
   Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton
   and Alden H. Wright
%D 2005
%P 327--331
%I ACM Press
%C Washington, D.C., USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0327.pdf
%X Genetic Programming uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the
   trees. The sufficiency principle requires that the set be sufficient to label the desired solution trees, often forcing the user to enlarge the set, thus also enlarging the
   search space. Structure-preserving crossover, STGP, CGP, and CFG-based GP give the user the power to reduce the space by specifying rules for valid tree construction, based
   on types, syntax, and heuristics. These rules in effect change the representation. However, in general the user may not be aware of the best representation, including
   heuristics, to solve a particular problem. Last year, ACGP methodology was introduced for extracting local problem-specific heuristics, that is for learning a local model
   of the problem domain. ACGP discovers representation, in the space of probabilistic representations, one that improves the search itself and that provides the user with
   heuristics about the domain. We discuss and illustrate the probabilistic representation.
%8 25-29  June
%Z Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006

%A Cezary Z. Janikow
%T Evolving problem heuristics with on-line ACGP
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2007)
%E Peter A. N. Bosman
%D 2007
%P 2503--2508
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, heuristics, machine learning, STGP, artificial ant
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2503.pdf
%X Genetic Programming uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the
   trees. The sufficiency principle requires that the set be sufficient to label the desired solution trees, often forcing the user to enlarge the set, thus also enlarging the
   search space. Structure-preserving crossover, STGP, CGP, and CFG-based GP give the user the power to reduce the space by specifying rules for valid tree construction:
   types, syntax, and heuristics. However, in general the user may not be aware of the best representation space, including heuristics, to solve a particular problem.
   Recently, the ACGP methodology for extracting problem-specific heuristics, and thus for learning model of the problem domain, was introduced with preliminary off-line
   results. This paper overviews ACGP, pointing out its strength and limitations in the off-line mode. It then introduces a new on-line model, for learning while solving a
   problem, illustrated with experiments involving the multiplexer and the Santa Fe trail.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A Cezary Z. Janikow
%A John Aleshunas
%A Mark W. Hauschild
%T Second order heuristics in ACGP
%B Optimization by building and using probabilistic models (OBUPM-2011)
%E Mark Hauschild and Martin Pelikan
%D 2011
%P 671--678
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X Genetic Programming explores the problem search space by means of operators and selection. Mutation and crossover operators apply uniformly, while selection is the driving
   force for the search. Constrained GP changes the uniform exploration to pruned non-uniform, skipping some subspaces and giving preferences to others, according to some
   heuristics. Adaptable Constrained GP is a methodology for discovery of such useful heuristics. Both methodologies have previously demonstrated their surprising capabilities
   using only first-order (parent-child) heuristics. Recently, they have been extended to second-order (parent-children) heuristics. This paper describes the second-order
   processing, and illustrates the usefulness and efficiency of this approach using a simple problem specifically constructed to exhibit strong second-order structure.
%8 12-16  July
%Z Also known as \cite2002066 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Jan Jannink
%T Cracking and Co-Evolving Randomizers
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 425--443
%I MIT Press
%K genetic algorithms, genetic programming, memory
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%O 20
%Z Uses protect mod. But doesnt give details.Uses Teller's READ and WRITE Uses IFGEN macro to evolve two separate functions in same tree Ref Kolmogorov (1965) = on density of
   information packing Ref James (1990) = on very long (10**170) sequence random numbers Initilises so store[x]=x+1 rather than zero.

%A Stefan Janson
%A Martin Middendorf
%T A hierarchical particle swarm optimizer for noisy and dynamic environments
%J Genetic Programming and Evolvable Machines
%V 7
%N 4
%D 2006
%P 329--354
%I 
%K Particle Swarm Optimization, PSO, Noisy functions, Dynamic functions
%X New Particle Swarm Optimisation (PSO) methods for dynamic and noisy function optimisation are studied in this paper. The new methods are based on the hierarchical PSO
   (H-PSO) and a new type of H-PSO algorithm, called Partitioned Hierarchical PSO (PH-PSO). PH-PSO maintains a hierarchy of particles that is partitioned into several
   sub-swarms for a limited number of generations after a change of the environment occurred. Different methods for determining the best time when to rejoin the sub-swarms and
   how to handle the topmost sub-swarm are discussed. A standard method for metaheuristics to cope with noise is to use function re-evaluations. To reduce the number of
   necessary re-evaluations a new method is proposed here which uses the hierarchy to find a subset of particles for which re-evaluations are particularly important. In
   addition, a new method to detect changes of the optimization function in the presence of noise is presented. It differs from conventional detection methods because it does
   not require additional function evaluations. Instead it relies on observations of changes that occur within the swarm hierarchy. The new algorithms are compared
   experimentally on different dynamic and noisy benchmark functions with a variant of standard PSO and H-PSO that are both provided with a change detection and response
   method.
%8 Decemeber

%A David Japikse
%A Oleg Dubitsky
%A Kerry N. Oliphant
%A Robert J. Pelton
%A Daniel Maynes
%A Jamin Bitter
%T Multi-variable, high order, performance Models (2005C)
%B 2005 ASME International Mechanical Engineering Congress \& Exposition
%D 2005
%P IMECE2005--79416
%I 
%I ASME
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, genetic expression programming, Numerical, Modeling, Turbomachinery, Statistics
%U http://www.conceptsnrec.com/pdf/IMECE2005-79416-R3.pdf
%X In the course of developing advanced data processing and advanced performance models, as presented in companion papers, a number of basic scientific and mathematical
   questions arose. This paper deals with questions such as uniqueness, convergence, statistical accuracy, training, and evaluation methodologies. The process of bringing
   together large data sets and using them, with outside data supplementation, is considered in detail. After these questions are focused carefully, emphasis is placed on how
   the new models, based on highly refined data processing, can best be used in the design world. The impact of this work on designs of the future is discussed. It is expected
   that this methodology will allow many designers to move well beyond contemporary design practices.
%8 November  5-11
%Z See also \citeIMECE2005-79414R3 Concepts NREC White River Jct., Vermont, 05001 USA. Brigham Young University

%A Gabriel Jarillo
%A Giancarlo Succi
%A Witold Pedrycz
%A Marek Reformat
%T Analysis of Software Engineering Data Using Computational Intelligence Techniques
%B 7th International Conference on Object Oriented Information Systems, OOIS'2001
%E Yingxu Wang and Shushma Patel and Ronald Johnston
%D 2001
%P 133--142
%I Springer
%C Calgary, Canada
%K genetic algorithms, genetic programming, SBSE
%X The accurate estimation of software development effort has major implications for the management of software development in the industry. Underestimates lead to time
   pressures that may compromise full functional development and thorough testing of the software product. On the other hand, overestimates can result in over allocation of
   development resources and personnel [7]. Many models for effort estimation have been developed during the past years; some of them use parametric methods with some degree
   of success, other kind of methods belonging to the computational intelligence family, such as Neural Networks (NN), have been also studied in this field showing more
   accurate estimations, and finally the Genetic programming (GP) techniques are being considered as promising tools for the prediction of effort estimation. Organizations are
   wandering how they can predict the quality of their software before it is used. Generally there are tree approaches to do so [1]: 1. - Predicting the number of defects in
   the system. 2. - Estimating the reliability of the system in terms of time and failure. 3. - Understanding the impact of the design and testing processes on defect counts
   and failure densities. Knowing the quality of the software allows the organization to estimate the amount of resources to be invested on its maintenance. Software
   maintenance is a factor that consumes most of the resources in many software organizations [2], therefore its worth it to be able to characterize, assess and predict
   defects in the software at early stages of its development in order to reduce maintenance costs. Maintenance involves activities such as correcting errors, maintaining
   software, and adapting software to deal with new environment requirements [2].
%8 27-29  August
%Z http://enel.ucalgary.ca/oois2001/programme.html

%A Harri J{\"a}ske
%T One-step-ahead prediction of sunspots with genetic programming
%B Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA)
%S Proceedings of the University of Vaasa, Nro. 13
%E Jarmo T. Alander
%D 1996
%P 79--88
%I University of Vaasa
%I Finnish Artificial Intelligence Society
%C Vaasa (Finland)
%K genetic algorithms, genetic programming, time series prediction , sunspots
%U http://www.uwasa.fi/cs/publications/2NWGA/node70.html#SECTION04700000000000000000
%X Timeinvariant nonlinear one-step-ahead prediction models were developed by genetic programming. As a test case benchmark sunspot series was used. Functional form and
   numerical parameters of the models were optimized. The generalisation ability, i.e. final suitability, of the predictors was assessed through crossvalidation. The results
   were compared to those of threshold autoregression and neural network -based predictors of the sunspot benchmarks found in literature. Standard GP-approach is shown not to
   be sufficient to solve this prediction problem as well as the methods in comparison do.
%O *on,*FIN,genetic programming,astronomy /sunspots,time series sunspots
%8 19.-23.~  August
%Z lil-gp, non standard GP parameters? "evolved models might not be numerically stable" page 86

%A Harri Jaske
%T On code reuse in genetic programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 201--206
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Jaske_1997_crGP.pdf
%8 13-16  July
%Z GP-97

%A Wojciech Jaskowski
%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Learning and Recognition of Hand-drawn Shapes using Generative Genetic Programming
%B Applications of Evolutionary Computing, EvoWorkshops2007: EvoCOMNET, EvoFIN, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, EvoTransLog
%S LNCS
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal
   Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang
%V 4448
%D 2007
%P 281--290
%I Springer Verlag
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X We describe a novel method of evolutionary visual learning that uses generative approach for assessing learner's ability to recognise image contents. Each learner,
   implemented as a genetic programming individual, processes visual primitives that represent local salient features derived from a raw input raster image. In response to
   that input, the learner produces partial reproduction of the input image, and is evaluated according to the quality of that reproduction. We present the method in detail
   and verify it experimentally on the real-world task of recognition of hand-drawn shapes.
%8 11-13  April
%Z EvoWorkshops2007

%A Wojciech Jaskowski
%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Genetic programming for cross-task knowledge sharing
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1620--1627
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, knowledge sharing, multitask learning, representation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1620.pdf
%X We consider multi-task learning of visual concepts within genetic programming (GP) framework. The proposed method evolves a population of GP individuals, with each of them
   composed of several GP trees that process visual primitives derived from input images. The two main trees are delegated to solving two different visual tasks and are
   allowed to share knowledge with each other by calling the remaining GP trees (sub-functions) included in the same individual. The method is applied to the visual learning
   task of recognising simple shapes, using generative approach based on visual primitives, introduced in [17]. We compare this approach to a reference method devoid of
   knowledge sharing, and conclude that in the worst case cross-task learning performs equally well, and in many cases it leads to significant performance improvements in one
   or both solved tasks.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Wojciech Jaskowski
%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Knowledge reuse in genetic programming applied to visual learning
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1790--1797
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Genetics-Based Machine Learning, knowledge reuse, pattern recognition
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1790.pdf
%X We propose a method of knowledge reuse for an ensemble of genetic programming-based learners solving a visual learning task. First, we introduce a visual learning method
   that uses genetic programming individuals to represent hypotheses. Individuals-hypotheses process image representation composed of visual primitives derived from the
   training images that contain objects to be recognised. The process of recognition is generative, i.e., an individual is supposed to restore the shape of the processed
   object by drawing its reproduction on a separate canvas. This canonical method is extended with a knowledge reuse mechanism that allows a learner to import genetic material
   from hypotheses that evolved for the other decision classes (object classes). We compare the performance of the extended approach to the basic method on a real-world tasks
   of handwritten character recognition, and conclude that knowledge reuse leads to significant convergence speedup and, more importantly, significantly reduces the risk of
   overfitting.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Wojciech Jaskowski
%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Evolutionary Learning with Cross-Class Knowledge Reuse for Handwritten Character Recognition
%B proceedings of the Planning to learn workshop, PlanLearn-07
%D 2007
%I 
%C Warsaw, Poland
%K genetic algorithms, genetic programming
%U http://www.ecmlpkdd2007.org/CD/workshops/PlanLearn/WS_PlanLearn_p2/WS_PlanLearn_p2.pdf
%X We propose a learning algorithm that reuses knowledge acquired in past learning sessions to improve its performance on a new learning task. The method concerns visual
   learning and uses genetic programming to represent hypotheses, each of them being a procedure that processes visual primitives derived from the training images. The process
   of recognition is generative, i.e., a procedure is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This basic method
   is extended with a knowledge reuse mechanism that allows learners to import genetic material from hypotheses that evolved for the other decision classes (object classes).
   We compare both methods on a task of handwritten character recognition, and conclude that knowledge reuse leads to significant improvement of classification accuracy and
   reduces the risk of overfitting.
%8 September  17
%Z Institute of Computing Science, Poznan University of Technology Piotrowo 2, 60965 Pozna, Poland

%A Wojciech Jaskowski
%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Winning Ant Wars: Evolving a Human-Competitive Game Strategy Using Fitnessless Selection
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 13--24
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Wojciech Jaskowski
%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Multi-task code reuse in genetic programming
%B GECCO-2008 Late-Breaking Papers
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 2159--2164
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, code Reuse, multi-task learning
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2159.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1389040

%A Wojciech Jaskowski
%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Evolving strategy for a probabilistic game of imperfect information using genetic programming
%J Genetic Programming and Evolvable Machines
%V 9
%N 4
%D 2008
%P 281--294
%I 
%K genetic algorithms, genetic programming
%X We provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. Ant Wars contestants are virtual ants collecting food on a
   grid board in the presence of a competing ant. BrilliAnt has been evolved through a competitive one-population coevolution using genetic programming and fitness less
   selection. In this paper, we detail the evolutionary setup that lead to BrilliAnt's emergence, assess its direct and indirect human-competitiveness, and describe the
   behavioural patterns observed in its strategy.
%8 Decemeber

%A Wojciech Jaskowski
%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Multitask Visual Learning Using Genetic Programming
%J Evolutionary Computation
%V 16
%N 4
%D 2008
%P 439--459
%I 
%K genetic algorithms, genetic programming
%X We propose a multi-task learning method of visual concepts within the genetic programming (GP) framework. Each GP individual is composed of several trees that process
   visual primitives derived from input images. Two trees solve two different visual tasks and are allowed to share knowledge with each other by commonly calling the remaining
   GP trees (sub functions) included in the same individual. The performance of a particular tree is measured by its ability to reproduce the shapes contained in the training
   images. We apply this method to visual learning tasks of recognizing simple shapes and compare it to a reference method. The experimental verification demonstrates that
   such multitask learning often leads to performance improvements in one or both solved tasks, without extra computational effort.
%8 Winter
%Z Part of special issue on Evolutionary Computer Vision \citeCagnoni:2008:EC

%A Wojciech Jaskowski
%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Genetic Programming for Generative Learning and Recognition of Hand-Drawn Shapes
%B Evolutionary Image Analysis and Signal Processing
%S Studies in Computational Intelligence
%E Stefano Cagnoni
%V 213
%D 2009
%P 73--90
%I Springer
%C Berlin / Heidelberg
%K genetic algorithms, genetic programming
%X We propose a novel method of evolutionary visual learning that uses a generative approach to assess the learner's ability to recognise image contents. Each learner,
   implemented as a genetic programming (GP) individual, processes visual primitives that represent local salient features derived from the input image. The learner analyses
   the visual primitives, which involves mostly their grouping and selection, eventually producing a hierarchy of visual primitives build upon the input image. Based on that
   it provides partial reproduction of the shapes of the analysed objects and is evaluated according to the quality of that reproduction.We present the method in detail and
   verify it experimentally on the real-world task of recognition of hand-drawn shapes. In particular, we show how GP individuals trained on examples from different decision
   classes can be combined to build a complete multiclass recognition system. We compare such recognition systems to reference methods, showing that our generative learning
   approach provides similar results. This chapter also contains detailed analysis of processing carried out by an exemplary individual.
%Z Institute of Computing Science, Poznan University of Technology,Poland EvoISAP, EvoNET, EvoStar

%A Akbar A. Javadi
%A Mohammad Rezania
%A Mohaddeseh {Mousavi Nezhad}
%T Evaluation of liquefaction induced lateral displacements using genetic programming
%J Computers and Geotechnics
%V 33
%N 4-5
%D 2006
%P 222--233
%I 
%K genetic algorithms, genetic programming, Geotechnical models, Soil liquefaction, Earthquake, Evolutionary computation, Evolutionary programming, Lateral displacement
%X Determination of liquefaction induced lateral displacements during earthquake is a complex geotechnical engineering problem due to the complex and heterogeneous nature of
   the soils and the participation of a large number of factors involved. In this paper, a new approach is presented, based on genetic programming (GP), for determination of
   liquefaction induced lateral spreading. The GP models are trained and validated using a database of SPT-based case histories. Separate models are presented to estimate
   lateral displacements for free face and for gently sloping ground conditions. It is shown that the GP models are able to learn, with a very high accuracy, the complex
   relationship between lateral spreading and its contributing factors in the form of a function. The attained function can then be used to generalise the learning to predict
   liquefaction induced lateral spreading for new cases not used in the construction of the model. The results of the developed GP models are compared with those of a commonly
   used multi linear regression (MLR) model and the advantages of the proposed GP model over the conventional method are highlighted.
%8 June -  July
%Z a Department of Engineering, University of Exeter, Exeter EX4 4QF, Devon, UK b Department of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

%A Akbar A. Javadi
%A Asaad Faramarzi
%A Alireza Ahangar-Asr
%A Moura Mehravar
%T Finite element analysis of three dimensional shallow foundation using artificial intelligence based constitutive model
%B Proceedings of the International Conference on Computing in Civil and Building Engineering
%E W. Tizani
%D 2010
%P 421
%I Nottingham University Press
%C Nottingham, UK
%K genetic algorithms, genetic programming, constitutive modelling, evolutionary computation, data mining, finite element
%U http://www.engineering.nottingham.ac.uk/icccbe/proceedings/html/211.htm
%O Paper 211
%8 30  June -2  July
%Z Uses GA to evolve polynomial model icccbe2010 http://www.engineering.nottingham.ac.uk/icccbe/proceedings/html/home.htm

%A Faizad Javed
%A Barrett R. Bryant
%A M. Crepinsek
%A Marjan Mernik
%A Alan Sprague
%T Context-free grammar induction using genetic programming
%B ACM-SE 42: Proceedings of the 42nd annual Southeast regional conference
%D 2004
%P 404--405
%I ACM Press New York, NY, USA
%C Huntsville, Alabama
%K genetic algorithms, genetic programming
%U http://portal.acm.org/ft_gateway.cfm?id=986635&type=pdf&coll=GUIDE&dl=GUIDE&CFID=59883361&CFTOKEN=89203485
%X While grammar inference is used in areas like natural language acquisition, syntactic pattern recognition, etc., its application to the programming language problem domain
   has been limited. We propose a new application area for grammar induction which intends to make domain-specific language development easier and finds a second application
   in renovation tools for legacy systems. The genetic programming approach is used for grammatical inference. Our earlier work used grammar-specific heuristic operators in
   tandem with non-random construction of the initial grammar population and succeeded in inducing small grammars.
%Z \onlineAvailablehttp://doi.acm.org/10.1145/986537.9866352007-09-09 Also known as \cite986635
%@ 1-58113-870-9

%A Faizan Javed
%A Marjan Mernik
%A Barrett R. Bryant
%A Alan Sprague
%T GenInc: An Incremental Context-Free Grammar Learning Algorithm for Domain-Specific Language Development
%B Proceedings of the 2007 International Conference on Machine Learning; Models, Technologies \& Applications, MLMTA 2007
%E Hamid R. Arabnia and Matthias Dehmer and Frank Emmert-Streib and Mary Qu Yang
%D 2007
%P 118--124
%I CSREA Press
%C Las Vegas Nevada, USA
%K genetic algorithms, genetic programming, Grammar Inference, Domain-Specific Languages, Incremental Learning
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.7594
%X While grammar inference (or grammar induction) has found extensive application in the areas of robotics, computational biology, speech and pattern recognition, its
   application to problems in programming language and software engineering domains has been limited. We have found a new application area for grammar inference which intends
   to make domain specific language development easier for domain experts not well versed in programming language design, and finds a second application in construction of
   renovation tools for legacy software systems. As a continuation of our previous efforts to infer context-free grammars (CFGs) for domain-specific languages which previously
   involved a genetic-programming based CFG inference system, we discuss improvements made to an incremental learning algorithm, called GenInc, for inferring context-free
   grammars with a core focus on facilitating domain-specific language development. We elaborate on the enhancements made to GenInc in the form of new operators, and conclude
   by discussing the results of applying GenInc to domain-specific languages.
%8 June  25-28

%A A. W. Jayawardena
%A N. Muttil
%A J. H. W. Lee
%T Comparative Analysis of Data-Driven and GIS-Based Conceptual Rainfall-Runoff Model
%J Journal of Hydrologic Engineering
%V 11
%N 1
%D 2006
%P 1--11
%I 
%K genetic algorithms, genetic programming
%X Modelling of the rainfall-runoff process is important in hydrology. Historically, researchers relied on conventional deterministic modeling techniques based either on the
   physics of the underlying processes, or on the conceptual systems which may or may not mimic the underlying processes. This study investigates the suitability of a
   conceptual technique along with a data-driven technique, to model the rainfall-runoff process. The conceptual technique used is based on the Xinanjiang model coupled with
   geographic information system (GIS) for runoff routing and the data-driven model is based on genetic programming (GP), which was used for rainfall-runoff modelling in the
   recent past. To verify GP's capability, a simple example with a known relation from fluid mechanics is considered first. For a small, steep-sloped catchment in Hong Kong,
   it was found that the conceptual model outperformed the data-driven model and provided a better representation of the rainfall-runoff process in general, and better
   prediction of peak discharge, in particular. To demonstrate the potential of GP as a viable data-driven rainfall-runoff model, it is successfully applied to two catchments
   located in southern China.
%8 January /  February
%Z c ASCE

%A Piotr Jedrzejowicz
%A Ewa Ratajczak-Ropel
%T Agent-Based Gene Expression Programming for Solving the RCPSP/max Problem
%B 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009
%S Lecture Notes in Computer Science
%E Mikko Kolehmainen and Pekka J. Toivanen and Bartlomiej Beliczynski
%V 5495
%D 2009
%P 203--212
%I Springer
%C Kuopio, Finland
%K genetic algorithms, genetic programming, gene expression programming
%O Revised Selected Papers
%8 April  23-25

%A Mark Jelasity
%T The Adaptationist Stance and Evolutionary Computation
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1859--1864
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K methodology, pedagogy and philosophy
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/MP-600.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Adam C. Jensen
%A Betty H. C. Cheng
%T On the use of genetic programming for automated refactoring and the introduction of design patterns
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 1341--1348
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, SBSE, Search-based software engineering
%X Maintaining an object-oriented design for a piece of software is a difficult, time-consuming task. Prior approaches to automated design refactoring have focused on making
   small, iterative changes to a given software design. However, such approaches do not take advantage of composition of design changes, thus limiting the richness of the
   refactoring strategies that they can generate. In order to address this problem, this paper introduces an approach that supports composition of design changes and makes the
   introduction of design patterns a primary goal of the refactoring process. The proposed approach uses genetic programming and software engineering metrics to identify the
   most suitable set of refactorings to apply to a software design. We illustrate the efficacy of this approach by applying it to a large set of published models, as well as a
   real-world case study
%8 7-11  July
%Z p1343 'Gamma design patterns, including Abstract Factory, Adapter, Bridge, Decorator, Prototype, and Proxy.' Search based refactoring QMOOD. Remodel = (UML design
   graph,transformation tree). O'Cinneide mini-transformations: abstraction, abstract access, delegation partial abstract, wrapper design patterns. ReMoDD. ECJ. JGraphT, JLog.
   Large cluster of SuSE linux enterprise server. Prolog. Also known as \cite1830731 GECCO-2010 A joint meeting of the nineteenth international conference on genetic
   algorithms (ICGA-2010) and the fifteenth annual genetic programming conference (GP-2010)

%A Kwang-Seuk Jeong
%A Dong-Kyun Kim
%A Peter Whigham
%A Gea-Jae Joo
%T Modelling Microcystis aeruginosa bloom dynamics in the Nakdong River by means of evolutionary computation and statistical approach
%J Ecological Modelling
%V 161
%N 1-2
%D 2003
%P 67--78
%I 
%K genetic algorithms, genetic programming, Multivariate linear regression, Microcystis aeruginosa, Algal blooms, Ecological modelling, Nakdong River
%U http://www.sciencedirect.com/science/article/B6VBS-47VRMKR-4/2/816a2fac74d51d8caefedf1f9c2055b0
%X Dynamics of a bloom-forming cyanobacteria (Microcystis aeruginosa) in a eutrophic river?reservoir hybrid system were modelled using a genetic programming (GP) algorithm and
   multivariate linear regression (MLR). The lower Nakdong River has been influenced by cultural eutrophication since construction of an estuarine barrage in 1987. During
   1994?1998, the average concentrations of nutrients and phytoplankton were: NO3-?N, 2.7 mg l-1; NH4+?N, 0.6 mg l-1; PO43-?P, 34.7 g l-1; and chlorophyll a, 50.2 g l-1.
   Blooms of M. aeruginosa occurred in summers when there were droughts. Using data from 1995 to 1998, GP and MLR were used to construct equation models for predicting the
   occurrence of M. aeruginosa. Validation of the model was done using data from 1994, a year when there were severe summer blooms. GP model was very successful in predicting
   the temporal dynamics and magnitude of blooms while MLR resulted rather insufficient predictability. The lower Nakdong River exhibits reservoir-like ecological dynamics
   rather than riverine, and for this reason a previous river mechanistic model failed to describe uncertainty and complexity. Results of this study suggest that an
   inductive-empirical approach is more suitable for modelling the dynamics of bloom-forming algal species in a river?reservoir transitional system.
%8 1  March
%Z a Department of Biology, Pusan National University, Jang-Jeon Dong, Gum-Jeong Gu, Busan 609-735, South Korea b Department of Information Science, University of Otago, PO
   Box 56, Dunedin, New Zealand

%A Gordan Jezic
%A Robert Kostelac
%A Ignac Lovrek
%A Vjekoslav Sinkovic
%T Genetic Algorithms for Scheduling Tasks with Non-negligible Intertask Communication onto Multiprocessors
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 518
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Guangfeng Jia
%A Yuehui Chen
%A Qiang Wu
%T A MEP and IP Based Flexible Neural Tree Model for Exchange Rate Forecasting
%B Fourth International Conference on Natural Computation, ICNC '08
%V 5
%D 2008
%P 299--303
%I 
%K genetic algorithms, genetic programming, MEP, financial problem, flexible neural tree model, foreign exchange rate forecasting, immune programming, multi expression
   programming, exchange rates, financial management, neural nets, trees (mathematics)
%X Forecasting exchange rate is an important financial problem that is received much more attentions because of its difficulty and practical applications. The problem of
   prediction of foreign exchange rates by using multi expression programming and immune programming based flexible neural tree (MEPIP-FNT) is presented in this paper. This
   work is an extension of our previously traditional FNT model which can optimize the architectures and the weights of flexible neuron model respectively. The novel MEPIPFNT
   model with the underlying immune theories is capable of evolving the architectures and the weights simultaneously. To demonstrate the efficiency of the model, we conduct
   three different datasets in our forecasting performance analysis.
%8 October
%Z Also known as \cite4667445

%A Qiang Jia
%A Wallace K. S. Tang
%T Synthesizing Chaotic Systems with Genetic Programming
%B 2010 International Workshop on Chaos-Fractals Theories and Applications (IWCFTA)
%D 2010
%P 132--136
%I 
%K genetic algorithms, genetic programming, optimisation, state dynamical equation, synthesising chaotic system, tree searching
%X In this paper, it is to apply genetic programming to explore some new chaotic systems. Based on a tree representation, each function in the state dynamical equation of a
   chaotic system can be well defined. Thus, through the optimisation process governed by genetic programming, it is demonstrated that some new potential forms can be
   determined, for which chaotic systems are obtained by having tuning of the coefficients.
%8 29-31  October
%Z City University of Hong Kong. Also known as \cite5671295

%A Mingda Jiang
%T A hierarchical genetic system for symbolic function identification
%R M.S. Thesis
%D 1992
%I 
%I University of Montana
%K genetic algorithms, genetic programming

%A Mingda Jiang
%A Alden H. Wright
%T An adaptive function identification system
%B Proceedings of the IEEE/ACM Conference on Developing and Managing Intelligent System Projects, Vienna, Virginia, USA
%D 1993
%P 47--53
%I 
%K genetic algorithms, genetic programming, Levenberg-Marquardt nonlinear regression algorithm, adaptive function identification system, adaptive system, expression-tree
   representation, symbolic function identification problem, adaptive systems, learning (artificial intelligence)
%X Given data in the form of a collection of (x,y) pairs of real numbers, the symbolic function identification problem is to find a functional model of the form y=f(x) that
   fits the data. This paper describes an adaptive system for solution of symbolic function identification problems that combines a genetic algorithm and the
   Levenberg-Marquardt nonlinear regression algorithm. The genetic algorithm uses an expression-tree representation rather than the more usual binary-string representation.
   Experiments were run with data generated using a wide variety of function models. The system was able to find a function model that closely approximated the data with a
   very high success rate
%8 March
%Z HGSFI, Ultrix, Unidata Inc. Also known as \cite248637

%A Mingda Jiang
%A Alden H. Wright
%T A Hierarchical Genetic System for Symbolic Function Identification
%B Proceedings of the 24th Symposium on the Interface: Computing Science and Statistics, College Station, Texas
%D 1992
%I 
%I University of Montana, Missoula, MT 59812
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/202012.html
%X Given data in the form of a collection of (x,y) pairs of real numbers, the symbolic function identification problem is to find a functional model of the form y = f(x) that
   fits the data. This paper describes a system for solution of symbolic function identification problems that combines a genetic algorithm and the Levenberg-Marquardt
   nonlinear regression algorithm. The genetic algorithm uses an expression-tree representation rather than the more usual binary-string representation. Experiments were run
   with data generated using a wide variety of function models. The system was able to find a function model that closely approximated the data with a very high success rate.
%8 March
%Z Also available as technical report, 26 pages. Does Symbolic regression but uses Levenberg-Marquadt statistical technique to adjust parameters to get closer (equivalent of
   local hill climbing?) Some case GP don't work on. Mentions Permutation but don't say how usefully it is

%A J. Jiang
%A D. Butler
%T An Adaptive Genetic Algorithm for Image Data Compression
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 83--87
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-201031-7

%A Tianzi Jiang
%T An Evolutionary Approach to Optimal Structuring Element Extraction for MST-Based Shapes Description
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Nanlin Jin
%A Edward P. K. Tsang
%T Relative Fitness and Absolute Fitness for Co-evolutionary Systems
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 331--340
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=331
%X The commonly adopted fitness which evaluates the performance of individuals in co-evolutionary systems is relative fitness. Relative fitness is a dynamic assessment subject
   to the other co-evolving population(s). Researchers apparently pay less attention to the use of absolute fitness functions in studying co-evolutionary algorithms than the
   use of relative fitness functions. One of our aims in this work is to formalise both relative fitness and absolute fitness for co-evolving systems. Another aim is to
   demonstrate the usage of absolute and relative fitness through a case study. We develop a co-evolutionary system by means of Genetic Programming to discover co-adapted
   strategies for a Basic Alternating-Offers Bargaining Problem. In this case, the relative fitness essentially drives co-evolution to converge to game-theoretic equilibrium.
   Whereas the relative fitness alone can not discover the whole view of co-evolutionary progress. The absolute fitness, on the other hand helps us to monitor the development
   of co-adaptive learning. Having analysed the micro-behaviour of the players' strategies, based on their absolute fitness, we can explain how the co-evolving populations
   converge to the perfect equilibria.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Nanlin Jin
%A Edward Tsang
%T Co-evolutionary Strategies for an Alternating-Offer Bargaining Problem
%B IEEE 2005 Symposium on Computational Intelligence and Games CIG'05
%E Graham Kendall and Simon Lucas
%D 2005
%P 211--217
%I IEEE Press
%I Computational Intelligence Society
%C Essex, UK
%K genetic algorithms, genetic programming, Co-evolution, GP, Bargaining Theory
%U http://cswww.essex.ac.uk/Research/CSP/finance/papers/JinTsa-Bargaining-Cig2005.pdf
%X We apply an Evolutionary Algorithm (EA) to solve the Rubinstein's Basic Alternating-Offer Bargaining Problem, and compare our experimental results with its analytic
   game-theoretic solution. The application of EA employs an alternative set of assumptions on the players' behaviours. Experimental outcomes suggest that the applied
   co-evolutionary algorithm, one of Evolutionary Algorithms, is able to generate convincing approximations of the theoretic solutions. The major advantages of EA over the
   game-theoretic analysis are its flexibility and ease of application to variants of Rubinstein Bargaining Problems and complicated bargaining situations for which theoretic
   solutions are unavailable.
%8 4-6  April

%A Nanlin Jin
%T Equilibrium Selection by Co-evolution for Bargaining Problems under Incomplete Information about Time Preferences
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2661--2668
%I IEEE Press
%I IEEE
%C Edinburgh, UK
%K genetic algorithms, genetic programming, co-evolution, game theory
%U http://cswww.essex.ac.uk/Research/CSP/finance/papers/Jin-IncompleteInfo-Cec2005.pdf
%X The main purpose of this work is to measure the impact of players' information completeness on the outcomes in dynamic strategic games. We apply Co-evolutionary Algorithms
   to solve four incomplete information bargaining problems and investigate the experimental outcomes on players' shares from agreements, the efficiency of agreements and the
   evolutionary time for convergence. Empirical analyses indicate that in the absence of complete information on the counterpart(s)' preferences, co-evolving populations are
   still able to select equilibriums which are Pareto-efficient and stationary. This property of the co-evolutionary algorithm supports its future applications on complex
   dynamic games.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-9363-5

%A Nanlin Jin
%T Indirect co-evolution for understanding belief in an incomplete information dynamic game
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 383--384
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Coevolution: Poster, belief, concept learning, game theory, heuristic methods, incomplete information, knowledge acquisition
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p383.pdf
%X This study aims to design a new co-evolution algorithm, Mixture Co-evolution which enables modelling of integration and composition of direct co-evolution and it indirect
   coevolution. This algorithm is applied to investigate properties of players' belief and of information incompleteness in a dynamic game.
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Nanlin Jin
%A Edward Tsang
%T Co-adaptive Strategies for Sequential Bargaining Problems with Discount Factors and Outside Options
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%D 2006
%P 7913--7920
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X Bargaining is fundamental in social activities. Game-theoretic methodology has provided theoretic solutions for certain abstract models. Even for a simple model, this
   method demands substantial human intelligent effort in order to solve game-theoretic equilibriums. The analytic complexity increases rapidly when more elements are included
   in the models. In our previous work, we have demonstrated how coevolutionary algorithms can be used to find approximations to game-theoretic equilibriums of bargaining
   models that consider bargaining costs only. In this paper, we study more complicated bargaining models, in which outside option is taken into account besides bargaining
   cost. Empirical studies demonstrate that evolutionary algorithms are efficient in finding near-perfect solutions. Experimental results reflect the compound effects of
   discount factors and outside options upon bargaining outcomes. We argue that evolutionary algorithm is a practical tool for generating reasonably good strategies for
   complicated bargaining models beyond the capability of game theory.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. best presentation in session
%@ 0-7803-9487-9

%A Nanlin Jin
%T Constraint-based co-evolutionary genetic programming for bargaining problems
%R Ph.D. Thesis
%D 2007
%I 
%I Department of Computer Science, University of Essex
%C UK
%K genetic algorithms, genetic programming
%U http://www.bracil.net/finance/papers/Jin-Bargaining-PhD2007.pdf

%A Nanlin Jin
%A Edward Tsang
%A Jin Li
%T A constraint-guided method with evolutionary algorithms for economic problems
%J Applied Soft Computing
%V 9
%N 3
%D 2009
%P 924--935
%I 
%K genetic algorithms, genetic programming, Constraint satisfaction, Economic problems
%U http://www.sciencedirect.com/science/article/B6W86-4V0TCY0-6/2/6b82133b94fa2c3580d4e43064120400
%X This paper presents an evolutionary algorithms based constrain-guided method (CGM) that is capable of handling both hard and soft constraints in optimization problems.
   While searching for constraint-satisfied solutions, the method differentiates candidate solutions by assigning them with different fitness values, enabling favorite
   solutions to be distinguished more likely and more effectively from unfavoured ones. We illustrate the use of CGM in solving two economic problems with optimization
   involved: (1) searching equilibriums for bargaining problems; (2) reducing the rate of failure in financial prediction problems. The efficacy of the proposed CGM is
   analysed and compared with some other computational techniques, including a repair method and a penalty method for the problem (1), a linear classifier and three neural
   networks for the problem (2), respectively. Our studies here suggest that the evolutionary algorithms based CGM compares favorably against those computational approaches.

%A Jian-Ping Jin
%T Alternative RNA Splicing-Generated Cardiac Troponin T Isoform Switching: A Non-Heart-Restricted Genetic Programming Synchronized in Developing Cardiac and Skeletal Muscles
%J Biochemical and Biophysical Research Communications
%V 225
%N 3
%D 1996
%P 883--889
%I 
%U http://www.sciencedirect.com/science/article/B6WBK-45N4ST7-14/2/925d3a91d563e35c558593bdd19ba17a
%Z Not on GP

%A Zhanli Jin
%A Yaowen Yang
%A Chee Kiong Soh
%T Application of fuzzy GA for optimal vibration control of smart cylindrical shells
%J Smart Materials and Structures
%V 14
%N 6
%D 2005
%P 1250--1264
%I 
%K genetic algorithms, genetic programming
%U http://stacks.iop.org/0964-1726/14/1250
%X a fuzzy-controlled genetic-based optimisation technique for optimal vibration control of cylindrical shell structures incorporating piezoelectric sensor/actuators (S/As) is
   proposed. The geometric design variables of the piezoelectric patches, including the placement and sizing of the piezoelectric S/As, are processed using fuzzy set theory.
   The criterion based on the maximisation of energy dissipation is adopted for the geometric optimization. A fuzzy-rule-based system (FRBS) representing expert knowledge and
   experience is incorporated in a modified genetic algorithm (GA) to control its search process. A fuzzy logic integrated GA is then developed and implemented. The results of
   three numerical examples, which include a simply supported plate, a simply supported cylindrical shell, and a clamped simply supported plate, provide some meaningful and
   heuristic conclusions for practical design. The results also show that the proposed fuzzy-controlled GA approach is more effective and efficient than the pure GA method.
%8 Decemeber
%Z PII: S0964-1726(05)07295-2 School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore

%A Yong-Gun Jo
%A Hoon Kang
%T Evolutionary Cellular Automata for Optimal Path Planning of Mobile Robots
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1443
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-037.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Yookyung Jo
%A Carl Lagoze
%A C. Lee Giles
%T Detecting research topics via the correlation between graphs and texts
%B Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD-2007
%E Pavel Berkhin and Rich Caruana and Xindong Wu
%D 2007
%P 370--379
%I ACM
%C San Jose, California, USA
%K genetic algorithms, genetic programming, Algorithms, Languages, Measurement, topic detection, graph mining, probabilistic measure, citation graphs, correlation of text and
   links
%X In this paper we address the problem of detecting topics in large-scale linked document collections. Recently, topic detection has become a very active area of research due
   to its utility for information navigation, trend analysis, and high-level description of data. We present a unique approach that uses the correlation between the
   distribution of a term that represents a topic and the link distribution in the citation graph where the nodes are limited to the documents containing the term. This tight
   coupling between term and graph analysis is distinguished from other approaches such as those that focus on language models. We develop a topic score measure for each term,
   using the likelihood ratio of binary hypotheses based on a probabilistic description of graph connectivity. Our approach is based on the intuition that if a term is
   relevant to a topic, the documents containing the term have denser connectivity than a random selection of documents. We extend our algorithm to detect a topic represented
   by a set of terms, using the intuition that if the co-occurrence of terms represents a new topic, the citation pattern should exhibit the synergistic effect. We test our
   algorithm on two electronic research literature collections, arXiv and Citeseer. Our evaluation shows that the approach is effective and reveals some novel aspects of topic
   detection.
%8 August  12-15
%Z GP literature used as one example

%A David Joffe
%T A Genetic Algorithm for a Stochastic Network Planning Problem
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 107--116
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Brad Johanson
%T Automated Fitness Raters for GP-Music System
%R M.S. Thesis
%D 1997
%I 
%I School of Computer Science, University of Birmingham
%C Birmingham, UK
%K genetic algorithms, genetic programming
%U http://graphics.stanford.edu/~bjohanso/gp-music/gp-music-auto-raters.ps.gz

%A Bradley E Johanson
%A Riccardo Poli
%T GP-Music: An Interactive Genetic Programming System for Music Generation with Automated Fitness Raters
%R Technical Report CSRP-98-13
%D 1998
%I 
%I University of Birmingham, School of Computer Science
%K genetic algorithms, genetic programming
%U http://graphics.stanford.edu/~bjohanso/gp-music/tech-report
%X In this paper we present the GP-Music System, an interactive system which allows users to evolve short musical sequences using interactive genetic programming, and its
   extensions aimed at making the system fully automated. The basic GP system works by using a genetic programming algorithm, a small set of functions for creating musical
   sequences, and a user interface which allows the user to rate individual sequences. With this user interactive technique it was possible to generate pleasant tunes over
   runs of 20 individuals over 10 generations. As the user is the bottleneck in interactive systems, the system takes rating data from a users run and uses it to train a
   neural network based automatic rater, or "auto rater", which can replace the user in bigger runs. Using this auto rater we were able to make runs of up to 50 generations
   with 500 individuals per generation. The best of run pieces generated by the auto raters were pleasant but were not, in general, as nice as those generated in user
   interactive runs.
%8 May

%A Brad Johanson
%A Riccardo Poli
%T GP-Music: An Interactive Genetic Programming System for Music Generation with Automated Fitness Raters
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 181--186
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/336573.html
%X In this paper we present the GP-Music System, an interactive system which allows users to evolve short musical sequences using interactive genetic programming. We also
   present an extension which uses a neural network to model a users preferences, then stands in for them during the evolutionary process. The use of this `automated fitness
   rater' allows the system to operate both with and without user interaction.
%8 22-25  July
%Z GP-98, see also \citeJohanson98
%@ 1-55860-548-7

%A Stefan J. Johansson
%T Evolving integer recurrences using genetic programming
%R Technical Report IR 402
%D 1996
%I 
%I Faculteit der Wiskunde en Informatica, VU Amsterdam
%C Holland
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/177550.html
%X his report addresses the problem of synthesizing integer recurrences by genetic programming (GP). A number of alternative approaches were proposed and tested by running
   thousands of experiments. In particular the following aspects were investigated: approaches to base cases, population size, different fitness measures and superiority of GP
   over random search. Results of the experiments showed that our approach (fixed base cases) is much better than the conventional one (evolved base cases) on...
%8 2  April
%Z Masters Thesis, CWI Shelf mark A-26012 CWI library 2-12-98

%A Stefan J. Johansson
%T Recurrences with Fixed Base Cases in Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 430
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Ulf Johansson
%A Rikard Konig
%A Lars Niklasson
%T The Truth is In There - Rule Extraction from Opaque Models Using Genetic Programming
%B Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference
%E Valerie Barr and Zdravko Markov
%D 2004
%I AAAI Press
%C Miami Beach, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Papers/FLAIRS/2004/Flairs04-113.pdf
%X A common problem when using complicated models for prediction and classification is that the complexity of the model entails that it is hard, or impossible, to interpret.
   For some scenarios this might not be a limitation, since the priority is the accuracy of the model. In other situations the limitations might be severe, since additional
   aspects are important to consider; e.g. comprehensibility or scalability of the model. In this study we show how the gap between accuracy and other aspects can be bridged
   by using a rule extraction method (termed G-REX) based on genetic programming. The extraction method is evaluated against the five criteria accuracy, comprehensibility,
   fidelity, scalability and generality. It is also shown how G-REX can create novel representation languages; here regression trees and fuzzy rules. The problem used is a
   data-mining problem from the marketing domain where the impact of advertising is predicted from investment plans. Several experiments, covering both regression and
   classification tasks, are evaluated. Results show that G-REX in general is capable of extracting both accurate and comprehensible representations, thus allowing high
   performance also in domains where comprehensibility is of essence.
%@ 1-57735-201-7

%A Ulf Johansson
%A Tuve Lofstrom
%A Rikard Konig
%A Lars Niklasson
%T Genetically Evolved Trees Representing Ensembles
%B Proceedings 8th International Conference on Artificial Intelligence and Soft Computing ICAISC
%S Lecture Notes on Artificial Intelligence (LNAI)
%E Leszek Rutkowski and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek Zurada
%V 4029
%D 2006
%P 613--622
%I Springer-Verlag
%C Zakopane, Poland
%K genetic algorithms, genetic programming
%X We have recently proposed a novel algorithm for ensemble creation called GEMS (Genetic Ensemble Member Selection). GEMS first trains a fixed number of neural networks (here
   twenty) and then uses genetic programming to combine these networks into an ensemble. The use of genetic programming makes it possible for GEMS to not only consider
   ensembles of different sizes, but also to use ensembles as intermediate building blocks. In this paper, which is the first extensive study of GEMS, the representation
   language is extended to include tests partitioning the data, further increasing flexibility. In addition, several micro techniques are applied to reduce overfitting, which
   appears to be the main problem for this powerful algorithm. The experiments show that GEMS, when evaluated on 15 publicly available data sets, obtains very high accuracy,
   clearly outperforming both straightforward ensemble designs and standard decision tree algorithms.
%8 June  25-29
%@ 3-540-35748-3

%A Ulf Johansson
%A Tuve Lofstrom
%A Rikard Konig
%A Lars Niklasson
%T Building Neural Network Ensembles using Genetic Programming
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 2239--2244
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X In this paper we present and evaluate a novel algorithm for ensemble creation. The main idea of the algorithm is to first independently train a fixed number of neural
   networks (here ten) and then use genetic programming to combine these networks into an ensemble. The use of genetic programming makes it possible to not only consider
   ensembles of different sizes, but also to use ensembles as intermediate building blocks. The final result is therefore more correctly described as an ensemble of neural
   network ensembles. The experiments show that the proposed method, when evaluated on 22 publicly available data sets, obtains very high accuracy, clearly outperforming the
   other methods evaluated. In this study several micro techniques are used, and we believe that they all contribute to the increased performance. One such micro technique,
   aimed at reducing overtraining, is the training method, called tombola training, used during genetic evolution. When using tombola training, training data is regularly
   resampled into new parts, called training groups. Each ensemble is then evaluated on every training group and the actual fitness is determined solely from the result on the
   hardest part.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Ulf Johansson
%A Rikard Konig
%A Lars Niklasson
%T Inconsistency - Friend or Foe
%B International Joint Conference on Neural Networks, IJCNN 2007
%D 2007
%P 1383--1388
%I 
%C Orlando, USA
%K genetic algorithms, genetic programming, G-REX tree, consistency criterion, evolutionary algorithms, inconsistency criterion, neural network ensembles, probability
   estimation, publicly available data sets, regression trees, rule extraction algorithms, data integrity, data mining, estimation theory, evolutionary computation, learning
   (artificial intelligence), probability, regression analysis
%X One way of obtaining accurate yet comprehensible models is to extract rules from opaque predictive models. When evaluating rule extraction algorithms, one frequently used
   criterion is consistency; i.e. the algorithm must produce similar rules every time it is applied to the same problem. Rule extraction algorithms based on evolutionary
   algorithms are, however, inherently inconsistent, something that is regarded as their main drawback. In this paper, we argue that consistency is an over valued criterion,
   and that inconsistency can even be beneficial in some situations. The study contains two experiments, both using publicly available data sets, where rules are extracted
   from neural network ensembles. In the first experiment, it is shown that it is normally possible to extract several different rule sets from an opaque model, all having
   high and similar accuracy. The implication is that consistency in that perspective is useless; why should one specific rule set be considered superior? Clearly, it should
   instead be regarded as an advantage to obtain several accurate and comprehensible descriptions of the relationship. In the second experiment, rule extraction is used for
   probability estimation. More specifically, an ensemble of extracted trees is used in order to obtain probability estimates. Here, it is exactly the inconsistency of the
   rule extraction algorithm that makes the suggested approach possible.
%8 12-17  August
%Z Also known as \cite4371160

%A Ulf Johansson
%T Obtaining Accurate and Comprehensible Data Mining Models - An Evolutionary Approach
%R Ph.D. Thesis doctoral thesis
%N 1086
%D 2007
%I 
%I Linkoping University, Department of Computer and Information Science
%C SE-581 83, Linkoping, Sweden
%K genetic algorithms, genetic programming, rule extraction, ensembles, data mining, artificial neural networks
%U http://bada.hb.se/bitstream/2320/2136/1/AvhandlingFinal.pdf
%X When performing predictive data mining, the use of ensembles is claimed to virtually guarantee increased accuracy compared to the use of single models. Unfortunately, the
   problem of how to maximise ensemble accuracy is far from solved. In particular, the relationship between ensemble diversity and accuracy is not completely understood,
   making it hard to efficiently use diversity for ensemble creation. Furthermore, most high-accuracy predictive models are opaque, i.e. it is not possible for a human to
   follow and understand the logic behind a prediction. For some domains, this is unacceptable, since models need to be comprehensible. To obtain comprehensibility, accuracy
   is often sacrificed by using simpler but transparent models; a trade-off termed the accuracy vs. comprehensibility trade-off. With this trade-off in mind, several
   researchers have suggested rule extraction algorithms, where opaque models are transformed into comprehensible models, keeping an acceptable accuracy. In this thesis, two
   novel algorithms based on Genetic Programming are suggested. The first algorithm (GEMS) is used for ensemble creation, and the second (G-REX) is used for rule extraction
   from opaque models. The main property of GEMS is the ability to combine smaller ensembles and individual models in an almost arbitrary way. Moreover, GEMS can use base
   models of any kind and the optimisation function is very flexible, easily permitting inclusion of, for instance, diversity measures. In the experimentation, GEMS obtained
   accuracies higher than both straightforward design choices and published results for Random Forests and AdaBoost. The key quality of G-REX is the inherent ability to
   explicitly control the accuracy vs. comprehensibility trade-off. Compared to the standard tree inducers C5.0 and CART, and some well-known rule extraction algorithms, rules
   extracted by G-REX are significantly more accurate and compact. Most importantly, G-REX is thoroughly evaluated and found to meet all relevant evaluation criteria for rule
   extraction algorithms, thus establishing G-REX as the algorithm to benchmark against.

%A Ulf Johansson
%A Rikard Konig
%A Tuve Lofstrom
%A Lars Niklasson
%T Increasing Rule Extraction Accuracy by Post-Processing GP Trees
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 3005--3010
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialised techniques on a variety of tasks. In this paper, we
   suggest a straightforward novel algorithm for post-processing of GP classification trees. The algorithm iteratively, one node at a time, searches for possible modifications
   that would result in higher accuracy. More specifically, the algorithm for each split evaluates every possible constant value and chooses the best. With this design, the
   post-processing algorithm can only increase training accuracy, never decrease it. In this study, we apply the suggested algorithm to GP trees, extracted from neural network
   ensembles. Experimentation, using 22 UCI datasets, shows that the post-processing results in higher test set accuracies on a large majority of datasets. As a matter of
   fact, for two setups of three evaluated, the increase in accuracy is statistically significant.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Ulf Johansson
%A Lars Niklasson
%T Evolving decision trees using oracle guides
%B IEEE Symposium on Computational Intelligence and Data Mining, CIDM '09
%D 2009
%P 238--244
%I 
%K genetic algorithms, genetic programming, data mining, decision trees, high-accuracy techniques, human inspection, neural network ensemble, opaque models, oracle guides,
   predictive models, rule extraction, transparent models, data mining, decision trees, neural nets
%X Some data mining problems require predictive models to be not only accurate but also comprehensible. Comprehensibility enables human inspection and understanding of the
   model, making it possible to trace why individual predictions are made. Since most high-accuracy techniques produce opaque models, accuracy is, in practice, regularly
   sacrificed for comprehensibility. One frequently studied technique, often able to reduce this accuracy vs. comprehensibility tradeoff, is rule extraction, i.e., the
   activity where another, transparent, model is generated from the opaque. In this paper, it is argued that techniques producing transparent models, either directly from the
   dataset, or from an opaque model, could benefit from using an oracle guide. In the experiments, genetic programming is used to evolve decision trees, and a neural network
   ensemble is used as the oracle guide. More specifically, the datasets used by the genetic programming when evolving the decision trees, consist of several different
   combinations of the original training data and 'oracle data', i.e., training or test data instances, together with corresponding predictions from the oracle. In total,
   seven different ways of combining regular training data with oracle data were evaluated, and the results, obtained on 26 UCI datasets, clearly show that the use of an
   oracle guide improved the performance. As a matter of fact, trees evolved using training data only had the worst test set accuracy of all setups evaluated. Furthermore,
   statistical tests show that two setups, both using the oracle guide, produced significantly more accurate trees, compared to the setup using training data only.
%8 30 2009-  April  2
%Z Also known as \cite4938655

%A Ulf Johansson
%A Cecilia Sonstrod
%A Tuve Lofstrom
%A Rikard Konig
%T Using Genetic Programming to Obtain Implicit Diversity
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 2454--2459
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles
   should be built from base classifiers that are both accurate and diverse. The question of how to balance these two properties in order to maximize ensemble accuracy is,
   however, far from solved and many different techniques for obtaining ensemble diversity exist. One such technique is bagging, where implicit diversity is introduced by
   training base classifiers on different subsets of available data instances, thus resulting in less accurate, but diverse base classifiers. In this paper, genetic
   programming is used as an alternative method to obtain implicit diversity in ensembles by evolving accurate, but different base classifiers in the form of decision trees,
   thus exploiting the inherent inconsistency of genetic programming. The experiments show that the GP approach outperforms standard bagging of decision trees, obtaining
   significantly higher ensemble accuracy over 25 UCI datasets. This superior performance stems from base classifiers having both higher average accuracy and more diversity.
   Implicitly introducing diversity using GP thus works very well, since evolved base classifiers tend to be highly accurate and diverse.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Ulf Johansson
%A Rikard Konig
%A Tuve Lofstrom
%A Lars Niklasson
%T Using Imaginary Ensembles to Select GP Classifiers
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 278--288
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X When predictive modeling requires comprehensible models, most data miners will use specialized techniques producing rule sets or decision trees. This study, however, shows
   that genetically evolved decision trees may very well outperform the more specialized techniques. The proposed approach evolves a number of decision trees and then uses one
   of several suggested selection strategies to pick one specific tree from that pool. The inherent inconsistency of evolution makes it possible to evolve each tree using all
   data, and still obtain somewhat different models. The main idea is to use these quite accurate and slightly diverse trees to form an imaginary ensemble, which is then used
   as a guide when selecting one specific tree. Simply put, the tree classifying the largest number of instances identically to the ensemble is chosen. In the experimentation,
   using 25 UCI data sets, two selection strategies obtained significantly higher accuracy than the standard rule inducer J48.
%8 7-9  April
%Z BNF grammar, parsimony pressure to lessen bloat, persistence, roulette wheel selection, p287 suggests opaque techniques (ANN, SVM, ensembles) will 'almost always' do better
   than rule sets or decision trees. Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Ulf Johansson
%A Rikard Konig
%A Lars Niklasson
%T Genetic rule extraction optimizing brier score
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 1007--1014
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, Genetics based machine learning
%X Most highly accurate predictive modelling techniques produce opaque models. When comprehensible models are required, rule extraction is sometimes used to generate a
   transparent model, based on the opaque. Naturally, the extracted model should be as similar as possible to the opaque. This criterion, called fidelity, is therefore a key
   part of the optimisation function in most rule extracting algorithms. To the best of our knowledge, all existing rule extraction algorithms targeting fidelity use 0/1
   fidelity, i.e., maximise the number of identical classifications. In this paper, we suggests and evaluate a rule extraction algorithm using a more informed fidelity
   criterion. More specifically, the novel algorithms, which is based on genetic programming, minimises the difference in probability estimates between the extracted and the
   opaque models, by using the generalised Brier score as fitness function. Experimental results from 26 UCI data sets show that the suggested algorithm obtained considerably
   higher accuracy and significantly better AUC than both the exact same rule extraction algorithm maximizing 0/1 fidelity, and the standard tree inducer J48. Somewhat
   surprisingly, rule extraction using the more informed fidelity metric normally resulted in less complex models, making sure that the improved predictive performance was not
   achieved on the expense of comprehensibility.
%8 7-11  July
%Z Also known as \cite1830668 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Ulf Johansson
%A Cecilia Sonstrod
%A Tuve Lofstrom
%T One Tree to Explain Them All
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 1443--1450
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Classification, clustering, data analysis and data mining, Learning classifier systems
%X Random forest is an often used ensemble technique, renowned for its high predictive performance. Random forests models are, however, due to their sheer complexity
   inherently opaque, making human interpretation and analysis impossible. This paper presents a method of approximating the random forest with just one decision tree. The
   approach uses oracle coaching, a recently suggested technique where a weaker but transparent model is generated using combinations of regular training data and test data
   initially labelled by a strong classifier, called the oracle. In this study, the random forest plays the part of the oracle, while the transparent models are decision trees
   generated by either the standard tree inducer J48, or by evolving genetic programs. Evaluation on 30 data sets from the UCI repository shows that oracle coaching
   significantly improves both accuracy and area under ROC curve, compared to using training data only. As a matter of fact, resulting single tree models are as accurate as
   the random forest, on the specific test instances. Most importantly, this is not achieved by inducing or evolving huge trees having perfect fidelity; a large majority of
   all trees are instead rather compact and clearly comprehensible. The experiments also show that the evolution outperformed J48, with regard to accuracy, but that this came
   at the expense of slightly larger trees.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A A. Johari
%A G. Habibagahi
%A A. Ghahramani
%T Prediction of Soil-Water Characteristic Curve Using Genetic Programming
%J Journal of Geotechnical and Geoenvironmental Engineering
%V 132
%N 5
%D 2006
%P 661--665
%I 
%K genetic algorithms, genetic programming
%X In this technical note, a genetic programming (GP) approach is employed to predict the soil-water characteristic curve (SWCC) of soils. The GP model requires an input
   terminal set that consists of initial void ratio, initial gravimetric water content, logarithm of suction normalised with respect to atmospheric air pressure, clay content,
   and silt content. The output terminal set consists of the gravimetric water content corresponding to the assigned input suction. The function set includes operators such as
   plus, minus, product, division, and power. Results from pressure plate tests carried out on clay, silty clay, sandy loam, and loam compiled in the SoilVision software were
   adopted as a database for developing and validating the genetic model. For this purpose, and after data digitisation, GP software (GPLAB) provided by MATLAB was employed
   for the analysis. Furthermore, GP simulations were compared with the experimental results as well as the models proposed by other investigators. This comparison indicated
   superior performance of the proposed model for predicting the SWCC.
%8 May
%Z c2006 ASCE

%A Maria John
%A David Panton
%A Kevin White
%T Genetic Algorithm for Regional Surveillance
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1573--1579
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-712.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Bryan H. Johnson
%T An Attempt to Evolve Cooperation Among Separately Evolved Structure in Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 117--126
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Clayton M. Johnson
%A James Farrell
%T Evolutionary Induction of Grammar Systems for Multi-agent Cooperation
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 101--112
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=101
%X We propose and describe a minimal cooperative problem that captures essential features of cooperative behaviour and permits detailed study of the mechanisms involved. We
   characterise this problem as one of language generation by cooperating grammars, and present initial results for language induction by pairs of right-linear grammars using
   grammatically based genetic programming. Populations of cooperating grammar systems were found to induce grammars for regular languages more rapidly than non-cooperating
   controls. Cooperation also resulted in greater absolute accuracy in the steady state, even though the control performance exceeded that of prior results for the induction
   of regular languages by a genetic algorithm.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Colin G. Johnson
%T Deriving genetic programming fitness properties by static analysis
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 298--307
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming, grammatical evolution
%U http://link.springer-ny.com/link/service/series/0558/papers/2278/22780298.pdf
%X The aim of this paper is to introduce the idea of using static analysis of computer programs as a way of measuring fitness in genetic programming. Such techniques extract
   information about the programs without explicitly running them, and in particular they infer properties which hold across the whole of the input space of a program. This
   can be applied to measure fitness, and has a number of advantages over measuring fitness by running members of the population on test cases. The most important advantage is
   that if a solution is found then it is possible to formally trust that solution to be correct across all inputs. This paper introduces these ideas, discusses various ways
   in which they could be applied, discusses the type of problems for which they are appropriate, and ends by giving a simple test example and some questions for future
   research.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Colin G. Johnson
%T What Can Automatic Programming Learn from Theoretical Computer Science?
%B The 2002 U.K. Workshop on Computational Intelligence (UKCI'02)
%E Xin Yao
%D 2002
%I 
%I eunite
%C Birmingham, U.K.
%K genetic algorithms, genetic programming, SBSE
%U http://kar.kent.ac.uk/13729/1/WhatColin1.pdf
%X This paper considers two (seemingly) radically different perspectives on the construction of software. On one hand, search-based heuristics such as genetic programming. On
   the other hand, the theories of programming which underpin mathematical program analysis and formal methods. The main part of the paper surveys possible links between these
   perspectives. In particular the contrast between inductive and deductive approaches to software construction are studied, and various suggestions are made as to how
   randomised search heuristics can be combined with formal approaches to software construction without compromising the rigorous provability of the results. The aim of the
   ideas proposed is to improve the efficiency, effectiveness and safety of search-based automatic programming.
%8 2-4  September
%Z http://www.cs.bham.ac.uk/~jxb/UKCI/program.shtml

%A Colin G. Johnson
%T Genetic programming with guaranteed constraints
%B Proceedings of the 4th International Conference on Recent Advances in Soft Computing
%E Ahmad Lotfi and Jon Garibaldi and Robert John
%D 2002
%P 134--140
%I The Nottingham Trent University
%C Nottingham, United Kingdom
%K genetic algorithms, genetic programming
%U http://www.cs.kent.ac.uk/pubs/2002/1545/content.pdf
%X Genetic programming is a powerful technique for automatically generating program code from a description of the desired functionality. However it is frequently distrusted
   by users because the programs are generated with reference to a training set, and there is no formal guarantee that the generated programs will operate as intended outside
   of this training set. This paper describes a way of including constraints into the fitness function of a genetic programming system, so that the evolution is guided towards
   a solution which satisfies those constraints and so that a check can be made when a solution satisfies those constraints. This is applied to a problem in mobile robotics.
%8 Decemeber  12-13
%Z http://www.rasc2002.info
%@ 1-84233-076-4

%A Colin G. Johnson
%T Artificial Immune System Programming for Symbolic Regression
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 350--358
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=350
%X Artificial Immune Systems are computational algorithms which take their inspiration from the way in which natural immune systems learn to respond to attacks on an organism.
   This paper discusses how such a system can be used as an alternative to genetic algorithms as a way of exploring program-space in a system similar to genetic programming.
   Some experimental results are given for a symbolic regression problem. The paper ends with a discussion of future directions for the use of artificial immune systems in
   program induction.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Colin Johnson
%T Genetic Programming with Fitness based on Model Checking
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 114--124
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming, evolution strategy, finite state machine FSM, CFA, AES, temporal logic, computational tree logic CTL, Stuttgart model-checking kit
   SMV, growth style mutation, SBSE
%X Model checking is a way of analysing programs and program-like structures to decide whether they satisfy a list of temporal logic statements describing desired behaviour.
   In this paper we apply this to the fitness checking stage in an evolution strategy for learning finite state machines. We give experimental results consisting of learning
   the control program for a vending machine.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007 No recombination. 30 runs failed to solve coffee and Tea vending
   problem
%@ 3-540-71602-5

%A Colin Johnson
%T Genetic Programming Crossover: Does it Cross Over?
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 97--108
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Colin Johnson
%T Teaching natural computation
%J IEEE Computational Intelligence Magazine
%V 4
%N 1
%D 2009
%P 24--30
%I 
%K computer science education, philosophical aspects computational ideas, computer science education, computing education, computing paradigms, natural computational property,
   philosophical debates, teaching
%X This paper consists of a discussion of the potential impact on computer science education of regarding computation as a property of the natural world, rather than just a
   property of artifacts specifically created for the purpose of computing. Such a perspective is becoming increasingly important: new computing paradigms based on the natural
   computational properties of the world are being created, scientific questions are being answered using computational ideas, and philosophical debates on the nature of
   computation are being formed. This paper discusses how computing education might react to these developments, goes on to discuss how these ideas can help to define computer
   science as a discipline, and reflects on our experience at Kent in teaching these subjects.
%8 February
%Z Also known as \cite4762307

%A Derek M. Johnson
%A Ankur Teredesai
%A Robert T. Saltarelli
%T Genetic Programming in Wireless Sensor Networks
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 96--107
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=96
%X Wireless sensor networks (WSNs) are becoming increasingly important as they attain greater deployment. New techniques for evolutionary computing (EC) are needed to address
   these new computing models. This paper describes a novel effort to develop a series of variations to evolutionary computing paradigms such as Genetic Programming to enable
   their operation within the wireless sensor network. The ability to compute evolutionary algorithms within the WSN has innumerable advantages including, intelligent-sensing,
   resource optimised communication strategies, intelligent-routing protocol design, novelty detection, etc to name a few. In this paper we first discuss an evolutionary
   computing algorithm that operates within a distributed wireless sensor network. Such algorithms include continuous evolutionary computing. Continuous evolutionary computing
   extends the concept of an asynchronous evolutionary cycle where each individual resides and communicates with its immediate neighbours in an asynchronous time-step and
   exchanges genetic material. We then describe the adaptations required to develop practicable implementations of evolutionary computing algorithms to effectively work in
   resource constrained environments such as WSNs. Several adaptations including a novel representation scheme, an approximate fitness computation method and a sufficient
   statistics based data reduction technique lead to the development of a GP implementation that is usable on the low-power, small footprint architectures typical to wireless
   sensor modes. We demonstrate the utility of our formulations and validate the proposed ideas using a variety of problem sets and describe the results.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Helen Johnson
%T EuroGP A biologist's persepective
%J EvoNEWS
%V 11
%D 1999
%P 11
%I 
%K genetic algorithms, genetic programming
%U http://evonet.lri.fr/evoweb/files/evonews/evonews11.pdf
%X More than 70 people attended EvoWorkshops-99 in Goteborg this May for four days of presentations on the state of the art in evolutionary computing. The event, which brought
   together the expertise of four EvoNet working groups, promised to be wide ranging and inspirational. Here, some of the participants report back.
%8 summer

%A Helen E. Johnson
%A Richard J. Gilbert
%A Michael K. Winson
%A Royston Goodacre
%A Aileen R. Smith
%A Jem J. Rowland
%A Michael A. Hall
%A Douglas B. Kell
%T Explanatory Analysis of the Metabolome Using Genetic Programming of Simple, Interpretable Rules
%J Genetic Programming and Evolvable Machines
%V 1
%N 3
%D 2000
%P 243--258
%I 
%K genetic algorithms, genetic programming, metabolome, tomato fruit, salinity, Fourier transform infra-spectroscopy (FTIR), chemometrics
%X Genetic programming, in conjunction with advanced analytical instruments, is a novel tool for the investigation of complex biological systems at the whole-tissue level. In
   this study, samples from tomato fruit grown hydroponically under both high- and low-salt conditions were analysed using Fourier-transform infrared spectroscopy (FTIR), with
   the aim of identifying spectral and biochemical features linked to salinity in the growth environment. FTIR spectra of whole tissue extracts are not amenable to direct
   visual analysis, so numerical modelling methods were used to generate models capable of classifying the samples based on their spectral characteristics. Genetic programming
   (GP) provided models with a better prediction accuracy to the conventional data modelling methods used, whilst being much easier to interpret in terms of the variables
   used. Examination of the GP-derived models showed that there were a small number of spectral regions that were consistently being used. In particular, the spectral region
   containing absorbances potentially due to a cyanide/nitrile functional group was identified as discriminatory. The explanatory power of the GP models enabled a chemical
   interpretation of the biochemical differences to be proposed. The combination of FTIR and GP is therefore a powerful and novel analytical tool that, in this study, improves
   our understanding of the biochemistry of salt tolerance in tomato plants.
%8 July
%Z Article ID: 264703

%A Helen E. Johnson
%A David Broadhurst
%A Royston Goodacre
%A Aileen R. Smith
%T Metabolic fingerprinting of salt-stressed tomatoes
%J Phytochemistry
%V 62
%N 6
%D 2003
%P 919--928
%I 
%K genetic algorithms, genetic programming
%X The aim of this study was to adopt the approach of metabolic fingerprinting through the use of Fourier transform infrared (FT-IR) spectroscopy and chemometrics to study the
   effect of salinity on tomato fruit. Two varieties of tomato were studied, Edkawy and Simge F1. Salinity treatment significantly reduced the relative growth rate of Simge F1
   but had no significant effect on that of Edkawy. In both tomato varieties salt-treatment significantly reduced mean fruit fresh weight and size class but had no significant
   affect on total fruit number. Marketable yield was however reduced in both varieties due to the occurrence of blossom end rot in response to salinity. Whole fruit flesh
   extracts from control and salt-grown tomatoes were analysed using FT-IR spectroscopy. Each sample spectrum contained 882 variables, absorbance values at different
   wavenumbers, making visual analysis difficult and therefore machine learning methods were applied. The unsupervised clustering method, principal component analysis (PCA)
   showed no discrimination between the control and salt-treated fruit for either variety. The supervised method, discriminant function analysis (DFA) was able to classify
   control and salt-treated fruit in both varieties. Genetic algorithms (GA) were applied to identify discriminatory regions within the FT-IR spectra important for fruit
   classification. The GA models were able to classify control and salt-treated fruit with a typical error, when classifying the whole data set, of 9% in Edkawy and 5% in
   Simge F1. Key regions were identified within the spectra corresponding to nitrile containing compounds and amino radicals. The application of GA enabled the identification
   of functional groups of potential importance in relation to the response of tomato to salinity.
%8 March
%Z PMID: 12590119 See \cite03_essa_153-163

%A Judy Johnson
%A Soundar Kumara
%T Coadaptation of Cooperative Players in an Iterated Prisoners Dilemma Game using an XML Based GA
%B Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference
%E Darrell Whitley
%D 2000
%P 147--154
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%8 8  July
%Z Part of \citewhitley:2000:GECCOlb

%A Martin Johnson
%T Sequence Generation Using Machine Language Evolved by Genetic Programming
%B Procceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02)
%E Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and Jong-Hwan Kim and Xin Yao
%D 2002
%I 
%C Orchid Country Club, Singapore
%K genetic algorithms, genetic programming
%8 18-22  November
%Z SEAL 2002
%@ 981-04-7522-5

%A Michael Patrick Johnson
%A Pattie Maes
%A Trevor Darrell
%T Evolving Visual Routines
%B ARTIFICIAL LIFE IV, Proceedings of the fourth International Workshop on the Synthesis and Simulation of Living Systems
%E Rodney A. Brooks and Pattie Maes
%D 1994
%P 198--209
%I MIT Press
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/402594.html
%X Traditional machine vision assumes that the vision system recovers a complete, labeled description of the world [Marr]. Recently, several researchers have criticized this
   model and proposed an alternative model which considers perception as a distributed collection of task-specific, task-driven visual routines [Aloimonos, Ullman]. Some of
   these researchers have argued that in natural living systems these visual routines are the product of natural selection [ramachandran]. So far, researchers have hand-coded
   task-specific visual routines for actual implementations (e.g. [Chapman]). In this paper we propose an alternative approach in which visual routines for simple tasks are
   evolved using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using Genetic
   Programming techniques [Koza]. The results obtained are promising: the evolved routines are able to correctly classify up to 93% of the images, which is better than the
   best algorithm we were able to write by hand.
%8 6-8  July
%Z alife-4 See also \citejohnson:1994:EVRAL

%A Michael Patrick Johnson
%T Evolving Visual Routines
%R M.S. Thesis
%D 1995
%I 
%I School or Architecture and Planning, MIT, USA
%K genetic algorithms, genetic programming, visual routines, active vision, machine learning
%U http://citeseer.ist.psu.edu/johnson94evolving.html
%8 September
%Z Extension of \citejohnson:1994:EVR Applies Genetic Programming to the problem of Active Vision

%A Michael Patrick Johnson
%A Pattie Maes
%A Trevor Darrell
%T Evolving Visual Routines
%J Artificial Life
%V 1
%N 4
%D 1994
%P 373--389
%I 
%K genetic algorithms, genetic programming, active vision, visual routines
%X Traditional machine vision assumes that the vision system recovers a complete, labeled description of the world [10]. Recently, several researchers have criticized this
   model and proposed an alternative model that considers perception as a distributed collection of task-specific, context-driven visual routines [1,12]. Some of these
   researchers have argued that in natural living systems these researchers have argued that in natural selection [11]. So far, researchers have hand-coded task-specific
   visual routines for actual implementations (e.g.,[3]). In this article we propose an alternative approach in which visual routines for simple tasks are created using an
   artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using genetic programming techniques
   [7]. The results obtained are promising: The evolved routines are able to process correctly up to 93percent of the test images, which is better than any algorithm we were
   able to write by hand.
%8 summer
%Z Extension of \citejohnson:1994:EVR

%A R. Colin Johnson
%T Genetic program auto-designs analog circuits
%J Electronic Engineering Times
%N 904
%D 1996
%I 
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/published/eetimes060396.html
%8 3  June
%Z short On-line publication

%A Soren Johnson
%T Swords vs. Plowshares: Using Genetic Algorithms in Turn-Based Strategy
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 76--85
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Mark Johnston
%A Thomas Liddle
%A Mengjie Zhang
%T A Linear Regression Approach to Numerical Simplification in Tree-Based Genetic Programming
%R Research report 09-7
%D 2009
%I 
%I School of Mathematics Statistics and Operations Research, Victoria University of Wellington
%C New Zealand
%K genetic algorithms, genetic programming
%U http://msor.victoria.ac.nz/twiki/pub/Main/ResearchReportSeries/msor09-07.pdf
%X We propose a novel approach to simplification in tree-based Genetic Programming to combat program bloat, based upon numerical relaxations of algebraic rules.We also
   separate proposal of simplifications (using linear regression, removing redundant children, and replacing small ranges with a constant) from an acceptance criterion that
   checks the effect of proposed simplifications on the evaluation of training examples, looking several levels up the tree.We test our simplification method on three
   classification datasets and conclude that the success of linear regression is data set dependent, that looking further up the tree can catch unwanted bad case
   simplifications, and that CPU time can be significantly reduced while maintaining classification accuracy on unseen examples.
%8 14  Decemeber
%Z Wine, Wisconsin, Coins

%A Mark Johnston
%A Thomas Liddle
%A Mengjie Zhang
%T A Relaxed Approach to Simplification in Genetic Programming
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 110--121
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X We propose a novel approach to program simplification in tree-based Genetic Programming, based upon numerical relaxations of algebraic rules. We also separate proposal of
   simplifications from an acceptance criterion that checks the effect of proposed simplifications on the evaluation of training examples, looking several levels up the tree.
   We test our simplification method on three classification datasets and conclude that the success of linear regression is dataset dependent, that looking further up the tree
   can catch ineffective simplifications, and that CPU time can be significantly reduced while maintaining classification accuracy on unseen examples.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A A. Jones
%T Writing Programs Using Genetic Algorithms
%R M.S. Thesis
%D 1991
%I 
%I Department of Computer Science, University of Manchester, United Kingdom
%K genetic algorithms, genetic programming

%A Antonia J. Jones
%T Nature's Way
%J Nature
%V 363
%N 6426
%D 1993
%P 222
%I 
%K genetic algorithms, genetic programming
%U http://adsabs.harvard.edu/abs/1993Natur.363..222J
%X Genetic Programming: On the Programming of Computers by Means of Natural Selection. By John R. Koza. MIT Press: 1992.
%O Book Review
%Z review of \citekoza:book

%A Alun Jones
%A Daniella Young
%A Janet Taylor
%A Douglas B. Kell
%A Jem J Rowland
%T Quantification of microbial productivity via multi-angle light scattering and supervised learning
%J Biotechnology and Bioengineering
%V 59
%N 2
%D 1998
%P 131--143
%I John Wiley and Sons
%K genetic algorithms, genetic programming, chemometrics, light scattering. microbial productivity
%X This article describes the use of chemometric methods for prediction of biological parameters of cell suspensions on the basis of their light scattering profiles. Laser
   light is directed into a vial or flow cell containing media from the suspension. The intensity of the scattered light is recorded at 18 angles. Supervised learning methods
   are then used to calibrate a model relating the parameter of interest to the intensity values. Using such models opens up the possibility of estimating the biological
   properties of fermentor broths extremely rapidly (typically every 4 sec), and, using the flow cell, without user interaction. Our work has demonstrated the usefulness of
   this approach for estimation of yeast cell counts over a wide range of values (10(5)-10(9) cells mL-1), although it was less successful in predicting cell viability in such
   suspensions.
%8 20  July
%Z PMID: 10099324

%A Lee W. Jones
%A Sameer H. Al-Sakran
%A John R. Koza
%T Automated Design of a Previously Patented Aspherical Optical Lens System by Means of Genetic Programming
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 33--48
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Automated design, optical lens system, aspherical lenses, developmental process, replication of previously patented invention,
   human-competitive result, Automated design, replication of previously patented invention
%X This chapter describes how genetic programming was used as an invention machine to automatically synthesise a complete design for an aspherical optical lens system (a type
   of lens system that is especially difficult to design and that offers advantages in terms of cost, weight, size, and performance over traditional spherical systems). The
   genetically evolved aspherical lens system duplicated the functionality of a recently patented aspherical system. The automatic synthesis was open-ended --- that is, the
   process did not start from a pre-existing good design and did not pre-specify the number of lenses, which lenses (if any) should be spherical or aspherical, the topological
   arrangement of the lenses, the numerical parameters of the lenses, or the non-numerical parameters of the lenses. The genetically evolved design is an instance of
   human-competitive results produced by genetic programming in the field of optical design.
%O 3
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Lee W. Jones
%A Sameer H. Al-Sakran
%A John R. Koza
%T Automated synthesis of a human-competitive solution to the challenge problem of the 2002 international optical design conference by means of genetic programming and a
   multi-dimensional mutation operation
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 823--830
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, automated design, human-competitive result, International optical design conference, invention machine, mutation operation, optical
   lens system
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p823.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Eric A. Jones
%A William T. Joines
%T Genetic design of electronic circuits
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 125--133
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, low-pass filter design, grammatical evolution
%8 13  July
%Z GECCO-99LB. Grammatical Evolution

%A Philip Jonkergouw
%A Ed Keedwell
%A Soon-Thiam Khu
%T Modelling Chlorine Decay in Water Networks with Genetic Programming
%B Adaptive and Natural Computing Algorithms
%S Springer Computer Series
%E Bernardete Ribeiro and Rudof F. Albrecht and Andrej Dobnikar and David W. Pearson and Nigel C. Steele
%D 2005
%P 206--209
%I Springer
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%X The disinfection of water supplies for domestic consumption is often achieved with the use of chlorine. Aqueous chlorine reacts with many harmful micro-organisms and other
   aqueous constituents when added to the water supply, which causes the chlorine concentration to decay over time. Up to a certain extent, this decay can be modelled using
   various decay models that have been developed over the last 50+ years. Assuming an accurate prediction of the chlorine concentration over time, a measured deviation from
   the values provided by such a decay model could be used as an indicator of harmful (intentional) contamination. However, current chlorine decay models have been based on
   assumptions that do not allow the modelling of another species, i.e. the species with which chlorine is reacting, thereby limiting their use for modelling the effect of a
   contaminant on chlorine. This paper investigates the use of genetic programming as a method for developing a mixed second-order chlorine decay model.
%8 21-23  March
%Z http://icannga05.dei.uc.pt/
%@ 3-211-24934-6

%A Natasha Jonoska
%A Phiset {Sa-Ardyen}
%A Nadrian C. Seeman
%T Computation by Self-assembly of DNA Graphs
%J Genetic Programming and Evolvable Machines
%V 4
%N 2
%D 2003
%P 123--137
%I 
%K DNA-computing, self-assembly, junction molecules, ligation, 3-SAT, graphs
%X Using three dimensional graph structure and DNA self-assembly we show that theoretically 3-SAT and 3-colourability can be solved in a constant number of laboratory steps.
   In this assembly, junction molecules and duplex DNA molecules are the basic building blocks. The graphs involved are not necessarily regular, so experimental results of
   self-assembling non regular graphs using junction molecules as vertices and duplex DNA molecules as edge connections are presented.
%8 June
%Z Special Issue on Biomolecular Machines and Artificial Evolution Article ID: 5122741

%A Natasa Jonoska
%T Theoretical and Experimental DNA Computation Published by: Springer-Verlag, Martyn Amos 172 pages, 78 figures, 2005, ISBN-10 3-540-65773-8
%J Genetic Programming and Evolvable Machines
%V 7
%N 3
%D 2006
%P 287--291
%I 
%K DNA computing
%O Book Review
%8 October

%A Per Jonsson
%A Jonas Barklund
%T Characterizing Signal Behaviour Using Genetic Programming
%B Evolutionary Computing
%S Lecture Notes in Computer Science
%E Terence C. Fogarty
%N 1143
%D 1996
%P 62--72
%I Springer-Verlag
%C University of Sussex, UK
%K genetic algorithms, genetic programming
%X Our overall goal is to detect automatically that a signal begins to deviate from its previous behaviours, using no other information than a sequence of samples of the
   signal. In order to detect such changes we use genetic programming to evolve an expression describing how the signal varies over time. One major difficulty when observing
   such signals is that they typically contain noise and other disturbances. Such disturbances makes it more difficult to find a useful expression characterising the signal.
   We have derived a new method that simultaneously evolves a numeral denoting the number of neighbours to use in a moving average of the signal, and an expression
   characterizing the smoothed signal.
%8 1-2  April
%Z The post-workshop proceedings of the 1996 AISB workshop on evolutionary computing.
%@ 3-540-61749-3

%A Istvan Jonyer
%A Akiko Himes
%T Improving Modularity in Genetic Programming Using Graph-Based Data Mining
%B Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference
%E Geoff C. J. Sutcliffe and Randy G. Goebel
%D 2006
%P 556--561
%I American Association for Artificial Intelligence
%C Melbourne Beach, Florida, USA
%K genetic algorithms, genetic programming, Machine Learning and Discovery
%U http://www.aaai.org/Papers/FLAIRS/2006/Flairs06-110.pdf
%X We propose to improve the efficiency of genetic programming, a method to automatically evolve computer programs. We use graph-based data mining to identify common aspects
   of highly fit individuals and modularising them by creating functions out of the subprograms identified. Empirical evaluation on the lawn mower problem shows that our
   approach is successful in reducing the number of generations needed to find target programs. Even though the graph-based data mining system requires additional processing
   time, the number of individuals required in a generation can also be greatly reduced, resulting in an overall speed-up.
%8 May  11-13
%Z cited by \citeSpector:2011:GECCO http://www.cs.miami.edu/~geoff/Conferences/FLAIRS-19/Schedule.shtml http://www.aaai.org/Press/Proceedings/flairs06.php

%A Andras Joo
%T Mining Evolving Learning Algorithms
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 73--84
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Andras Joo
%A Juan Pablo Neirotti
%T Towards identifying salient patterns in genetic programming individuals
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1885--1886
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Poster
%X A practical method for the offline extraction and analysis of salient patterns from tree-based genetic programming (GP) individuals is proposed. The method is contrasted
   with Tackett's algorithm [7] and it is shown that relying solely on frequency and fitness profiles for the salient pattern identification can be misleading. To amend
   Tackett's work a formula for measuring saliency is proposed. A method for separating inert and salient patterns is also discussed.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Elsa Jordaan
%A Arthur Kordon
%A Leo Chiang
%A Guido Smits
%T Robust Inferential Sensors based on Ensemble of Predictors generated by Genetic Programming
%B Parallel Problem Solving from Nature - PPSN VIII
%S LNCS
%E Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\vno Ata Kab\'an and Hans-Paul
   Schwefel
%V 3242
%D 2004
%P 522--531
%I Springer-Verlag Berlin
%C Birmingham, UK
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=522
%X Inferential sensors are mathematical models used to predict the quality variables of industrial processes. One factor limiting the widespread use of soft sensors in the
   process industry is their inability to cope with non-constant noise in the data and process variability. A novel approach for inferential sensors design with increased
   robustness is proposed in the paper. It is based on three techniques. The first technique increases robustness by using explicit nonlinear functions derived by Genetic
   Programming. The second technique applies multi-objective model selection on a Pareto-front to guarantee the right balance between accuracy and complexity. The third
   technique uses ensembles of predictors for more consistent estimates and possible self-assessment capabilities. The increased robustness of the proposed sensor is
   demonstrated on a number of industrial applications.
%8 18-22  September
%Z PPSN-VIII
%@ 3-540-23092-0

%A Elsa Jordaan
%A Jaap {den Doelder}
%A Guido Smits
%T Novel Approach to Develop Rheological Structure-Property Relationships Using Genetic Programming
%B Parallel Problem Solving from Nature - PPSN IX
%S LNCS
%E Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao
%V 4193
%D 2006
%P 322--331
%I Springer-Verlag Berlin
%C Reykjavik, Iceland
%K genetic algorithms, genetic programming, rheology, molar mass distribution.
%X Rheological structure-property models play a crucial role in the manufacturing and processing of polymers. Traditionally rheological models are developed by design of
   experiments that measure a rheological property as a function of the moments of molar mass distributions. These empirical models lack the capacity to apply to a wide range
   of distributions due the limited availability of experimental data. In recent years fundamental models were developed to satisfy a wider range of distributions, but they
   are in terms of variables not readily available during processing or manufacturing. Genetic programming can be used to bridge the gap between the practical, but limited,
   empirical models and the more general, but less practical, fundamental models. This is a novel approach of generating rheological models that are both practical and valid
   for a wide set of distributions.
%8 9-13  September
%Z PPSN-IX
%@ 3-540-38990-3

%A K. J{\o}rgensen
%A B. Elfrink
%A M. Keijzer
%A V. Babovic
%T Analysis of long term morphological changes: A data mining approach
%B Proceedings of the International Conference on Coastal Engineering
%D 2000
%I 
%C Australia
%K genetic algorithms, genetic programming

%A L. Jourdan
%A M. Basseur
%A E.-G. Talbi
%T Hybridizing exact methods and metaheuristics: A taxonomy
%J European Journal of Operational Research
%V 199
%N 3
%D 2009
%P 620--629
%I 
%K genetic algorithms, genetic programming, Taxonomy, Combinatorial optimisation, Metaheuristics, Exact methods
%U http://www.sciencedirect.com/science/article/B6VCT-4S8K9FW-5/2/da4a040e6d29d78527bb46fcab2eeacd
%X The interest about hybrid optimisation methods has grown for the last few years. Indeed, more and more papers about cooperation between heuristics and exact techniques are
   published. In this paper, we propose to extend an existing taxonomy for hybrid methods involving heuristic approaches in order to consider cooperative schemes between exact
   methods and metaheuristics. First, we propose some natural approaches for the different schemes of cooperation encountered, and we analyse, for each model, some examples
   taken from the literature. Then we recall and complement the proposed grammar and provide an annotated bibliography.

%A Ari Juels
%A Martin Wattenberg
%T Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms
%R Technical Report CSD-94-834
%D 1995
%I 
%I Department of Computer Science, University of California at Berkeley
%C USA
%K genetic algorithms, genetic programming
%U http://citeseer.nj.nec.com/juels94stochastic.html
%X We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimisers.
   In particular, we address four problems to which GAs have been applied in the literature: the maximum-cut problem, Koza's 11-multiplexer problem, MDAP (the Multiprocessor
   Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hill climbing methods are able to achieve results comparable or superior to
   those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a
   stochastic hill-climbing algorithm can lead to improvements in the encoding used by a GA.
%8 18  July
%Z "We demonstate that simple stochastic hillcliming methods are able to achieve results comparable or superior to those obtained by the GAs". 4 GAs one is Koza's
   11-multiplexor. citeseer.nj.nec.com/juels94stochastic.html may be slightly different from CSD-94-834

%A Hugues Juille
%T Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces
%B Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)
%E Larry J. Eshelman
%D 1995
%P 351--358
%I Morgan Kaufmann San Francisco, CA, USA
%C Pittsburgh, PA, USA
%K genetic algorithms, Sorting Networks, Stochastic Search
%U http://www.demo.cs.brandeis.edu/papers/icga95.ps
%X Let us call a non-deterministic incremental algorithm one that is able to construct any solution to a combinatorial problem by selecting incrementally an ordered sequence
   of choices that defines this solution, each choice being made non-deterministically. In that case, the state space can be represented as a tree, and a solution is a path
   from the root of that tree to a leaf. This paper describes how the simulated evolution of a population of such non-deterministic incremental algorithms offers a new
   approach for the exploration of a state space, compared to other techniques like Genetic Algorithms (GA), Evolutionary Strategies (ES) or Hill Climbing. In particular, the
   efficiency of this method, implemented as the Evolving Non-Determinism (END) model, is presented for the sorting network problem, a reference problem that has challenged
   computer science. Then, we shall show that the END model remedies some drawbacks of these optimization techniques and even outperforms them for this problem. Indeed, some
   16-input sorting networks as good as the best known have been built from scratch, and even a 25-year-old result for the 13-input problem has been improved by one
   comparator.
%8 15-19  July
%@ 1-55860-370-0

%A Hugues Juille
%A Jordan B. Pollack
%T Parallel Genetic Programming and Fine-Grained SIMD Architecture
%B Working Notes for the AAAI Symposium on Genetic Programming
%E E. V. Siegel and J. R. Koza
%D 1995
%P 31--37
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025, USA
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Library/Symposia/Fall/fs95-01.php
%X As tile field of Genetic Programming (GP) matures and its breadth of application increases, the need for parallel implementations becomes absolutely necessary. The
   transputer-based system recently presented by Koza ([8]) is one of the rare such parallel implementations. Until today, no implementation has been proposed for parallel GP
   using a SIMD architecture, except for a data-parallel approach ([16]), although others have exploited workstation farms and pipelined supercomputers. One reason is
   certainly the apparent difficulty of dealing with the parallel evaluation of different S-expressions when only a single instruction can be executed at the same time on
   every processor. The aim of this paper is to present such an implementation of parallel GP on a SIMD system, where each processor can efficiently evaluate a different
   S-expression. We have implemented this approach on a MasPar MP-2 computer, and will present some timing results. To the extent that SIMD machines, like the MasPar are
   available to offer cost-effective cycles for scientific experimentation, this is a useful approach.
%8 10--12  November
%Z AAAI-95f GP. Part of \citesiegel:1995:aaai-fgp \em Telephone: 415-328-3123 \em Fax: 415-321-4457 \em email info@aaai.org \em URL: http://www.aaai.org/ tic-tak-toe
   coevolution

%A Hugues Juille
%A Jordan B. Pollack
%T Massively Parallel Genetic Programming
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 339--358
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming, coevolution, competitive fitness, spirals problem
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/356
%X As the field of Genetic Programming (GP) matures and its breadth of application increases, the need for parallel implementations becomes absolutely necessary. The
   transputer-based system presented in [Koza95] is one of the rare such parallel implementations. Until today, no implementation has been proposed for parallel GP using a
   SIMD architecture, except for a data-parallel approach [tufts95], although others have exploited workstation farms and pipelined supercomputers. One reason is certainly the
   apparent difficulty of dealing with the parallel evaluation of different S-expressions when only a single instruction can be executed at the same time on every processor.
   The aim of this chapter is to present such an implementation of parallel GP on a SIMD system, where each processor can efficiently evaluate a different S-expression. We
   have implemented this approach on a MasPar MP-2 computer, and will present some timing results. To the extent that SIMD machines, like the MasPar are available to offer
   cost-effective cycles for scientific experimentation, this is a useful approach.
%O 17
%Z tic-tak-toe, intertwined spirals, coevolution
%@ 0-262-01158-1

%A Hugues Juille
%A Jordan B. Pollack
%T Dynamics of Co-evolutionary Learning
%B Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior: From animals to animats 4
%E Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer and Jordan Pollack and Stewart W. Wilson
%D 1996
%P 526--534
%I MIT Press Cambridge, MA, USA
%C Cape Code, USA
%K genetic algorithms, genetic programming, Spirals, Coevolution
%U http://www.cs.brandeis.edu/~hugues/papers/SAB_96.ps.gz
%X Co-evolutionary learning, which involves the embedding of adaptive learning agents in a fitness environment which dynamically responds to their progress, is a potential
   solution for many technological chicken and egg problems, and is at the heart of several recent and surprising successes, such as Sim's artificial robot and Tesauro's
   backgammon player. We recently solved the two spirals problem, a difficult neural network benchmark classification problem, using the genetic programming primitives set up
   by [ \citekoza:book ]. Instead of using absolute fitness, we use a relative fitness [ \citeicga93:angeline ] based on a competition for coverage of the data set. As the
   population reproduces, the fitness function driving the selection changes, and subproblem niches are opened, rather than crowded out. The solutions found by our method have
   a symbiotic structure which suggests that by holding niches open.
%8 9-13  September
%Z SAB-96
%@ 0-262-63178-4

%A Hugues Juille
%A Jordan B Pollack
%T Co-evolving Intertwined Spirals
%B Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming
%E Lawrence J. Fogel and Peter J. Angeline and Thomas Baeck
%D 1996
%P 461--467
%I MIT Press Cambridge, MA, USA
%C San Diego
%K genetic algorithms, genetic programming, Spirals, Coevolution
%U http://citeseer.ist.psu.edu/201284.html
%X We recently solved the two spirals problem, a difficult neural network benchmark classification problem, using the genetic programming primitives set up by [ \citekoza:book
   ]. Instead of using absolute fitness, we use a relative fitness based on a competition for coverage of the data set. This is a form of co-evolutionary search because the
   fitness function changes with the population. Because niches are opened by proportionate reproduction, rather than crowded out, and because of the crossover operator, we
   find solutions which have a nice modular structure. Our experiments used our Massively Parallel Genetic Programming (MPGP) system running on a SIMD machine of 4096
   processors, the Maspar MP-2.
%8 February  29-  March  3
%Z EP-96 http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383 Massively Parallel Genetic Programming MPGP on SIMD machine of 4096 processors, the Maspar MP-2
%@ 0-262-06190-2

%A Hugues Juille
%A Jordan B. Pollack
%T Coevolving the Ideal Trainer: Application to the Discovery of Cellular Automata Rules
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 519--527
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, Cellular Automata
%U http://www.cs.brandeis.edu/~hugues/papers/GP_98.ps.gz
%X Coevolution provides a framework to implement search heuristics that are more elaborate than those driving the exploration of the state space in canonical evolutionary
   systems. However, some drawbacks have also to be overcome in order to ensure continuous progress on the long term. This paper presents the concept of coevolutionary
   learning and introduces a search procedure which successfully addresses the underlying impediments in coevolutionary search. The application of this algorithm to the
   discovery of cellular automata rules for a classification task is described. This work resulted in a significant improvement over previously known best rules for this task.
%8 22-25  July
%Z SGA-98

%A Hugues Juille
%A Jordan Pollack
%T Coevolutionary Arms Race Improves Generalization
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Hugues Juille
%A Jordan B. Pollack
%T A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction
%B Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98) Tenth Conference on Innovative Applications of Artificial Intelligence (IAAI-98)
%D 1998
%I AAAI Press Books
%C Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, search, massively parallel systems, inductive learning, DFA induction
%U http://www.cs.brandeis.edu/~hugues/papers/AAAI_98.ps.gz
%X In the field of Operation Research and Artificial Intelligence, several stochastic search algorithms have been designed based on the theory of global random search
   (Zhigljavsky, 1991). Basically, those techniques iteratively sample the search space with respect to a probability distribution which is updated according to the result of
   previous samples and some predefined strategy. Genetic Algorithms (GAs) (Goldberg, 1989) or Greedy Randomised Adaptive Search Procedures (GRASP) (Feo & Resende, 1995) are
   two particular instances of this paradigm. we present SAGE, a search algorithm based on the same fundamental mechanisms as those techniques. However, it addresses a class
   of problems for which it is difficult to design transformation operators to perform local search because of intrinsic constraints in the definition of the problem itself.
   For those problems, a procedural approach is the natural way to construct solutions, resulting in a state space represented as a tree or a DAG. The aim of this paper is to
   describe the underlying heuristics used by SAGE to address problems belonging to that class. The performance of SAGE is analysed on the problem of grammar induction and its
   successful application to problems from the recent Abbadingo DFA learning competition is presented.
%8 26-30  July

%A Hugues Juille
%T Methods for Statistical Inference: Extending the Evolutionary Computation Paradigm
%R Ph.D. Thesis
%D 1999
%I 
%I Department of Computer Science, Brandeis University
%K genetic algorithms, genetic programming, Coevolutionary Learning, Stochastic Search
%U http://www.demo.cs.brandeis.edu/papers/hugues_thesis.pdf
%X In many instances, Evolutionary Computation (EC) techniques have demonstrated their ability to tackle ill-structured and poorly understood problems against which
   traditional Artificial Intelligence (AI) search algorithms fail. The principle of operation behind EC techniques can be described as a statistical inference process which
   implements a sampling-based strategy to gather information about the state space, and then exploits this knowledge for controlling search. However, this statistical
   inference process is supported by a rigid structure that is an integral part of an EC technique. For instance, \em schemas seem to be the basic components that form this
   structure in the case of Genetic Algorithms (GAs). Therefore, it is important that the encoding of a problem in an EC framework exhibits some regularities that correlate
   with this underlying structure. Failure to find an appropriate representation prevents the evolutionary algorithm from making accurate decisions. This dissertation
   introduces new methods that exploit the same principles of operation as those embedded in EC techniques and provide more flexibility for the choice of the structure
   supporting the statistical inference process. The purpose of those methods is to generalize the EC paradigm, thereby expanding its domain of applications to new classes of
   problems. Two techniques implementing those methods are described in this work. The first one, named SAGE, extends the sampling-based strategy underlying evolutionary
   algorithms to perform search in trees and directed acyclic graphs. The second technique considers coevolutionary learning, a paradigm which involves the embedding of
   adaptive agents in a fitness environment that dynamically responds to their progress. Coevolution is proposed as a framework in which evolving agents would be permanently
   challenged, eventually resulting in continuous improvement of their performance. After identifying obstacles to continuous progress, the concept of an ``Ideal'' trainer is
   presented as a paradigm which successfully achieves that goal by maintaining a pressure toward adaptability. The different algorithms discussed in this dissertation have
   been applied to a variety of difficult problems in learning and combinatorial optimization. Some significant achievements that resulted from those experiments concern: (1)
   the discovery of new constructions for 13-input sorting networks using fewer comparators than the best known upper bound, (2) an improved procedure for the induction of
   DFAs from sparse training data which ended up as a co-winner in a grammar inference competition, and (3) the discovery of new cellular automata rules to implement the
   majority classification task which outperform the best known rules. By describing evolutionary algorithms from the perspective of statistical inference techniques, this
   research work contributes to a better understanding of the underlying search strategies embedded in EC techniques. In particular, an extensive analysis of the
   coevolutionary paradigm identifies two fundamental requirements for achieving continuous progress. Search and machine learning are two fields that are closely related. This
   dissertation emphasizes this relationship and demonstrates the relevance of the issue of generalization in the context of coevolutionary races.
%8 May
%Z p169

%A Bryant A. Julstrom
%T Contest Length, Noise, and Reciprocal Altruism in the Population of a Genetic Algorithm for the Iterated Prisoner's Dilemma
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 88--93
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-201031-7

%A Bryant A. Julstrom
%T Strings of Weights as Chromosomes in Genetic Algorithms for the Traveling Salesman Problem
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 100--106
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Bryant A. Julstrom
%T Insertion Decoding Algorithms and Initial Tours in a Weight-Coded GA for TSP
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 528--534
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Bryant A. Julstrom
%T The Maximum Weight Parameter in a Weight-Coded GA for TSP
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z GP-98LB

%A Bryant A. Julstrom
%T Redundant Genetic Encodings May Not Be Harmful
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 791
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://condor.stcloudstate.edu/~julstrom/ga_papers/gecco99short.html
%X In a redundant genetic encoding, several distinct chromosomes represent each candidate solution to the target problem. Such an encoding would seem to hinder genetic search
   by allowing competing representations of the same information. Tests using a GA for the 3-cycle problem (3CP), which seeks to partition n = 3k points in the plane into
   3-cycles of minimum total length, indicate that this is not necessarily so.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Bryant A. Julstrom
%T Comparing Darwinian, Baldwinian, and Lamarckian search in a genetic algorithm for the 4-Cycle problem
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 134--138
%I 
%C Orlando, Florida, USA
%K Genetic Algorithms
%8 13  July
%Z GECCO-99LB

%A Bryant A. Julstrom
%T Manipulating Valid Solutions in a Genetic Algorithm for the Bounded-Diameter Minimum Spanning Tree Problem
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 247--254
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp

%A Anthony B. Jung
%A James P. Bennett
%T Development of striatal dopaminergic function. I. Pre- and postnatal development of mRNAs and binding sites for striatal D1 (D1a) and D2 (D2a) receptors
%J Developmental Brain Research
%V 94
%N 2
%D 1996
%P 109--120
%I 
%U http://www.sciencedirect.com/science/article/B6SYW-47G1W7V-2/2/82536e82898d98ddcc2fef6c92792a86
%Z Not on GP

%A Jae-Yoon Jung
%A James A. Reggia
%T Evolving an autonomous agent for non-Markovian reinforcement learning
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 971--978
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X In this paper, we investigate the use of nested evolution in which each step of one evolutionary process involves running a second evolutionary process. We apply this
   approach to build an evolutionary system for reinforcement learning (RL) problems. Genetic programming based on a descriptive encoding is used to evolve the neural
   architecture, while an evolution strategy is used to evolve the connection weights. We test this method on a non-Markovian RL problem involving an autonomous foraging
   agent, finding that the evolved networks significantly outperform a rule-based agent serving as a control. We also demonstrate that nested evolution, partitioning into
   subpopulations, and crossover operations all act synergistically in improving performance in this context.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Tae-min Jung
%A Young-Seol Lee
%A Sung-Bae Cho
%T Mobile interface for adaptive image refinement using interactive evolutionary computing
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Due to developing mobile devices and providing services like mobile blogs, people can easily share their thought and experience, at any place and any time. A picture is an
   important datum to record and share their thought and experience, while we can easily take pictures with a mobile device that has a camera in it. However, the quality is
   usually poor without image refinement. Many mobile devices provide a simply interface to improve the quality, but require knowledge of predefined filters or image
   enhancement to control the parameters. It causes the user to feel inconvenient in mobile environments for their real-time editing pictures. In this paper, we propose a
   novel image enhancement interface in consideration of the accessibility to the mobile environment and various constraints. A usability test with various images has been
   conducted to show its usefulness, and the proposed interface achieved better performance than the other through the SUS test.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5585966

%A Robert Junges
%A Franziska Klugl
%T Evolution for modeling: a genetic programming framework for SeSAm
%B GECCO 2011 Evolutionary computation and multi-agent systems and simulation (ECoMASS) - fifth annual workshop
%E William Rand and Forrest Stonedahl
%D 2011
%P 551--558
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X Developing a valid agent-based simulation model is not always straight forward, but involves a lot of prototyping, testing and analysing until the right low-level behaviour
   is fully specified and calibrated. Our aim is to replace the try and error search of a modeller by adaptive agents which learn a behaviour that then can serve as a source
   of inspiration for the modeler. In this contribution, we suggest to use genetic programming as the learning mechanism. For this aim we developed a genetic programming
   framework integrated into the visual agent-based modeling and simulation tool SeSAm, providing similar easy-to-use functionality.
%8 12-16  July
%Z Also known as \cite2002047 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Robert-Paul Juster
%A Bruce S. McEwen
%A Sonia J. Lupien
%T Allostatic load biomarkers of chronic stress and impact on health and cognition
%J Neuroscience \& Biobehavioral Reviews
%V 35
%N 1
%D 2010
%P 2--16
%I 
%K genetic algorithms, genetic programming, Allostatic load, Chronic stress, Aging, Resilience, Health, Cognition, Biomedicine
%U http://www.sciencedirect.com/science/article/B6T0J-4XF83T1-1/2/ba6b3d4794b04ffafb547bc67f45f581
%X The allostatic load model expands the stress-disease literature by proposing a temporal cascade of multi-systemic physiological dysregulations that contribute to disease
   trajectories. By incorporating an allostatic load index representing neuroendocrine, immune, metabolic, and cardiovascular system functioning, numerous studies have
   demonstrated greater prediction of morbidity and mortality over and beyond traditional detection methods employed in biomedical practice. This article reviews theoretical
   and empirical work using the allostatic load model vis-a-vis the effects of chronic stress on physical and mental health. Specific risk and protective factors associated
   with increased allostatic load are elucidated and policies for promoting successful aging are proposed.
%8 September
%Z survey

%A M. Kaboudan
%A M. Vance
%T Statistical Evaluation of Symbolic Regression Forecasting of Time-Series
%B Proceedings of the International Federation of Automatic Control Symposium on Computation in Economics, Finance and Engineering: Economic Systems
%D 1998
%I 
%C Cambridge, UK
%K genetic algorithms, genetic programming
%8 June

%A M. Kaboudan
%T Forecasting Stock Returns Using Genetic Programming in C++
%B Proceedings of 11th Annual Florida Artificial Intelligence International Research Symposium
%E Diane J. Cook
%D 1998
%I AAAI Press
%C Sanibel Island, Florida, USA
%K genetic algorithms, genetic programming
%8 May  18-20
%Z FLAIRS-98
%@ 1-57735-051-0

%A M. A. Kaboudan
%T A GP Approach to Distinguish Chaotic from Noisy Signals
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 187--191
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A M. A. Kaboudan
%T Statistical Evaluation of Genetic Programming
%B Fifth International Conference: Computing in Economics and Finance
%E David A. Belsley and Christopher F. Baum
%D 1999
%P 148
%I 
%I Society for Computational Economics
%C Boston College, MA, USA
%K genetic algorithms, genetic programming, GP-QUICK
%U http://www.lv.psu.edu/mak7/GP-Stat.htm
%X A recent advance in genetic computations is the heuristic prediction model (symbolic regression), which have received little statistical scrutiny. Diagnostic checks of
   genetically evolved models (GEMs) as a forecasting method are therefore essential. This requires assessing the statistical properties of errors produced by GEMs. Since the
   predicted models and their forecasts are produced artificially by a computer program, little controls the final model specification. However, it is of interest to
   understand the final specification and to know the statistical characteristics of its errors, particularly if artificially produced models furnish better forecasts than
   humanly conceived ones. This paper's main concern is the statistical analysis of errors from genetically evolved models. Genetic programming (GP) is one of two
   computational algorithms for evolving regression models, the other being evolutionary programming (EP). GP-QUICK computer code written in C ++ evolves the regression models
   for this study. GP-QUICK replicates an original GP program in LISP by Koza. Both are designed to evolve regression models randomly, finding one that replicates the series'
   data-generating process best. Prediction errors from GP evolved regression models are tested for whiteness (or autocorrelation) and for normality. Well-established
   diagnostic tools for linear time-series modeling apply also to nonlinear models. Only diagnostic methods using errors without having to replicate the models that produced
   them are selected and applied to series. This restriction is avoids reproducing the resulting genetically evolved equations. These equations are generated by a random
   selection mechanism almost impossible to replicate with GP unless the process is deterministic, and they are usually too complex for standard statistical software to
   reproduce and analyze. The diagnostic methods are selected for their simplicity and speed of execution without sacrificing reliability. This paper contains four other
   sections. One presents the diagnostic tools to determine the statistical properties of residuals produced by GEMs. Residuals from evolved models representing systems with
   known characteristics are used to evaluate the statistical performance of GEMs. Another furnishes six data-generating processes representing linear, linear-stochastic,
   nonlinear, nonlinear-stochastic, and pseudo-random systems for which models are evolved and residuals computed. The final contains those residuals' diagnostics. Diagnostic
   tools include the Kolmogorov-Smirnov test for whiteness developed by Durbin (1969) in addition to statistical testing of the null hypotheses that the fitted residuals'
   mean, skewness, and kurtosis are independently equal to zero. Conclusions and future research are given.
%O Book of Abstracts
%8 24-26  June
%Z CEF'99 RePEc:sce:scecf9:1031 23 Nov 1999: Our printers barf if given GP-Stat.prn 22 Aug 2004 http://ideas.repec.org/p/sce/scecf9/1031.html CEF number 1031

%A M. A. Kaboudan
%T Genetic Programming Prediction of Stock Prices
%J Computational Economics
%V 6
%N 3
%D 2000
%P 207--236
%I 
%K genetic algorithms, genetic programming
%8 Decemeber

%A M. Kaboudan
%T A Measure of Time Series Predictability Using Genetic Programming Applied to Stock Returns
%J Journal of Forecasting
%V 18
%D 1999
%P 345--357
%I 
%K genetic algorithms, genetic programming

%A M. A. Kaboudan
%T Genetic Evolution of Regression Models for Business and Economic Forecasting
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 2
%D 1999
%P 1260--1268
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, forecasting
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143
%@ 0-7803-5537-7 (Microfiche)

%A M. A. Kaboudan
%T Evaluation Of Forecasts Produced By Genetically Evolved Models
%B Computing in Economics and Finance
%D 2000
%I 
%I Society for Computational Economics
%C Universitat Pompeu Fabra, Barcelona, Spain
%K genetic algorithms, genetic programming
%U http://fmwww.bc.edu/cef00/papers/paper331.pdf
%X Genetic programming (or GP) is a random search technique that emerged in the late 1980s and early 1990s. A formal description of the method was introduced in Koza (1992).
   GP applies to many optimisation areas. One of them is modelling time series and using those models in forecasting. Unlike other modeling techniques, GP is a computer
   program that 'searches' for a specification that replicates the dynamic behaviour of observed series. To use GP, one provides operators (such as +, -, *, ?, exp, log, sin,
   cos, ... etc.) and identifies as many variables thought best to reproduce the dependent variable's dynamics. The program then randomly assembles equations with different
   specifications by combining some of the provided variables with operators and identifies that specification with the minimum sum of squared errors (or SSE). This process is
   an iterative evolution of successive generations consisting of thousands of the assembled equations where only the fittest within a generation survive to breed better
   equations also using random combinations until the best one is found. Clearly from this simple description, the method is based on heuristics and has no theoretical
   foundation. However, resulting final equations seem to produce reasonably accurate forecasts that compare favourably to forecasts humanly conceived specifications produce.
   With encouraging results difficult to overlook or ignore, it is important to investigate GP as a forecasting methodology. This paper attempts to evaluate forecasts
   genetically evolved models (or GEMs) produce for experimental data as well as real world time series.The organisation of this paper in four Sections. Section 1 contains an
   overview of GEMs. The reader will find lucid explanation of how models are evolved using genetic methodology as well as features found to characterise GEMs as a modeling
   technique. Section 2 contains descriptions of simulated and real world data and their respective fittest identified GEMs. The MSE and a new alpha-statistic are presented to
   compare models' performances. Simulated data were chosen to represent processes with different behavioral complexities including linear, linear-stochastic, nonlinear,
   nonlinear chaotic, and nonlinear-stochastic. Real world data consist of two time series popular in analytical statistics: Canadian lynx data and sunspot numbers.
   Predictions of historic values of each series (used in generating the fittest model) are also presented there. Forecasts and their evaluations are in Section 3. For each
   series, single- and multi-step forecasts are evaluated according to the mean squared error, normalised mean squared error, and alpha- statistic. A few concluding remarks
   are in the discussion in Section 4.
%8 6-8  July
%Z 22 August 2004 http://ideas.repec.org/p/sce/scecf0/331.html CEF number 331

%A M. A. Kaboudan
%T Genetically evolved models and normality of their fitted residuals
%J Journal of Economic Dynamics and Control
%V 25
%N 11
%D 2001
%P 1719--1749
%I 
%I Society for Computational Economics
%K genetic algorithms, genetic programming, Model evaluation, Sunspot numbers, Canadian lynx data
%U http://www.sciencedirect.com/science/article/B6V85-43DKSHS-2/1/814779519703b0e20b2ed476f932e7e5
%X This paper evaluates performance of genetically evolved models. GPQuick, a genetic programming software written in C++, is used to evolve best-fit regression models for
   simulated and real world data. Simulated data are twelve time series with different but known dynamical structures. Predicted values from best models are compared with
   originally simulated data and the residuals are statistically evaluated. The results suggest that genetic programming approximates less complex and less noisy data better
   than it does more complex and noisy data. GPQuick is then used to evolve models of real world data extracted from Canadian lynx and sunspot numbers.
%8 1  November
%Z JEL Classification: C63; C45; C52. cf. CEF'2000.

%A M. A. Kaboudan
%T Compumetric Forecasting of Crude Oil Prices
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 283--287
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, ANN
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =
%@ 0-7803-6658-1

%A M. A. Kaboudan
%T Short-Term Compumetric Forecast of Crude Oil Prices
%B Modeling and Control of Economic Systems 2001 -- A Proceedings volume from the 10th IFAC Symposium
%E R. Neck
%D 2003
%P 365--370
%I Elsevier Science Ltd Oxford
%C Klagenfurt, Austria
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B86BF-4PF22NC-17/2/96bb656b1958ddb535464abece56273c
%X Forecasting oil prices remains an important empirical issue. This paper compares three forecasts of short-term oil prices using two compumetric methods and naive random
   walk. Compumetric methods use model specifications generated by computers with limited human intervention. Users are responsible only for selecting the appropriate set of
   explanatory variables. The compumetric methods employed here are genetic programming and artificial neural networks. The variable to forecast is monthly US imports FOB oil
   prices. Each method is used to forecast one and three months ahead. The results suggest that neural networks deliver better predictions.
%8 6-8  September

%A M. Kaboudan
%T GP forecasts of stock prices for profitable trading
%B Evolutionary Computation in Economics and Finance
%S Studies in Fuzziness and Soft Computing
%E Shu-Heng Chen
%V 100
%D 2002
%P 359--382
%I Physica Verlag
%K genetic algorithms, genetic programming
%O 19
%8 2002
%Z http://btobsearch.barnesandnoble.com/booksearch/isbnInquiry.asp?sourceid=00395996645644787198&btob=Y&endeca=1&isbn=3790814768&itm=2
%@ 3-7908-1476-8

%A M. A. Kaboudan
%T Forecasting with computer-evolved model specifications: a genetic programming application
%J Computers and Operations Research
%V 30
%N 11
%D 2003
%P 1661--1681
%I 
%K genetic algorithms, genetic programming, Computational methods, Nonlinear dynamic systems, Time series, Sunspot numbers
%U http://www.sciencedirect.com/science/article/B6VC5-47P1N3H-1/2/d89d466d6ed20bb2d2da43b3701f351b
%X This paper uses genetic programming (GP) to evolve model specifications of time series data. GP is a computerized random search optimisation algorithm that assembles
   equations until it identifies the fittest one. The technique is applied here to artificially simulated data first then to real-world sunspot numbers. One-step-ahead
   forecasts produced by the fittest of computer-evolved models are evaluated and compared with alternatives. The results suggest that GP may produce reasonable forecasts if
   their user selects appropriate input variables and comprehends the process investigated. Further, the technique appears promising in forecasting noisy complex series
   perhaps better than other existing methods. It is suitable for decision makers who set high priority on obtaining accurate forecasts rather than on probing into and
   approximating the underlying data generating process. This paper contains a brief introduction and an evaluation of the use of genetic programming (GP) in forecasting time
   series. GP is a computerized random search optimization technique based upon Darwin's theory of evolution. The algorithm is first applied to model and forecast artificially
   simulated linear and nonlinear time series. Results are used to evaluate the effectiveness of GP as a forecasting technique. It is then applied to model and forecast
   sunspot numbers--the most frequently analyzed and forecasted series. An autoregressive and a threshold nonlinear dynamical systems to capture the dynamics of the irregular
   sunspot numbers' cycle were tested using GP. The latter delivered estimated equations yielding the lowest mean square error ever reported for the series. This paper
   demonstrates that GP's forecasting capabilities depend on the structure and complexity of the process to model. Skills and intuition of GP's user are its limitation.
%8 September

%A M. A. Kaboudan
%T Forecasting Demand for Natural Gas Using GP-Econometric Integrated Systems
%B Computing in Economics and Finance
%D 2003
%I 
%I Society for Computational Economics
%C University of Washington, Seattle, USA
%K genetic algorithms, genetic programming
%U http://bulldog2.redlands.edu/fac/mak_kaboudan/cef2003/
%X genetic programming (GP) is used in econometrics to predict US demand for natural gas using two recursive systems of equations. The first contains econometric models
   estimated using two-stage-least-squares (2SLS). These deliver estimates of policy-making parameters. The system contains four demand equations representing consuming
   sectors and an identity for total US. The second is to deliver forecasts of exogenous variables in the first using GP. GP can deliver relatively accurate predictions but
   its evolved equations are not useful in policy-making. For comparison, ARIMA models are used as input into the 2SLS system to compete with GP. Further, GP demand equations
   are evolved and used to obtain a different forecast altogether. The two forecasts are then compared with a forecast available from the US Department of Energy (DOE).
   Econometric and GP models deliver forecasts with different merits. Econometric models are concerned with estimating measures of interactions between a dependent variable
   and each of the independent variables. They provide for what if scenarios fundamental in policy-making that GP does not. The evolved equations are random combinations of
   variables and terminals that may not capture interactions between variables. Their forecasts may outperform those available using standard statistical techniques.
   Therefore, GP may add value to econometric models.
%8 11-13  July
%Z 22 August 2004 http://ideas.repec.org/p/sce/scecf3/44.html CEF 2003

%A M. A. Kaboudan
%T Genetic Programming Software to Forecast Time Series
%B Computing in Economics and Finance
%D 2003
%I 
%I Society for Computational Economics
%C University of Washington, Seattle, USA
%K genetic algorithms, genetic programming
%U http://bulldog2.redlands.edu/fac/mak_kaboudan/cef2003/
%X Genetic programming (GP) is an optimisation technique useful in forecasting. GP software is available freely on the Internet or can be purchased commercially. Free software
   demands advanced programming skills, while commercial software may be expensive. This paper introduces TSGP software developed to forecast time series. It is free to
   download with instructions, works in windows environment, is user-friendly, does not require programming skills, delivers comprehensible output, and reports statistics a
   time series analyst, statistician, or econometrician finds desirable. This introduction benefits forecasting researchers and practitioners. Genetic programming (GP) emerged
   in the late 1980s and early 1990s. Koza was first to introduce a formal description of the technique. GP applies to many optimisation areas including modelling time series.
   Unlike other modelling techniques, GP is a computerised search for specifications that replicate patterns of observed series. Users of GP software provide input files
   containing mathematical operators and values of variables. The program is designed to randomly assemble specifications of equations until it finds the best one. That
   equation, its fitted values, residuals, and evaluation statistics are written to output files. Such automated search for specifications makes GP an attractive algorithm.
   TSGP stands for time series genetic programming. The software is available at HYPERLINK "http://www.compumetrica.com" www.compumetrica.com. It is an expansion of a code in
   Koza's 1990 GP book written in LISP that was converted to C by Andy Singleton in 1994. TSGP gets its instructions from a configuration file containing self-reproduction,
   crossover, and mutation rates, names of input variables, population size, number of generations, minimum threshold error (set at 0.0001), and operators (including standard
   ones: +, -, *, %, and sqrt, where % is protected division as well as two other sets the user selects from: set 1: sin and cos; set 2: ln and exp). In addition to protected
   division, the program also contains these protections: If in (x(y), y = 0, then (x/y) = 1. If in y1/2, y < 0, then y1/2 = -| y|1/2.
%8 11-13  July
%Z 22 August 2004 http://ideas.repec.org/p/sce/scecf3/97.html CEF 2003 number 97

%A Mak Kaboudan
%T Spatiotemporal forecasting of housing prices by use of genetic programming
%D 2004
%I 
%K genetic algorithms, genetic programming
%U http://bulldog2.redlands.edu/fac/mak_kaboudan/agb_kaboudan_paper.pdf
%X Complexity of space-time analysis remains a major problem faced by forecasters. Theoretical issues and forecast inaccuracy emanate from specification error, aggregation
   error, measurement error, and perhaps model complexity. Because such problems are mainly statistical in nature, employing techniques not based on statistical methods is
   tested here. Two computational techniques (genetic programming and neural networks) are employed to demonstrate their potential. Their forecasts can help deliver sequences
   of maps of the same geographic region depicting future temporal changes.
%O A paper presented during the 16th Annual Meeting of the Association of Global Business in Cancun Mexico, November 18-21, 2004.
%8 November
%Z http://falcon.jmu.edu/~damanpfx/

%A Mahmoud A. Kaboudan
%A Qingfeng ``Wilson'' Liu
%T Forecasting quarterly US demand for natural gas
%J Information Technology for Economics and Management
%V 2
%N 1
%D 2004
%I 
%K genetic algorithms, genetic programming
%U http://www.item.woiz.polsl.pl/issue2.1/pdf/forecastingquarterlyusdemandfornaturalgas.pdf
%X forecasting demand for natural gas in the short run. The method used combines genetic programming with a two-stage least squares (2SLS) regression system of equations. In
   the system developed, each of US consuming sectors is represented by a regression model. These models quantify each sector's demand elasticity and produce a four-year-ahead
   forecast of quarterly consumption of gas. Genetic programming (GP) is used here to obtain accurate predictions of exogenous variables to use as inputs into the 2SLS system
   of equations. GP is a computerised search algorithm that identifies equations that can forecast well. The proposed method delivered interesting nonlinear equations that
   seem to produce a reasonable forecast.
%Z http://www.item.woiz.polsl.pl/

%A Mak Kaboudan
%T Extended daily exchange rates forecasts using wavelet temporal resolutions
%J New Mathematics and Natural Computing
%V 1
%D 2005
%P 79--107
%I 
%K genetic algorithms, genetic programming
%X Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended
   forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the
   random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond
   one-step-ahead) that are better than naive random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and
   sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen
   exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US
   dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and
   wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

%A Mak Kaboudan
%T Wavelets in Multi-step-ahead forecasting
%B The 16th IFAC World Congress
%E Pavel Zitek
%D 2005
%I Elsevier Science Ltd
%I IFAC
%C Prague
%K genetic algorithms, genetic programming, sunspot numbers
%U http://bulldog2.redlands.edu/fac/mak_kaboudan/wavelets_in_forecasting.pdf
%X the possibility of obtaining long-into-the-future reliable forecasts of observed nonlinear cyclical phenomena. Unsmoothed monthly sunspot numbers that are
   characteristically cyclical with nonlinear dynamics as well as their wavelet-transformed and wavelet-denoised series are forecasted through October 2008. The objective is
   to determine whether modelling wavelet-conversions of a series provides reasonable forecasts. Two computational techniques neural networks and genetic programming are used
   to model the dynamics of the series. Statistical comparison of their ex post forecasts is then used to identify the data set and computational technique to use under the
   circumstances.
%O A paper presented during
%8 July  4-8
%Z Additional info from http://www.ifac.cz/ http://www.ecampus.com/book/008045108X
%@ 0-08-045108-X

%A Mak Kaboudan
%T Spatiotemporal forecasting of home prices: aGIS application
%B The 16th IFAC World Congress
%E Pavel Zitek
%D 2005
%I Elsevier Science Ltd
%I IFAC
%C Prague
%K genetic algorithms, genetic programming
%U http://bulldog2.redlands.edu/fac/mak_kaboudan/kaboudanprices3.pdf
%X Computational techniques may be useful in modelling and forecasting spatiotemporal data. Statistical challenges that emanate from specification error, aggregation error,
   measurement error, and perhaps model complexity among other problems encourage employing computational techniques. Genetic programming and neural networks are two such
   techniques that are robust with respect to autocorrelation, multicollinearity, and stationarity problems statistical and econometric methods encounter. These two
   computational techniques are employed to demonstrate their potential in producing dynamic forecasts of spatial data. Such forecasts can then help produce sequences of maps
   of the same geographic region depicting future temporal changes.
%O A paper presented during
%8 July  4-8
%Z Additional info from http://www.ifac.cz/ http://www.ecampus.com/book/008045108X
%@ 0-08-045108-X

%A Mak Kaboudan
%T Computational Forecasting of Two Exchange Rates
%B The 4th International Workshop on Computational Intelligence in Economics and Finance (CIEF'2005)
%E Paul P. Wang
%D 2005
%P (CIEF-10)
%I 
%C Marriott City Center, Salt Lake City, Utah, USA
%K genetic algorithms, genetic programming, neural networks, wavelets
%U http://bulldog2.redlands.edu/fac/mak_kaboudan/kaboudan_cief05.pdf
%X genetic programming and artificial neural networks are employed to forecast two different exchange rates, US dollar/Japanese Yen and US dollar/Taiwan dollar. Extended
   forecasts (that go beyond one-step-ahead) obtained using the computational techniques were compared with naive random walk predictions of the two exchange rates.
   Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions.
   Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead
   random walk predictions of the exchange rate.
%8 July  21-26
%Z http://www.aiecon.org/cief2005/schedule.htm

%A Mak Kaboudan
%T Genetic programming for spatiotemporal forecasting of housing prices
%B Handbook of Research on Nature-Inspired Computing for Economics and Management
%E Jean-Philippe Rennard
%V II
%D 2007
%P 851--868
%I Idea Group Inc.
%C 1200 E. Colton Ave
%K genetic algorithms, genetic programming, ANN, TSGP, C++,
%X This chapter compares forecasts of the median neighbourhood prices of residential single-family homes in Cambridge, Massachusetts, using parametric and nonparametric
   techniques. Prices are measured over time (annually) and over space (by neighborhood). Modelling variables characterised by space and time dynamics is challenging.
   Multi-dimensional complexities due to specification, aggregation, and measurement errors thwart use of parametric modeling, and nonparametric computational techniques
   (specifically genetic programming and neural networks) may have the advantage. To demonstrate their efficacy, forecasts of the median prices are first obtained using a
   standard statistical method: weighted least squares. Genetic programming and neural networks are then used to produce two other forecasts. Variables used in modelling
   neighbourhood median home prices include economic variables such as neighbourhood median income and mortgage rate, as well as spatial variables that quantify location. Two
   years out-of-sample forecasts comparisons of median prices suggest that genetic programming may have the edge.
%O LV
%@ 1-59140-984-5

%A Mak Kaboudan
%T Computational Forecasting of Wavelet-Converted Monthly Sunspot Numbers
%J Journal of Applied Statistics
%V 33
%N 9
%D 2006
%P 925--941
%I 
%K genetic algorithms, genetic programming, Wavelets, thresholding, neural networks, sunspot numbers
%X Monthly average sunspot numbers follow irregular cycles with complex nonlinear dynamics. Statistical linear models constructed to forecast them are therefore inappropriate
   while nonlinear models produce solutions sensitive to initial conditions. Two computational techniques 'neural networks' and 'genetic programming' that have their
   advantages are applied instead to the monthly numbers and their wavelet-transformed and wavelet-denoised series. The objective is to determine if modelling
   wavelet-conversions produces better forecasts than those from modeling a series' observed values. Because sunspot numbers are indicators of geomagnetic activity their
   forecast is important. Geomagnetic storms endanger satellites and disrupt communications and power systems on Earth.
%8 November
%Z http://www.tandf.co.uk/journals/titles/02664763.asp

%A Mak Kaboudan
%T Biologically Inspired Algorithms for Financial Modelling Published by: Springer, A. Brabazon and M. O'Neill, 2006, ISBN 3-540-26252-0, \$85
%J Genetic Programming and Evolvable Machines
%V 7
%N 3
%D 2006
%P 285--286
%I 
%K genetic algorithms, genetic programming
%O Book Review
%8 October
%Z review of \citeBrabazon:2006:BIAS

%A Mak (Mahmoud) Kaboudan
%T GP versus GLS Spatial Index Models to Forecast Single-Family Home Prices
%J New Mathematics and Natural Computation
%V 4
%N 2
%D 2008
%P 143--163
%I 
%K genetic algorithms, genetic programming, generalised least squares, hedonic model, spatial index, home prices
%X This paper investigates use of genetic programming regression models to forecast home values. Neighbourhood prices in a city are represented by a quarterly index. Index
   values are ratios of each local neighborhood to the global city average real price of homes sold. Relative average neighbourhood home attributes, local socioeconomic
   characteristics, spatial measures, and real mortgage rates explain spatiotemporal variations in the index. To examine efficacy of model estimation, forecasts obtained using
   genetic programming are compared with those obtained using generalised least squares. Out-of-sample genetic programming predictions of home prices obtained using spatial
   index models deliver reasonable forecasts of home prices.
%8 July

%A Mak Kaboudan
%T Genetic Programming Forecasting of Real Estate Prices of Residential Single Family Homes in Southern California
%J Journal of Real Estate Literature
%V 16
%N 2
%D 2008
%P 219--239
%I 
%K genetic algorithms, genetic programming
%X Use of an artificial intelligence technique, genetic programming (GP), is introduced here to predict real estate residential single family home prices. GP is a computerised
   random search technique that can deliver regression-like models. Spatiotemporal model specifications of periodic average neighbourhood prices are implemented to predict
   individual property prices. Average price variations are explained in terms of changes in home attributes, spatial attributes, and temporal economic variables. Quarterly
   data (2000-2005) from two cities in Southern California are used to obtain GP and standard statistical models (generalised least square - GLS). Results obtained suggest
   that forecasts from city neighborhood average price GP equations may have advantage over forecasts from GLS equations and over forecasts from models estimated using city
   aggregated data.
%Z house price prediction in US of america http://business.fullerton.edu/finance/jrel/

%A Mak Kaboudan
%T A two-stage multi-agent system to predict the unsmoothed monthly sunspot numbers
%J International Journal of Mathematics and Computer Sciences
%V 5
%N 3
%D 2009
%P 136--143
%I 
%K genetic algorithms, genetic programming, Computational techniques, discrete wavelet transformations, solar cycle prediction, sunspot numbers
%U http://www.waset.org/journals/ijmcs/v5/v5-3-21.pdf
%X A multi-agent system is developed here to predict monthly details of the upcoming peak of the 24th solar magnetic cycle. While studies typically predict the timing and
   magnitude of cycle peaks using annual data, this one uses the unsmoothed monthly sunspot number instead. Monthly numbers display more pronounced fluctuations during periods
   of strong solar magnetic activity than the annual sunspot numbers. Because strong magnetic activities may cause significant economic damages, predicting monthly variations
   should provide different and perhaps helpful information for decision-making purposes. The multi-agent system developed here operates in two stages. In the first, it
   produces twelve predictions of the monthly numbers. In the second, it uses those predictions to deliver a final forecast. Acting as expert agents, genetic programming and
   neural networks produce the twelve fits and forecasts as well as the final forecast. According to the results obtained, the next peak is predicted to be 156 and is expected
   to occur in October 2011, with an average of 136 for that year.
%8 Summer

%A Mak Kaboudan
%T A genetic programming/neural network multi-agent system to forecast the S\&P/Case-Shiller home price index for Los Angeles
%B Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization
%E Shu-Heng Chen and Yasushi Kambayashi and Hiroshi Sato
%D 2011
%P 1--18
%I IGI Global
%K genetic algorithms, genetic programming
%U http://www.igi-global.com/bookstore/Chapter.aspx?TitleId=46196
%X Successful decision-making by home-owners, lending institutions, and real estate developers among others is dependent on obtaining reasonable forecasts of residential home
   prices. For decades, home-price forecasts were produced by agents using academically well-established statistical models. In this chapter, several modelling agents will
   compete and cooperate to produce a single forecast. A cooperative multi-agent system (MAS) is developed and used to obtain monthly forecasts (April 2008 through March 2010)
   of the S&P/Case-Shiller home price index for Los Angeles, CA (LXXR). Monthly housing market demand and supply variables including conventional 30-year fixed real mortgage
   rate, real personal income, cash out loans, homes for sale, change in housing inventory, and construction material price index are used to find different independent models
   that explain percentage change in LXXR. An agent then combines the forecasts obtained from the different models to obtain a final prediction.
%O 1
%@ 1-60566-898-2

%A Ilan Kadar
%A Ohad Ben-Shahar
%A Moshe Sipper
%T Evolving boundary detectors for natural images via Genetic Programming
%B 19th International Conference on Pattern Recognition, ICPR 2008
%D 2008
%P 1--4
%I 
%C Tampa, Florida, USA
%K genetic algorithms, genetic programming, computer vision, learning (artificial intelligence), boundary detection, boundary detectors, computer vision, filter kernels, human
   visual system, human-marked boundaries, human-marked boundary maps, learning approach, learning framework, natural images, primate visual system
%X Boundary detection constitutes a crucial step in many computer vision tasks. We present a novel learning approach to automatically construct a boundary detector for natural
   images via Genetic Programming (GP). Our approach aims to use GP as a learning framework for evolving computer programs that are evaluated against human-marked boundary
   maps, in order to accurately detect and localize boundaries in natural images. Our GP system is unique in that it combines filter kernels that were inspired by models of
   processing in the early stages of the primate visual system, but makes no assumption about what constitutes a boundary, thus avoiding the need to make ad-hoc intuitive
   definitions. By testing the evolved boundary detectors on a benchmark set of natural images with associated human-marked boundaries, we show performance to be
   quantitatively competitive with existing computer-vision approaches. Moreover, we show that our evolved detector provides insights into the mechanisms underlying boundary
   detection in the human visual system.
%8 Decemeber  8-11
%Z Also known as \cite4761581

%A Ilan Kadar
%A Ohad Ben-Shahar
%A Moshe Sipper
%T Evolution of a local boundary detector for natural images via genetic programming and texture cues
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1887--1888
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Poster
%X Boundary detection constitutes a crucial step in many computer vision tasks. We present a learning approach for automatically constructing high-performance local boundary
   detectors for natural images via genetic programming (GP). Our GP system is unique in that it combines filter kernels that were inspired by models of processing in the
   early stages of the primate visual system, but makes no assumptions about what constitutes a boundary, thus avoiding the need to make ad hoc intuitive definitions. By
   testing our evolved boundary detectors on a highly challenging benchmark set of natural images with associated human-marked boundaries, we show performance that outperforms
   most existing approaches.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Petr Kadlec
%A Bogdan Gabrys
%A Sibylle Strandt
%T Data-driven Soft Sensors in the process industry
%J Computers \& Chemical Engineering
%V 33
%N 4
%D 2009
%P 795--814
%I 
%K genetic algorithms, genetic programming, Soft Sensors, Process industry, Data-driven models, PCA, ANN
%U http://www.sciencedirect.com/science/article/B6TFT-4VDS8G1-1/2/f93212bf3e52875c4130ab4c34570170
%X In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process
   monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of
   data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry,
   etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely
   realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular
   Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main
   contributions of this work.
%Z Survey

%A Stefan Kahrs
%T Genetic programming with primitive recursion
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 941--942
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming: Poster, grammatical evolution, primitive recursion, program transformation, theory
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p941.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Talip Kahyaoglu
%T Optimization of the pistachio nut roasting process using response surface methodology and gene expression programming
%J LWT - Food Science and Technology
%V 41
%N 1
%D 2008
%P 26--33
%I 
%K genetic algorithms, genetic programming, Pistachio nut, Roasting, Response surface, Optimization
%U http://www.sciencedirect.com/science/article/B6WMV-4NFXDRG-2/2/af126f2eab53c54caa0cefff78e6558e
%X Roasted pistachio nuts are consumed as snack foods and used as ingredients in confectionery, chocolates and ice-cream industries. Response surface methodology (RSM) and
   Gene Expression Programming (GEP) were used to optimize the roasting process for production of the pistachios in shell, kernel, and ground-kernel forms over a range of
   temperature (100-180degrees C) and for various times (10-60min). The moisture content and color parameters (L, a, b and yellowness index (YI)) were evaluated during
   roasting and modeled by RSM and GEP. The moisture content changes of the pistachios during roasting were successfully described by RSM and GEP models. The results showed
   that the L, a and b values could be used as parameters for the development of the predictive models during roasting of in shell pistachios, but the color of kernel and
   ground-kernel pistachios could be monitored by measuring only a and a, b values, respectively. The quadratic models developed by RSM adequately described the changes in
   selected color parameters during roasting. The GEP models were found to be slightly better than RSM models. The response surface of desirability function was used
   successfully in optimization procedure of pistachio nut roasting.

%A Jeevan J. Kalanithi
%T Co-Evolution of Predator and Prey Behaviors in a Simulated Environment using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 86--94
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A T. Kalganova
%A J. Miller
%A N. Lipnitskaya
%T MultipleValued Combinational Circuits Synthesized using Evolvable Hardware Approach
%D 1998
%I 
%Z T. Kalganova, J. F. Miller, N. Lipnitskaya, MultipleValued Combinational Circuits Synthesized using Evolvable Hardware Approach. Proc. of the 7th Workshop on Post-Binary
   Ultra Large Scale Integration Systems (ULSI'98) in association with ISMVL'98, Fukuoka, Japan. IEEE Press, 1998b.

%A Tatiana Kalganova
%A Julian F. Miller
%A Terence C. Fogarty
%T Some Aspects of an Evolvable Hardware Approach for Multiple-Valued Combinational Circuit Design
%B Evolvable Systems: From Biology to Hardware Second International Conference, ICES '98
%S LNCS
%E Moshe Sipper and Daniel Mange and Andres Perez-Uribe
%V 1478
%D 1998
%P 78--89
%I Springer-Verlag
%C Lausanne, Switzerland
%8 September  23-25
%Z 382 pp., Softcover Available online in SpringerLink
%@ 3-540-64954-9

%A T. Kalganova
%A J. Miller
%T Evolving More Efficient Digital Circuits by Allowing Circuit Layout Evolution and Multi-Objective Fitness
%B The First NASA/DoD Workshop on Evolvable Hardware
%E Adrian Stoica and Jason Lohn and Didier Keymeulen
%D 1999
%P 54--63
%I IEEE Computer Society 1730 Massachusetts Avenue, N.W., Washington, DC 20036-1992, USA
%I Jet Propulsion Laboratory, California Institute of Technology
%C Pasadena, California
%K genetic algorithms, genetic programming, evolvable hardware
%X We use evolutionary search to design combinational logic circuits. The technique is based on evolving the functionality and connectivity of a rectangular array of logic
   cells whose dimension is defined by the circuit layout. The main idea of this approach is to improve quality of the circuits evolved by the genetic algorithm (GA) by
   reducing the number of active gates used. We accomplish this by combining two ideas: 1) using multi-objective fitness function; 2) evolving circuit layout. It will be shown
   that using these two approaches allows us to increase the quality of evolved circuits. The circuits are evolved in two phases. Initially the genome fitness in given by the
   percentage of output bits that are correct. Once 100\% functional circuits have been evolved, the number of gates actually used in the circuit is taken into account in the
   fitness function. This allows us to evolve circuits with 100\% functionality and minimise the number of active gates in circuit structure. The population is initialised
   with heterogeneous circuit layouts and the circuit layout is allowed to vary during the evolutionary process. Evolving the circuit layout together with the function is one
   of the distinctive features of proposed approach. The experimental results show that allowing the circuit layout to be flexible is useful when we want to evolve circuits
   with the smallest number of gates used. We find that it is better to use a fixed circuit layout when the objective is to achieve the highest number of 100\% functional
   circuits. The two-fitness strategy is most effective when we allow a large number of generations.
%8 19-21  July
%Z EH1999 http://cism.jpl.nasa.gov/events/nasa_eh/
%@ 0-7695-0256-3

%A Tatiana Kalganova
%A Julian F. Miller
%A Terence C. Fogarty
%T Evolution of the Digital Circuits with Variable Layouts
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1235
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, EHW, evolvable hardware, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-449.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Tatiana Kalganova
%T An Extrinsic Function-Level Evolvable Hardware Approach
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 60--75
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=60
%X The function level evolvable hardware approach to synthesize the combinational multi-valued and binary logic functions is proposed in first time. The new representation of
   logic gate in extrinsic EHW allows us to describe behaviour of any multi-input multi-output logic function. The circuit is represented in the form of connections and
   functionalities of a rectangular array of building blocks. Each building block can implement primitive logic function or any multi-input multi-output logic function defined
   in advance. The method has been tested on evolving logic circuits using half adder, full adder and multiplier. The effectiveness of this approach is investigated for
   multi-valued and binary arithmetical functions. For these functions either method appears to be much more efficient than similar approach with two-input one-output cell
   representation.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A T. Kalganova
%T Bidirectional Incremental Evolution in Extrinsic Evolvable Hardware
%B The Second NASA/DoD workshop on Evolvable Hardware
%E Jason Lohn and Adrian Stoica and Didier Keymeulen
%D 2000
%P 65--74
%I IEEE Computer Society 1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA
%I Jet Propulsion Laboratory, California Institute of Technology
%C Palo Alto, California
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/kalganova00bidirectional.html
%X Evolvable Hardware (EHW) has been proposed as a new technique to design complex systems. Often, complex systems turn out to be very difficult to evolve. The problem is that
   a general strategy is too difficult for the evolution process to discover directly. This paper proposes a new approach that performs incremental evolution in two
   directions: from complex system to sub-systems and from subsystems back to complex system. In this approach, incremental evolution gradually decomposes a complex problem
   into some sub-tasks. In a second step, we gradually make the tasks more challenging and general. Our approach automatically discovers the sub-tasks, their sequence as well
   as circuit layout dimensions. Our method is tested in a digital circuit domain and compared to direct evolution. We show that our bidirectional incremental approach can
   handle more complex, harder tasks and evolve them more effectively, then direct evolution.
%8 13-15  July
%Z EH2000 http://csdl.computer.org/comp/proceedings/eh/2000/0762/00/0762toc.htm http://ic-www.arc.nasa.gov/ic/eh2000/index.html
%@ 0-7695-0762-X

%A T. Kalganova
%A I. Baradavka
%T A probabilistic approach to analyse the evolutionary process in circuit design
%B Proc. of the 9th LMPU Conference
%D 2002
%P 689--696
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%X One of the actual problems in the evolvable hardware is the evolvability of logic circuits. In order to understand better the nature of existing problem, the probabilistic
   analysis can be used. This paper aims to investigate how the circuit layout evolution is carried out. This is interesting thing to do for two main reasons. Firstly, to
   investigate what type of genes mostly influence on the algorithm performance in evolvable hardware. Secondly, to see how effective an allocation of active logic gates might
   be in a digital circuit design task. In order to achieve this goal we investigate the genotypes of the best chromosomes which bring some improvements in evolutionary
   process. The logic circuits have been evolved using circuit layout evolution.

%A T. Kalganova
%A I. M. Karol
%A J. C. Werner
%A N. I. Silkou
%A N. G. Lipnitskaya
%T Probability prediction method of throat cancer with use of discriminate function
%B 2nd International Belarusian-Polish Conference on Otorhinolaryngology: Actual Problems in Otorhinolaryngology
%D 2003
%I 
%C Grodno
%K genetic algorithms, genetic programming
%U http://www.geocities.com/jamwer2002/russo.pdf
%8 29-30  May
%Z in Russian

%A Maximos Kaliakatsos-Papakostas
%A Michael Epitropakis
%A Andreas Floros
%A Michael Vrahatis
%T Interactive Evolution of 8-bit melodies with Genetic Programming towards finding aesthetic measures for sound
%B Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012
%S LNCS
%E Penousal Machado and Juan Romero and Adrian Carballal
%V 7247
%D 2012
%P 141--152
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming
%X The efficient specification of aesthetic measures for music as a part of modelling human conception of sound is a challenging task and has motivated several research works.
   It is not only targeted to the creation of automatic music composers and raters, but also reinforces the research for a deeper understanding of human noesis. The aim of
   this work is twofold: first, it proposes an Interactive Evolution system that uses Genetic Programming to evolve simple 8-bit melodies. The results obtained by subjective
   tests indicate that evolution is driven towards more user-preferable sounds. In turn, by monitoring features of the melodies in different evolution stages, indications are
   provided that some sound features may subsume information about aesthetic criteria. The results are promising and signify that further study of aesthetic preference through
   Interactive Evolution may accelerate the progress towards defining aesthetic measures for sound and music.
%8 11-13  April
%Z Part of \citeMachado:2012:EvoMusArt EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012

%A Vyacheslav Kalmykov
%T The Integral Algorithm of Organization and Evolution of the Living Up to Culture - the Possible Instrument for Genetic Programming
%B The 1st Online Workshop on Soft Computing (WSC1)
%D 1996
%I Nagoya University, Japan
%I Research Group on ECOmp of the Society of Fuzzy Theory and Systems (SOFT)
%C http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/
%K genetic algorithms, genetic programming
%U http://www.calresco.org/kalmykov/vlkiaoe.txt
%X The paper present correct physicomathematical formulating the invariant operational scheme of organization (space correlations) and evolution (time correlations) of the
   living, including a new generalized conception of information. This new methodological innovation would permit the creation of a programs that solve problems in the full
   sense (in essence and integrally), and not only as imitation. The chief elements of the proposed operational schemes are as follows: - elementary operations on information,
   energy and matter; life is realization of some combinations of these operations; the combinations form a mathematical group; - general definitions of control, reproduction
   and creation operations; interdependence of these integrative operations (on elementary operations) in the organism; - generalized conception of information; - consecutive
   stages of arising and evolution of the organisms; - the generalized criterion of the life evolution direction; - the generalized definition of life, culture, functional
   elements of culture ...
%8 19--30  August
%Z Artificial life Alife ? See discussion page at WSC1 email WSC1 organisers wsc@bioele.nuee.nagoya-u.ac.jp

%A Ruchi Kalra
%A M. C. Deo
%T Genetic programming for retrieving missing information in wave records along the west coast of India
%J Applied Ocean Research
%V 29
%N 3
%D 2007
%P 99--111
%I 
%K genetic algorithms, genetic programming, ANN, GP, Missing data, Soft computing, Wave heights
%U http://www.sciencedirect.com/science/article/B6V1V-4RH2SVM-1/2/b6c7570e30be137676dfac0cb711a4db
%X Instruments such as floating wave rider buoys provide wave data over a long period in a continuous manner; however such information invariably contains missing values
   resulting from the instrument and telemetry system that is damaged, malfunctioning or otherwise non-operational. The problem of restoring missing wave heights is attempted
   in this paper using one of the latest soft computing tools, namely, Genetic Programming (GP). The gaps in the time series of significant wave heights collected at every 3h
   for a period of four years from January 2000 to December 2003 are filled in at six selected buoy locations along the west coast of India. The performance of GP was judged
   in terms of the error statistics of bias, root mean square error, correlation coefficient and scatter index. The methodology demonstrated reliable results with fairly good
   overall agreement between the restored wave records and actual measurements.
%Z Discused by \citeGandomi2008338 and \citeDeo2008340

%A Ruchi Kalra
%A M. C. Deo
%A Raj Kumar
%A Vijay K. Agarwal
%T Genetic Programming to Estimate Coastal Waves from Deep Water Measurements
%J International Journal of Ecology \& Development
%V 10
%N S08
%D 2008
%P 67--76
%I 
%K genetic algorithms, genetic programming, Wave data, wave mapping, geometric programming, neural networks
%U http://www.ceser.res.in/ceserp/index.php/ijed/article/view/439
%X Satellites gather vast quantities of ocean wave data worldwide and such measurements are available to ocean scientists and engineers at low costs. However corresponding
   information is more useful in deeper sea with open or exposed locations rather than nearshore locations involving complex bathymetric effects. The technique based on the
   approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) to map remote sensed deep-water waves with coastal waves
   was attempted by the authors in the past (Kalra et al (2005, a, b)). This paper presents an application of a relatively new soft computing tool called Genetic Programming
   for this purpose. Significant wave heights at a number of locations over a track parallel to the coastline are used to estimate the significant wave heights at a nearshore
   site. The success of the method adopted was confirmed from the satisfactory error measures it produced during the testing carried out following the training. The results
   are also compared with those derived using artificial neural networks (ANN). In general it was found that the spatial mapping of wave heights done by genetic programming
   rivals that by ANN.
%8 Summer
%Z Department of Civil Engineering, IIT Bombay ISRO, Ahmedabad, 380 015, India

%A Sesha Kalyur
%T Error Driven Parallelization of a Genetic Program
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 127--134
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Sejal Kamani
%T Behavior Learning and Individual Cooperation in Autonomous Agents as a Result of Interaction Dynamics with the Environment
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 135--144
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Uday Kamath
%A Kenneth {De Jong}
%A Amarda Shehu
%T An Evolutionary-based Approach for Feature Generation: Eukaryotic Promoter Recognition
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 277--284
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Biometrics, bioinformatics and biomedical applications
%X Prediction of promoter regions continues to be a challenging subproblem in mapping out eukaryotic DNA. While this task is key to understanding the regulation of
   differential transcription, the gene-specific architecture of promoter sequences does not readily lend itself to general strategies. To date, the best approaches are based
   on Support Vector Machines (SVMs) that employ standard "spectrum" features and achieve promoter region classification accuracies from a low of 84percent to a high of
   94percent depending on the particular species involved. In this paper, we propose a general and powerful methodology that uses Genetic Programming (GP) techniques to
   generate more complex and more gene-specific features to be used with a standard SVM for promoter region identification. We evaluate our methodology on three data sets from
   different species and observe consistent classification accuracies in the 94-95percent range. In addition, because the GP-generated features are gene-specific, they can be
   used by biologists to advance their understanding of the architecture of eukaryotic promoter regions.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A A. R. Kambekar
%A M. C. Deo
%T Wave simulation and forecasting using wind time history and data-driven methods
%J Ships and Offshore Structures
%V 5
%N 3
%D 2010
%I 
%K genetic algorithms, genetic programming, wave simulation, wave forecasting, wind time history, model trees
%X Simulation and forecasting of significant wave heights and average zero-cross wave periods in real time are done for a specified location, given the past observed sequence
   of wind speed and wind direction. This is based on time series forecasting implemented using the two recent data-driven methods of genetic programming (GP) and model trees
   (MT). The wave buoy measurements made at eight different offshore locations around the west as well as the east coast in India are considered. Both genetic programming and
   model trees perform satisfactorily in the given task of wind-wave simulation and forecasting as reflected in the values of the six different error statistics employed to
   assess the performance of developed models over testing sets of data. Although the magnitudes of error statistics do not indicate a significant difference between the
   performance of GP and MT, qualitative scatter diagrams and time histories showed the tendency of MT to estimate higher waves more correctly.
%8 253--266
%Z Department of Civil Engineering, Indian Institute of Technology-Bombay, Powai, Mumbai 400 076, India

%A Yoshitaka Kameya
%A Junichi Kumagai
%A Yoshiaki Kurata
%T Accelerating genetic programming by frequent subtree mining
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1203--1210
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, building blocks, frequent subtree mining, probabilistic model building genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1203.pdf
%X One crucial issue in genetic programming (GP) is how to acquire promising building blocks efficiently. In this paper, we propose a GP method (called GPTM, GP with Tree
   Mining) which protects the subtrees repeatedly appearing in superior individuals. Currently GPTM uses a FREQT-like efficient data mining method to find such subtrees. GPTM
   is evaluated by three benchmark problems, and the results indicate that GPTM is comparable to or better than POLE, one of the most advanced probabilistic model building GP
   methods, and finds the optimal individual earlier than the standard GP and POLE.
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389332 GPsys-2b, Java. Santa Fe Ant, Royal Tree.

%A Shotaro Kamio
%A Hitoshi Iba
%T A Co-evolutionary Approach to Parallel Distributed Genetic Programming
%B Proceedings of the 4th International Workshop on Emergent Synthesis - IWES'02
%D 2002
%P 23--28
%I 
%C Kobe University, Japan
%K genetic algorithms, genetic programming
%8 9-10  May
%Z http://www.race.u-tokyo.ac.jp/uedalab/IWES/IWES02/

%A Shotaro Kamio
%A Hongwei Liu
%A Hideyuki Mitsuhasi
%A Hitoshi Iba
%T Researches on Ingeniously Behaving Agents
%B 2003 NASA/DoD Conference on Evolvable Hardware
%E Jason Lohn and Ricardo Zebulum and James Steincamp and Didier Keymeulen and Adrian Stoica and Michael I. Ferguson
%D 2003
%P 208--220
%I IEEE Computer Society 10662 Los Vaqueros Circle, P.O. Box 3014, Los Alamitos, CA, 90720-1314, USA
%I NASA Ames Research Center
%C Chicago, Illinois
%U EHW http://ehw.jpl.nasa.gov
%X We have been studying the techniques for evolutionary robotics and experimenting with various robots applied evolutionary methods. We have paid special attentions to real
   robots and multi-agent problems related to them. In this research domain, we name them as "ingeniously behaving agents" (IBA). This paper shows several techniques developed
   in our IBA laboratory and their experimental results applied to simulations and real robots.
%8 9-11  July
%Z EH2003 http://ic.arc.nasa.gov/projects/eh2003/
%@ 0-7695-1977-6

%A Shotaro Kamio
%A Hideyuki Mitsuhashi
%A Hitoshi Iba
%T Integration of Genetic Programming and Reinforcement Learning for Real Robots
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2723
%D 2003
%P 470--482
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, Evolutionary Robotics
%X We propose an integrated technique of genetic programming (GP) and reinforcement learning (RL) that allows a real robot to execute real-time learning. Our technique does
   not need a precise simulator because learning is done with a real robot. Moreover, our technique makes it possible to learn optimal actions in real robots. We show the
   result of an experiment with a real robot AIBO and represents the result which proves proposed technique performs better than traditional Q-learning method.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40602-6

%A Shotaro Kamio
%A Hitoshi Iba
%T Real-time adaptation technique to real robots: An experiment with a humanoid robot
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 506--513
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Shotaro Kamio
%A Hitoshi Iba
%T Evolutionary Construction of a Simulator for Real Robots
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 2202--2209
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Evolutionary intelligent agents
%U http://www.iba.k.u-tokyo.ac.jp/papers/2004/kamioCEC2004.pdf
%X In order to acquire useful motions of a real-world robot, it is necessary to carry out learning in a real environment. However, learning is difficult within a real
   environment. In addition, the acceleration of learning is required for a practical execution. In this paper, we propose an approach to the learning acceleration using data
   retrieved from the real environment. This consists of the method of automatically constructing the simulator from real data and of learning a robot controller with the
   simulator. The experimental results suggest that our GP-based technique enables the effective controller learning.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Shotaro Kamio
%A Hideyuki Mitsuhashi
%A Hitoshi Iba
%T Integration of Genetic Programming and Reinforcement Learning for Real Robots
%J IPSJ Transactions on Mathematical Modeling and Applications
%V 45
%N 2
%D 2004
%P 33--142
%I 
%K genetic algorithms, genetic programming
%X We propose an integrated technique of genetic programming (GP) and reinforcement learning (RL) that allows a real robot to execute real-time learning. Our technique does
   not need a precise simulator because learning is done with a real robot. Moreover, our technique makes it possible to learn optimal actions in real robots. We show the
   result of an experiment with a real robot AIBO and represents the result which proves proposed technique performs better than traditional Q-learning method.
%Z Language: Japanese http://www.fujipress.jp/finder/search_main.php?mediaID=J7&getsubmit=on&mediaselect=mediatitle&SortOrder=descending&lang=all

%A Shotaro Kamio
%A Hitoshi Iba
%T Adaptation technique for integrating genetic programming and reinforcement learning for real robots
%J IEEE Transactions on Evolutionary Computation
%V 9
%N 3
%D 2005
%P 318--333
%I 
%K genetic algorithms, genetic programming, adaptive systems, humanoid robots, learning (artificial intelligence), legged locomotion, AIBO four-legged robot, HOAP-1 humanoid
   robot, Q-learning method, adaptation technique, box-moving task, reinforcement learning, Adaptation evolutionary computation, box moving, real robot, reinforcement learning
   (RL)
%X We propose an integrated technique of genetic programming (GP) and reinforcement learning (RL) to enable a real robot to adapt its actions to a real environment. Our
   technique does not require a precise simulator because learning is achieved through the real robot. In addition, our technique makes it possible for real robots to learn
   effective actions. Based on this proposed technique, we acquire common programs, using GP, which are applicable to various types of robots. Through this acquired program,
   we execute RL in a real robot. With our method, the robot can adapt to its own operational characteristics and learn effective actions. In this paper, we show experimental
   results from two different robots: a four-legged robot AIBO and a humanoid robot HOAP-1. We present results showing that both effectively solved the box-moving task; the
   end result demonstrates that our proposed technique performs better than the traditional Q-learning method.
%8 June
%Z INSPEC Accession Number: 8465512 Graduate Sch. of Frontier Sci., Univ. of Tokyo, Chiba, Japan. Reinforcement learning (RL) is outside the GP loop. Q-table 168 or 238
   states, RL ten hours or 6 hours. 6 way IF. GP run ten minutes. Target and box are colour coded. Precise simulation of both robots is not possible. p 330 GP "learning some
   general knowledge...not limited to a particular robot".

%A Motoki Kamitani
%A Tadashi Ae
%T Augmented interactive evolutionary computation for composition
%J International Journal of Technology, Policy and Management
%V 4
%N 4
%D 2005
%P 337--352
%I Inderscience Publishers
%K genetic algorithms, genetic programming, interactive evolutionary computation, sequence generation, prediction agent, hidden Markov model, music composition, partial
   sequences.
%U http://www.inderscience.com/link.php?id=6616
%X we propose an augmented interactive evolutionary computation technique to generate a symbol sequence, which is composed of several partial sequences. We introduce
   two-levels of feedback mechanism for evaluations, where the inner cycle induces an evolution of prediction agent for evaluation realised by a hidden Markov model, and the
   outer cycle induces an interaction with the user by selecting the candidate generated by the prediction agent. We describe, first, the process of augmented interactive
   evolutionary computation, and discuss the cooperative generation of sequences, which affects an anticipatory effective creation of formed sequence such as a music score.
   Next, we show several experimental results, which provide the generation of partial sequences and formed sequence.
%O Special Issue on Developments in Decision Technologies
%8 March ~28
%Z http://www.inderscience.com/browse/index.php?journalID=28 Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima 739-8527, Japan. '
   Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima 739-8527, Japan

%A Thomas Kammeyer
%A R. K. Belew
%T Stochastic Context-Free Grammar Induction with a Genetic Algorithm Using Local Search
%B Foundations of Genetic Algorithms IV
%E Richard K. Belew and Michael Vose
%D 1996
%I Morgan Kaufmann
%C University of San Diego, CA, USA
%K genetic algorithms, CFG
%8 3--5  August
%Z FOGA4 Variable length chromosome with introns used to specify stochastic grammar with BNF like syntax
%@ 1-55860-460-X

%A Michael Kampouridis
%A Edward Tsang
%T EDDIE for investment opportunities forecasting: Extending the search space of the GP
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X In this paper we present a new version of a GP-based financial forecasting tool called EDDIE. The novelty of this new version (EDDIE 8), is its enlarged search space, where
   we allow the GP to search in the space of the technical indicators, in order to form its Genetic Decision Trees. In this way, EDDIE 8 is not constrained in using
   pre-specified indicators, but it is left up to the GP to choose the optimal ones. We then proceed to compare EDDIE 8 with EDDIE 7, which is based on previous EDDIE
   versions; EDDIE 7 has a smaller space where the indicators are pre-specified by the user and are part of EDDIE 8's space. Results show that thanks to the bigger search
   space, new and improved solutions can be found by EDDIE 8. However, there are cases where EDDIE 8 can still be outperformed by its predecessor. Analysis shows that this
   depends on the nature of the solutions. If the solutions come from EDDIE 8's search space, then EDDIE 8 can find them and perform better; if, however, solutions come from
   the smaller search space of EDDIE 7, then EDDIE 8 is having difficulties focusing in such a small space and is thus outperformed by EDDIE 7.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586094

%A Michael Kampouridis
%A Shu-Heng Chen
%A Edward Tsang
%T The Market Fraction Hypothesis under Different Genetic Programming Algorithms
%B Information Systems for Global Financial Markets: Emerging Developments and Effects
%E Alexander Y. Yap
%D 2011
%P 37--54
%I IGI global
%K genetic algorithms, genetic programming
%U http://www.amazon.com/Information-Systems-Global-Financial-Markets/dp/1613501625
%X In a previous work, inspired by observations made in many agent-based financial models, we formulated and presented the Market Fraction Hypothesis, which basically predicts
   a short duration for any dominant type of agents, but then a uniform distribution over all types in the long run. We then proposed a two-step approach, a rule-inference
   step, and a rule-clustering step, to test this hypothesis. We employed genetic programming as the rule inference engine, and applied self-organising maps to cluster the
   inferred rules. We then ran tests for 10 international markets and provided a general examination of the plausibility of the hypothesis. However, because of the fact that
   the tests took place under a GP system, it could be argued that these results are dependent on the nature of the GP algorithm. This chapter thus serves as an extension to
   our previous work. We test the Market Fraction Hypothesis under two new different GP algorithms, in order to prove that the previous results are rigorous and are not
   sensitive to the choice of GP. We thus test again the hypothesis under the same 10 empirical datasets that were used in our previous experiments. Our work shows that
   certain parts of the hypothesis are indeed sensitive on the algorithm. Nevertheless, this sensitivity does not apply to all aspects of our tests. This therefore allows us
   to conclude that our previously derived results are rigorous and can thus be generalised.
%O 3
%8 November
%@ 1-61350-162-5

%A Manoj Kandpal
%A Kalyan Mynampati Chakravarthy
%A S. Lakshminarayanan
%T A genetic programming based methodology for variable interaction determination in multivariate dynamical systems
%B The 2010 International Conference on Modelling, Identification and Control (ICMIC)
%D 2010
%P 173--178
%I 
%C Okayama, Japan
%K genetic algorithms, genetic programming, multivariate dynamical system, parameter estimation technique, variable interaction determination, multivariable systems, parameter
   estimation
%U http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5553570
%X In many systems, the determination of variable interaction structures using data is of central importance. For example, biological systems mainly comprise of a cascade of
   various interrelated metabolites/reactions such that the occurrence of one event depends on the occurrence of a prior event or a set of events of the same or different
   nature. This time dependent inter-correlation between the variables, once deciphered, can help in gaining a better understanding the mechanisms governing the systems and
   paves way for their manipulation. In the present paper, genetic programming and standard parameter estimation techniques are used to determine such relationships from noise
   corrupted datasets.
%8 July  17-19
%Z Dept. of Chem. & Biomol. Eng., Nat. Univ. of Singapore, Singapore, Singapore. www.gbv.de/dms/tib-ub-hannover/637808703.pdf Also known as \cite5553570

%A Moonyoung Kang
%A Jungseok Shin
%A Tuan Hao Hoang
%A R. I. (Bob) McKay
%A Daryl Essam
%A Naoki Mori
%A Xuan Hoai Nguyen
%T Code Duplication and Developmental Evaluation in Genetic Programming
%B Proceedings of the 2006 Asia-Pacific Workshop on Intelligent and Evolutionary Systems
%D 2006
%P 181--191
%I 
%C Seoul, Korea
%K genetic algorithms, genetic programming
%U http://sc.snu.ac.kr/PAPERS/compression.pdf
%X We investigate a hypothesis, that structured, replicated code can be promoted by evaluation during development, and that this is the cause of the good performance of
   algorithms using developmental evaluation. We use compression as a tool to measure replication of code in this research. Our results show that evaluation during development
   does not promote replicated structured code. Hence we are left with two problems, explaining why developmental evaluation systems exhibit good performance, and
   understanding how replicated, structured coding has arisen in the genotype of natural biological systems.
%8 November
%Z Kang, Moonyoung1, Shin, Jungseok2, Hoang, Tuan Hao3, McKay, RI (Bob)4, Essam, Daryl5, Mori, Naoki6, and Nguyen, Xuan Hoai7 1 Seoul National University, Seoul, Korea 2 Seoul
   National University, Seoul, Korea 3 University of New South Wales @ ADFA, Canberra, Australia 4 Seoul National University, Seoul, Korea 5 University of New South Wales @
   ADFA, Canberra, Australia 6 Osaka Prefecture University, Osaka, Japan 7 VietNam Military Technical Academy, Hanoi, VietNam

%A Young-Min Kang
%A Hwan-Gue Cho
%A Ee-Taek Lee
%T An efficient control over human running animation with extension of planar hopper model
%J The Journal of Visualization and Computer Animation
%V 10
%N 4
%D 1999
%P 215--224
%I 
%K genetic algorithms, genetic programming, animation, human gait, energy control
%U http://www3.interscience.wiley.com/cgi-bin/abstract/68501003/START
%X The most important goal of character animation is to efficiently control the motions of a character. Until now, many techniques have been proposed for human gait animation.
   Some techniques have been created to control the emotions in gaits such as tired walking and brisk walking by using parameter interpolation or motion data mapping. Since it
   is very difficult to automate the control over the emotion of a motion, the emotions of a character model have been generated by creative animators. This paper proposes a
   human running model based on a one-legged planar hopper with a self-balancing mechanism. The proposed technique exploits genetic programming to optimize movement and can be
   easily adapted to various character models. We extend the energy minimization technique to generate various motions in accordance with emotional specifications. Copyright c
   1999 John Wiley & Sons, Ltd.

%A Zhou Kang
%A Yan Li
%A Hugo {de Garis}
%A Li-Shan Kang
%T A Multi-Level And Multi-Scale Evolutionary Modeling System For Scientific Data
%B Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN'02
%D 2002
%P 737--742
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE
%C Hilton Hawaiian Village Hotel, Honolulu, Hawaii
%K genetic algorithms, genetic programming
%X The discovery of scientific laws is always built on the basis of scientific experiments and observed data. Any real world complex system must be controlled by some basic
   laws, including macroscopic level, submicroscopic level and microscopic level laws. How to discover its necessity-laws from these observed data is the most important task
   of data mining (DM) and KDD. Based on the evolutionary computation, this paper proposes a multi-level and multi -scale evolutionary modeling system which models the
   macro-behavior of the system by ordinary differential equations while models the micro- behavior of the system by natural fractals. This system can be used to model and
   predict the scientific observed time series, such as observed data of sunspot and precipitation of flood season, and always get good results.
%8 12-17  May
%Z IJCNN 2002 Held in connection with the World Congress on Computational Intelligence (WCCI 2002)
%@ 0-7803-7278-6

%A Mark Kanok
%T The Genetically Determined Dream Team
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 145--152
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Wolfgang Kantschik
%A Peter Dittrich
%A Markus Brameier
%A Wolfgang Banzhaf
%T Meta-Evolution in Graph GP
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 15--28
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=15
%X In this contribution we introduce the evolution of operators for Genetic Programming by means of Genetic Programming. Specifically, meta-evolution of recombination
   operators in graph-based GP is applied and compared to other methods for the variation of recombination operators in graph-based GP. We demonstrate that a straightforward
   application of recombination operators onto themselves does not work well. After introducing an additional level of recombination operators (the meta level) which are
   recombining a pool of recombination operators, even self-recombination on the additional becomes feasible. We show that the overall performance of this system is better
   than in other variants of graph GP. As a test problem we use speaker recognition
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP Genome is a graph. Evolves genetic operators (also represented as graphs) which act on the graphs.
%@ 3-540-65899-8

%A Wolfgang Kantschik
%A Wolfgang Banzhaf
%T Linear-Tree GP and its comparison with other GP structures
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 302--312
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Linear tree structure, GP representation, Crossover
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=302
%X In recent years different genetic programming (GP) structures have emerged. Today, the basic forms of representation for genetic programs are tree, linear and graph
   structures. In this contribution we introduce a new kind of GP structure which we call linear-tree. We describe the linear-tree-structure, as well as crossover and mutation
   for this new GP structure in detail. We compare linear-tree programs with linear and tree programs by analyzing their structure and results on different test problems.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Wolfgang Kantschik
%A Wolfgang Banzhaf
%T Linear-Graph GP---A new GP Structure
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 83--92
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%X In recent years different genetic programming (GP) structures have emerged. Today, the basic forms of representation for genetic programs are tree, linear and graph
   structures. In this contribution we introduce a new kind of GP structure which we call linear-graph, it is a further development of the linear-Tree structure. We describe
   the linear-graph structure, as well as crossover and mutation for this new GP structure in detail. We compare linear-graph programs with linear and tree programs by
   analyzing their structure and results on different test problems.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Sanjay Kapoor
%T A Variable Complexity Genetic Algorithm for Job Allocation
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 153--160
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Ilker Fatih Kara
%T Prediction of shear strength of FRP-reinforced concrete beams without stirrups based on genetic programming
%J Advances in Engineering Software
%D 2011
%I 
%K genetic algorithms, genetic programming, Gene expression programming, Fibre reinforced polymers, Shear strength, Concrete beams
%U http://www.sciencedirect.com/science/article/B6V1P-52FTG6M-1/2/cc6e82009d687d7917fc13cd45df6fc8
%X The use of fibre reinforced polymer (FRP) bars to reinforce concrete structures has received a great deal of attention in recent years due to their excellent corrosion
   resistance, high tensile strength, and good non-magnetisation properties. Due to the relatively low modulus of elasticity of FRP bars, concrete members reinforced
   longitudinally with FRP bars experience reduced shear strength compared to the shear strength of those reinforced with the same amounts of steel reinforcement. This paper
   presents a simple yet improved model to calculate the concrete shear strength of FRP-reinforced concrete slender beams (a/d > 2.5) without stirrups based on the gene
   expression programming (GEP) approach. The model produced by GEP is constructed directly from a set of experimental results available in the literature. The results of
   training, testing and validation sets of the model are compared with experimental results. All of the results show that GEP is a strong technique for the prediction of the
   shear capacity of FRP-reinforced concrete beams without stirrups. The performance of the GEP model is also compared to that of four commonly used shear design provisions
   for FRP-reinforced concrete beams. The proposed model produced by GEP provides the most accurate results in calculating the concrete shear strength of FRP-reinforced
   concrete beams among existing shear equations provided by current provisions. A parametric study is also carried out to evaluate the ability of the proposed GEP model and
   current shear design guidelines to quantitatively account for the effects of basic shear design parameters on the shear strength of FRP-reinforced concrete beams.
%O In Press, Corrected Proof

%A Vassilios K. Karakasis
%A Andreas Stafylopatis
%T Data Mining based on Gene Expression Programming and Clonal Selection
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 1621--1628
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, AIS, Gene Expression Programming
%X A hybrid evolutionary technique is proposed for data mining tasks, which combines the Clonal Selection Principle with Gene Expression Programming (GEP). The proposed
   algorithm introduces the notion of Data Class Antigens, which is used to represent a class of data. The produced rules are evolved by a clonal selection algorithm, which
   extends the recently proposed CLONALG algorithm. In the present algorithm, among other new features, a receptor editing step has been incorporated. Moreover, the rules
   themselves are represented as antibodies, which are coded as GEP chromosomes, in order to exploit the flexibility and the expressiveness of such encoding. The algorithm is
   tested on some benchmark problems of the UCI repository, and in particular on the set of MONK problems and the Pima Indians Diabetes problem. In both problems, the results
   in terms of prediction accuracy are very satisfactory, albeit slightly less accurate than those obtained by a standard GEP technique. In terms of convergence rate and
   computational efficiency, however, the technique proposed here markedly outperforms the standard GEP algorithm.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Vasileios K. Karakasis
%A Andreas Stafylopatis
%T Efficient Evolution of Accurate Classification Rules Using a Combination of Gene Expression Programming and Clonal Selection
%J IEEE Transactions on Evolutionary Computation
%V 12
%N 6
%D 2008
%P 662--678
%I 
%K genetic algorithms, genetic programming, gene expression programming, artificial immune systems, data mining, pattern classificationCLONALG algorithm, classification rules,
   clonal selection algorithm, clonal selection principle, data class antigen, data mining tasks, genotype-phenotype coincidence, hybrid evolutionary technique, immune system,
   receptor editing step
%X A hybrid evolutionary technique is proposed for data mining tasks, which combines a principle inspired by the immune system, namely the clonal selection principle, with a
   more common, though very efficient, evolutionary technique, gene expression programming (GEP). The clonal selection principle regulates the immune response in order to
   successfully recognize and confront any foreign antigen, and at the same time allows the amelioration of the immune response across successive appearances of the same
   antigen. On the other hand, gene expression programming is the descendant of genetic algorithms and genetic programming and eliminates their main disadvantages, such as the
   genotype-phenotype coincidence, though it preserves their advantageous features. In order to perform the data mining task, the proposed algorithm introduces the notion of a
   data class antigen, which is used to represent a class of data, the produced rules are evolved by our clonal selection algorithm (CSA), which extends the recently proposed
   CLONALG algorithm. In CSA, among other new features, a receptor editing step has been incorporated. Moreover, the rules themselves are represented as antibodies that are
   coded as GEP chromosomes in order to exploit the flexibility and the expressiveness of such encoding. The proposed hybrid technique is tested on a set of benchmark problems
   in comparison to GEP. In almost all problems considered, the results are very satisfactory and outperform conventional GEP both in terms of prediction accuracy and
   computational efficiency.
%8 Decemeber
%Z Also known as \cite4633339

%A Murat Karakus
%T Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP)
%J Computer \& Geosciences
%D 2010
%I 
%K genetic algorithms, genetic programming, Symbolic regression (SR), Elasticity modulus, Compressive strength, Tensile strength, Granitic rocks
%U http://www.sciencedirect.com/science/article/B6V7D-51J36C7-1/2/c4feed49145a702b62cf7ac917871262
%X Symbolic Regression (SR) analysis, employing a genetic programming (GP) approach, was used to analyse laboratory strength and elasticity modulus data for some granitic
   rocks from selected regions in Turkey. Total porosity (n), sonic velocity (vp), point load index (Is) and Schmidt Hammer values (SH) for test specimens were used to develop
   relations between these index tests and uniaxial compressive strength ([sigma]c), tensile strength ([sigma]t) and elasticity modulus (E). Three GP models were developed.
   Each GP model was run more than 50 times to optimise the GP functions. Results from the GP functions were compared with the measured data set and it was found that simple
   functions may not be adequate in explaining strength relations with index properties. The results also indicated that GP is a potential tool for identifying the key and
   optimal variables (terminals) for building functions for predicting the elasticity modulus and the strength of granitic rocks.
%O In Press, Corrected Proof

%A Ilkka Karanta
%A Topi Mikkola
%A Catherine Bounsaythip
%A Olli Jokinen
%A Juha Savola
%T Solving Wood Collection Problem using Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1787
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-757.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Hillol Kargupta
%A David E. Goldberg
%A Liwei Wang
%T Extending The Class of Order-k Delineable Problems For The Gene Expression Messy Genetic Algorithm
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Genetic Algorithms
%8 13-16  July
%Z GP-97

%A Hillol Kargupta
%T Relation learning in gene expression: Introns, variable length representation, and all that
%D 1997
%I 
%C East Lansing, MI, USA
%K genetic algorithms, introns
%O Position paper at the Workshop on Exploring Non-coding Segments and Genetics-based Encodings at ICGA-97
%8 21  July
%Z http://www.aic.nrl.navy.mil/~aswu/icga97/

%A Hillol Kargupta
%A Kakali Sarkar
%T Function Induction, Gene Expression, And Evolutionary Representation Construction
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 313--320
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-885.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Hillol Kargupta
%A B. H. Park
%T Fast construction of distributed and decomposed evolutionary representation
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 139--148
%I 
%C Orlando, Florida, USA
%K Walsh analysis
%8 13  July
%Z GECCO-99LB

%A Hillol Kargupta
%T Editorial: Computation in Gene Expression
%J Genetic Programming and Evolvable Machines
%V 3
%N 2
%D 2002
%P 111--112
%I 
%K genetic algorithms
%8 June
%Z Special issue on Gene Expression. Article ID: 408584

%A Hillol Kargupta
%A Samiran Ghosh
%T Toward Machine Learning Through Genetic Code-like Transformations
%J Genetic Programming and Evolvable Machines
%V 3
%N 3
%D 2002
%P 231--258
%I 
%K genetic algorithms, genetic code, gene expression, representation construction, machine learning
%X The gene expression process in nature involves several representation transformations of the genome. Translation is one among them; it constructs the amino acid sequence in
   proteins from the nucleic acid-based mRNA sequence. Translation is defined by a code book, known as the universal genetic code. This paper explores the role of genetic code
   and similar representation transformations for enhancing the performance of inductive machine learning algorithms. It considers an abstract model of genetic code-like
   transformations (GCTs) introduced elsewhere [21] and develops the notion of randomised GCTs. It shows that randomized GCTs can construct a representation of the learning
   problem where the mean-square-error surface is almost convex quadratic and therefore easier to minimise. It considers the functionally complete Fourier representation of
   Boolean functions to analyse this effect of such representation transformations. It offers experimental results to substantiate this claim. It shows that a linear
   classifier like the Perceptron [38] can learn non-linear XOR and DNF functions using a gradient-descent algorithm in a representation constructed by randomized GCTs. The
   paper also discusses the immediate challenges that must be solved before the proposed technique can be used as a viable approach for representation construction in machine
   learning.
%8 September
%Z Article ID: 5091790

%A Muhammad Rezaul Karim
%A Conor Ryan
%T A New Approach to Solving 0-1 Multiconstraint Knapsack Problems Using Attribute Grammar with Lookahead
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 250--261
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming, grammatical evolution: poster
%X In this paper, we introduce a new approach to genotype-phenotype mapping for Grammatical Evolution (GE) using an attribute grammar (AG) to solve 0-1 multiconstraint
   knapsack problems. Previous work on AGs dealt with constraint violations through repeated remapping of non-terminals, which generated many introns, thus decreasing the
   power of the evolutionary search. Our approach incorporates a form of lookahead into the mapping process using AG to focus only on feasible solutions and so avoid repeated
   remapping and introns. The results presented in this paper show that the proposed approach is capable of obtaining high quality solutions for the tested problem instances
   using fewer evaluations than existing methods.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Muhammad Rezaul Karim
%A Conor Ryan
%T A Simple Improvement Heuristic for Attributed Grammatical Evolution with Lookahead to Solve the Multiple Knapsack Problem
%B Proceedings of the 5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011
%S Lecture Notes in Computer Science
%E Geuk Lee and Daniel Howard and Dominik Slezak
%V 6935
%D 2011
%P 274--281
%I Springer
%C Daejeon, Korea
%K genetic algorithms, genetic programming, Grammatical Evolution
%X In this paper, we introduce a simple improvement heuristic to be used with Attribute Grammar with Lookahead approach (AG+LA), a recently proposed mapping approach for
   Grammatical Evolution (GE) using an attribute grammar (AG) to solve the Multiple Knapsack Problem (MKP). The results presented in this paper show that the proposed
   improvement heuristic can improve the quality of solutions obtained by AG+LA with little computational effort.
%8 September  22-24

%A Muhammad Karim
%A Conor Ryan
%T Degeneracy Reduction or Duplicate Elimination? An Analysis on the Performance of Attributed Grammatical Evolution with Lookahead to Solve the Multiple Knapsack Problem
%B Nature Inspired Cooperative Strategies for Optimization (NICSO 2011)
%S Studies in Computational Intelligence
%E David Pelta and Natalio Krasnogor and Dan Dumitrescu and Camelia Chira and Rodica Lung
%V 387
%D 2012
%P 247--266
%I Springer
%C Cluj-Napoca, Romania
%K genetic algorithms, genetic programming, grammatical evolution, attribute grammar
%X This paper analyses the impact of having degenerate code and duplicate elimination in an attribute grammar with look ahead (AG+LA) approach, a recently proposed mapping
   process for Grammatical Evolution (GE) using attribute grammar (AG) with a lookahead feature to solve heavily constrained multiple knapsack problems (MKP). Degenerate code,
   as used in DNA, is code in which different codons can represent the same thing. Many developmental systems, such as (GE), use a degenerate encoding to help promote neutral
   mutations, that is, minor genetic changes that do not result in a phenotypic change. Early work on GE suggested that at least some level of degeneracy has a significant
   impact on the quality of search when compared to the system with none. Duplicate elimination techniques, as opposed to degenerate encoding, are employed in decoder-based
   Evolutionary Algorithms (EAs) to ensure that the newly generated solutions are not already contained in the current population. The results and analysis show that it is
   crucial to incorporate duplicate elimination to improve the performance of AG+LA. Reducing level of degeneracy is also important to improve search performance, specially
   for the large instances of the MKP.
%O 18

%A Rikard Karlsson
%T Sound localization for a humanoid robot by means of Genetic Programming
%R M.S. Thesis
%D 1998
%I 
%I Complex Systems Group, Chalmers University of Technology
%C S-41296, G\"oteborg, Sweden
%K genetic algorithms, genetic programming, Elvis
%X A linear GP system has been used to solve the problem of sound localization for an autonomous humanoid robot, with two microphones functioning as ears. To determine the
   angle to a sound source a genetically evolved program was used in a loop over a stereo sample stream, where the genetic program gets the latest sample pair plus feedback
   from the previous run as input. The precision of the evolved genetic programs was largely dependent on the experimental setup. When training on a sawtooth wave from a fixed
   distance the smallest standard deviation of the error was 8 degrees. After letting the distance to the same sound source vary the standard deviation of the error was 23
   degrees. With a human voice as sound source at varying distances the standard deviation of the error was up to 41 degrees.
%8 Decemeber

%A Rikard Karlsson
%A Peter Nordin
%A Mats Nordahl
%T Sound Localization for a Humanoid Robot Using Genetic Programming
%B Real-World Applications of Evolutionary Computing
%S LNCS
%E Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter
   and Terence C. Fogarty
%V 1803
%D 2000
%P 65--76
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming, memory, demes
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=65
%8 17  April
%Z GP individual run iteratively, reading current inputs and its previous outputs (saved in two memories) as well as generating two real valued outputs. Training data
   presented in as time series one sample (8 or 16bits at 16kHz). 40 samples. Pop 40000 split into ten demes. Homologous crossover. Ears important. EvoWorkshops 2000: EvoIASP,
   EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61
%@ 3-540-67353-9

%A Ulya Rahmet Karpuzcu
%T Automatic Verilog Code Generation through Grammatical Evolution
%B Genetic and Evolutionary Computation Conference (GECCO2005) workshop program
%E Franz Rothlauf and Misty Blowers and J\"urgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J\"orn Grahl and Gregory Hornby and Edwin D. de Jong and
   Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor\`a and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and
   Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton
   and Alden H. Wright
%D 2005
%P 394--397
%I ACM Press
%C Washington, D.C., USA
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0394.pdf
%X We investigate the automatic generation of Verilog code, representing digital circuits through Grammatical Evolution (GE). Preliminary tests using a simple full adder
   generation problem have been performed.
%8 25-29  June
%Z Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006

%A Charles L. Karr
%A Ken Borgelt
%T Modeling A Grinding Circuit Using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1785
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-702.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Charles L. Karr
%A Barry Weck
%T Solutions to Systems of Nonlinear Equations Via Genetic Algorithm
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1786
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-703.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A F. Karr
%T Programmation g\'en\'etique pour un probl\`eme de contr\^ole, Interfa\ccage avec Maple
%R M.S. Thesis
%E M. Schoenauer
%D 1996
%I 
%I l'Ecole Polytechnique. Palaiseau
%K genetic algorithms, genetic programming, Maple
%O Rapport de stage d'option de l'Ecole Polytechnique. Palaise au
%8 Juin
%Z in French - English title would be: Genetic Programming for optimal control. Interface with Maple

%A Vijay Karunamurthy
%T A Genetic Programming Approach to the Dynamic Portfolio Rebalancing Problem
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 100--108
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/Karunamurthy.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A J. Kaschel
%A Gunnar Kobernik
%A Bernd Meier
%A Tobias Teich
%T Genetic Algorithm, Avoiding of Deadlocks and Gantt-Chart-Generation for the Job Shop Scheduling Problem
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 792
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A S. S. Kashid
%A Rajib Maity
%T Hydroclimatological Approach for Monthly Basin Scale Streamflow Prediction using Genetic Programming
%B Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009, Tumkur, Karnataka, India, December 16-18, 2009
%E Bhanu Prasad and Pawan Lingras and Ashwin Ram
%D 2009
%P 1235--1249
%I IICAI
%K genetic algorithms, genetic programming

%A Satishkumar Shahajirao Kashid
%T Basin-scale streamflow forecasting using hydrometeorological and hydroclimatological inputs
%R Ph.D. Thesis
%D 2010
%I 
%I Mumbai : IIT
%C India
%Z 043:551.482 Kas, 226166 http://www.library.iitb.ac.in/archives/Mar-01-11.html supervisor Subimal Ghosh

%A S. S. Kashid
%A Subimal Ghosh
%A Rajib Maity
%T Streamflow prediction using multi-site rainfall obtained from hydroclimatic teleconnection
%J Journal of Hydrology
%V 395
%N 1-2
%D 2010
%P 23--38
%I 
%K genetic algorithms, genetic programming, El Nino Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO), Outgoing Longwave Radiation (OLR), Mahanadi
   River, Hydroclimatic teleconnection
%U http://www.sciencedirect.com/science/article/B6V6C-51921DB-1/2/1998f2cc7e20cdc0fc4d6f78d8795381
%X Simultaneous variations in weather and climate over widely separated regions are commonly known as hydroclimatic teleconnections. Rainfall and runoff patterns, over
   continents, are found to be significantly teleconnected, with large-scale circulation patterns, through such hydroclimatic teleconnections. Though such teleconnections
   exist in nature, it is very difficult to model them, due to their inherent complexity. Statistical techniques and Artificial Intelligence (AI) tools gain popularity in
   modelling hydroclimatic teleconnection, based on their ability, in capturing the complicated relationship between the predictors (e.g. sea surface temperatures) and
   predictand (e.g., rainfall). Genetic Programming is such an AI tool, which is capable of capturing nonlinear relationship, between predictor and predictand, due to its
   flexible functional structure. In the present study, gridded multi-site weekly rainfall is predicted from El Nino Southern Oscillation (ENSO) indices, Equatorial Indian
   Ocean Oscillation (EQUINOO) indices, Outgoing Longwave Radiation (OLR) and lag rainfall at grid points, over the catchment, using Genetic Programming. The predicted
   rainfall is further used in a Genetic Programming model to predict streamflows. The model is applied for weekly forecasting of stream flow in Mahanadi River, India, and
   satisfactory performance is observed.

%A Nadav Kashtan
%A Uri Alon
%T Spontaneous evolution of modularity and network motifs
%J Proceedings of the National Academy of Sciences
%V 102
%N 39
%D 2005
%P 13773--13778
%I 
%K genetic algorithms, genetic programming, EHW, NAND, ANN, demes, parallel GA, MFINDER1.2
%U http://www.pnas.org/cgi/reprint/102/39/13773.pdf
%X Biological networks have an inherent simplicity: they are modular with a design that can be separated into units that perform almost independently. Furthermore, they show
   reuse of recurring patterns termed network motifs. Little is known about the evolutionary origin of these properties. Current models of biological evolution typically
   produce networks that are highly nonmodular and lack understandable motifs. Here, we suggest a possible explanation for the origin of modularity and network motifs in
   biology. We use standard evolutionary algorithms to evolve networks. A key feature in this study is evolution under an environment (evolutionary goal) that changes in a
   modular fashion. That is, we repeatedly switch between several goals, each made of a different combination of subgoals. We find that such modularly varying goals lead to
   the spontaneous evolution of modular network structure and network motifs. The resulting networks rapidly evolve to satisfy each of the different goals. Such switching
   between related goals may represent biological evolution in a changing environment that requires different combinations of a set of basic biological functions. The present
   study may shed light on the evolutionary forces that promote structural simplicity in biological networks and offers ways to improve the evolutionary design of engineered
   systems.
%8 September  27
%Z Elistist selection, high mutation rate, fitness parsimony genotype pressure. pop size=1000 or 2000. crossover. Goal switched every 20 generations. Z-score Z = (Nreal -
   Nrand) / sigma. Fixed genome genetic algorithm. Quantifying Modularity. Evolution with nonmodular random goals did not yield modular networks. Modularly varying give
   evolution of modularity and motifs. The two functions had shared subproblems -- modularly varying goals MVG. Rapid target function swapping -> Q=0.54 (ie high modularity).
   But typically used 11 NAND rather than 10 NAND evolved with fixed fitness target. With randomly chosen goals (ie no common sub goals) evolved networks typically are not
   modular. Modular seed rapidly loses modularity. Fixed? architecture feed forward multi-layer (4 layers) perceptron? MLP. pop size=600. Feedback (output to level -1 etc)
   allowed in NAND circuit. Every 10 generations copies of the 50 best networks from each island were added to each of the other islands, replacing eliminated networks. 4x2
   binary picture. Bifan and diamond motifs common but also some anti-motifs less common in evolved modular ANN than occurred in random ANN. Table 5. Modularity measure of
   several biological networks E. coli transcription network Neuronal network of C. elegans (threshold = 5) Signal transduction in human cells Over the course of many goal
   changes, modularly varying goals seem to guide the population toward a region of network space that contains fitness peaks for each of the goals in close proximity. This
   region seems to correspond to modular networks. High-resolution figures, a citation map, links to PubMed and Google Scholar, etc., can be found at:
   www.pnas.org/cgi/content/full/102/39/13773 Supplementary material can be found at: www.pnas.org/cgi/content/full/0503610102/DC1 This article cites 28 articles, 8 of which
   you can access for free at: www.pnas.org/cgi/content/full/102/39/13773#BIBL This article has been cited by other articles:
   www.pnas.org/cgi/content/full/102/39/13773#otherarticles

%A Nadav Kashtan
%A Elan Noor
%A Uri Alon
%T Varying environments can speed up evolution
%J Proceedings of the National Academy of Sciences
%V 104
%N 34
%D 2007
%P 13711--13716
%I 
%K genetic algorithms NAND, ANN, synthetic tRNA, biological physics, modularity, optimization, systems biology
%U http://www.pnas.org/cgi/reprint/104/34/13711
%X Simulations of biological evolution, in which computers are used to evolve systems toward a goal, often require many generations to achieve even simple goals. It is
   therefore of interest to look for generic ways, compatible with natural conditions, in which evolution in simulations can be speeded. Here, we study the impact of
   temporally varying goals on the speed of evolution, defined as the number of generations needed for an initially random population to achieve a given goal. Using computer
   simulations, we find that evolution toward goals that change over time can, in certain cases, dramatically speed up evolution compared with evolution toward a fixed goal.
   The highest speedup is found under modularly varying goals, in which goals change over time such that each new goal shares some of the subproblems with the previous goal.
   The speedup increases with the complexity of the goal: the harder the problem, the larger the speedup. Modularly varying goals seem to push populations away from local
   fitness maxima, and guide them toward evolvable and modular solutions. This study suggests that varying environments might significantly contribute to the speed of natural
   evolution. In addition, it suggests a way to accelerate optimisation algorithms and improve evolutionary approaches in engineering.
%8 21  August
%Z Not a GP but interesting?

%A Hironobu Katagiri
%A Kotaro Hirasawa
%A Jinglu Hu
%A Junichi Murata
%T Network Structure Oriented Evolutionary Model -- Genetic Network Programming--and Its Comparison with
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 179
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster, GP, Evolutionary Computation, Network Structure, Planning, Tileworld
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Hironobu Katagiri
%A Kotaro Hirasawa
%A Jinglu Hu
%A Junichi Murata
%T Network Structure Oriented Evolutionary Model-Genetic Network Programming-and its Comparison with Genetic Programming
%B 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Erik D. Goodman
%D 2001
%P 219--226
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, GNP, tileworld
%8 9-11  July
%Z GECCO-2001LB GNP form network structures. Judgement node, processing node. (predefined number of nodes, p221, data flow)

%A Hironobu Katagiri
%A Kotaro Hirasawa
%A Jinglu Hu
%A Junichi Murata
%T A New Model To Realize Variable Size Genetic Network Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 890
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, poster paper, GNP, network structure, program size, Tileworld
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Hironobu Katagiri
%A Kotaro Hirasawa
%A Jinglu Hu
%A Junichi Murata
%T A New Model to Realize Variable Size Genetic Network Programming - A Case Study with the Tileworld Problem
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 279--286
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming, GNP
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp

%A Kengo Katayama
%A Hiroyuki Narihisa
%T Iterated Local Search Approach using Genetic Transformation to the Traveling Salesman Problem
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 321--328
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-819.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Hooman Katirai
%T Filtering Junk E-Mail: A Performance Comparison between Genetic Programming and Naive Bayes
%D 1999
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/310632.html
%X This paper describes the application of genetic programming as a novel approach to the problem of filtering junk e-mail. We benchmark our results against the common
   standard: the naive Bayes classifier. While the genetically programmed classifier demonstrated a precision comparable to that of naive Bayes, it was slightly outperformed
   in recall. Since both learning methods gave similar results, it is recommended that a larger study be undertaken to ascertain whether these differences are indeed
   statistically significant. Further it is recommended that the performance of these classifiers be tested in a richer feature space more typical of real-world classifiers.
   Although the genetically programming classifier greatly outperformed the naive Bayes classifier in speed, it is concluded that a more efficient implementation of naive
   Bayes needs to be used in order to provide a fair comparison. We show that when left unabated, e-mail signatures also known as taglines reduce the value of several
   important features in junk e-mail detection; however it is also shown that these e-mail signatures may be harvested as advantageous features if some of their components are
   removed and noted as a feature. We therefore recommend that a better parser capable of meeting this criteria be implemented. To aid the reader in the theoretical aspects of
   our work, we have included introductory background for both approaches, including a full derivation of the generative naive Bayes model.
%O 4A Year student project
%8 10  September

%A Saul Kato
%T A Discrete Artificial Organic Chemistry and Search for Autocatalysis
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 54--63
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z Three diemnsional finite state cellular automata This volume contains 22 papers written and submitted by students describing their term projects for the course in
   artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Ahmad Kattan
%A Riccardo Poli
%T Evolutionary Lossless Compression with GP-ZIP
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 2468--2472
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X In this paper we propose a new approach for applying Genetic Programming to loss-less data compression based on combining well-known lossless compression algorithms. The
   file to be compressed is divided into chunks of a predefined length, and GP is asked to find the best possible compression algorithm for each chunk in such a way to
   minimise the total length of the compressed file. This technique is referred to as ''GP-zip''. The compression algorithms available to GP-zip (its function set) are:
   Arithmetic coding (AC), Lempel-Ziv-Welch (LZW), Unbounded Prediction by Partial Matching (PPMD), Run Length Encoding (RLE), and Boolean Minimisation. In addition, two
   transformation techniques are available: Burrows-Wheeler Transformation (BWT) and Move to Front (MTF). In experimentation with this technique, we show that when the file to
   be compressed is composed of heterogeneous data fragments (as is the case, for example, in archive files), GP-zip is capable of achieving compression ratios that are
   superior to those obtained with well-known compression algorithms.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Ahmed Kattan
%A Riccardo Poli
%T Evolutionary lossless compression with GP-ZIP*
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1211--1218
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, AC, Boolean minimisation BWT, GP-zip, GP-zip*, Lossless data compression, LZW, MTF, PPMD, RLE
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1211.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389333

%A Ahmed Kattan
%A Riccardo Poli
%T Genetic-Programming Based Prediction of Data Compression Saving
%B 9th International Conference, Evolution Artificielle, EA 2009
%S Lecture Notes in Computer Science
%E Pierre Collet and Nicolas Monmarche and Pierrick Legrand and Marc Schoenauer and Evelyne Lutton
%V 5975
%D 2009
%P 182--193
%I Springer
%C Strasbourg, France
%K genetic algorithms, genetic programming, Compression, Byte frequency distribution, Decision tree
%X We use Genetic Programming (GP) to generate programs that predict the data compression ratio for compression algorithms. GP evolves programs with multiple components. One
   component analyses statistical features extracted from the files' byte frequency distribution to come up with a compression ratio prediction. Another component does the
   same but by analysing statistical features extracted from the files' raw ASCII representation. A further (evolved) component acts as a decision tree to determine the
   overall output (compression ratio estimation) returned by an individual. The decision tree produces its result based on a series of comparisons among statistical features
   extracted from the files and the outputs of the two prediction components. The evolved decision tree has the choice to select either the outputs of the two compression
   prediction trees or alternatively, to integrate them into an evolved mathematical formula. Experiments with the proposed approach show that GP is able to accurately
   estimate the compression ratio of unseen files thereby avoiding the need to run multiple compressions on a file to decide which one provide best results.
%O Revised Selected Papers
%8 October  26-28
%Z EA'09 Published 2010

%A Ahmed Kattan
%A Mohammed Al-Mulla
%A Francisco Sepulveda
%A Riccardo Poli
%T Detecting Localised Muscle Fatigue during Isometric Contraction using Genetic Programming
%B International Conference on Evolutionary Computation (ICEC 2009)
%E Agostinho Rosa
%D 2009
%P 292--297
%I 
%C Madeira, Portugal
%K genetic algorithms, genetic programming
%U http://www.ahmedkattan.com/index_files/Camera_ready.pdf
%X We propose the use of Genetic Programming (GP) to generate new features to predict localised muscles fatigue from pre-filtered surface EMG signals. In a training phase, GP
   evolves programs with multiple components. One component analyses statistical features extracted from EMG to divide the signals into blocks. The blocks' labels are decided
   based on the number of zero crossings. These blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means
   clustering is applied to group similar data blocks. Each cluster is then labeled into one of three types (Fatigue, Transition-to-Fatigue and Non-Fatigue) according to the
   dominant label among its members. Once a program is evolved that achieves good classification, it can be used on unseen signals without requiring any further evolution.
   During normal operation the data are again divided into blocks by the first component of the program. The blocks are again projected onto a two-dimensional Euclidean space
   by the two other components of the program. Finally blocks are labelled according to the k-nearest neighbours. The system alerts the user of possible approaching fatigue
   once it detects a Transition-to-Fatigue. In experimentation with the proposed technique, the system provides very encouraging results.
%8 5-7  October
%Z http://www.icec.ijcci.org/Abstracts/2009/ICEC_2009_Abstracts.htm

%A Ahmed Kattan
%A Alexandros Agapitos
%A Riccardo Poli
%T Unsupervised Problem Decomposition using Genetic Programming
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 122--133
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X We propose a new framework based on Genetic Programming (GP) to automatically decompose problems into smaller and simpler tasks. The frame-work uses GP at two levels. At
   the top level GP evolves ways of splitting the fitness cases into subsets. At the lower level GP evolves programs that solve the fitness cases in each subset. The top level
   GP programs include two components. Each component receives a training case as the input. The components' outputs act as coordinates to project training examples onto a 2-D
   Euclidean space. When an individual is evaluated, K-means clustering is applied to group the fitness cases of the problem. The number of clusters is decided based on the
   density of the projected samples. Each cluster then invokes an independent GP run to solve its member fitness cases. The fitness of the lower level GP individuals is
   evaluated as usual. The fitness of the high-level GP individuals is a combination of the fitness of the best evolved programs in each of the lower level GP runs. The
   proposed framework has been tested on several symbolic regression problems and has been seen to significantly outperforming standard GP systems.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Ahmed Kattan
%A Edgar Galvan-Lopez
%A Riccardo Poli
%A Michael O'Neill
%T GP-Fileprints: File Types Detection Using Genetic Programming
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 134--145
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X We propose a novel application of Genetic Programming (GP): the identification of file types via the analysis of raw binary streams (i.e., without the use of meta data). GP
   evolves programs with multiple components. One component analyses statistical features extracted from the raw byte-series to divide the data into blocks. These blocks are
   then analysed via another component to obtain a signature for each file in a training set. These signatures are then projected onto a two-dimensional Euclidean space via
   two further (evolved) program components. K-means clustering is applied to group similar signatures. Each cluster is then labelled according to the dominant label for its
   members. Once a program that achieves good classification is evolved it can be used on unseen data without requiring any further evolution. Experimental results show that
   GP compares very well with established file classification algorithms (i.e., Neural Networks, Bayes Networks and J48 Decision Trees).
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Ahmed Kattan
%A Riccardo Poli
%T Evolutionary synthesis of lossless compression algorithms with GP-zip3
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Here we propose GP-zip3, a system which uses Genetic Programming to find optimal ways to combine standard compression algorithms for the purpose of compressing files and
   archives. GP-zip3 evolves programs with multiple components. One component analyses statistical features extracted from the raw data to be compressed (seen as a sequence of
   8-bit integers) to divide the data into blocks. These blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means
   clustering is applied to group similar data blocks. Each cluster is then labelled with the optimal compression algorithm for its member blocks. Once a program that achieves
   good compression is evolved, it can be used on unseen data without the requirement for any further evolution. GP-zip3 is similar to its predecessor, GP-zip2. Both systems
   outperform a variety of standard compression algorithms and are faster than other evolutionary compression techniques. However, GP-zip2 was still substantially slower than
   off-the-shelf algorithms. GP-zip3 alleviates this problem by using a novel fitness evaluation strategy. More specifically, GP-zip3 evolves and then uses decision trees to
   predict the performance of GP individuals without requiring them to be used to compress the training data. As shown in a variety of experiments, this speeds up evolution in
   GP-zip3 considerably over GP-zip2 while achieving similar compression results, thereby significantly broadening the scope of application of the approach.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5585956

%A Ahmed Jamil Kattan
%T Evolutionary Synthesis of Lossless Compression Algorithms: the GP-zip Family
%R Ph.D. Thesis
%D 2010
%I 
%I School of Computer Science and Electronic Engineering, University of Essex
%C UK
%K genetic algorithms, genetic programming
%U www.ahmedkattan.com/PhD.pdf
%X Data Compression algorithms have existed from almost forty years. Many algorithms have been developed. Each of which has their own strengths and weaknesses. Each works best
   with the data types they were designed to work for. No Compression algorithm can compress all data types effectively. Nowadays files with a complex internal structure that
   stores data of different types simultaneously are in common use (e.g., Microsoft Office documents, PDFs, computer games, HTML pages with online images, etc.). All of these
   situations (and many more) make lossless data compression a difficult, but increasingly significant, problem. The main motivation for this thesis was the realisation that
   the development of data compression algorithms capable to deal with heterogeneous data has significantly slowed down in the last few years. Furthermore, there is relatively
   little research on using Computational Intelligence paradigms to develop reliable universal compression systems. The primary aim of the work presented in this thesis is to
   make some progress towards turning the idea of using artificial evolution to evolve human-competitive general-purpose compression system into practice. We aim to improve
   over current compression systems by addressing their limitations in relation to heterogeneous data, particularly archive files. Our guiding idea is to combine existing,
   well-known data compression schemes in order to develop an intelligent universal data compression system that can deal with different types of data effectively. The system
   learns when to switch from one compression algorithm to another as required by the particular regularities in a file. Genetic Programming (GP) has been used to automate
   this process. This thesis contributes to the applications of GP in the lossless data compression domain. In particular we proposed a series of intelligent universal
   compression systems: the GP-zip family. We presented four members of this family, namely, GP-zip, GP-zip*, GP-zip2 and GP-zip3. Each new version addresses the limitations
   of previous systems and improves upon them. In addition, this thesis presents a new learning technique that specialised on analysing continues stream of data, detect
   different patterns within them and associate these patterns with different classes according to the user need. Hence, we extended this work and explored our learning
   technique applications to the problem of the analysing human muscles EMG signals to predict fatigue onset and the identification of file types. This thesis includes an
   extensive empirical evaluation of the systems developed in a variety of real world situations. Results have revealed the effectiveness of the systems.
%8 October

%A Ahmed Kattan
%A Riccardo Poli
%T Evolution of human-competitive lossless compression algorithms with GP-zip2
%J Genetic Programming and Evolvable Machines
%V 12
%N 4
%D 2012
%P 335--364
%I 
%K genetic algorithms, genetic programming
%X We propose GP-zip2, a new approach to loss less data compression based on Genetic Programming (GP). GP is used to optimally combine well-known loss-less compression
   algorithms to maximise data compression. GP-zip2 evolves programs with multiple components. One component analyses statistical features extracted by sequentially scanning
   the data to be compressed and divides the data into blocks. These blocks are projected onto a two-dimensional Euclidean space via two further (evolved) program components.
   K-means clustering is then applied to group similar data blocks. Each cluster is labelled with the optimal compression algorithm for its member blocks. After evolution,
   evolved programs can be used to compress unseen data. The compression algorithms available to GP-zip2 are: Arithmetic coding, Lempel-Ziv-Welch, Unbounded Prediction by
   Partial Matching, Run Length Encoding, and Bzip2. Experimentation shows that the results produced by GP-zip2 are human-competitive, being typically superior to
   well-established human-designed compression algorithms in terms of the compression ratios achieved in heterogeneous archive files.
%8 Decemeber
%Z GP chooses when to switch between 5 well known algorithms (LZW, PPMD, Run-length encoding and Bzip2) and which to use. Three trees co-evolve to do work between themselves
   (splitter tree, two feature extraction, cf \citelangdon:book). Error in previous Kattan GP-zip work. Threshold theta on splitter tree output, Davis Bouldin index (DBI).
   variable length header in output file. Compares well to WinRar. Amazon EC2 AWS cloud computer used. Model of use assumes compress once (eg DVD) uncompressed many times.
   Single v. two pass approaches. Arithmetic coding.

%A Gal Katz
%A Doron Peled
%T Model Checking-Based Genetic Programming with an Application to Mutual Exclusion
%B Tools and Algorithms for the Construction and Analysis of Systems
%S LNCS
%E C. R. Ramakrishnan and Jakob Rehof
%V 4963
%D 2008
%P 141--156
%I Springer
%C Budapest, Hungary
%K genetic algorithms, genetic programming, STGP, linear temporal logic
%X Two approaches for achieving correctness of code are verification and synthesis from specification. Evidently, it is easier to check a given program for correctness
   (although not a trivial task by itself) than to generate algorithmically correct-by-construction code. However, formal verification may give quite limited information about
   how to correct the code. Genetic programming repeatedly generates mutations of code, and then selects the mutations that remain for the next stage based on a fitness
   function, which assists in converging into a correct program. We use a model checking procedure to provide the fitness value in every stage. As an example, we generate
   algorithms for mutual exclusion, using this combination of genetic programming and model checking. The main challenge is to select a fitness function that will allow
   constructing correct solutions with minimal effort. We present our considerations behind the selection of a fitness function based not only on the classical outcome of
   model checking, i.e., the existence of an error trace, but on the complete graph constructed during the model checking process.
%O Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2008
%8 March  29-  April  6
%Z Fitness based on graph created by model checking, only 4 fitness levels per ... but then summed to give combined fitness. Mutation only. Automatic conversion between Buchi
   and Streett FSM. Approach is doublly exponential: p148 "The translation may result in a doubly exponential blowup" Deadlock and livelock detection. Parsimony for bloat
   control leads to smaller programs.

%A Gal Katz
%A Doron Peled
%T Genetic Programming and Model Checking: Synthesizing New Mutual Exclusion Algorithms
%B Automated Technology for Verification and Analysis
%S Lecture Notes in Computer Science
%V 5311
%D 2008
%P 33--47
%I Springer
%K genetic algorithms, genetic programming, SBSE, EmCTL, LTL
%U http://www.springerlink.com/content/92q064217k04531h/
%X Recently, genetic programming and model checking were combined for synthesizing algorithms that satisfy a given specification \citeKatz:2008:TACAS,\citeeurogp07:johnson. In
   particular, we demonstrated this approach by developing a tool that was able to rediscover the classical mutual exclusion algorithms \citeKatz:2008:TACAS with two or three
   global bits. In this paper we extend the capabilities of the model checking-based genetic programming and the tool built to experiment with this approach. In particular, we
   add qualitative requirements involving locality of variables and checks, which are typical of realistic mutual exclusion algorithms. The genetic process mimics the actual
   development of mutual exclusion algorithms, by starting with an existing correct solution, which does not satisfy some performance requirements, and converging into a
   solution that satisfies these requirements. We demonstrate this by presenting some nontrivial new mutual exclusion algorithms, discovered with our tool.
%Z ATVA 2008 Buchi automata. p40 EXSPACE, 2EXPTIME. Three shared bits, memory. Tarjan's strongly connected graph algorithm. Runtime <14mins.

%A Gal Katz
%A Doron Peled
%T Synthesizing Solutions to the Leader Election Problem Using Model Checking and Genetic Programming
%B 5th International Haifa Verification Conference, HVC 2009
%S Lecture Notes in Computer Science
%E Kedar S. Namjoshi and Andreas Zeller and Avi Ziv
%V 6405
%D 2009
%P 117--132
%I Springer
%C Haifa, Israel
%K genetic algorithms, genetic programming, SBSE
%X In recent papers [13,14,15], we demonstrated a methodology for developing correct-by-design programs from temporal logic specification using genetic programming. Model
   checking the temporal specification is used to calculate the fitness function for candidate solutions, which directs the search from initial randomly generated programs
   towards correct solutions. This method was successfully demonstrated by constructing solutions for the mutual exclusion problem; later, we also imposed some realistic
   constraints on access to variables. While the results were encouraging for using the genetic synthesis method, the mutual exclusion example includes some limitations that
   fit well with the constraints of model checking: the goal was finding a fixed finite state program, and its state space was moderately small. Here, in a more realistic
   setting, we challenge the problem of synthesising a solution for the well known leader election problem; under this problem, a circular, unidirectional network with message
   passing is seeking the identity of a process with a maximal value. This identity, once found, can be used for synchronisation, breaking symmetry and other network
   applications. The problem is challenging since it is parametric, and the state space of the solutions grows up exponentially with the number of processes.
%O Revised Selected Papers published 2011
%8 October  19-22
%Z Hardware and Software: Verification and Testing. Tool. mutation only, 'correct-by-design' O(n**2), 'model checking of instances in an incremental way' n 'grows with the
   fitness level of the candidate solution'. 'large confidence'. Evolved programs 'tend to be specific to some ring sizes', 'additional structural checks'.

%A Gal Katz
%A Doron Peled
%T Code Mutation in Verification and Automatic Code Correction
%B 16th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2010
%S Lecture Notes in Computer Science
%E Javier Esparza and Rupak Majumdar
%V 6015
%D 2010
%P 435--450
%I Springer
%C Paphos, Cyprus
%K genetic algorithms, genetic programming, SBSE
%X Model checking can be applied to finite state systems in order to find counterexamples showing that they do not satisfy their specification. This was generalized to handle
   parametric systems under some given constraints, usually using some inductive argument. However, even in the restricted cases where these parametric methods apply, the
   assumption is usually of a simple fixed architecture, e.g., a ring. We consider the case of nontrivial architectures for communication protocols, for example, achieving a
   multi party interaction between arbitrary subsets of processes. In this case, an error may manifest itself only under some particular architectures and interactions, and
   under some specific values of parameters. We apply here our model checking based genetic programming approach for achieving a dual task: finding an instance of a protocol
   which is suspicious of being bogus, and automatically correcting the error. The synthesis tool we constructed is capable of generating various mutations of the code. Moving
   between them is guided by model checking analysis. In the case of searching for errors, we mutate only the architecture and related parameters, and in the case of fixing
   the error, we mutate the code further in order to search for a corrected version. As a running example, we use a realistic nontrivial protocol for multiparty interaction.
   This protocol, published in a conference and a journal, is used as a building block for various systems. Our analysis shows this protocol to be, as we suspected, erroneous;
   specifically, the protocol can reach a livelock situation, where some processes do not progress towards achieving their interactions. As a side effect of our experiment, we
   provide a correction for this important protocol obtained through our genetic process.
%8 20-28  March
%Z Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2010

%A Gal Katz
%A Doron Peled
%T MCGP: A Software Synthesis Tool Based on Model Checking and Genetic Programming
%B 8th International Symposium on Automated Technology for Verification and Analysis, ATVA 2010
%S Lecture Notes in Computer Science
%E Ahmed Bouajjani and Wei-Ngan Chin
%V 6252
%D 2010
%P 359--364
%I Springer
%C Singapore
%K genetic algorithms, genetic programming
%X We present MCGP - a tool for generating and correcting code, based on our synthesis approach combining deep Model Checking and Genetic Programming. Given an LTL
   specification, genetic programming is used for generating new candidate solutions, while deep model checking is used for calculating to what extent (i.e., not only whether)
   a candidate solution program satisfies a property. The main challenge is to construct from the result of the deep model checking a fitness function that has a good
   correlation with the distance of the candidate program from a correct solution. The tool allows the user to control various parameters, such as the syntactic building
   blocks, the structure of the programs, and the fitness function, and to follow their effect on the convergence of the synthesis process.
%8 September  21-24
%Z ATVA

%A Paul Kaufmann
%A Marco Platzner
%T Advanced techniques for the creation and propagation of modules in cartesian genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1219--1226
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, automatically defined functions (ADFs), cartesian genetic programming, crossover operator, embedded cartesian genetic programming
   (ECGP), module acquisition
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1219.pdf
%X The choice of an appropriate hardware representation model is key to successful evolution of digital circuits. One of the most popular models is cartesian genetic
   programming, which encodes an array of logic gates into a chromosome. While several smaller circuits have been successfully evolved on this model, it lacks scalability. A
   recent approach towards scalable hardware evolution is based on the automated creation of modules from primitive gates. In this paper, we present two novel approaches for
   module creation, an age-based and a cone-based technique. Further, we detail a cone-based crossover operator for use with cartesian genetic programming. We evaluate the
   different techniques and compare them with related work. The results show that age-based module creation is highly effective, while cone-based approaches are only
   beneficial for regularly structured, multiple output functions such as multipliers.
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389334 Virtual FPGA. ECGP. One-row CGP (linear GP). Cones (convergent paths). Cone based crossover. 5-even parity,
   electromyographic signals. ES pop=5.

%A Paul Kaufmann
%A Tobias Knieper
%A Marco Platzner
%T A novel hybrid evolutionary strategy and its periodization with multi-objective genetic optimizers
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%X This work investigates the effects of the periodization of local and global multi-objective search algorithms. To this, we introduce a model for periodisation and define a
   new multi-objective evolutionary algorithm adopting concepts from Evolutionary Strategies and NSGA-II. We show that our method, especially when periodised with standard
   multi-objective genetic algorithms, excels for the evolution of digital circuits on the Cartesian Genetic Programming model as well as on some standard benchmarks such as
   the ZDT6.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586541

%A Devinder Kaur
%A Dustin Baumgartner
%T A Comparative Analysis of Neuro-fuzzy and Grammatical Evolution Models for Simulating Field-Effect Transistors
%B World Congress on Computer Science and Information Engineering, CSIE 2009, 2009 WRI
%E Mark Burgin and Masud H. Chowdhury and Chan H. Ham and Simone A. Ludwig and Weilian Su and Sumanth Yenduri
%D 2009
%P 179--183
%I IEEE Computer Society
%C Los Angeles, California, USA
%K genetic algorithms, genetic programming, Grammatical Evolution, Neuro Fuzzy Inference System, Field Effect Transistor Modeling
%X In this paper we have developed fuzzy inference system models for a field-effect transistor. The hope is to see if such techniques can be used for inventing future
   semiconductor based devices. Three modeling techniques have been used. Neuro Fuzzy based on grid partitioning and Neuro Fuzzy based on cluster partitioning create Sugeno
   Fuzzy Inference Systems, which are trained with a neural network back propagation method. The third modeling technique is based on Grammatical Evolution, where a grammar
   template in the form of rules is evolved using genetic algorithms based evolutionary techniques. This grammar template is based on the Mamdani Fuzzy Inference System.
   Experimental results indicate that all models produce acceptable levels of performance, some even have an error rate that is nearly negligible.
%8 March  31-  April  2

%A Nikolaos Kavvadias
%A Vasiliki Giannakopoulou
%A Spiridon Nikolaidis
%T Development of a customized processor architecture for accelerating genetic algorithms
%J Microprocessors and Microsystems
%V 31
%N 5
%D 2007
%P 347--359
%I 
%K genetic algorithms, genetic programming, 89.20.Ff, Embedded systems, Field-programmable gate arrays, Application-specific processors, Hardware description languages
%U http://www.sciencedirect.com/science/article/B6V0X-4MT5K1Y-1/2/c2a2d447c74f5cfcb3dec1eb0125163f
%X In this paper, a new programmable RISC processor architecture named VGP-I is proposed, aiming to the acceleration of genetic algorithms in embedded systems. Compared to
   other GA engines, the VGP-I specification defines a compact instruction set supporting multiple operator types, with scalable instruction encodings, programmer-visible and
   auxiliary registers and optional extensions. Apart from the programmable accelerator approach, VGP-I instructions have been tightly integrated to the Nios II soft-core
   processor as well. For performance assessment, a cycle-accurate reference VGP-I model has been developed while VGP-I subsets have been realized on a prototype
   microarchitecture and as Nios II custom instructions, both verified on programmable logic. Performance improvements on the execution of genetic operators are typically at
   the level of two orders of magnitude with application kernels written in ANSI C being accelerated by about 20 times due to the usage of GA instruction set extensions.

%A Riki Kawaguchi
%A Julia Bailey-Serres
%T mRNA sequence features that contribute to translational regulation in Arabidopsis
%J Nucleic Acids Research
%V 33
%N 3
%D 2005
%P 955--965
%I 
%K genetic algorithms, genetic programming
%X DNA microarrays were used to evaluate the regulation of the proportion of individual mRNA species in polysomal complexes in leaves of Arabidopsis thaliana under control
   growth conditions and following a mild dehydration stress (DS). The analysis determined that the percentage of an individual gene transcript in polysomes (ribosome loading)
   ranged from over 95 to <5percent. DS caused a decrease in ribosome loading from 82 to 72percent, with maintained polysome association for over 60percent of the mRNAs with
   an increased abundance. To identify sequence features responsible for translational regulation, ribosome loading values and features of full-length mRNA sequences were
   compared. mRNAs with extreme length or high GU content in the 5'-untranslated regions (5'-UTRs) were generally poorly translated. Under DS, mRNAs with both a high GC
   content in the 5'-UTR and long open reading frame showed a significant impairment in ribosome loading. Evaluation of initiation A+1UG codon context revealed distinctions in
   the frequency of adenine in nucleotides -10 to -1 (especially at -4 and -3) in mRNAs with different ribosome loading values. Notably, the mRNA features that contribute to
   translational regulation could not fully explain the variation in ribosome loading, indicating that additional factors contribute to translational regulation in
   Arabidopsis.
%Z PMID: "despite considerable effort, we were unable to identify motifs that augment or impair ribosome loading under DS using publicly available motif explore programs [e.g.
   MEME, GPRM \citeYuh-JyhHu:2003:NAR and SLASH (52)] (data not shown)."

%A H. Gunes Kayacyk
%A A. Nur Zincir-Heywood
%A Malcolm Heywood
%T Evolving Successful Stack Overflow Attacks for Vulnerability Testing
%B 21st Annual Computer Security Applications Conference (ACSAC'05)
%D 2005
%P 225--234
%I IEEE Computer Society
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.acsac.org/2005/papers/119.pdf
%X The work presented in this paper is intended to test crucial system services against stack overflow vulnerabilities. The focus of the test is the user-accessible variables,
   that is to say, the inputs from the user as specified at the command line or in a configuration file. The tester is defined as a process for automatically generating a wide
   variety of user-accessible variables that result in malicious buffers (an exploit). In this work, the search for successful exploits is formulated as an optimisation
   problem and solved using evolutionary computation. Moreover the resulting attacks are passed through the Snort misuse detection system to observe the detection (or not) of
   each exploit.
%8 Decemeber
%Z note incorrect spelling of first author

%A Hilmi Gunes Kayacik
%A Malcolm Heywood
%A Nur Zincir-Heywood
%T On evolving buffer overflow attacks using genetic programming
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 2
%D 2006
%P 1667--1674
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Real-World Applications, intrusion detection systems, linear genetic programming, mimicry attacks, security
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1667.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A H. G. Kayacik
%A A. N. Zincir-Heywood
%A M. I. Heywood
%T Automatically Evading IDS using GP Authored Attacks
%B IEEE Symposium on computational Intelligence in Security and Defense Applications
%D 2007
%P 153--160
%I IEEE Press
%C Honolulu
%K genetic algorithms, genetic programming, mimicry attack generation, vulnerability testing
%X A mimicry attack is a type of attack where the basic steps of a minimalist core attack are used to design multiple attacks achieving the same objective from the same
   application. Research in mimicry attacks is valuable in determining and eliminating weaknesses of detectors. In this work, we provide a genetic programming based automated
   process for designing all components of a mimicry attack relative to the Stide detector under a vulnerable Traceroute application. Results indicate that the automatic
   process is able to generate mimicry attacks that reduce the alarm rate from 65percent of the original attack, to 2.7percent, effectively making the attack indistinguishable
   from normal behaviors.
%8 April  1-5
%Z http://www.cidefense.org/

%A H. Gunes Kayacik
%A Malcolm Iain Heywood
%A A. Nur Zincir-Heywood
%T Evolving Buffer Overflow Attacks with Detector Feedback
%B Applications of Evolutionary Computing, EvoWorkshops2007: EvoCOMNET, EvoFIN, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, EvoTransLog
%S LNCS
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal
   Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and Shengxiang Yang
%V 4448
%D 2007
%P 11--20
%I Springer Verlag
%I EvoStar
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X A mimicry attack is an exploit in which basic behavioural objectives of a minimalist ’core’ attack are used to design multiple attacks achieving the same objective from the
   same application. Research in mimicry attacks is valuable in determining and eliminating detector weaknesses. In this work, we provide a process for evolving all components
   of a mimicry attack relative to the Stide (anomaly) detector under a Traceroute exploit. To do so, feedback from the detector is directly incorporated into the fitness
   function, thus guiding evolution towards potential blind spots in the detector. Results indicate that we are able to evolve mimicry attacks that reduce the detector anomaly
   rate from ~67percent of the original core exploit, to less than 3%, effectively making the attack indistinguishable from normal behaviours.
%8 11-13  April
%Z EvoWorkshops2007

%A Hilmi Gunes Kayacik
%T Can the Best Defense be a Good Offense? Evolving (Mimicry) Attacks for Detector Vulnerability Testing under a black-box Assumption
%R Ph.D. Thesis
%D 2009
%I 
%I Dalhousie University
%C Halifax, Nova Scotia, Canada
%K genetic algorithms, genetic programming
%U http://web.cs.dal.ca/~kayacik/PhD/GK_Thesis.pdf
%X This thesis proposes a black-box approach for automating attack generation by way of Evolutionary Computation. The proposed black-box approach employs just the anomaly rate
   or detection feedback from the detector. Assuming a black-box access in vulnerability testing presents a scenario different from a white-box access assumption, since the
   attacker does not posses sufficient knowledge to constrain the scope of the attack. As such, this thesis contributes by providing a black-box vulnerability testing tool for
   identifying detector weaknesses and aiding detector research in designing detectors which are robust against evasion attacks. The proposed approach focuses on stack buffer
   overflow attacks on a 32-bit Intel architecture and aims to optimise the various characteristics of the attack. Three components exist in a common stack buffer overflow
   attack: the shellcode, NoOP and return address components. Therefore, automation of attack generation is realised in three stages: (1) identifying the suitable NoOP and
   return address components, (2) designing the shellcode at the assembly level, and (3) designing the shellcode at the system call level. The first and second stage address
   the evasion of misuse detectors by employing obfuscation, whereas the third stage addresses the evasion of anomaly detectors by employing mimicry attacks. In short, the
   proposed approach takes the form of a black-box search process where the attacks are rewarded according to two main criteria: (a) their ability to carry out the malicious
   intent, while (b) minimising or eliminating the detectable attack characteristics. Furthermore, it is demonstrated that there are two parts to buffer overflow attacks: (i)
   the preamble and (ii) the exploit. Therefore, the anomaly rate of the whole attack is calculated on both parts. Additionally, the proposed approach supports multi-objective
   optimisation, where multiple characteristics of attacks can be improved. The proposed approach is evaluated against six detectors and four vulnerable applications. The
   results show that attacks which the proposed approach generates under a black-box assumption are as effective as the attacks in generated under a white-box assumption
   adopted by previous work.
%8 March
%Z slides: http://web.cs.dal.ca/~kayacik/PhD/GK_Defense_Slides.pdf

%A H. {Gunes Kayacik}
%A A. Nur Zincir-Heywood
%A Malcolm I. Heywood
%A Stefan Burschka
%T Generating mimicry attacks using genetic programming: A benchmarking study
%B IEEE Symposium on Computational Intelligence in Cyber Security, CICS '09
%D 2009
%P 136--143
%I 
%K genetic algorithms, genetic programming, benchmark testing, black-box approach, commodity anomaly detection system, evolutionary mimicry attack generation, intrusion
   detection, multiobjective genetic programming, open-source anomaly detection system, penetration testing, target anomaly detection, vulnerability testing approach,
   vulnerable UNIX application, benchmark testing, program testing, security of data
%X Mimicry attacks have been the focus of detector research where the objective of the attacker is to generate multiple attacks satisfying the same generic exploit goals for a
   given vulnerability. In this work, multi-objective Genetic programming is used to establish a 'black-box' approach to mimicry attack generation. No knowledge is made of
   internal data structures of the target anomaly detector, only the anomaly rate reported by the detector. Such a 'black box' methodology enables a vulnerability testing
   approach where both open-source and commodity anomaly detection systems can be tested. The approach successfully identifies exploits when benchmarked over four detectors
   and four applications.
%8 30  March -  April  2
%Z Also known as \cite4925101

%A Hilmi Gunes Kayacik
%A A. Nur Zincir-Heywood
%A Malcolm I. Heywood
%T Can a good offense be a good defense? Vulnerability testing of anomaly detectors through an artificial arms race
%J Applied Soft Computing
%V 11
%N 7
%D 2011
%P 4366--4383
%I 
%K genetic algorithms, genetic programming, Computer security, Intrusion detection, Evasion attacks, Arms race
%U http://www.sciencedirect.com/science/article/B6W86-517J230-1/2/84e06f47c1845a8bc71256b74a86b16d
%X Intrusion detection systems, which aim to protect our IT infrastructure are not infallible. Attackers take advantage of detector vulnerabilities and weaknesses to evade
   detection, hence hindering the effectiveness of the detectors. To do so, attackers generate evasion attacks which can eliminate or minimise the detection while successfully
   achieving the attacker's goals. This work proposes an artificial arms race between an automated white-hat attacker and various anomaly detectors for the purpose of
   identifying detector weaknesses. The proposed arms race aims to automate the vulnerability testing of the anomaly detectors so that the security experts can be more
   proactive in eliminating detector vulnerabilities.
%8 October

%A Hilmi Gunes Kayacik
%A A. Nur Zincir-Heywood
%A Malcolm I. Heywood
%T Evolutionary computation as an artificial attacker: generating evasion attacks for detector vulnerability testing
%J Evolutionary Intelligence
%V 4
%N 4
%D 2011
%P 243--266
%I Springer
%K genetic algorithms, genetic programming, Engineering, Computer security, Intrusion detection, Anomaly detection, Evasion attacks, Evolutionary computation, Artificial arms
   race
%X Intrusion detection systems protect our infrastructures by monitoring for signs of intrusions. However, intrusion detection systems are themselves susceptible to
   vulnerabilities, which the attackers take advantage of to evade detection. In particular, we focus on evasion attacks in which the attacker aims to generate a stealthy
   attack that eliminates or minimises the likelihood of detection. Attackers achieve stealth by mimicking normal behaviour while achieving the attack goals, hence bypassing
   the detector. Previous work focused on generating evasion attacks using the internal knowledge of the detectors, hence adopting a 'white-box' access to the detector. On the
   other hand, we adopt a 'black-box' approach and propose an evolutionary attacker based on Genetic Programming. The access of our 'black-box' approach is limited to the
   feedback of the detector such as anomaly rates and delays. We compare our black-box approach with various white-box approaches to investigate its effectiveness. In doing
   so, the impact of anomalies from the break-in stage of the attacks and the delays based on locality frame counts are also discussed. This is particularly important if the
   performance comparison is to reflect the real capabilities of detectors.
%8 Decemeber

%A C. Kayadelen
%A O. Gunaydin
%A M. Fener
%A A. Demir
%A A. Ozvan
%T Modeling of the angle of shearing resistance of soils using soft computing systems
%J Expert Systems with Applications
%V 36
%N 9
%D 2009
%P 11814--11826
%I 
%K genetic algorithms, genetic programming, Genetic expression programming, Neural networks, Adaptive Neuro Fuzzy, Angle of shearing resistance of soils
%U http://www.sciencedirect.com/science/article/B6V03-4W3HX4S-1/2/c5bf935abc4eb5a42707a84dd6e518ea
%X Precise determination of the effective angle of shearing resistance ([phi]') value is a major concern and an essential criterion in the design process of the geotechnical
   structures, such as foundations, embankments, roads, slopes, excavation and liner systems for the solid waste. The experimental determination of [phi]' is often very
   difficult, expensive and requires extreme cautions and labour. Therefore many statistical and numerical modelling techniques have been suggested for the [phi]' value.
   However they can only consider no more than one parameter, in a simplified manner and do not provide consistent accurate prediction of the [phi]' value. This study explores
   the potential of Genetic Expression Programming, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) computing paradigm in the prediction of [phi]' value of
   soils. The data from consolidated-drained triaxial tests (CID) conducted in this study and the different project in Turkey and literature were used for training and testing
   of the models. Four basic physical properties of soils that cover the percentage of fine grained (FG), the percentage of coarse grained (CG), liquid limit (LL) and bulk
   density (BD) were presented to the models as input parameters. The performance of models was comprehensively evaluated some statistical criteria. The results revealed that
   GEP model is fairly promising approach for the prediction of angle of shearing resistance of soils. The statistical performance evaluations showed that the GEP model
   significantly outperforms the ANN and ANFIS models in the sense of training performances and prediction accuracies.

%A Saheeb Ahmed Kayani
%A Muhammad Afzaal Malik
%T Automated Design of Mechatronic Systems using Bond-Graph Modeling and Simulation and Genetic Programming
%B International Bhurban Conference on Applied Sciences Technology, IBCAST 2007
%D 2007
%P 104--110
%I 
%K genetic algorithms, genetic programming, advanced object oriented modeling, advanced object oriented simulation, bond-graph modeling, mechatronic systems, state equations,
   bond graphs, mechatronics
%X All modern dynamic engineering systems can be characterized as mechatronic systems. The multi-domain nature of a mechatronic system makes it difficult to model using a
   single modeling technique over the whole system as varying sets of system variables are required. Bond-Graphs offer an advanced object oriented modeling and simulation
   technique. They are domain independent allowing straight forward and efficient model composition, classification and analysis. Bond-Graph model of the mechatronic system
   can be directly simulated on a digital computer using simulation software like 20-Sim and Modelica graphically or manipulated mathematically to yield state equations using
   a simplified set of power and energy variables. The simulation scheme can be augmented to synthesize designs for mechatronic systems using genetic programming as a tool for
   open ended search. This research paper presents results of experiments conducted to combine Bond-Graph modeling and simulation with genetic programming. A comprehensive
   review of the methodology is also included and the results are compared using different simulation softwares and conclusions drawn by research groups working on mechatronic
   systems and genetic programming internationally.
%8 January
%Z Also known as \cite4379917

%A Saheeb Ahmed Kayani
%A Muhammad Afzaal Malik
%T Combining bond-graphs with genetic programming for unified/automated design of mechatronic or multi domain dynamic systems
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2007)
%E Peter A. N. Bosman
%D 2007
%P 2515--2518
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, bond graphs, multi domain dynamic or mechatronic systems, unified/automated design, verification
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2515.pdf
%X The multi domain nature of a mechatronic system makes it difficult to model using a single modelling technique over the whole system as varying sets of system variables are
   required. Bond-Graphs offer an advanced object oriented and polymorphic modeling and simulation technique. Bond-Graph model of the mechatronic system can be directly
   simulated on a digital computer using simulation software like 20-Sim graphically or manipulated mathematically to yield state equations using a simplified set of power and
   energy variables. The simulation scheme can be augmented to synthesise designs for mechatronic systems employing genetic programming as a tool for open ended search. This
   research paper presents results of an experiment developed to combine Bond-Graphs with genetic programming for unified and automated design of mechatronic or multi domain
   dynamic systems.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A Saheeb Ahmed Kayani
%T Search for human competitive results in open ended automated synthesis of a primordial mechatronic system
%B GECCO-2008 Graduate Student Workshops
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 1827--1830
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, Bond-graphs, dynamic analysis, multi domain dynamic or Mechatronic systems, Physical design Realization, topology synthesis,
   unified/automated design
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1827.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1388981

%A Saheeb Ahmed Kayani
%A Muhammad Afzaal Malik
%T Bond-graphs + genetic programming: analysis of an automatically synthesized rotary mechanical system
%B GECCO-2008 Late-Breaking Papers
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 2165--2168
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, Bond-graphs, dynamic analysis, dynect oriented modelling, multi energy domain dynamic or Mechatronic systems, Physical design
   Realization, Rotary Mechanical systems, topology synthesis, unified/automated design
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2165.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1389041

%A Saheeb Ahmed Kayani
%T Theoretical foundations of automated synthesis using Bond-Graphs and genetic programming
%B 4th International Conference on Emerging Technologies, ICET 2008
%D 2008
%P 11--16
%I 
%C Rawalpindi, Pakistan
%K genetic algorithms, genetic programming, Darwinian evolution concept, Darwinian natural selection concept, automated invention machine, automated mechatronic system design
   methodology, automated mechatronic system synthesis, bond graph modeling, bond graph simulation, computer program, dynamic system, evolutionary computation, feedback
   scheme, fuzzy logic, genetic programming paradigm, machine intelligence, multienergy domain character, neural network, object-oriented polymorphic graphical description,
   physical system design, search method, unified notation scheme, bond graphs, digital simulation, mechanical engineering computing, mechatronics, object-oriented programming
%X Automated synthesis refers to design of physical systems using any of the models proposed for machine intelligence like evolutionary computation, neural networks and fuzzy
   logic. Mechatronic systems are mixed or hybrid systems as they combine elements from different energy domains. These dynamic systems are inherently complex and capturing
   underlying energy behavior among interacting sub-systems is difficult owing to the variety in the composition of the mechatronic systems and also due to the limitation
   imposed by conventional modeling techniques unable to handle more than one energy domain. Bond-graph modeling and simulation is an advanced domain independent, object
   oriented and polymorphic graphical description of physical systems. The universal modeling paradigm offered by bond-graphs is well suited for mechatronic systems as it can
   represent their multi energy domain character using a unified notation scheme. Genetic programming is one of the most promising evolutionary computation techniques. The
   genetic programming paradigm is modeled on Darwinian concepts of evolution and natural selection. Genetic programming starts from a high level statement of a problem's
   requirements along with a fitness criterion and attempts to produce a computer program that provides a solution to the problem. Combining unified modeling and analysis
   tools offered by bond-graphs with topologically open ended synthesis and search capability of genetic programming, a novel automated design methodology has been developed
   for generating mechatronic systems designs using an integrated synthesis, analysis and feedback scheme which comes close to the definition of a true automated invention
   machine. This research paper develops a theoretical foundation for automated synthesis and design of mechatronic systems using bond-graphs and genetic programming.
%8 18-19  October
%Z Also known as \cite4777466

%A Jan Kazimierczak
%T An Approach to Evolvable Hardware representing the Knowledge Base in an Automatic Programming System
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 492--497
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Evolvable Hardware
%8 13-16  July
%Z GP-97

%A Martin A. Keane
%A John R. Koza
%A James P. Rice
%T Finding an impulse response function using genetic programming
%B Proceedings of the 1993 American Control Conference
%V III
%D 1993
%P 2345--2350
%I 
%I American Automatic Control Council
%C Evanston, IL, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/acc1993.pdf
%X For many practical problems of control engineering, it is desirable to find a function, such as the impulse response function or transfer function, for a system for which
   one does not have an analytical model. The finding of the function, in symbolic form, that satisfies the requirements of the problem (rather than merely finding a single
   point) is usually not possible when one does not have an analytical model of the system. This paper illustrates how the recently developed genetic programming paradigm, can
   be used to find an approximation to the impulse response, in symbolic form, for a linear time-invariant system using only the observed response of the system to a
   particular known forcing function. The method illustrated can then be applied to other problems in control engineering that require the finding of a function in symbolic
   form.

%A Martin A. Keane
%A Jessen Yu
%A John R. Koza
%T Automatic Synthesis of Both Topology and Tuning of a Common Parameterized Controller for Two Families of Plants using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 496--504
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/499019.html
%X This paper demonstrates that genetic programming can be used to automatically create the design for both the topology and parameter values (tuning) for a common
   parameterized controller for all the plants in two families of plants that are representative of typical industrial processes. The genetically evolved controller is
   "general" in the sense that it contains free variables representing the characteristics of the particular plant. The genetically evolved controller outperforms the
   controller designed with conventional techniques. In addition, the genetically evolved controller infringes on an early patented invention in the field of control
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Jonathon Keats
%T The New Explorers John Koza Has Built an Invention Machine Its creations earn patents, outperform humans, and will soon fly to space. All it needs now is a few worthy
   challenges
%J Popular Science Magazine
%D 2006
%I 
%K genetic algorithms, genetic programming
%U http://www.popsci.com/popsci/thenewexplorers/
%X Its creations earn patents, outperform humans, and will soon fly to space. All it needs now is a few worthy challenges
%8 April
%Z See also http://tech.groups.yahoo.com/group/genetic_programming/message/4002

%A David Keaveney
%A Colm O'Riordan
%T Evolving robust strategies for an abstract real-time strategy game
%B IEEE Symposium on Computational Intelligence and Games, CIG 2009
%D 2009
%P 371--378
%I 
%K genetic algorithms, genetic programming, abstract real-time strategy game, imperfect spatial information, multiple spatial environments, parallel turns, progressive
   refinement planning technique, robust strategies, turn-based strategy game, game theory, games of skill, strategic planning
%X This paper presents an analysis of evolved strategies for an abstract real-time strategy (RTS) game. The abstract RTS game used is a turn-based strategy game with
   properties such as parallel turns and imperfect spatial information. The automated player used to learn strategies uses a progressive refinement planning technique to plan
   its next immediate turn during the game. We describe two types of spatial tactical coordination which we posit are important in the game and define measures for both. A set
   of ten strategies evolved in a single environment are compared to a second set of ten strategies evolved across a set of environments. The robustness of all of evolved
   strategies are assessed when playing each other in each environment. Also, the levels of coordination present in both sets of strategies are measured and compared. We wish
   to show that evolving across multiple spatial environments is necessary to evolve robustness into our strategies.
%8 September
%Z Also known as \cite5286453

%A David Keaveney
%A Colm O'Riordan
%T Evolving Coordination for Real-Time Strategy Games
%J IEEE Transactions on Computational Intelligence and AI in Games
%V 3
%N 2
%D 2011
%P 155--167
%I 
%K genetic algorithms, genetic programming, abstract real-time strategy game, automated player, board game, evolutionary computation, progressive refinement planning,
   real-time strategy games, computer games, evolutionary computation
%X The aim of this work is to show that evolutionary computation techniques (genetic programming in this case) can be used to evolve coordination in real-time strategy games.
   An abstract real-time strategy game is used for our experiments, similar to a board game but with many of the properties that define real-time strategy games. We develop an
   automated player that uses a progressive refinement planning technique when determining its next immediate turn in our abstract real-time strategy game. We describe two
   types of coordination which we believe are important in the game and then define measurements for both. We perform twenty co-evolutionary runs for our automated player and
   then analyse the history of each run with respect to the success of the solutions found and their level of coordination. We wish to show that as the evolutionary process
   progresses both the quality and the level of coordination in the solutions found increases.
%8 June
%Z Also known as \cite5755185

%A Christian Keber
%A Matthias G. Schuster
%T Option Valuation With Generalized Ant Programming
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 74--81
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, artificial life, adaptive behavior, agents, ant colony optimization, ant algorithm, ant programming, option valuation, symbolic
   regression
%U ftp://cs.ucl.ac.uk/genetic/papers/gecco2002/gecco-2002-01.pdf
%X For the valuation of American put options exact pricing formulas haven't as yet been derived We therefore determine analytical approximations for pricing such options by
   introducing the Generalised Ant Programming (GAP) approach applicable to all problems in which the search space of feasible solutions consists of computer programs. GAP is
   a new method inspired by Genetic Programming as well as by Ant Algorithms. Applying our GAP-approximations for the valuation of American put options on non-dividend paying
   stocks to experimental data as well as huge validation data sets we can show that our formulas deliver accurate results and outperform other formulas presented in the
   literature.
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Edward Keedwell
%A Ajit Narayanan
%A Dragan Savic
%T Using Genetic Algorithms to Extract Rules From Trained Neural Networks
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 793
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Ga-805.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Edward Keedwell
%A Ajit Narayanan
%T Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems
%D 2005
%P 221--237
%I Wiley
%K genetic algorithms, genetic programming
%U http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470021756.html
%O 9
%Z PART 3: FUTURE TECHNIQUES 9. Genetic Programming. 9.1 Method. 9.2 Application guidelines. 9.3 Bioinformatics applications. 9.4 Background. 9.5 Summary of chapter. 9.6
   References. Eg \citelangdon:2004:ECDM, \citeKell:2000:GECCO
%@ 0-470-02175-6

%A Nick Keenan
%T Statistical Investigations of Genetic Algorithms and Genetic Programming
%D 1993
%P 22
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/ICGA-93-GP-Abstracts.ps.Z
%O Notes from Genetic Programming Workshop at ICGA-93
%Z "This analysis implies that there is a finite limit to the effectiveness of genetic programming."

%A Maarten Keijzer
%T Efficiently Representing Populations in Genetic Programming
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 259--278
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/276
%X The chapter compares two representations for genetic programming. One is the commonly used Lisp S-Expression which uses the problem specific terminals and functions defined
   before a run as an alphabet. The other is a minimal Directed Acyclic Graph (DAG) that uses a variable alphabet of complete subtrees. This chapter will show that the DAG
   representation can replace S-Expression representation without any change in the functionality of a genetic programming system. In certain situations the amount of memory
   needed to represent a population can be reduced enormously when using a DAG. The implementation of Automatically Defined Functions (ADFs) in a DAG gives rise to the
   definitions of a divergent ADF, and a compact ADF. The latter can represent huge programs in S-Expression format with a few elements.
%O 13
%@ 0-262-01158-1

%A Maarten Keijzer
%T Implicitly Defined Functions as an alternative to GP-schemata
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 107--111
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Maarten Keijzer
%A Vladan Babovic
%T Dimensionally Aware Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1069--1076
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-420.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Maarten Keijzer
%T Genetic Programming in Hydraulic Engineering
%B 3rd DHI Software Conference \& DHI Software Courses
%D 1999
%I 
%I Danish Hydraulic Institute
%C Helsingor, Denmark
%K genetic algorithms, genetic programming
%U http://www.dhi.dk/softcon/abstract/105.doc
%X Genetic Programming (Koza 1993), is a general method for the induction of computer programs by training. Applications of genetic programming include but are not limited by:
   symbolic regression, decision tree induction, robot control, feature detection and system identifictation. This paper will describe some of the unique aspects of genetic
   programming in the field of system identification and will give an example in sediment transport
%8 7-11  June
%Z http://www.dhi.dk/softcon/index.htm

%A Maarten Keijzer
%T Scientific Discovery using Genetic Programming
%B GECCO-99 Student Workshop
%E Una-May O'Reilly
%D 1999
%P 365--366
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, data mining, scientific discovery
%U http://projects.dhi.dk/d2k/Publications/GPinSD.htm
%X One of the greatest challenges facing organisations and individuals is how to turn their rapidly expanding data stores into accessible, and actionable knowledge (Fayyad et
   al, 1996). Knowledge Discovery in Databases (KDD) is concerned with extracting such useful information from data stores. We view data mining (DM) as a step in this larger
   process called the KDD process. In a DM step one can use genetic programming (GP) (Koza, 1992; Babovic 1996).
%8 13  July
%Z GECCO-99WKS Part of wu:1999:GECCOWKS

%A Maarten Keijzer
%A Vladan Babovic
%T Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff - Introductory Investigations
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 76--90
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=76
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Maarten Keijzer
%A Vladan Babovic
%T Genetic Programming within a Framework of Computer-Aided Discovery of Scientific Knowledge
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 543--550
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW091.pdf
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Maarten Keijzer
%A Conor Ryan
%A Michael O'Neill
%A Mike Cattolico
%A Vladan Babovic
%T Ripple Crossover in Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 74--86
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, grammatical evolution, Context Free Grammars, Crossover, Intrinsic Polymorphism
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=74
%X This paper isolates and identifies the effects of the crossover operator used in Grammatical Evolution. This crossover operator has already been shown to be adept at
   combining useful building blocks and to outperform engineered crossover operators such as Homologous Crossover. This crossover operator, Ripple Crossover is described in
   terms of Genetic Programming and applied to two benchmark problems. Its performance is compared with that of traditional sub-tree crossover on populations employing the
   standard functions and terminal set, but also against populations of individuals that encode Context Free Grammars. Ripple crossover is more effective in exploring the
   search space of possible programs than sub-tree crossover. This is shown by examining the rate of premature convergence during the run. Ripple crossover produces
   populations whose fitness increases gradually over time, slower than, but to an eventual higher level than that of sub-tree crossover.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A M. Keijzer
%A V. Babovic
%A C. Ryan
%A M. O'Neill
%A M. Cattolico
%T Adaptive Logic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 42--49
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, logic programming, grammatical evolution, units of, measurement, strong typing
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Maarten Keijzer
%A J. J. Merelo
%A G. Romero
%A M. Schoenauer
%T Evolving Objects: a general purpose evolutionary computation library
%B EA-01, Evolution Artificielle, 5th International Conference in Evolutionary Algorithms
%D 2001
%P 231--244
%I 
%K genetic algorithms, genetic programming
%U http://www.lri.fr/~marc/EO/EO-EA01.ps.gz
%X his paper presents the evolving objects library (EOlib), an object-oriented framework for evolutionary computation (EC) that aims to provide a flexible set of classes to
   build EC applications. EOlib design objective is to be able to evolve any object in which fitness makes sense.

%A Maarten Keijzer
%T Scientific Discovery using Genetic Programming
%R Ph.D. Thesis
%D 2002
%I 
%I Danish Technical University
%C Lyngby, Denmark
%K genetic algorithms, genetic programming
%U http://www.cs.vu.nl/~mkeijzer/publications/thesis/
%X Genetisk programmering er i stand til at producere computer programmer, automatisk pa baggrund af eksempler pa programmernes virkning i en simulering. Da matematiske udtryk
   er en veldefineret delmangde af symbolske computer programmer og kan disse ogsa bestemmes under genetisk programmerings paradigmet. Empirisk bestemmelse af matematiske
   udtryk kaldes symbolsk regression. I dette arbejde bliver genetisk programmering udvidet til, et varktoj der ikke bare "fitter data", men ogsa giver korrekte fysiske
   dimensioner. De vasentligste bidrag i dette arbejde opsummeres ved: Symbolske udtryk, udledt ved hjalp af genetisk programmering kan gores tilgangelige for analyse og
   fortolkning, ved at lade dimensionsbetragtninger stotte eller begranse sogerummet. Dette er opnaet ved at Et standard genetisk programmerings-varktoj er blevet modificeret
   til at producerer udtryk som hovedsagligt er dimensionelt konsistente. Dette modificerede system er anvendt til at malrette genetisk sogning mod dimensionelt korrekte
   udtryk via sakaldt "preferential bias". Et nyt genetisk programmeringsvarktoj er blevet introduceret, som kan producere udtryk baseret pa kontekst-folsomme bibetingelser.
   Det er blevet demonstreret at dette system kan implementere malrettet sogning som via sakaldt "declarative bias" giver mulighed for at
%8 March

%A Maarten Keijzer
%A Michael O'Neill
%A Conor Ryan
%A Mike Cattolico
%T Grammatical Evolution Rules: The mod and the Bucket Rule
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 123--130
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming, grammatical evolution
%X We present an alternative mapping function called the Bucket Rule, for Grammatical Evolution, that improves upon the standard modulo rule. Grammatical Evolution is applied
   to a set of standard Genetic Algorithm problem domains using two alternative grammars. Applying GE to GA problems allows us to focus on a simple grammar whose effects are
   easily analysable.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Maarten Keijzer
%A Vladan Babovic
%T Declarative and Preferential Bias in GP-based Scientific Discovery
%J Genetic Programming and Evolvable Machines
%V 3
%N 1
%D 2002
%P 41--79
%I 
%K genetic algorithms, genetic programming, symbolic regression, strong typing, coercion typing, empirical equations, hydraulics
%X This work examines two methods for evolving dimensionally correct equations on the basis of data. It is demonstrated that the use of units of measurement aids in evolving
   equations that are amenable to interpretation by domain specialists. One method uses a strong typing approach that implements a declarative bias towards correct equations,
   the other method uses a coercion mechanism in order to implement a preferential bias towards the same objective. Four experiments using real-world, unsolved scientific
   problems were performed in order to examine the differences between the approaches and to judge the worth of the induction methods. Not only does the coercion approach
   perform significantly better on two out of the four problems when compared to the strongly typed approach, but it also regularizes the expressions it induces, resulting in
   a more reliable search process. A trade-off between type correctness and ability to solve the problem is identified. Due to the preferential bias implemented in the
   coercion approach, this trade-off does not lead to sub-optimal performance. No evidence is found that the reduction of the search space achieved through declarative bias
   helps in finding better solutions faster. In fact, for the class of scientific discovery problems the opposite seems to be the case.
%8 March
%Z Article ID: 395989

%A Maarten Keijzer
%A Mike Cattolico
%T An example of the use of context-sensitive constraints in the ALP system
%B GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2002
%P 128--132
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York
%K genetic algorithms, genetic programming, grammatical evolution
%8 8  July
%Z Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic
   Programming Conference (GP-2002) part of barry:2002:GECCO:workshop

%A Maarten Keijzer
%T Improving Symbolic Regression with Interval Arithmetic and Linear Scaling
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 70--82
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=70
%X The use of protected operators and squared error measures are standard approaches in symbolic regression. It will be shown that two relatively minor modifications of a
   symbolic regression system can result in greatly improved predictive performance and reliability of the induced expressions. To achieve this, interval arithmetic and linear
   scaling are used. An experimental section demonstrates the improvements on 15 symbolic regression problems.
%8 14-16  April
%Z Cited by \citeNi:2012:ieeeTEC. EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%T Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=issue&issn=0302-9743&volume=3003
%8 5-7  April
%Z EuroGP'2004
%@ 3-540-21346-5

%A Maarten Keijzer
%T Alternatives in Subtree Caching for Genetic Programming
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 328--337
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=328
%X We examine a number of subtree caching mechanisms that are capable of adapting during the course of a run while maintaining a fixed size cache of already evaluated
   subtrees. A cache update and flush mechanism is introduced as well as the benefits of vectorised evaluation over the standard case-by-case evaluation method for interpreted
   genetic programming systems are discussed. The results show large benefits for the use of even very small subtree caches. One of the approaches studied here can be used as
   a simple add-on module to an existing genetic programming system, providing an opportunity to improve the runtime efficiency of such a system.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%T Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, ACO, GEP, GNP
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/
%8 26  July
%Z GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A Maarten Keijzer
%T Scaled Symbolic Regression
%J Genetic Programming and Evolvable Machines
%V 5
%N 3
%D 2004
%P 259--269
%I 
%K genetic algorithms, genetic programming, linear regression, symbolic regression
%X Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of
   linear regression are reviewed on their applicability for use in symbolic regression. It will be proven that the use of a scaled error measure, in which the error is
   calculated after scaling, is expected to perform better than its unscaled counterpart on all possible symbolic regression problems. As the method (i) does not introduce
   additional parameters to a symbolic regression run, (ii) is guaranteed to improve results on most symbolic regression problems (and is not worse on any other problem), and
   (iii) has a well-defined upper bound on the error, scaled squared error is an ideal candidate to become the standard error measure for practical applications of symbolic
   regression.
%8 September
%Z Article ID: 5272971

%T Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%I Springer Berlin
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=issue&issn=0302-9743&volume=3447
%8 30  March  - 1  April
%Z EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Maarten Keijzer
%A Conor Ryan
%A Gearoid Murphy
%A Mike Cattolico
%T Undirected Training of Run Transferable Libraries
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 361--370
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=361
%X This paper investigates the robustness of Run Transferable Libraries(RTLs) on scaled problems. RTLs are provide GP with a library of functions which replace the usual
   primitive functions provided when approaching a problem. The RTL evolves from run to run using feedback based on function usage, and has been shown to outperform GP by an
   order of magnitude on a variety of scalable problems. RTLs can, however, also be applied across a em domain of related problems, as well as across a range of scaled
   instances of a single problem. To this successfully, it will need to balance a range of functions. We introduce a problem that can deceive the system into converging to a
   sub-optimal set of functions, and demonstrate that this is a consequence of the greediness of the library update algorithm. We demonstrate that a much simpler, truly
   evolutionary, update strategy doesn't suffer from this problem, and exhibits far better optimisation properties than the original strategy.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Maarten Keijzer
%A Martin Baptist
%A Vladan Babovic
%A Javier {Rodriguez Uthurburu}
%T Determining equations for vegetation induced resistance using genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1999--2006
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Real World Applications, equation induction, hydraulics, hydrology, measurement units
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1999.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052 7. CONCLUSIONS Four formulations for waterdepth-related resistance induced by vegetation are studied and compared. Two have been explicitly derived by a
   scientist building upon the extensive literature on this subject. One expression was derived by dimensionally aware genetic programming, and finally one expression was
   created by manually analysing and improving the genetic programming equation. It was found that the genetic programming equations were superior to the manually derived
   equations, both on their performance on synthetic training and laboratory testing data, and in the economy of detail that needs to be modelled. The manually improved
   expression was found to be in good agreement with previous expressions found in the literature, and performed competitive with the detailed model that was used to generate
   the training data.
%@ 1-59593-010-8

%T GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%D 2006
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, algorithms, design, experimentation, performance
%U http://portal.acm.org/citation.cfm?id=1143997
%X These proceedings contain the papers presented at the 8th Annual Genetic and Evolutionary Computation COnference (GECCO-2006), held in Seattle, Washington, USA., July 8-12,
   2006. In our second year sponsored by the ACM Special Interest Group on Evolutionary Computation (SIGEVO), we've seen a further evolution of the field toward practical
   importance. Although the main genetic algorithm track remains the largest in the number of submitted papers, the real world applications track is starting to close the gap
   rapidly. As an ACM publication, the GECCO-2006 proceedings are available online in the ACM Digital Library. This guarantees a broader dissemination of Darwinian and other
   nature-inspired computation methods, and will likely increase the relevance of the field even further. A total of 446 papers were submitted to 15 separate tracks, with 205
   (46percent) accepted as full, eight-page papers for publication and oral presentation. Double-blind reviews were conducted by over 400 reviewers. On average, each paper was
   evaluated by four reviewers. In addition, 143 papers were accepted as posters with two-page abstracts included in the proceedings. With 10 workshops, 32 tutorials, sessions
   in Evolutionary Computation in Practice, late-breaking papers, awards in human-competitive results, GECCO-2006 has lived up to its motto of one conference, many
   miniconferences. Also this year's GECCO thrives on diversity.
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060 Also available on CD-ROM
%@ 1-59593-010-8

%A Maarten Keijzer
%A James Foster
%T Crossover Bias in Genetic Programming
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 33--43
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X Path length, or search complexity, is an under studied phenomenon in genetic programming. Unlike size and depth measures, path length directly measures the balancedness or
   skewedness of a tree. Here a close relative to path length, called visitation length, is studied. It is shown that a population undergoing standard crossover will introduce
   a crossover bias in the visitation length. This bias is due to inserting variable length subtrees at various levels of the tree. The crossover bias takes the form of a
   covariance between the sizes and levels in the trees that form a population. It is conjectured that the crossover bias directly determines the size distribution of trees in
   genetic programming. Theorems are presented for the one-generation evolution of visitation length both with and without selection. The connection between path length and
   visitation length is made explicit.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%T GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%I ACM New York, NY, USA
%I ACM SIGEVO
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming
%U http://portal.acm.org/toc.cfm?id=1389095&type=proceeding&coll=GUIDE&dl=GUIDE&CFID=84352265&CFTOKEN=86679895
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081.

%A Brandon Keim
%T Computer Program Self-Discovers Laws of Physics
%J Wired
%D 2009
%I 
%K genetic algorithms, genetic programming, Eureqa
%U http://www.wired.com/wiredscience/2009/04/newtonai/
%8 April  2
%Z Petabyte Age, Hod Lipson \citeScience09:Schmidt, Martha Pollack, Atherton

%A Brandon Keim
%T Download Your Own Robot Scientist
%J Wired
%D 2009
%I 
%K genetic algorithms, genetic programming, Eureqa
%U http://www.wired.com/wiredscience/2009/12/download-robot-scientist/
%8 Decemeber  3
%Z Hod Lipson, Wikswo http://ccsl.mae.cornell.edu/eureqa

%A Mike J. Keith
%A Martin C. Martin
%T Genetic Programming in C++: Implementation Issues
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 285--310
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%X The purpose of our current research is to investigate the design and implementation of a Genetic Programming platform in C++, with primary focus on efficiency and
   flexibility. In this chapter we consider the lower level implementation aspects of such a platform, specifically, the Genome Interpreter. The fact that Genetic Programming
   is a computationally expensive task means that the overall efficiency of the platform in both memory and time is crucial. In particular, the node representation is the key
   part of the implementation in which the overhead will be magnified. We first compare a number of ways of storing the topology of the tree. The most efficient representation
   overall is one in which the program tree is a linear array of nodes in prefix order as opposed to a pointer based tree structure. We consider trade-offs with other linear
   representations, namely postfix and arbitrary positioning of functions and their arguments. We then consider how to represent which function or terminal each node
   represents, and demonstrate a very efficient one to two byte representation. Finally, we integrate these approaches and offer a prefix/jump-table (PJT) approach which
   results in a very small overhead per node in both time and space compared to the other approaches we investigated. In addition to being efficient, our interpreter is also
   very flexible. Finally, we discuss approaches for handling flow control, encapsulation, recursion, and simulated parallel programming.
%O 13
%Z Contrasts 5 different genome interpreters (postfix, prefix, Mixfix). Code based on C and C++. Population of trees. check later 1995 for postscript on GP ftp site

%A Richard J. Gilbert
%A Jem J. Rowland
%A Douglas B. Kell
%T Genomic computing: explanatory modelling for functional genomics
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 551--557
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW045.pdf
%X Many newly discovered genes are of unknown function. DNA microarrays are a method for determining the expression levels of all genes in an organism for which a complete
   genome sequence is available. By comparing the expression changes under different conditions it should be possible to assign functions to these genes. However, many
   hundreds of thousands of data points may be produced over a series of experiments. Genetic programming provided simple explanatory rules for gene function from such
   datasets, where previous approaches had not succeeded.
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO DNA chip, microarray mRNA, baker's yeast saccharomyces cerevisiae. Protected divide by zero =10**15, range protection 6 way classification via 6
   classifiers in one individual. Linear GP Five demes (7500 each). 5% migration. p554 2000 generations. Single point crossover. Conditional branches, loops. Infinite loops
   trapped. "new insights into biological systems at the genomic level". "not been previously reported". Sec 4. p553 2467 genes x 79??? These missing (data) points were
   assigned the mean values for the column. log expression. bloat fitness penalty.
%@ 1-55860-708-0

%A Douglas B. Kell
%A Robert M. Darby
%A John Draper
%T Genomic Computing. Explanatory Analysis of Plant Expression Profiling Data Using Machine Learning
%J Plant Physiology
%V 126
%N 3
%D 2001
%P 943--951
%I 
%K genetic algorithms, genetic programming
%U http://www.plantphysiol.org/cgi/content/full/126/3/943
%8 July
%Z PMID: 11457944

%A Douglas Kell
%T Defence against the flood
%J Bioinformatics World
%D 2002
%P 16--18
%I 
%K genetic algorithms, genetic programming
%U http://dbkgroup.org/Papers/biwpp16-18_as_publ.pdf
%8 January /  February
%Z high level

%A Douglas B. Kell
%T Metabolomics and Machine Learning: Explanatory Analysis of Complex Metabolome Data Using Genetic Programming to Produce Simple, Robust Rules
%J Molecular Biology Reports
%V 29
%N 1-2
%D 2002
%P 237--241
%I 
%K genetic algorithms, genetic programming
%U http://www.ingentaconnect.com/content/klu/mole/2002/00000029/F0020001/05102807
%X There is a clear trend in post-genomic studies to understand gene function, pharmaceutical mode of action, cytotoxicity and the like by expression profiling at the level of
   the transcriptome, the proteome and the metabolome. Our interest is focused on the latter.
%Z Article ID: 5102807 Kluwer Academic Publishers

%A Douglas B. Kell
%T Genotype-phenotype mapping: genes as computer programs
%J Trends in Genetics
%V 18
%N 11
%D 2002
%P 555--559
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6TCY-46WPK82-4/2/4c365fb0bda43abc86c236b146030879
%X The effects of genes on phenotype are mediated by processes that are typically unknown but whose determination is desirable. The conversion from gene to phenotype is not a
   simple function of individual genes, but involves the complex interactions of many genes; it is what is known as a nonlinear mapping problem. A computational method called
   genetic programming allows the representation of candidate nonlinear mappings in several possible trees. To find the best model, the trees are `evolved' by processes akin
   to mutation and recombination, and the trees that more closely represent the actual data are preferentially selected. The result is an improved tree of rules that represent
   the nonlinear mapping directly. In this way, the encoding of cellular and higher-order activities by genes is seen as directly analogous to computer programs. This analogy
   is of utility in biological genetics and in problems of genotype-phenotype mapping.
%8 November
%Z Opinion

%A Robert E. Keller
%A Wolfgang Banzhaf
%T Genetic Programming using Genotype-Phenotype Mapping from Linear Genomes into Linear Phenotypes
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 116--122
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X In common genetic programming approaches, the space of genotypes, that is the search space, is identical to the space of phenotypes, that is the solution space. Facts and
   theories from molecular biology suggest the introduction of non-identical genospaces and phenospaces, and a generic genotype-phenotype mapping which maps unconstrained
   genotypes into syntactically correct phenotypes. Neutral variants come into effect due to this mapping. They enhance genetic diversity and allow for escaping local optima
   in phenospace via high-dimensional saddle surfaces in genospace. We propose a concrete mapping that maps linear binary genotypes into linear phenotypes of an arbitrary
   context-free programming language. Empirical results are presented which show that the mapping improves the performance of GP under mutation and reproduction.
%8 28--31  July
%Z GP-96

%A Robert E. Keller
%A Wolfgang Banzhaf
%T Genetic Programming using Genotype-Phenotype Mapping from Linear Genomes into Linear Phenotypes
%B Genetic Programming 1996: Video Proceedings of the First Annual Conference
%D 1996
%I Sound Photo Synthesis
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://photosynthesis.com/space/gp96.html
%8 28--31  July
%Z GP-96. Presentation of \citekeller:1996:gpmlg2lp

%A Robert E. Keller
%A Wolfgang Banzhaf
%A Klaus Weinert
%A Jorn Mehnen
%T CAD Surface Reconstruction from Digitized 3D Point Data with Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98LB

%A Robert E. Keller
%A Wolfgang Banzhaf
%A Jorn Mehnen
%A Klaus Weinert
%T CAD Surface Reconstruction from Digitized 3D Point Data with a Genetic Programming/Evolution Strategy hybrid
%B Advances in Genetic Programming 3
%E Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline
%D 1999
%P 41--65
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1764
%X Surface reconstruction is a hard problem in the industrial core domain of computer-aided design (CAD) applications. A workpiece must be represented in some standard CAD
   object description format such that its representation can be efficiently used in a CAD process like redesign. To that end, a digitising process represents the object
   surface as a weakly-structured discrete and digitized set of 3D points. Surface reconstruction attempts to transform this representation into an efficient CAD
   representation. Certain classic approaches produce inefficient reconstructions of surface areas that do not correspond to construction logic. Here, a new reconstruction
   principle along with empiric results is presented which yields logical and efficient representations. This principle is implemented as a
   Genetic-Programming/Evolution-Strategy-based software system.
%O 3
%8 June
%Z AiGP3
%@ 0-262-19423-6

%A Robert E. Keller
%A Wolfgang Banzhaf
%T The Evolution of Genetic Code in Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1077--1082
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-438.ps
%X In most Genetic Programming (GP) approaches, the space of genotypes, that is the search space, is identical to the space of phenotypes, that is the solution space.
   Developmental approaches, like Developmental Genetic Programming (DGP), distinguish between genotypes and phenotypes and use a genotypephenotype mapping prior to fitness
   evaluation of a phenotype. To perform this mapping, DGP uses a problem-specific manually designed genetic code, that is a mapping from genotype components to phenotype
   components. The employed genetic code is critical for the performance of the underlying search process. Here, the evolution of genetic code is introduced as a novel
   approach for enhancing the search process. It is hypothesized that code evolution improves the performance of developmental approaches by enabling them to beneficially
   adapt the fitness landscape during search. As the first step of investigation, this article empirically shows the operativeness of code evol...
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Robert E. Keller
%A Wolfgang Banzhaf
%T Evolution of Genetic Code on a Hard Problem
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 50--56
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, genetic code, real-world problem, noise filtering, developmental genetic programming, genotype-phenotype mapping, self-adaptation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Robert E. Keller
%A Walter A. Kosters
%A Martijn {van der Vaart}
%A Martijn D. J. Witsenburg
%T Genetic Programming Produces Strategies for Agents in a Dynamic Environment
%B Proceedings of the Fourteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02)
%E Hendrik Blockeel and Marc Denecker
%D 2002
%P 171--178
%I 
%I BNVKI, Dutch and the Belgian AI Association
%C Leuven, Belgium
%K genetic algorithms, genetic programming, DAI, MAS
%U http://www.liacs.nl/home/kosters/gpas.ps
%8 21-22  October
%Z Katholieke Universiteit Leuven and Universite Libre de Bruxelles in collaboration with PharmaDM and under the auspices of BNVKI/AIABN (the Belgian-Dutch Association for
   Artificial Intelligence), SIKS (School for Information and Knowledge Systems), and SNN (the Foundation for Neural Networks). Distributed Agents evolved to play 2 reduced
   versions of chess "poor man's chess" and "pseudo chess". Summarised in \citelangdon:2002:bnaic02ec2

%A R. E. Keller
%A R. Poli
%T Linear genetic programming of metaheuristics
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1753--1753
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming: Poster, metaheuristics, optimisation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1753.pdf
%X We suggest a flavour of linear Genetic Programming in domain-specific languages that acts as a hyperheuristic (HH).
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A R. E. Keller
%A R. Poli
%T Linear Genetic Programming of Parsimonious Metaheuristics
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 4508--4515
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X We use a form of grammar-based linear Genetic Programming (GP) as a hyperheuristic, i.e., a search heuristic on the space of heuristics. This technique is guided by domain
   specific languages that one designs taking inspiration from elementary components of specialised heuristics and metaheuristics for a domain. We demonstrate this approach
   for travelling salesman problems for which we test different languages, including one containing a looping construct. Experimentation with benchmark instances from the
   TSPLIB shows that the GP hyperheuristic routinely and rapidly produces parsimonious metaheuristics that find tours whose lengths are highly competitive with the best
   real-valued lengths from literature.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Robert E. Keller
%A Riccardo Poli
%T Cost-benefit investigation of a Genetic-Programming Hyperheuristic
%B Evolution Artificielle, 8th International Conference
%S Lecture Notes in Computer Science
%E Nicolas Monmarch\'e and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton
%V 4926
%D 2007
%P 13--24
%I Springer
%C Tours, France
%K genetic algorithms, genetic programming, grammar
%X in previous work, we have introduced an effective, grammar-based, linear Genetic-Programming hyperheuristic, i.e., a search heuristic on the space of heuristics. Here we
   further investigate this approach in the context of search performance and resource usage. For the chosen realistic travelling salesman problems it shows that the
   hyperheuristic routinely produces metaheuristics that find tours whose lengths are highly competitive with the best results from literature, while population size, genotype
   size, and run time can be kept very moderate.
%O Revised Selected Papers
%8 29-31  October
%Z EA'07

%A R. Keller
%A R. Poli
%T Toward Subheuristic Search
%B Proceedings of the IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 3148--3155
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X In previous work, we have introduced an effective, resource-efficient and self-adapting hyperheuristic that uses Genetic Programming (GP) as its method of search in the
   space of domain-specific metaheuristics. GP employs user-provided, local heuristics from which it produces these metaheuristics (MHs). Here, we show that the hyperheuristic
   performs even better when working at the subheuristic level, i.e., when building MHs from generic components and specific elementary operations. In particular, this
   approach supports efficiency of the better MHs. Specifically, these MHs do not excessively iterate local search steps, i.e., their good performance comes from smart
   patterns of calls of the provided, basic components. Also, a moderate reduction of the maximum allowed MH size does not reduce performance significantly.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Robert E. Keller
%A Riccardo Poli
%T Self-Adaptive Hyperheuristic and Greedy Search
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 3801--3808
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X In previous work, we have introduced an effective and resource-efficient hyperheuristic that uses Genetic Programming as its search heuristic on the space of heuristics.
   Here, we show that the hyperheuristic performs better than purely greedy and even only mostly greedy flavours of hill climbing. We also introduce a generic principle that
   allows the hyperheuristic to automatically find good parameter values for its effective and efficient search.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Robert E. Keller
%A Riccardo Poli
%T Subheuristic search and scalability in a hyperheuristic
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 609--610
%I ACM New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C Atlanta, GA, USA
%K bounded labels, genetic algorithms (GA), greedy heuristics, labelled spanning trees, local search, Evolutionary combinatorial optimisation: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p609.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389216

%A D'ondria L. Kennard
%T Using Genetic Algorithm and Decision Trees to produce a Hybrid Classification System
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 161--170
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Claire J. Kennedy
%T Evolutionary Higher-Order Concept Learning
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bris.ac.uk/Publications/Papers/1000281.pdf
%X Current concept learners are limited in their applicability as they generally rely on comparatively poor knowledge representation facilities (e.g. attribute value pairs,
   flattened horn clauses). The work carried out in support of my thesis has involved extending concept learning to a higher order setting by developing a novel representation
   based on closed Escher terms for highly structured data. The added expressiveness offered by the proposed representation results in an explosion of the search space, which
   is compounded by the increased complexity of its structure. This paper describes an investigation into the use of genetic programming techniques to allow the exploitation
   of higher-order features during the induction of structured concept descriptions.
%8 22-25  July
%Z GP-98LB

%A Claire J. Kennedy
%A Christophe Giraud-Carrier
%T A Depth Controlling Strategy for Strongly Typed Evolutionary Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 879--885
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, evolution strategies and evolutionary programming
%U http://faculty.cs.byu.edu/~cgc/Research/Publications/GECCO1999.pdf
%X This paper presents a dynamic strategy for monitoring the depth of program trees evolved by STEPS (Strongly Typed Evolutionary Programming System). STEPS evolves
   higher-order functional programs in the form of trees, which are allowed to grow or shrink to fit the size of the problem, via specialised genetic operators. Thus, the need
   for arbitrary cut-off mechanisms is eliminated.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) problems of
   bloat in GP, Strongly typed Evolutionary Programming System STEPS, Escher programs, STGP, 6 types of mutation, editing. Bench marks tennis (Tom Mitchell), michalski's
   train, animal ??Available as technical report of the university of bristol http://citeseer.ist.psu.edu/438901.html ??
%@ 1-55860-611-4

%A Claire J. Kennedy
%A C. Giraud-Carrier
%A D. W. Bristol
%T Predicting Chemical Carcinogenesis Using Structural Information Only
%B Third European Conference on the Principles of Data Mining and Knowledge Discovery
%D 1999
%P 360--365
%I Springer
%K genetic algorithms, genetic programming
%U http://www.cs.bris.ac.uk/Publications/Papers/1000393.pdf
%X This paper reports on the application of the Strongly Typed Evolutionary Programming System (STEPS) to the PTE2 challenge, which consists of predicting the carcinogenic
   activity of chemical compounds from their molecular structure and the outcomes of a number of laboratory analyses. Most contestants so far have relied heavily on results of
   short term toxicity (STT) assays. Using both types of information made available, most models incorporate attributes that make them strongly dependent on STT results.
   Although such models may prove to be accurate and informative, the use of toxicological information requires time cost and in some cases substantial use of laboratory
   animals. If toxicological information only makes explicit, properties implicit in the molecular structure of chemicals, then provided a sufficiently expressive
   representation language, accurate solutions may be obtained from the structural information only. Such solutions may offer more tangible insight into the mechanistic paths
   and features that govern chemical toxicity as well as prediction based on virtual chemistry for the universe of compounds.
%8 September
%@ 3-540-66490-4

%A Claire Julia Kennedy
%T Strongly Typed Evolutionary Programming
%R Ph.D. Thesis
%D 1999
%I 
%I Computer Science, University of Bristol
%C UK
%K genetic algorithms, genetic programming, STGP
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/claire_j_kennedy/kennedy_phdthesis.pdf
%X As the potential of applying machine learning techniques to perplexing problems is realised, increasingly complex problems are being tackled, requiring intricate
   explanations to be induced. Escher is a functional logic language whose higher-order constructs allow arbitrarily complex observations to be captured and highly expressive
   generalisations to be conveyed. The work presented in this thesis alleviates the challenging problem of identifying an underlying structure normally required to search the
   resulting hypothesis space efficiently. This is achieved through STEPS, an evolutionary based system that allows the vast space of highly expressive Escher programs to be
   explored. STEPS provides a natural upgrade of the evolution of concept descriptions to the higher-order level. In particular STEPS uses the individual-as-terms approach to
   knowledge representation where all the information provided by an example is localised as a single closed term so that examples of arbitrary complexity can be treated in a
   uniform manner. STEPS also supports Lambda abstractions as arguments to higher-order functions thus enabling the invention of new functions not contained in the original
   alphabet. Finally, STEPS provides a number of specialised genetic operators for the design of specific concept learning strategies. STEPS has been successfully applied to a
   number of complex real world problems, including the international PTE2 challenge. This problem involves the prediction of the Carcinogenic activity of a test set of 30
   chemical compounds. The results produced by STEPS rank joint second if the hypothesis must be interpretable and joint first if interpretability is sacrificed for increased
   accuracy.
%8 Decemeber

%A Claire J. Kennedy
%T First Steps Towards Using Genetic Programming to Solve a Distributed Radio Frequency Management Problem
%B 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Erik D. Goodman
%D 2001
%P 234--238
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, STEPS, STGP, Escher program
%8 9-11  July
%Z GECCO-2001LB

%A James Kennedy
%T Review of Engelbrecht's Fundamentals of Computational Swarm Intelligence
%J Genetic Programming and Evolvable Machines
%V 8
%N 1
%D 2007
%P 107--109
%I 
%K genetic algorithms, genetic programming, PSO
%8 March
%Z Resource Review

%A Paul Joseph Kennedy
%T Simulation of the Evolution of Single Celled Organisms with Genome, Metabolism and Time-Varying Phenotype
%R Ph.D. Thesis
%D 1999
%I 
%I University of Technology, Sydney
%C Australia
%K genetic algorithms
%U http://zahir.socs.uts.edu.au:9673/Paul/Papers/Thesis.zip
%X A novel model of a biological cell is presented. Primary features in the cell are a genome and metabolism. Pairs of genome and metabolism coevolve with a genetic algorithm
   (GA) to produce cells that can survive in simple environments. Evolution of the genome is Darwinian, whereas evolution of the metabolism has Lamarckian features through
   acquired chemical concentrations being inherited. Fitness is more closely correlated with the mother cell than with the father. A biologically inspired double-strand genome
   model is presented. Double-stranded genomes admit a large increase in the number of schemata represented by each genome compared to single-strand encodings. This gives GAs
   more information to use and allows faster search. Simple implementation of a biologically inspired algorithm for inversion also becomes possible, as well as a compression
   of data on the genome. Increased rates of inversion showed an increase in population convergence. Double-stranded genomes impose constraints between strands that decrease
   the overall rate of population convergence. Four-bit bases from a parallel genomic language are encoded on the genome. The parallel genomic language, following the operon
   model of Jacob and Monod, allows genes to be placed on the genome at any loci and allows easy implementation of an inversion operator. The genome and chemical metabolism of
   a cell in our model have a close relationship. Genomes specify allowable families of enzyme-catalysed chemical reactions and families of chemicals that may diffuse through
   the cell membrane at increased rate. Chemicals produced from metabolic processes regulate genes and allow expression of proteins from the genome. We introduce the
   "bootstrapping" problem: evolution of cells stable in simple environments from random genomes and initial simple metabolic conditions. Experiments show that solution of the
   "bootstrapping" problem is much easier with coevolution than when the initial metabolic conditions remain fixed. A gallery of cellular survival strategies is given. Genes
   in the population are diverse because there is a variety of equally valid solutions to the problem posed by the environment. Solution to the "bootstrapping" problem is
   hindered because fitness functions cannot differentiate between cells using myopic solutions rather than long-term strategies. Cells with myopic strategies attain high
   fitness but produce offspring with high probability of cell death (ie, when the myopic solution begins to fail). A novel solution, where fitness of parents is retroactively
   modified when the fitness of offspring becomes known, reduces the number of cells exhibiting myopic strategies.
%8 July
%Z Fri, 15 Jun 2001 12:16:19 +1000 To: genetic-programming@cs.stanford.edu I applied some more biological notions to GAs in my PhD thesis. In that, I built a model of
   single-celled organisms and bred populations of them to live in simple environments. The cell models had a double-stranded (DNA inspired) genome and a chemical-kinetic
   metabolism. Operons on the genome encoded enzymes to control reactions in the metabolism and the metabolism itself instantiated the simulated enzyme molecules from the
   genome template. Some of the complexities I added from biology were: - operons (for a model of gene regulation) - a gene expression algorithm (transcription and translation
   algorithms) - a double stranded genome (not diploidy) - the inversion genetic operator - a language that allows genes to appear at any locus on the genome - a phenotype
   that interacts with the genome for its lifetime rather than just at the start. The simulation was interesting but big and slow. It generated so much information that it was
   a bit difficult to work out how it was solving a problem. PhD thesis is here: http://zahir.socs.uts.edu.au:9673/Paul/research_html Since then I've looked at abstracting the
   biological concepts out of the big simulation into simpler models (with no differential equations!). This work has focused on the double-stranded genome and inversion
   operator. I have a paper at GECCO about this work. Currently I'm interested in the (overly simplified) idea that biological cells exist as systems in isolation to their
   genome. (That's not to say that a cell can exist without its genome). Without the genome the cell is a sort of "default" system. As you add genes to the genome you add
   enzymes to the system which kicks the metabolism into different areas of (chemical) reaction space. I see this kind of phenotype as a "tempered" phenotype - tempered by
   genes rather than completely specified. I'm looking forward to discussing some of these ideas at the gene expression workshop at GECCO next month. Cheers, Paul.

%A Paul J. Kennedy
%A Thomas R. Osborn
%T Operon expression and regulation with spiders
%B Gene Expression: the Missing Link in Evolutionary
%D 2000
%P 161--166
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, grammar
%8 8  July
%Z GECCO-2000WKS Part of wu:2000:GECCOWKS

%A Paul J. Kennedy
%A Thomas R. Osborn
%T A Model of Gene Expression and Regulation in an Artificial Cellular Organism
%J Complex Systems
%V 13
%N 1
%D 2001
%P 33--59
%I 
%K genetic algorithms, genetic programming
%U http://www.complex-systems.com/pdf/13-1-3.pdf
%X Gene expression and regulation may be viewed as a parallel parsing algorithm---translation from a genomic language to a phenotype. We describe a model of gene expression
   and regulation based on the operon model of Jacob and Monod. Operons are groups of genes regulated in the same way. An artificial cellular metabolism expresses operons
   encoded on a genome in a parallel genomic language. This is accomplished using an abstract entity called a spider. A genetic algorithm is used to evolve the simulated cells
   to adapt to a simple environment. Genomes are subjected to recombination, mutation, and inversion operators. Observations from this experiment suggest four areas to
   explore: dynamic environments for the evolution of regulation, advantages of time lags inherent in the expression algorithm, sensitivity of our genomic language, and
   noncoding regions on the genome. Issues relating to the application of the expression model to evolutionary computation are discussed.

%A Robert A. {Kennelly, Jr.}
%T Genetic Evolution of Shape-Altering Programs for Supersonic Aerodynamics
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 100--109
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1997:GAGPs ADFs, variable number of iterations
%@ 0-18-205981-2

%A Robert A. {Kennelly, Jr.}
%T Genetic Evolution of Shape-Altering Programs for Supersonic Aerodynamics
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 112--120
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A Simon Kent
%T Diagnosis of Oral Cancer using Genetic Programming
%R Technical Report CSTR-96-14 ; CNES-96-04
%D 1996
%I 
%I Brunel University
%C Uxbridge, Middlesex, UB8 3PH, UK
%K genetic algorithms, genetic programming, classification
%U http://citeseer.ist.psu.edu/37859.html
%X Genetic Programming is a relatively new technique for the automatic discovery of computer programs which offer solutions to complex problems. It is being applied to an ever
   increasing number of application areas with good results. This report presents some introductory work on a classification problem. The Genetic Programming technique is used
   to evolve programs which are able to diagnose oral cancer and pre-cancer in a sample of patients. Real data on the habits and lifestyles of patients is...
%8 July

%A Simon Kent
%A Dimitris Dracopoulos
%T Bulk Synchronous Parallelisation of Genetic Programming
%R Technical Report CSTR-96-13 ; CNES-96-02
%D 1996
%I 
%I Brunel University
%C Uxbridge, Middlesex, UB8 3PH, UK
%K genetic algorithms, genetic programming, parallel computing
%8 July
%Z as \citeBSPoGP:Kent

%A Dimitris C. Dracopoulos
%A Simon Kent
%T Bulk Synchronous Parallelisation of Genetic Programming
%B Applied parallel computing : industrial strength computation and optimization ; Proceedings of the third International Workshop, PARA '96
%E Jerzy Wa\'sniewski
%D 1996
%P 216--226
%I Springer Verlag
%C Berlin, Germany
%K genetic algorithms, genetic programming, parallel computing
%U http://citeseer.ist.psu.edu/dracopoulos96bulk.html
%X A parallel implementation of Genetic Programming (GP) is described, using the Bulk Synchronous Parallel Programming (BSP) model, as implemented by the Oxford BSP library.
   Two approaches to the parallel implementation of GP are examined. The first is based on global parallelisation while the second implements the island model for evolutionary
   algorithms. It is shown that considerable speedup of the GP execution can be achieved and that the BSP model is very suitable for parallelisation of...
%Z cf \citeBSPoGPTR:Kent

%A D. C. Dracopoulos
%A Simon Kent
%T Genetic Programming for Prediction and Control
%J Neural Computing and Applications
%V 6
%N 4
%D 1997
%P 214--228
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/188085.html
%X The relatively "new" field of genetic programming has received a lot of attention during the last few years. This is because of its potential for generating functions which
   are able to solve specific problems. This paper begins with an extensive overview of the field, highlighting its power and limitations and providing practical tips and
   techniques for the successful application of GP in general domains. Following this, emphasis is placed on the application of genetic programming to prediction ...
%Z Demes ring v. star, oral cancer, statellite control, Santa Fe Ant

%A Simon Kent
%T Evolutionary Approaches to Robot Path Planning
%R Ph.D. Thesis
%D 1999
%I 
%I Department of Information Systems and Computing, Brunel University
%C Uxbridge, Middlesex, UB8 3PH, United Kingdom.
%K genetic algorithms, genetic programming
%U http://bura.brunel.ac.uk/handle/2438/1276
%X The ultimate goal in robotics is to create machines which are more independent and rely less on humans to guide them in their operation. There are many sub-systems which
   may be present in such a robot, one of which is path planning the ability to determine a sequence of positions or configurations between an initial and goal position within
   a particular obstacle cluttered workspace. Many classical path planning techniques have been developed, but these tend to have drawbacks such as their computational
   requirements; the suitability of the plans they produce for a particular application; or how well they are able to generalise to unseen problems. In recent years,
   evolutionary based problem solving techniques have seen a rise in popularity, possibly coinciding with the improvement in the computational power afforded researches by
   successful developments in hardware. These techniques adopt some of the features of natural evolution and mimic them in a computer. The increase in the number of
   publications in the areas of Genetic Algorithms (GA) and Genetic Programming (GP) demonstrate the success achieved when applying these techniques to ever more problem
   areas. This dissertation presents research conducted to determine whether there is a place for Evolutionary Approaches, and specifically GA and GP, in the development of
   future path planning techniques.
%8 March
%Z Advisor Dracopoulos, D C

%A Simon Kent
%A Nayna Patel
%T Artificial intelligence makes computers lazy
%J International Journal of Industrial and Systems Engineering
%V 1
%D 2006
%P 519--532
%I Inderscience Publishers
%K genetic algorithms, genetic programming, artificial intelligence, classification, medical diagnosis, path planning
%U http://www.inderscience.com/link.php?id=10390
%X This paper looks at the age-old problem of trying to instil some degree of intelligence in computers. Genetic Algorithms (GA) and Genetic Programming (GP) are techniques
   that are used to evolve a solution to a problem using processes that mimic natural evolution. This paper reflects on the experience gained while conducting research
   applying GA and GP to two quite different problems: Medical Diagnosis and Robot Path Planning. An observation is made that when these algorithms are not applied correctly
   the computer seemingly exhibits lazy behaviour, arriving at a suboptimal solutions. Using examples, this paper shows how this 'lazy' behaviour can be overcome.
%O Int. J. of Industrial and Systems Engineering
%8 July ~18

%A Kevin Kerr
%T A Parallel Genetic Algorithm to Evolve VLSI Circuits
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z GP-98LB

%A Matthew W. Kessler
%T Avoiding Two-Bit Crossovers in Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%P 115--119
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/13724.html
%X We investigate the utility of weighting the crossover points in genetic programming. The depth-fair crossover (DFC) operator is introduced as an alternative to the standard
   90/10 weight heuristic. The DFC weight heuristic performs better that the standard 90/10 weight heuristic in the clique domain. Preliminary results also indicate it will
   perform better in other applications.
%8 22-25  July
%Z GP-98LB url refers to joint paper with Thomas D. Haynes

%A Matthew Kessler
%A Thomas Haynes
%T Avoiding Two-bit Crossovers in Genetic Programming
%B Proceedings of the 1999 ACM Symposium on Applied Computing
%E Janice Carroll and Hisham Haddad and Dave Oppenheim and Barrett Bryant and Gary B. Lamont
%D 1999
%P 319--323
%I ACM Press
%K genetic algorithms, genetic programming
%X We use collective memory to integrate weak and strong search heuristics to find cliques in FC, a family of graphs. We construct FC such that pruning partial solutions will
   be ineffective. Each weak heuristic maintains a local cache of the collective memory. We examine the impact on the distributed search of the distribution of the collective
   memory, the search algorithms, and our family of graphs. We find the distributed search performs better than the individual searches, even though the space of partial
   solutions is combinatorial.
%Z (GA track) See also \citekessler:1998:a2xGP

%A J. H. D. Keukelaar
%T Topics in Soft Computing
%R Ph.D. Thesis
%D 2002
%I 
%I Department of Numerical Analysis and Computer Science, Royal Institute of Technology
%C Stockholm
%K genetic algorithms, genetic programming, dataflow
%U http://citeseer.ist.psu.edu/567095.html
%X This thesis discusses visual programming languages, representation of uncertainty in geographical data and a combination of genetic programming and optimisation. A new
   visual programming language is described, based on a novel version of the dataflow paradigm. In this version, cyclic graphs are replaced with nested graphs, which also have
   other uses. Furthermore, the programs become more structured, readable and scalable. This language is then formally defined using a novel extension of plex grammars.
   Various representations of uncertainty in geographical data are discussed, including some novel ones based on rough sets. Various novel measures are developed, and used in
   two experiments that verify the usefulness of the representations chosen. Furthermore, a novel theory of topological relations between uncertain data is presented. A novel
   combination of genetic programming and optimization is presented. This has been implemented in a system that is in actual use. The system is described, as is the
   combination. An experiment has been done to test the performance of this combination, and in this experiment it performed better than plain genetic programming.
%O The Pennsylvania State University CiteSeer Archives
%8 January
%Z Kungl Tekniska Hogskolan
%@ 91-7283-242-8

%A Cathy Key
%T (formerly ES-212) Non-reciprocal Altruism and the Evolution of Paternal Care
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1313--1320
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-051.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Amir Hosein Keyhanipour
%A Maryam Piroozmand
%A Kambiz Badie
%T A GP-adaptive web ranking discovery framework based on combinative content and context features
%J Journal of Informetrics
%V 3
%N 1
%D 2009
%P 78--89
%I 
%K genetic algorithms, genetic programming, Document ranking, Classifier designing, LETOR, LAGEP
%U http://www.sciencedirect.com/science/article/B83WV-4V99602-2/2/dbdb4475cf1bfdaf20f775edd1aa4636
%X The problem of ranking is a crucial task in the web information retrieval systems. The dynamic nature of information resources as well as the continuous changes in the
   information demands of the users has made it very difficult to provide effective methods for data mining and document ranking. Regarding these challenges, in this paper an
   adaptive ranking algorithm is proposed named GPRank. This algorithm which is a function discovery framework, uses the relatively simple features of web documents to provide
   suitable rankings using a multi-layer/multi-population genetic programming architecture. Experiments done, illustrate that GPRank has better performance in comparison with
   well-known ranking techniques and also against its full mode edition.
%8 January

%A Didier Keymeulen
%A Masaya Iwata
%A Yasuo Kuniyoshi
%A Tetsuya Higuchi
%T On-line Model-based Learning using Evolvable Hardware for a Robotics Tracking System
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 816--823
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K Evolutionary Robotics
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%T The Third NASA/DoD workshop on Evolvable Hardware
%E Didier Keymeulen and Adrian Stoica and Jason Lohn and Ricardo S. Zebulum
%D 2001
%I IEEE Computer Society 1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA
%I Jet Propulsion Laboratory, California Institute of Technology
%C Long Beach, California
%K genetic algorithms, evolvable hardware
%U EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/
%8 12-14  July
%@ 0-7695-1180-5

%A E. Khamehchi
%A F. Rashidi
%A H. Omranpour
%A S. {Shiry Ghidary}
%A A. Ebrahimian
%A H. Rasouli
%T Intelligent System for Continuous Gas Lift Operation and Design with Unlimited Gas Supply
%J Journal of Applied Sciences
%V 9
%N 10
%D 2009
%P 1889--1897
%I Asian Network for Scientific Information
%K genetic algorithms, genetic programming, mutation, cross over, gas lift, optimization, depth of injection
%U http://scialert.net/qredirect.php?doi=jas.2009.1889.1897&linkid=pdf
%X Gas lift is one of a number of processes used to artificially lift oil or water from wells where there is insufficient reservoir pressure to produce the well. The process
   involves injecting gas through the tubing-casing annulus. Injected gas aerates the fluid to reduce its density; the formation pressure is then able to lift the oil column
   and forces the fluid out of the well bore. Gas may be injected continuously or intermittently, depending on the producing characteristics of the well and the arrangement of
   the gas-lift equipment. To enhance the financial revenues this operation has usually always been a subject for optimization to reach the most rewarding design before its
   operational establishment. Evolutionary approaches have recently been successfully applied to almost every aspect of engineering problems. This study reviews the general
   facts and ideas related to the gas lift and its optimization and further focus on the application and evaluation of genetic programming for such a purpose. It has been
   concluded that genetic programming is fully capable in aiding faster gas lift optimizations while is also stable and applicable to a very broad range of operating
   conditions. The merits and draw backs are finally compared with the neural network approach.

%A Asifullah Khan
%A Anwar M. Mirza
%A Abdul Majid
%T Optimizing Perceptual Shaping of a Digital Watermark Using Genetic Programming
%J Iranian Journal of Electrical and Computer Engineering (IJECE)
%V 3
%N 2
%D 2004
%P 144--150
%I 
%K genetic algorithms, genetic programming
%X Embedding of a digital watermark in an electronic document is proving to be a feasible solution for copyright protection and authentication purposes. In this paper, we
   present an innovative scheme of perceptually shaping watermark to the cover images. A watermark is generally embedded in the selected coefficients of the transformed image
   using a carefully chosen watermarking strength. Choice of a good watermarking strength, to perceptually shape the watermark according to the cover image is crucial to make
   a tradeoff between the two conflicting properties, namely: robustness and imperceptibility of the watermark. Traditionally, a constant watermarking strength obtained from
   spatial activity masking and heuristics has been used for all the selected coefficients during embedding. We consider this tradeoff as an optimisation problem and have
   investigated an evolutionary optimisation technique to find optimal/near-optimal perceptual shaping function for DCT based watermarking system. The new scheme provides an
   excellent tradeoff between the robustness and imperceptibility and is image adaptive. Improved resistance to attacks, especially against JPEG compression of quality
   7percent and Gaussian noise of variance 17000 has been observed
%8 Summer-Fall
%Z http://www.ijece.org/

%A Asifullah Khan
%A Abdul Majid
%A Anwar M. Mirza
%T Combination and optimization of classifiers in gender classification using genetic programming
%J International Journal of Knowledge-Based and Intelligent Engineering Systems
%V 9
%N 1
%D 2005
%P 1--11
%I 
%K genetic algorithms, genetic programming, gender classification, principal component analysis, eigenface, jackknife technique, receiver operating characteristics curve, area
   under the convex hull, AUROC
%U http://iospress.metapress.com/link.asp?id=j0mnt4t904cebhh8
%X we have investigated the problem of gender classification using frontal facial images. Four different classifiers, namely K-means, k-nearest neighbours, Linear Discriminant
   Analysis and Mahalanobis Distance Based classifiers are compared. Receiver operating characteristics (ROC) curve along with the area under the convex hull (AUCH) have been
   used as the performance measures of the classifiers at different feature subsets. To measure the overall performance of a classifier with single scalar value, the new
   scheme of finding the area under the convex hull of AUCH of ROC curves (AUCH of AUCHS) is proposed. It has been observed that, when the number of macro features is
   increased beyond 5, the AUCH saturates and even decreases for some classifiers, illustrating the curse of dimensionality. We then used genetic programming to combine
   classifiers and thus evolved an optimum combined classifier (OCC), producing better performance than the individual classifiers. We found that using only two features, the
   OCC has comparable performance to that of original classifier using 20 macro features. It produces true positive rate values as high as 0.94 corresponding to false positive
   rate as low as 0.15 for 1: 3 train to testing ratio. We also observed that heterogeneous combination of classifiers is more promising than the homogenous combination.

%A Asifullah Khan
%T Intelligent Perceptual Shaping of a Digital Watermark
%R Ph.D. Thesis
%D 2006
%I 
%I Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology
%C Topi, Pakistan
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Intelligent_perceptual_shaping_WM_asif.pdf
%X Embedding of a digital watermark in a digital media is proving to be a workable solution for many of the recent problems like copyright protections and content
   authentication. However, the embedding of a digital watermark in a digital media is not without constraints. This requires perceptual shaping of a watermark in context of
   Human Visual System (HVS). The goal of this thesis is to develop a new watermarking scheme based on intelligent shaping of a digital watermark using GP. To achieve this
   goal, the research focuses on making efficient tradeoffs between two of the most important, but contradicting properties of a watermarking system; robustness and
   imperceptibility. This thesis makes the following contributions: (1) An analysis of the importance of perceptual shaping of a watermark in making a trade off between
   robustness and imperceptibility is performed, (2) intelligent search technique, like GP, is used to exploit the characteristics of HVS in evolving superior perceptual
   shaping functions, (3) the concept of bonus fitness has been proposed to implement multi-objective fitness function, in the GP simulation. This helps in simultaneously
   handling the estimated robustness and imperceptibility requirements during embedding stage, and actual robustness during decoding stage, (4) we realize that perceptual
   shaping of a watermark is not only important for making a superior trade off, but could also be used to tailor the watermark in accordance to an anticipated attack, (5)
   watermarking systems are becoming more and more sophisticated, as such this thesis, using intelligent search technique like GP, points towards the solution strategy of many
   complex issues in watermarking that are difficult to be computed analytically. A series of empirical investigations are performed to analyse the performance of the
   genetically evolved perceptual shaping functions (GPSFs) using standard benchmark, which shows the effectiveness of our approach.
%8 May

%A Asifullah Khan
%A Anwar M. Mirza
%A Abdul Majid
%T Intelligent perceptual shaping of a digital watermark: Exploiting Characteristics of human visual system
%J International Journal of Knowledge-Based and Intelligent Engineering Systems
%V 10
%N 3
%D 2006
%P 213--223
%I 
%K genetic algorithms, genetic programming, Watermarking, perceptual model, Human Visual System (HVS), Discrete Cosine Transform (DCT), spread spectrum and JPEG
%U http://iospress.metapress.com/link.asp?id=1rvgbkb5wtt3005g
%X we present a method for developing a Genetic Perceptual Model (GPM) applicable to a watermarking system. The proposed technique exploits the characteristics of human visual
   system using a Genetic Programming (GP) approach. We employ a tradeoff between watermark robustness and imperceptibility, as an optimisation criterion in the GP search. The
   resultant GPM is a combination of frequency, luminance sensitivity and contrast masking, enabling us to shape the watermark according to the cover image. Our investigations
   have shown that the evolved GPM provides maximum allowable imperceptible alterations to the Discrete Cosine Transform coefficients of a cover image. Comparative studies in
   terms of watermark imperceptibility and bit correct ratio performance have been carried out. The performance of the GPM has been analysed for various watermarking schemes.

%A Asifullah Khan
%T A novel approach to decoding: Exploiting anticipated attack information using genetic programming
%J International Journal of Knowledge-Based and Intelligent Engineering Systems
%V 10
%N 5
%D 2006
%P 337--346
%I 
%K genetic algorithms, genetic programming, Watermarking, Genetic Programming (GP), Decoder, Discrete Cosine Transform (DCT), and Sufficient Statistics
%U http://iospress.metapress.com/openurl.asp?genre=article&issn=1327-2314&volume=10&issue=5&spage=337
%X In a water marking system, the decoder structures are mostly fixed. They do not account for the normal processing or intentional attacks. In the present work, a method of
   automatically modifying the decoder structure in accordance to the given cover image and conceivable attack is illustrated. The proposed Genetic Programming based watermark
   decoding scheme is a blind one. It exploits the search space regarding types of dependencies of the decoder on different factors. Especially, information pertaining to
   watermarked cover coefficients is used to reduce host interference, while the conceivable-attack information is used to circumvent the anticipated distortion. The actual
   performance of the genetic decoder is assessed through experiments, which justify the use of intelligent search techniques in signal detection/decoding. Simulation results
   show that the resultant genetic decoder has superior performance as compared to the conventional decoder against the attacks of Checkmark benchmark.

%A Asifullah Khan
%A Anwar M. Mirza
%T Genetic perceptual shaping: Utilizing cover image and conceivable attack information during watermark embedding
%J Information Fusion
%V 8
%N 4
%D 2007
%P 354--365
%I 
%K genetic algorithms, genetic programming, Watermarking, Perceptual model, Discrete cosine transform (DCT), Bit correct ratio (BCR), JPEG, Human visual system (HVS)
%X We describe a new watermarking scheme based on intelligent shaping of a digital watermark using Genetic Programming (GP). The proposed method, in addition to achieving a
   superior tradeoff between watermark robustness and imperceptibility, is also able to structure the watermark in accordance with an anticipated attack. This has been
   achieved by simultaneously hiding the watermark as well as spreading and fusing it in such a way to resist the conceivable attack. Robustness versus imperceptibility
   tradeoff and increase in bit correct ratio after attack, have been employed as the optimisation criteria in the GP search. The concept of bonus fitness has been used to
   implement multi-objective fitness based GP evolution. Experiments on standard images indicate that such watermark shaping functions could be developed that are cover image
   independent and enhance imperceptibility. They offer high resistance against removal and interference attacks of Checkmark benchmark.
%8 October
%Z Faculty of Computer Science and Engineering, Ghulam Ishaq Khan (GIK) Institute of Engineering Science and Technology, Topi-23460, Swabi, Pakistan Lena

%A Asifullah Khan
%A Syed Gibran Javed
%T Predicting regularities in lattice constants of GdFeO$\sb 3$-type perovskites
%J Acta Crystallographica Section B: Structural Science
%V 64
%N 1
%D 2008
%P 120--122
%I 
%K genetic algorithms, genetic programming, GdFeO3
%U http://journals.iucr.org/b/
%X This work correlates the lattice constant of GdFeO8-type perovskites with the ionic radii of the cations using genetic programming. The resultant prediction models of the
   lattice constant are in the form of mathematical expressions.
%8 February
%Z Also known as \citeKhan:so5010

%A Asifullah Khan
%A Imran Usman
%T Intelligent Perceptual Shaping in Digital Watermarking
%B Information Hiding and Applications
%S Studies in Computational Intelligence
%V 227
%D 2009
%P 115--139
%I Springer
%K genetic algorithms, genetic programming
%X With the rapid technological advancement in the development, storage and transmission of digital content, watermarking applications are both growing in number and becoming
   complex. This has prompted the use of computational intelligence in watermarking, especially for thwarting attacks. In this context, we describe the development of a new
   watermarking system based on intelligent perceptual shaping of a digital watermark using Genetic Programming (GP). The proposed approach uses optimum embedding strength
   together with appropriate DCT position selection and information pertaining to conceivable attack in order to achieve superior tradeoff in terms of the two conflicting
   properties in digital watermarking, namely, robustness and imperceptibility. This tradeoff is achieved by developing superior perceptual shaping functions using GP, which
   learn the content of a cover image by exploiting the sensitivities/insensitivities of Human Visual System (HVS) as well as attack information. The improvement in
   imperceptibility and bit correct ratio after attack are employed as the multi-objective fitness criteria in the GP search.
%O 6
%Z Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore 45650 Islamabad, Pakistan

%A Gul Muhammad Khan
%A Julian Francis Miller
%A David M. Halliday
%T Coevolution of intelligent agents using cartesian genetic programming
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 1
%D 2007
%P 269--276
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Cartesian Genetic Programming, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, Evolvable Hardware, artificial neural
   networks, brain
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p269.pdf
%X A coevolutionary competitive learning environment for two antagonistic agents is presented. The agents are controlled by a new kind of computational network based on a
   compartmentalised model of neurons. The genetic basis of neurons is an important [27] and neglected aspect of previous approaches. Accordingly, we have defined a collection
   of chromosomes representing various aspects of the neuron: soma, dendrites and axon branches, and synaptic connections. Chromosomes are represented and evolved using a form
   of genetic programming (GP) known as Cartesian GP. The network formed by running the chromosomal programs, has a highly dynamic morphology in which neurons grow, and die,
   and neurite branches together with synaptic connections form and change in response to environmental interactions. The idea of this paper is to demonstrate the importance
   of the genetic transfer of learned experience and life time learning. The learning is a consequence of the complex dynamics produced as a result of interaction
   (coevolution) between two intelligent agents. Our results show that both agents exhibit interesting learning capabilities.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Gul Muhammad Khan
%A Julian F. Miller
%A David M. Halliday
%T A developmental model of neural computation using cartesian genetic programming
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2007)
%E Peter A. N. Bosman
%D 2007
%P 2535--2542
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, cartesian genetic programming, brain
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2535.pdf
%X The brain has long been seen as a powerful analogy from which novel computational techniques could be devised. However, most artificial neural network approaches have
   ignored the genetic basis of neural functions. In this paper we describe a radically different approach. We have devised a compartmental model of a neuron as a collection
   of seven chromosomes encoding distinct computational functions representing aspects of real neurons. This model allows neurons, dendrites, and axon branches to grow, die
   and change while solving a computational problem. This also causes the synaptic morphology to change and affect the information processing. Since the appropriate
   computational equivalent functions of neural computation are unknown, we have used a form of genetic programming known as Cartesian Genetic Programming (CGP) to obtain
   these functions. We have evaluated the learning potential of this system in the context of solving a well known agent based learning scenario, known as wumpus world and
   obtained promising results.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A Gul Muhammad Khan
%A Julian Francis Miller
%A David M. Halliday
%T Developing neural structure of two agents that play checkers using cartesian genetic programming
%B GECCO-2008 Late-Breaking Papers
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 2169--2174
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, artificial neural networks, cartesian genetic programming, checkers, co-evolution, computational development
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2169.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1389042

%A Gul Muhammad Khan
%A Julian F. Miller
%A David Halliday
%T In Search of Intelligent Genes: The Cartesian Genetic Programming Computational Neuron (CGPCN)
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 574--581
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, cartesian genetic programming
%X Biological neurons are extremely complex cells whose morphology grows and changes in response to the external environment. Yet, artificial neural networks (ANNs) have
   represented neurons as simple computational devices. It has been evident for a long time that ANNs have learning abilities that are insignificant compared with some of the
   simplest biological brains. We argue that we understand enough neuroscience to create much more sophisticated models. In this paper, we report on our attempts to do this.We
   identify and evolve seven programs that together represents a neuron which grows post evolution into a complete 'neurological' system. The network that occurs by running
   the programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change. We have evaluated the
   capability of these networks for playing the game of checkers. Our method has no board evaluation function, no explicit learning rules and no human expertise at playing
   checkers is used. The learning abilities of these networks are encoded at a genetic level rather than at the phenotype level of neural connections.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Gul Muhammad Khan
%A Julian F. Miller
%T Evolution of cartesian genetic programs capable of learning
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 707--714
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, cartesian genetic programming
%X We propose a new form of Cartesian Genetic Programming (CGP) that develops into a computational network capable of learning. The developed network architecture is inspired
   by the brain. When the genetically encoded programs are run, a networks develops consisting of neurons, dendrites, axons, and synapses which can grow, change or die. We
   have tested this approach on the task of learning how to play checkers. The novelty of the research lies mainly in two aspects: Firstly, chromosomes are evolved that encode
   programs rather than the network directly and when these programs are executed they build networks which appear to be capable of learning and improving their performance
   over time solely through interaction with the environment. Secondly, we show that we can obtain learning programs much quicker through co-evolution in comparison to the
   evolution of agents against a minimax based checkers program. Also, co-evolved agents show significantly increased learning capabilities compared to those that were evolved
   to play against a minimax-based opponent.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Gul Muhammad Khan
%A Julian F. Miller
%A David M. Halliday
%T Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture
%J Evolutionary Computation
%V 19
%N 3
%D 2011
%P 469--523
%I 
%K genetic algorithms, genetic programming, cartesian genetic programming, Artificial Neural Networks, ANN, Co-evolution, Generative and developmental approaches, Learning and
   memory
%X Although artificial neural networks have taken their inspiration from natural neurological systems they have largely ignored the genetic basis of neural functions. Indeed,
   evolutionary approaches have mainly assumed that neural learning is associated with the adjustment of synaptic weights. The goal of this paper is to use evolutionary
   approaches to find suitable computational functions that are analogous to natural subcomponents of biological neurons and demonstrate that intelligent behaviour can be
   produced as a result of this additional biological plausibility. Our model allows neurons, dendrites, and axon branches to grow or die so that synaptic morphology can
   change and affect information processing while solving a computational problem. The compartmental model of neuron consists of a collection of seven chromosomes encoding
   distinct computational functions inside neuron. Since the equivalent computational functions of neural components are very complex and in some cases unknown, we have used a
   form of genetic programming known as Cartesian Genetic Programming (CGP) to obtain these functions. We start with a small random network of soma, dendrites, and neurites
   that develops during problem solving by executing repeatedly the seven chromosomal programs that have been found by evolution. We have evaluated the learning potential of
   this system in the context of a well known single agent learning problem, known as Wumpus World. We also examined the harder problem of learning in a competitive
   environment for two antagonistic agents, in which both agents are controlled by independent CGP Computational Networks (CGPCN). Our results show that the agents exhibit
   interesting learning capabilities.

%A Gul Muhammad Khan
%A Julian F. Miller
%T The CGP Developmental Network
%B Cartesian Genetic Programming
%S Natural Computing Series
%E Julian F. Miller
%D 2011
%P 255--291
%I Springer
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%U http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7
%X In this chapter we will describe a developmental form of Cartesian Genetic Programming (CGP) known as a CGP Developmental Network (CGPDN). The CGPDN is a kind of
   constructivist artificial neural network in which the neuron is represented by seven evolved CGP programs. These programs are each responsible for some neuro-inspired
   aspect of the artificial neuron (i.e. soma, dendrites, axons, synapses and neurite branches). The network is usually initialised with a few neurons. However, when the
   evolved programs are executed the network can develop into a network of arbitrary complexity while simultaneously solving a computational problem. We have tested this model
   on two well known problem in artificial intelligence: Wumpus World and Checkers (Draughts). The role of CGP is to evolve programs that encode the capability of learning,
   rather than learnt information directly. All specific learnt information is acquired post-evolution while solving problems.
%O 9
%Z part of \citeMiller:CGP

%A Maryam Mahsal Khan
%A Gul Muhammad Khan
%A Julian F. Miller
%T Evolution of neural networks using Cartesian Genetic Programming
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%X A novel Neuroevolutionary technique based on Cartesian Genetic Programming is proposed (CGPANN). ANNs are encoded and evolved using a representation adapted from the CGP.
   We have tested the new approach on the single pole balancing problem. Results show that CGPANN evolves solutions faster and of higher quality than the most powerful
   algorithms of Neuroevolution in the literature.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586547

%A Maryam Mahsal Khan
%A Gul Muhammad Khan
%A Julian F. Miller
%T Efficient representation of Recurrent Neural Networks for Markovian/non-Markovian Non-linear Control Problems
%B 10th International Conference on Intelligent Systems Design and Applications (ISDA 2010)
%D 2010
%P 615--620
%I 
%K genetic algorithms, genetic programming, cartesian genetic programming, Markovian-nonMarkovian nonlinear control problems, evolutionary search, generalised networks, neural
   architecture, neuroevolutionary techniques, recurrent artificial neural network, recurrent neural networks, standard benchmark control problem, Markov processes,
   neurocontrollers, nonlinear control systems, recurrent neural nets
%X A novel representation of Recurrent Artificial neural network is proposed for non-linear Markovian and non-Markovian control problems. The network architecture is inspired
   by Cartesian Genetic Programming. The neural network attributes namely weights, topology and functions are encoded using Cartesian Genetic Programming. The proposed
   algorithm is applied on the standard benchmark control problem: double pole balancing for both Markovian and non-Markovian cases. Results demonstrate that the network has
   the ability to generate neural architecture and parameters that can solve these problems in substantially fewer number of evaluations in comparison to earlier
   neuroevolutionary techniques. The power of Recurrent Cartesian Genetic Programming Artificial Neural Network (RCGPANN) is its representation which leads to a thorough
   evolutionary search producing generalised networks.
%8 29  November  -  Decemeber  1
%Z Also known as \cite5687197

%A Maryam Mahsal Khan
%A Gul Muhammad Khan
%T A novel NeuroEvolutionary algorithm: Cartesian genetic programming evolved artificial neural network (CGPANN)
%B Proceedings of the 8th International Conference on Frontiers of Information Technology
%D 2010
%P 48:1--48:4
%I ACM
%C Islamabad, Pakistan
%K genetic algorithms, genetic programming, cartesian genetic programming, ANN, neuroevolution, inverted pendulum, pole balancing
%X Cartesian Genetic Programming based Neuroevolutionary algorithm is proposed. It encodes the neural network attributes namely weights, topology and functions and then
   evolves them for best possible weight, topology and function. The architecture generated are both feedforward and recurrent. The proposed algorithm is applied on the
   standard benchmark control problem: balancing single and double pole at both markovian and non-markovian states. Results demonstrate that CGPANN has the potential to
   generate neural architecture and parameters in substantially fewer number of evaluations in comparison to earlier neuroevolutionary techniques. The power of CGPANN is its
   representation which leads to a thorough evolutionary search producing generalized networks. This opens new avenues of applying the proposed technique to any non-linear and
   dynamic problem.
%Z University of Engineering & Technology, Peshawar, Pakistan

%A Maryam Mahsal Khan
%A Gul Muhammad Khan
%A Julian Francis Miller
%T Evolution of Optimal ANNs for Non-Linear Control Problems using Cartesian Genetic Programming
%B Proceedings of the 2010 International Conference on Artificial Intelligence, ICAI 2010, July 12-15, 2010, Las Vegas Nevada, USA, 2 Volumes
%E Hamid R. Arabnia and David de la Fuente and Elena B. Kozerenko and Jos\'e Angel Olivas and Rui Chang and Peter M. LaMonica and Raymond A. Liuzzi and Ashu M. G. Solo
%D 2010
%P 339--346
%I CSREA Press
%K genetic algorithms, genetic programming, cartesian genetic programming

%A Rahman Khatibi
%A Mohammad Ali Ghorbani
%A Mahsa Hasanpour Kashani
%A Ozgur Kisi
%T Comparison of three artificial intelligence techniques for discharge routing
%J Journal of Hydrology
%D 2011
%I 
%K genetic algorithms, genetic programming, Inter-comparison, Model pluralism, Discharge routing, Artificial intelligence modelling, GP, ANFIS, ANN, Kizilirmak
%U http://www.sciencedirect.com/science/article/B6V6C-52G2370-1/2/930aa6b55c99eef1f1b8abf473b2e17e
%X The inter-comparison of three artificial intelligence (AI) techniques are presented using the results of river flow/stage timeseries, that are otherwise handled by
   traditional discharge routing techniques. These models comprise Artificial Neural Network (ANN), Adaptive Nero-Fuzzy Inference System (ANFIS) and Genetic Programming (GP),
   which are for discharge routing of Kizilirmak River, Turkey. The daily mean river discharge data with a period between 1999 and 2003 were used for training and testing the
   models. The comparison includes both visual and parametric approaches using such statistic as Coefficient of Correlation (CC), Mean Absolute Error (MAE) and Mean Square
   Relative Error (MSRE), as well as a basic scoring system. Overall, the results indicate that ANN and ANFIS have mixed fortunes in discharge routing, and both have different
   abilities in capturing and reproducing some of the observed information. However, the performance of GP displays a better edge over the other two modelling approaches in
   most of the respects. Attention is given to the information contents of recorded timeseries in terms of their peak values and timings, where one performance measure may
   capture some of the information contents but be ineffective in others. Thus, this makes a case for compiling knowledge base for various modelling techniques.
%O In Press, Corrected Proof

%A Mehrdad Khatir
%A Amir Hossein Jahangir
%A Hamid Beigy
%T Investigating the Baldwin Effect on Cartesian Genetic Programming Efficiency
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 2360--2364
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, Cartesian Genetic Programming, Baldwin Effect, Phenotypic Plasticity, Digital Circuit, Reinforcement Learning.
%X Cartesian Genetic Programming (CGP) has an unusual genotype representation which makes it more efficient than Genetic programming (GP) in digital circuit design problem.
   However, to the best of our knowledge, all methods used in evolutionary design of digital circuits deal with rugged, complex search space, which results in long running
   time to obtain successful evolution. Therefore, employing a method to guide evolution in these spaces can facilitate achieving more reasonable results. It has been claimed
   that a two-step evolutionary scenario caused by benefit and cost of learning called Baldwin effect can guide evolution in the biology and artificial life. Therefore, we
   have been motivated to examine this effect on CGP. We observe using this scenario the success rate and evolution time of CGP improves dramatically especially when size of
   chromosomes increases.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Namrata Khemka
%A Scott Novakowski
%A Gerald Hushlak
%A Christian Jacob
%T Evolutionary design of dynamic SwarmScapes
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 827--834
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, interactive evolution, interactive evolutionary art, swarm intelligence, swarm-based painting, Generative systems, developmental
   systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p827.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389257

%A Chirag D. Khopkar
%T Solving the Art Gallery Problem via Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 110--119
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Taghi M. Khoshgoftaar
%A Yi Liu
%A Naeem Seliya
%T Genetic Programming-Based Decision Trees for Software Quality Classification
%B Proceedings of the Fifteenth International Conference on Tools with Artificial Intelligence (ICTAI 03)
%D 2003
%P 374--383
%I IEEE Computer Society
%C Los Alamitos, California
%K genetic algorithms, genetic programming, decision trees, program testing, software metrics, software quality, C4.5 decision tree, GP-based decision trees, S-expression
   tree, automated genetic programming, classification model, misclassification cost, multiobjective optimization, multiple criteria, program module, risk-based classes,
   simultaneous optimization, software development, software inspection, software metrics, software quality classification, software system, software testing, tree-structure
%X The knowledge of the likely problematic areas of a software system is very useful for improving its overall quality. Based on such information, a more focused software
   testing and inspection plan can be devised. Decision trees are attractive for a software quality classification problem which predicts the quality of program modules in
   terms of risk-based classes. They provide a comprehensible classification model which can be directly interpreted by observing the tree-structure. A simultaneous
   optimisation of the classification accuracy and the size of the decision tree is a difficult problem, and very few studies have addressed the issue. This paper presents an
   automated and simplified genetic programming (gp) based decision tree modelling technique for the software quality classification problem. Genetic programming is ideally
   suited for problems that require optimisation of multiple criteria. The proposed technique is based on multi-objective optimisation using strongly typed GP. In the context
   of an industrial high-assurance software system, two fitness functions are used for the optimization problem: one for minimising the average weighted cost of
   misclassification, and one for controlling the size of the decision tree. The classification performances of the GP-based decision trees are compared with those based on
   standard GP, i.e., S-expression tree. It is shown that the GP-based decision tree technique yielded better classification models. As compared to other decision tree-based
   methods, such as C4.5, GP-based decision trees are more flexible and can allow optimisation of performance objectives other than accuracy. Moreover, it provides a practical
   solution for building models in the presence of conflicting objectives, which is commonly observed in software development practice.
%8 3-5  November
%Z Inspec Accession Number: 7862146

%A Taghi M. Khoshgoftaar
%A Yi Liu
%A Naeem Seliya
%T Module-Order Modeling using an Evolutionary Multi-Objective Optimization Approach
%B Proceedings of the 10th IEEE International Symposium on Software Metrics (METRICS '04)
%D 2004
%P 159--169
%I IEEE Computer Society
%K genetic algorithms, genetic programming, software fault tolerance, software metrics, software process improvement, module-order model, multiobjective optimization,
   risk-based rankings, software faults, software quality, software reliability improvements, telecommunications software system
%X The problem of quality assurance is important for software systems. The extent to which software reliability improvements can be achieved is often dictated by the amount of
   resources available for the same. A prediction for risk-based rankings of software modules can assist in the cost-effective delegation of the limited resources. A
   module-order model (MOM) is used to gauge the performance of the predicted rankings. Depending on the software system under consideration, multiple software quality
   objectives may be desired for a MOM; e.g., the desired rankings may be such that if 20% of modules were targeted for reliability enhancements then 80% of the faults would
   be detected. In addition, it may also be desired that if 50% of modules were targeted then 100% of the faults would be detected. Existing works related to MOM(s) have used
   an underlying prediction model to obtain the rankings, implying that only the average, relative, or mean square errors are minimized. Such an approach does not provide an
   insight into the behavior of a MOM, the performance of which focuses on how many faults are accounted for by the given percentage of modules enhanced. We propose a
   methodology for building MOM (s) by implementing a multiobjective optimization with genetic programming. It facilitates the simultaneous optimization of multiple
   performance objectives for a MOM. Other prediction techniques, e.g., multiple linear regression and neural networks, cannot achieve multiobjective optimization for MOM(s).
   A case study of a high-assurance telecommunications software system is presented. The observed results show a new promise in the modeling of goal-oriented software quality
   estimation models.
%Z Also known as \cite1357900

%A Taghi M. Khoshgoftaar
%A Yi Liu
%A Naeem Seliya
%T A Multiobjective Module-Order Model for Software Quality Enhancement
%J IEEE Transactions on Evolutionary Computation
%V 8
%N 6
%D 2004
%P 593--608
%I 
%K genetic algorithms, genetic programming, module-order model (MOM), multiobjective optimization (MOO), software metrics, software quality estimation, SBSE
%X The knowledge, prior to system operations, of which program modules are problematic is valuable to a software quality assurance team, especially when there is a constraint
   on software quality enhancement resources. A cost-effective approach for allocating such resources is to obtain a prediction in the form of a quality-based ranking of
   program modules. Subsequently, a module-order model (MOM) is used to gauge the performance of the predicted rankings. From a practical software engineering point of view,
   multiple software quality objectives may be desired by a MOM for the system under consideration: e.g., the desired rankings may be such that 100percent of the faults should
   be detected if the top 50percent of modules with highest number of faults are subjected to quality improvements. Moreover, the management team for the same system may also
   desire that 80percent of the faults should be accounted if the top 20percent of the modules are targeted for improvement. Existing work related to MOM(s) use a quantitative
   prediction model to obtain the predicted rankings of program modules, implying that only the fault prediction error measures such as the average, relative, or mean square
   errors are minimized. Such an approach does not provide a direct insight into the performance behavior of a MOM. For a given percentage of modules enhanced, the performance
   of a MOM is gauged by how many faults are accounted for by the predicted ranking as compared with the perfect ranking. We propose an approach for calibrating a
   multi-objective MOM using genetic programming. Other estimation techniques, e.g., multiple linear regression and neural networks cannot achieve multi objective optimization
   for MOM(s). The proposed methodology facilitates the simultaneous optimization of multiple performance objectives for a MOM. Case studies of two industrial software systems
   are presented, the empirical results of which demonstrate a new promise for goal-oriented software quality modeling.
%8 Decemeber
%Z lilgp. Also known as \cite1369249

%A Taghi M. Khoshgoftaar
%A Yi Liu
%T A Multi-Objective Software Quality Classification Model Using Genetic Programming
%J IEEE Transactions on Reliability
%V 56
%N 2
%D 2007
%P 237--245
%I 
%K genetic algorithms, genetic programming, decision trees, genetic algorithms, software metrics, software quality, software reliability, genetic programming-based decision
   tree model, multiobjective software quality classification model, risk-based software quality prediction, software fault-prone module, software metrics, software quality
   assurance, software quality-improvement, software reliability
%X A key factor in the success of a software project is achieving the best-possible software reliability within the allotted time & budget. Classification models which provide
   a risk-based software quality prediction, such as fault-prone & not fault-prone, are effective in providing a focused software quality assurance endeavor. However, their
   usefulness largely depends on whether all the predicted fault-prone modules can be inspected or improved by the allocated software quality-improvement resources, and on the
   project-specific costs of misclassifications. Therefore, a practical goal of calibrating classification models is to lower the expected cost of misclassification while
   providing a cost-effective use of the available software quality-improvement resources. This paper presents a genetic programming-based decision tree model which
   facilitates a multi-objective optimization in the context of the software quality classification problem. The first objective is to minimize the Modified Expected Cost of
   Misclassification, which is our recently proposed goal-oriented measure for selecting & evaluating classification models. The second objective is to optimize the number of
   predicted fault-prone modules such that it is equal to the number of modules which can be inspected by the allocated resources. Some commonly used classification
   techniques, such as logistic regression, decision trees, and analogy-based reasoning, are not suited for directly optimizing multi-objective criteria. In contrast, genetic
   programming is particularly suited for the multi-objective optimization problem. An empirical case study of a real-world industrial software system demonstrates the
   promising results, and the usefulness of the proposed model
%8 June

%A Bijan KHosraviani
%T Organization Design Optimization using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 109--117
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/KHosraviani.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Bijan KHosraviani
%A Raymond E. Levitt
%A John R. Koza
%T Organization Design Optimization Using Genetic Programming
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP056.pdf
%X This paper describes how we use Genetic Programming (GP) techniques to help project managers find near optimal designs for their project organisations. We use GP as a
   postprocessor optimiser for the project organisation design simulator Virtual Design Team (VDT). Decision making policy and individual/sub-team properties, activity
   assignments and percentage allocation for each activity are varied by GP, and the effect on quality and duration of the project is compared via a fitness function. The
   solutions found by GP compare favourably with the bes
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Bijan KHosraviani
%T An Evolutionary Approach for Project Organization Design: Producing Human-Competitive Results using Genetic Programming
%R Ph.D. Thesis
%D 2005
%I 
%I Department of Civil and Environmental Engineering, Stanford
%K genetic algorithms, genetic programming
%U http://crgp.stanford.edu/publications/dissertations/KHosraviani_2005.pdf
%X I discuss why I chose the evolutionary computing approach as opposed to classical optimisation methodology, and show some of the advantages and limitations of the
   evolutionary approach. There was no formal theory of project organisation design nor any analysis tools for predicting the performance of project organisations prior to the
   development of VDT in the 1990s. Not surprisingly, therefore, research by C.B. Tatum in the early 1980s found that current human-developed project organisation structures
   are the result of "natural" trial-and-error evolutionary processes. By applying the evolutionary computing approach to organisation design, my model is thus actually
   mimicking the nature of human organisation design. In addition, I demonstrate how my approach can create a powerful Human-Computer Interaction (HCI) environment that can
   motivate humans to think "outside the box" when designing project organisations. Using a combination of "intellective" (theorem proving) and "emulation" (natural,
   empirical) experiments, I validate the postprocessor's "near-optimal" solutions against findings of organisational contingency theory and human-derived solutions for a set
   of real test cases. By showing that "optimal" structure depends on the relative emphasis of time, cost and process quality outcome metrics, I extend contingency theory to
   develop a richer "micro-contingency theory" for project organisations. This research represents a significant step towards closing the relevance gap between organisation
   theory and organisation practice by addressing the issues of organisational design prescriptively. I analyse alternatives in terms of fitness functions that evaluate
   specific designs for "survival" and "reproduction" in the spirit of contingency theory. Finally, the thesis concludes with a summary of the contributions of this research
   in the three areas of organisation science, project management, and computer science.
%O submitted as a doctoral dissertation to Stanford University
%8 Decemeber

%A Mehdi Khoury
%A Frank Guerin
%A George Macleod Coghill
%T Finding Semi-Quantitative Physical Models Using Genetic Programming
%B The 6th annual UK Workshop on Computational Intelligence
%E Xue Z. Wang and Rui Fa Li
%D 2006
%P 245--252
%I 
%C Leeds, UK
%K genetic algorithms, genetic programming, fuzzy, qualitative modelling, semi quantitative modelling
%U http://www.csd.abdn.ac.uk/~mkhoury/fuzzy%20evolution2.pdf
%X Model learning often implies exploring a vast search space of possible hypotheses in the hope of finding a solution. Qualitative model learners are mostly based on
   Inductive Logic Programming (ILP), which is a systematic method which tends to be well fitted for exploring solutions in a narrow search space. We present a
   semi-quantitative model learner that uses Genetic Programming (GP), which is well suited for exploring a broad search space. We learn simple physical systems based on a
   formalism involving both crisp numbers and fuzzy quantity spaces. We use the ECJ framework,1 and the fitness of a model is set to be optimal when it covers all positive
   examples. Several experiments are performed to learn and reuse models of physical systems of increasing complexity; firstly a u-tube, then coupled tanks, and finally
   cascading tanks. Results show that the system can approximate the target models in reasonably good conditions, and that there is still scope for optimisation.
%8 4-6  September

%A Mehdi Khoury
%A Frank Guerin
%A George M. Coghill
%T Learning dynamic models of compartment systems by combining symbolic regression with fuzzy vector envisionment
%B Genetic and Evolutionary Computation Conference (GECCO2007) workshop program
%E Tina Yu
%D 2007
%P 2769--2776
%I ACM Press New York, NY, USA
%C London, United Kingdom
%K genetic algorithms, genetic programming, dynamic biological model, dynamic compartmental model, fuzzy vector envisionment, measurement, metabolic pathways,
   semi-quantitative modelling, S-system, symbolic regression, u-tube
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2769.pdf
%X This paper is concerned with the learning of dynamic models of compartmental systems visualised as networks of interconnected tanks. This is intended as an intermediary
   step to learn more complex dynamic biological systems such as metabolic pathways. Our present aim is to learn systems of differential equations from time series data to
   capture physical models of increasing complexity (u-tube, cascaded tanks, and coupled tanks). To do so, we use Symbolic Regression in Genetic Programming and combine it
   with a fuzzy representation which has inherent differential capabilities (Fuzzy Vector Envisionment). We use the ECJ framework to implement the learner. Present results
   show that the system can approximate the target models and that the use of a weighted fitness function seems to accelerate the learning process.
%8 7-11  July
%Z Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071

%A Mehdi Khoury
%A Honghai Liu
%T Classifying 3D Human Motions by Mixing Fuzzy Gaussian Inference with Genetic Programming
%B Second International Conference on Intelligent Robotics and Applications, ICIRA 2009
%S Lecture Notes in Computer Science
%E Ming Xie and Youlun Xiong and Caihua Xiong and Honghai Liu and Zhencheng Hu
%V 5928
%D 2009
%P 55--66
%I Springer
%C Singapore
%K genetic algorithms, genetic programming
%X This paper combines the novel concept of Fuzzy Gaussian Inference(FGI) with Genetic Programming (GP) in order to accurately classify real natural 3d human Motion Capture
   data. FGI builds Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions, providing a suitable modelling paradigm for such noisy
   data. Genetic Programming (GP) is used to make a time dependent and context aware filter that improves the qualitative output of the classifier. Results show that FGI
   outperforms a GMM-based classifier when recognizing seven different boxing stances simultaneously, and that the addition of the GP based filter improves the accuracy of the
   FGI classifier significantly.
%8 Decemeber  16-18

%A Mehdi Khoury
%A Honghai Liu
%T Extending evolutionary Fuzzy Quantile Inference to classify partially occluded human motions
%B IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X This work presents a framework that combines the concept of Fuzzy Quantile Inference(FQI) with Genetic Programming (GP) in order to accurately classify real natural 3d
   human Motion Capture data. FQI is a generalisation of Fuzzy Gaussian Inference. It builds Fuzzy Membership Functions that map to hidden Probability Distributions underlying
   human motions, providing a suitable modelling paradigm for such noisy data. Genetic Programming (GP) is used to make a time dependent and context aware filter that improves
   the qualitative output of the classifier. Results show that FQI outperforms a GMM-based classifier when recognising six different boxing stances simultaneously, and that
   the addition of the GP based filter improves the accuracy of the FQI classifier significantly. A mechanism allowing the FQI extended framework to deal with occluded data
   reasonably well is also integrated.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5584623

%A Soon Thiam Khu
%A Shie-Yui Liong
%A Vladan Babovic
%A Henrik Madsen
%A Nitin Muttil
%T Genetic Programming and Its Application in Real-Time Runoff Forecasting
%J Journal of the American Water Resources Association
%V 37
%N 2
%D 2001
%P 439--451
%I American Water Resources Association
%K genetic algorithms, genetic programming, Runoff forecasting, Rainfall-runoff models, Storms, NAM rainfall-runoff simulation model, MIKE II hydrodynamic model, NAMKAL,
   France, Orgeval River, Ru des Avenelles, Ru de Bourgogne, Ru de Rognon
%U http://www.awra.org/jawra/papers/J99178.html
%8 April
%Z AWRA Paper Number 99178

%A Arman Kiani-B
%A M. R. Akbarzadeh-T
%T Automatic Text Summarization Using: Hybrid Fuzzy GA-GP
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 5465--5471
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X A novel technique is proposed for summarising text using a combination of Genetic Algorithms (GA) and Genetic Programming (GP) to optimise rule sets and membership
   functions of fuzzy systems. The novelty of the proposed algorithm is that fuzzy system is optimized for extractive based text summarizing. In this method GP is used for
   structural part and GA for the string part (Membership functions). The goal is to develop an optimal intelligent system to extract important sentences in the texts by
   reducing the redundancy of data. The method is applied in 3 test documents and compared with the standard fuzzy systems as well as two other commercial summarisers:
   Microsoft word and Copernic Summarizer. Simulations demonstrate several significant improvements with the proposed approach.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Raihan H. Kibria
%A You Li
%T Optimizing the Initialization of Dynamic Decision Heuristics in DPLL SAT Solvers Using Genetic Programming
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 331--340
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050331.pdf
%X The Boolean satisfiability problem (SAT) has many applications in electronic design automation (EDA) as well as theoretical computer science. Most SAT solvers for EDA
   problems use the DPLL algorithm and conflict analysis dependent decision heuristics. When the search starts, the heuristics have little or no information about the
   structure of the CNF. In this work, an algorithm for initialising dynamic decision heuristics is evolved using genetic programming. The open-source SAT solver MiniSAT v1.12
   is used. Using the best algorithm evolved, an advantage was found for solving unsatisfiable EDA SAT problems.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A K. Kiguchi
%A H. Miyaji
%A K. Watanabe
%A K. Izumi
%A T. Fukuda
%T Generation of an optimal architecture of neuro force controllers for robot manipulators in unknown environments using genetic programming with fuzzy fitness evaluation
%J Soft Computing - A Fusion of Foundations, Methodologies and Applications
%V 5
%N 3
%D 2001
%P 237--242
%I 
%K genetic algorithms, genetic programming, Robot manipulator, Force control, Neuro controller, Fuzzy evaluation
%X we have applied genetic programming to generate an optimal architecture of neuro force controllers for robot manipulators in any environment. In order to perform precise
   force control in unknown environments, the optimal structured neuro force controller is generated using genetic programming with fuzzy fitness evaluation. After the
   architecture of the neuro controller has been optimised for any kinds of environments, it can be applied for a robot contact task with an unknown environment in on-line
   manner using its own adaptation ability. An effective crossover operation is proposed for the efficient evolution of the controllers. The simulation has been carried out to
   evaluate the effectiveness of the proposed robot force controller.
%8 June

%A Kohki Kikuchi
%A Fumio Hara
%T Evolutionary Design of Morphology and Intelligence in Robotic System Using Genetic Programming
%B From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior
%E Rolf Pfeifer and Bruce Blumberg and Jean-Arcady Meyer and Stewart W. Wilson
%D 1998
%I MIT Press
%C Zurich, Switzerland
%K genetic algorithms, genetic programming
%8 August  17-21
%Z http://www.isab.org.uk/confs/sab98.php included in google books May 2008
%@ 0-262-66144-6

%A Dae Wook Kim
%A Sang Kyoon Kim
%A Hang Joon Kim
%T An Extraction Method of a Car License Plate using a Distributed Genetic Algorithm
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 500
%I MIT Press
%C Stanford University, CA, USA
%K Genetic Algorithms
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96 GA paper

%A Hyun-Soo Kim
%A Sang-Gyu Lee
%A Seung-Soo Han
%A Hyeon Bae
%A Tae-Ryong Jeon
%A Sungshin Kim
%T A Comparison of Optimization Methods for the Transparent Conducting Oxide Application of Ga-doped ZnO
%B Fourth International Conference on Natural Computation, ICNC '08
%V 1
%D 2008
%P 126--130
%I 
%K genetic algorithms, genetic programming, error back-propagation algorithm, fractional factorial design, neural networks, optimal process conditions, optimization methods,
   particle swarm optimization, transparent conducting oxide, backpropagation, dielectric thin films, electrical engineering computing, gallium, neural nets, particle swarm
   optimisation, zinc compounds
%X In this paper, statistical experimental design is used to characterize the transparent conducting oxide process of Ga-doped ZnO. Fractional factorial design with three
   center points are employed. In the process modeling, neural networks trained by the error back-propagation algorithm and genetic programming are applied to map the
   relationships between several input factors and resistivity. Both modeling methods are typical modeling methods for local and global approaches. Subsequently, both genetic
   algorithms and particle swarm optimization are used to identify the optimal process conditions to minimize resistivity. The results of the two approaches are compared, and
   the optimized resistivity found by the particle swarm method was slightly better than that found by genetic algorithms. More importantly, repeated applications of particle
   swarm optimization yielded process conditions with smaller standard deviations, implying greater consistency in recipe generation.
%8 October
%Z Also known as \cite4666824

%T Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, biological modeling/ breast cancer, biological modelling, classifiers, coevolution, constraint handling, control system design,
   controlling search, design applications, devices developement and applications, dynamic and parallel ec, ec techniques, ecological modelling and information ecosystems,
   engineering applications, evolutionary markets, evolutionary scheduling, evolvable hardware, evolving neural networks, fitness, games and game like tasks, hybrid systems,
   image processing applications, image/ signal processing, intelligent agents, learning and search spaces, local search optimization, medical applications, multi-agent
   systems and cultural algorithms, multi-objective optimization, network applications, new paradigms, novel applications, novel themes, operations research applications,
   representations, revisiting the fossil record, robotic applications, stroganoff, system modeling and control, theory and foundations, time series
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =
%@ 0-7803-6658-1

%A Jungwon Kim
%A Peter Bentley
%T Immune Memory and Gene Library Evolution in the Dynamic Clonal Selection Algorithm
%J Genetic Programming and Evolvable Machines
%V 5
%N 4
%D 2004
%P 361--391
%I 
%K AIS, artificial immune systems, dynamic clonal selection, immune memory, gene library evolution, intrusion detection
%X We describe two extensions to the original DynamiCS: (1) the deletion of memory detectors that are no longer valid and (2) the simulation of gene library evolution.
   Firstly, DynamiCS is extended in order to decrease the false positive (FP) error rates caused by memory detectors. The extended DynamiCS eliminates memory detectors when
   they show a poor degree of self-tolerance to new antigens. This system is tested to determine whether surviving memory detectors no longer cause high FP error rates. The
   results show a marked decrease in FP errors produced by the system but an increase in the amount of Co-stimulation required. The large amount of costimulation can render
   the system weak for intrusion detection. The second extension to DynamiCS is proposed to resolve this problem. It employs the use of hypermutation to produce the effect of
   gene library evolution. This is designed to fine-tune generated memory detectors so that the system obtains higher true positive (TP) detection rates without increasing the
   amount of co-stimulation. The new extension is tested to determine whether it gains high TP detection rates without increasing the amount of costimulation as the result of
   gene library evolution. The test results prove that hyper-mutation leads the progress of gene library evolution and thus produces immature detectors that are more tuned to
   cover existing non-self antigens.
%8 Decemeber
%Z Article ID: 5272967

%A Dong-Kyun Kim
%A Hongqing Cao
%A Kwang-Seuk Jeong
%A Friedrich Recknagel
%A Gea-Jae Joo
%T Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms
%J Ecological Modelling
%V 203
%N 1-2
%D 2007
%P 147--156
%I 
%K genetic algorithms, genetic programming, Machine learning, Regulated river, Evolutionary computation, Algebraic function model, Rule-based model, Microcystis aeruginosa,
   Sensitivity analysis
%X Two algorithms of evolutionary computation, an algebraic function model and a rule-based model, were applied for model development with respect to 8 years of limnological
   data from the lower Nakdong River. The aim of the modelling was to reproduce the abundances of the phytoplankton species, Microcystis aeruginosa, based on physical,
   chemical and meteorological parameters. The algebraic function model overestimated or underestimated abundance values, but correctly recognised the timing of high
   abundances. The rule-based model detected not only the timing of algal blooms well but also the magnitude of abundances. Sensitivity analysis indicates that high water
   temperature influences high abundances of M. aruginosa. In addition, dissolved oxygen, pH, nitrate and phosphate are shown to be explainable in relation to deoxygeneration,
   carbon dioxide transformation and nutrient limitations.
%O Special Issue on Ecological Informatics: Biologically-Inspired Machine Learning, 4th Conference of the International Society for Ecological Informatics
%8 24  April
%Z a Department of Biology, Pusan National University, Jang-Jeon Dong, Gum-Jeong Gu, Busan 609-735, South Korea b School of Earth and Environmental Sciences, University of
   Adelaide, SA 5005, Australia

%A Dong-Kyun Kim
%A Bob Mckay
%A Haisoo Shin
%A Yun-Geun Lee
%A Xuan Hoai Nguyen
%T Ecological application of evolutionary computation: Improving water quality forecasts for the Nakdong River, Korea
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Water quality is an important global issue, requiring effective management, which needs good predictive tools. While good methods for lake water quality prediction have
   previously been developed, accurate prediction of river water quality has hitherto been difficult. This project combines process-model and data mining approaches through
   evolutionary methods, resulting in tools for more effective water management. Although the work is still in its preliminary stages, error rates of the predictive models are
   already around half those resulting from representative applications of either pure process-based or pure data mining approaches.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586060

%A Kyoung Min Kim
%A Sung-Soo Lim
%A Sung-Bae Cho
%T User Adaptive Answers Generation for Conversational Agent Using Genetic Programming
%B Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Proceedings
%S Lecture Notes in Computer Science
%E Zheng Rong Yang and Richard M. Everson and Hujun Yin
%V 3177
%D 2004
%P 813--819
%I Springer
%I IEEE
%C Exeter, UK
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3177&spage=813
%X Recently, it seems to be interested in the conversational agent as an effective and familiar information provider. Most of conversational agents reply to user's queries
   based on static answers constructed in advance. Therefore, it cannot respond with flexible answers adjusted to the user, and the stiffness shrinks the usability of
   conversational agents. In this paper, we propose a method using genetic programming to generate answers adaptive to users. In order to construct answers, Korean grammar
   structures are defined by BNF (Backus Naur Form), and it generates various grammar structures using genetic programming (GP). We have applied the proposed method to the
   agent introducing a fashion web site, and certified that it responds more flexibly to users queries
%8 August  25-27
%Z http://www.dcs.ex.ac.uk/ideal04/ Dept. of Computer Science, Yonsei University, 134 Shinchon-dong, Seodaemoon-ku, Seoul 120-749, Korea
%@ 3-540-22881-0

%A Kyung-Joong Kim
%A Sung-Bae Cho
%T Evolved neural networks based on cellular automata for sensory-motor controller
%J Neurocomputing
%V 69
%N 16-18
%D 2006
%P 2193--2207
%I 
%K genetic algorithms, genetic programming, Evolutionary neural network, Incremental evolution, Multi-module integration, Cellular automata, Mobile robot control
%X Constructing the controller of a mobile robot has several issues to be addressed: how to automate behaviour generation procedure, how to insert available domain knowledge
   effectively, and how to hybrid these methods in an integrated manner. There has been extensive work to construct an optimal neural network for controlling a mobile robot by
   evolutionary approaches such as genetic algorithm, genetic programming, and so on. However, evolutionary approaches have a difficulty to design the controller that conducts
   complex behaviours. In order to overcome this shortcoming, we propose an incremental evolution method for neural networks based on cellular automata and a method of
   combining several evolved modules by a rule-based approach. The incremental evolution method evolves the neural network by starting with simple environment and gradually
   making it more complex. The multi-modules integration method can make complex behaviors by combining several modules evolved or programmed to do simple behaviours.
   Simulation results show the potential of the incremental evolution and multi-module integration methods as sophisticated techniques to make the evolved neural network to do
   complex behaviours. In this paper, we attempt to investigate the applicability of cellular automata-based neural networks and propose sophisticated techniques for the
   generation of high-level behaviours.
%8 October

%A Kyung-Joong Kim
%A Adrian Wong
%A Hod Lipson
%T Automated synthesis of resilient and tamper-evident analog circuits without a single point of failure
%J Genetic Programming and Evolvable Machines
%V 11
%N 1
%D 2010
%P 35--59
%I 
%K genetic algorithms, genetic programming, evolvable hardware, Analog circuit, Robustness, Evolutionary strategies, Low-pass filter, Hardware implementation, Tamper-evident
   circuits, coevolution
%X This study focuses on the use of genetic programming to automate the design of robust analog circuits. We define two complementary types of failure modes: partial
   short-circuit and partial disconnect, and demonstrated novel circuits that are resilient across a spectrum of fault levels. In particular, we focus on designs that are
   uniformly robust, and unlike designs based on redundancy, do not have any single point of failure. We also explore the complementary problem of designing tamper-proof
   circuits that are highly sensitive to any change or variation in their operating conditions. We find that the number of components remains similar both for robust and
   standard circuits, suggesting that the robustness does not necessarily come at significant increased circuit complexity. A number of fitness criteria, including surrogate
   models and co-evolution were used to accelerate the evolutionary process. A variety of circuit types were tested, and the practicality of the generated solutions was
   verified by physically constructing the circuits and testing their physical robustness.
%8 March
%Z sec 4.5 "We tested the validity of the evolved circuits by building them in reality.". sec 5 "no single point of failure. Surprisingly, this robustness did not come at a
   significant increase in circuit complexity..."

%A Peter S. Kim
%T Evolution of a State-Evaluation Function for the Game of Nim via Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 120--127
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Jung-Jib Kim
%A Byoung-Tak Zhang
%T Effects of Selection Schemes in Genetic Programming for Time Series Prediction
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 1
%D 1999
%P 252--258
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, time series
%U http://bi.snu.ac.kr/Publications/Conferences/International/CEC99_Kim.pdf
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143 A Study on Effects of Selection Schemes
   in Genetic Programming for Time Series Prediction, Jung-Jib Kim, Master Thesis, Dept. of Computer Engineering, Seoul National University, February 2000
%@ 0-7803-5537-7 (Microfiche)

%A Jungwon Kim
%A Peter Bentley
%T Negative selection and niching by an artificial immune system for network intrusion detection
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 149--158
%I 
%C Orlando, Florida, USA
%8 13  July
%Z GECCO-99LB

%A DaeEun Kim
%T Structural Risk Minimization on Decision Trees Using An Evolutionary Multiobjective Optimization
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 338--348
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=338
%X Inducing decision trees is a popular method in machine learning. The information gain computed for each attribute and its threshold helps finding a small number of rules
   for data classification. However, there has been little research on how many rules are appropriate for a given set of data. An evolutionary multi-objective optimisation
   approach with genetic programming will be applied to the data classification problem in order to find the minimum error rate for each size of decision trees. Following
   structural risk minimisation suggested by Vapnik, we can determine a desirable number of rules with the best generalisation performance. A hierarchy of decision trees for
   classification performance can be provided and it is compared with C4.5 application.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A DaeEun Kim
%T Analyzing Sensor States and Internal States in the Tartarus Problem with Tree State Machines
%B Parallel Problem Solving from Nature - PPSN VIII
%S LNCS
%E Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\vno Ata Kab\'an and Hans-Paul
   Schwefel
%V 3242
%D 2004
%P 551--560
%I Springer-Verlag Berlin
%C Birmingham, UK
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=551
%X The Tartarus problem is a box pushing task in a grid world environment. It is one of difficult problems for purely reactive agents to solve, and thus a memory-based control
   architecture is required. This paper presents a novel control structure, called tree state machine, which has an evolving tree structure for sensorimotor mapping and also
   encodes internal states. As a result, the evolutionary computation on tree state machines can quantify internal states and sensor states needed for the problem. Tree state
   machines with a dynamic feature of sensor states are demonstrated and compared with finite state machines and GP-automata. It is shown that both sensor states and memory
   states are important factors to influence the behaviour performance of an agent.
%8 18-22  September
%Z PPSN-VIII
%@ 3-540-23092-0

%A DaeEun Kim
%T Memory analysis and significance test for agent behaviours
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 151--158
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Artificial Life Evolutionary Robotics, Adaptive Behavior, computational effect, finite state machines, grid world problem, internal
   states
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p151.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Kangil Kim
%A R. I. (Bob) McKay
%A Dharani Punithan
%T Sampling Bias in Estimation of Distribution Algorithms for Genetic Programming Using Prototype Trees
%B PRICAI 2010: Trends in Artificial Intelligence, 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30-September 2, 2010. Proceedings
%S Lecture Notes in Computer Science
%E Byoung-Tak Zhang and Mehmet A. Orgun
%V 6230
%D 2010
%P 100--111
%I Springer
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.1007/978-3-642-15246-7

%A Kangil Kim
%A Min Hyeok Kim
%A Bob McKay
%T Structural difficulty in estimation of distribution genetic programming
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1459--1466
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X Estimation of Distribution Algorithms were introduced into Genetic Programming over 15 years ago, and have demonstrated good performance on a range of problems, but there
   has been little research into their limitations. We apply two such algorithms - scalar and vectorial Stochastic Grammar GP - to Daida's well-known Lid problem, to better
   understand their ability to learn specific structures. The scalar algorithm performs poorly, but the vectorial version shows good overall performance. We then extended
   Daida's problem to explore the vectorial algorithm's ability to find even more specific structures, finding that the performance fell off rapidly as the specificity of the
   required structure increased. Thus although this particular system has less severe structural difficulty issues than standard GP, it is by no means free of them. Track:
   Genetic Programming
%8 12-16  July
%Z Also known as \cite2001772 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Kangil Kim
%A Bob (R. I.) Mckay
%T Stochastic Diversity Loss and Scalability in Estimation of Distribution Genetic Programming
%J IEEE Transactions on Evolutionary Computation
%I 
%K genetic algorithms, genetic programming, Estimation of Distribution Algorithm (EDA), Evolutionary Computation (EC), Genetic Programming (GP), Likelihood Weighting (LW),
   Probabilistic Prototype Tree (PPT), diversity loss, sampling bias, sampling drift
%X In Estimation of Distribution Algorithms (EDA), probability models hold accumulating evidence on the location of an optimum. Stochastic sampling drift has been heavily
   researched in EDA optimisation, but not in EDAs applied to Genetic Programming (EDA-GP). We show that, for EDA-GPs using Probabilistic Prototype Tree (PPT) models,
   stochastic drift in sampling and selection is a serious problem, inhibiting scaling to complex problems. Problems requiring deep dependence in their probability structure
   see such rapid stochastic drift that the usual methods for controlling drift are unable to compensate. We propose a new alternative, analogous to likelihood weighting of
   evidence. We demonstrate in a small-scale experiment that it does counteract the drift, sufficiently to leave EDA-GP systems subject to similar levels of stochastic drift
   to other EDAs.
%O Accepted for future publication
%Z also known as \cite6189777

%A Kyong-joong Kim
%A Sung-bae Cho
%T Integration of Multiple Neural Networks Evolved on Cellular Automata by Action Selection Mechanism
%D 2001?
%I 
%K cellular automata
%U http://citeseer.ist.psu.edu/521166.html
%X There has been extensive research of developing the controller for a mobile robot. Especially, several researchers have constructed the mobile robot controller that can
   avoid obstacles, evade predators, or catch moving prey by evolutionary algorithms such as genetic algorithm and genetic programming. In this line of research, we have also
   presented a method of applying CAM-Brain, evolved neural networks based on cellular automata (CA), to control a mobile robot. However, this approach has a limitation to
   make the robot to perform appropriate behavior in complex environments. In this paper, we have attempted to solve this problem by combining several modules evolved to do a
   simple behavior by Maes's Action Selection Mechanism. Experimental results show that this approach has potential to develop a sophisticated neural controller for complex
   environments.
%O The Pennsylvania State University CiteSeer Archives
%Z Not a GP paper Similarly http://citeseer.ist.psu.edu/517213.html is not a GP paper

%A MinHyeok Kim
%A Robert Ian (Bob) McKay
%A Nguyen Xuan Hoai
%A Kangil Kim
%T Operator Self-Adaptation in Genetic Programming
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 215--226
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X We investigate the application of adaptive operator selection rates to Genetic Programming. Results confirm those from other areas of evolutionary algorithms: adaptive rate
   selection out-performs non-adaptive methods, and among adaptive methods, adaptive pursuit out-performs probability matching. Adaptive pursuit combined with a reward policy
   that rewards the overall fitness change in the elite worked best of the strategies tested, though not uniformly on all problems.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A MinHyeok Kim
%A Robert Ian (Bob) McKay
%A Dong-Kyun Kim
%A Xuan Hoai Nguyen
%T Evolutionary Operator Self-Adaptation with Diverse Operators
%B Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012
%S LNCS
%E Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta
%V 7244
%D 2012
%P 230--241
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Adaptive operator selection, Adaptive pursuit, Probability matching, Multi-armed bandit, Evolutionary algorithm
%X Operator adaptation in evolutionary computation has previously been applied to either small numbers of operators, or larger numbers of fairly similar ones. This paper
   focuses on adaptation in algorithms offering a diverse range of operators. We compare a number of previously-developed adaptation strategies, together with two that have
   been specifically designed for this situation. Probability Matching and Adaptive Pursuit methods performed reasonably well in this scenario, but a strategy combining
   aspects of both performed better. Multi-Arm Bandit techniques performed well when parameter settings were suitably tailored to the problem, but this tailoring was
   difficult, and performance was very brittle when the parameter settings were varied.
%8 11-13  April
%Z Part of \citeMoraglio:2012:GP EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012

%A Minkyu Kim
%A Ying L. Becker
%A Peng Fei
%A Una-May O'Reilly
%T Constrained Genetic Programming to Minimize Overfitting in Stock Selection
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 179--195
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%O 12
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008

%A Seung Joon Kim
%A Sanghoun Oh
%A Young Geun Lee
%A Moon Gu Jeon
%A In S. Kim
%A Joon Ha Kim
%T A control methodology for the feed water temperature to optimize SWRO desalination process using genetic programming
%J Desalination
%V 247
%N 1-3
%D 2009
%P 190--199
%I 
%K genetic algorithms, genetic programming, Seawater reverse osmosis (SWRO)
%U http://www.sciencedirect.com/science/article/B6TFX-4X502WT-P/2/35e0f68a8e3e5dcddf34a87ddbc4703a
%X This paper presents a novel methodology to determine an optimized control method for feed water temperature in a seawater reverse osmosis (SWRO) desalination process using
   genetic programming (GP) which is an evolutionary algorithm used to find functional forms through training data. Two functional models were determined by GP with operation
   data collected over four years from Fujairah SWRO plant. The models showed high accuracy (>99.0percent) in terms of the average error rate between the observed and the
   predicted values. The first model involved the permeate water flow rate with a functional temperature correction factor (TCF), water transfer coefficient, and net driving
   pressure (NDP) and the second is the salt passage ratio with a functional TCF, salt transfer coefficient, and total dissolved solids (TDS) in the feed. To determine the
   optimized control of the feed water temperature, a new control methodology with the two functional models was proposed and applied to a simulation of the feed water
   temperature, which showed better performance in terms of the permeate flow rate. Applying the optimized control of feed water temperatures to a plant under identical
   operational conditions, it was found that the permeate flow rate could be increased by approximately 900 m3/day under a steady condition of 600 ppm in permeate TDS.

%A Seung Joon Kim
%A Young Geun Lee
%A Sanghoun Oh
%A Yun Seok Lee
%A Young Mi Kim
%A Moon Gu Jeon
%A Sangho Lee
%A In S. Kim
%A Joon Ha Kim
%T Energy saving methodology for the SWRO desalination process: control of operating temperature and pressure
%J Desalination
%V 247
%N 1-3
%D 2009
%P 260--270
%I 
%K genetic algorithms, genetic programming, Seawater reverse osmosis (SWRO)
%U http://www.sciencedirect.com/science/article/B6TFX-4X502WT-Y/2/733f20864f4a10d23d73aef497596e27
%X This study proposes a new operation methodology for energy saving in the Fujairah seawater reverse osmosis (SWRO) plant, as the optimum feed pressure is determined at the
   controlled operating temperature. To this end, two functional models were developed by genetic programming (GP) using two-year operational data. The data revealed that the
   required feed pressure for the plant operation was potentially overestimated. Based on the developed models, simulation of a three-step sequential control was carried out
   to reduce and optimise the required feed pressure. The simulation results first indicate that the temperature control significantly reduces the required feed pressure at a
   reasonably high temperature. Second, as the permeate water flow rate (PFR) is determined by the optimised feed pressure instead of the permeate pressure actually used to
   maintain a steady PFR in Fujairah, the required feed pressure could be substantially reduced. As a result, the proposed methodology can potentially reduce the required feed
   pressure, by approximately 10 bar, under the identical performance of both PFR and permeate water total dissolved solids (TDS). This study implies that the optimization of
   operation and management of MSF-hybridized SWRO processes can considerably improve the efficiency of the desalination process in terms of energy and, eventually, cost
   saving.

%A A. Kinane
%A V. Muresan
%A N. O'Connor
%T Towards an optimised VLSI design algorithm for the constant matrix multiplication problem
%B Proceedings of the IEEE International Symposium on Circuits and Systems, ISCAS 2006
%D 2006
%I IEEE
%K genetic algorithms, genetic programming
%X The efficient design of multiplierless implementations of constant matrix multipliers is challenged by the huge solution search spaces even for small scale problems.
   Previous approaches tend to use hill-climbing algorithms risking sub-optimal results. The proposed algorithm avoids this by exploring parallel solutions. The computational
   complexity is tackled by modelling the problem in a format amenable to genetic programming and hardware acceleration. Results show an improvement on state of the art
   algorithms with future potential for even greater savings.
%O 4 pp., CD-ROM
%8 21-24  May
%Z Centre for Digital Video Process., Dublin City Univ., Ireland
%@ 0-7803-9389-9

%A Josh King
%T OSU-GP: Attribute Selection using Genetic Programming
%B INLG 2008 Fifth International Natural Language Generation Conference
%E Michael White and Crystal Nakatsu and David McDonald
%D 2008
%P 227--226
%I The Association for Computational Linguistics
%I SIGGEN, the ACL Special Interest Group on Natural Language Generation
%C Salt Fork Resort and Conference Center, Ohio, USA
%K genetic algorithms, genetic programming, Poster
%U http://www.aclweb.org/anthology-new/W/W08/W08-1137.pdf
%X This system's approach to the attribute selection task was to use a genetic programming algorithm to search for a solution to the task. The evolved programs for the
   furniture and people domain exhibit quite naive behavior, and the DICE and MASI scores on the training sets reflect the poor human likeness of the programs.
%8 June  12-14
%Z ECJ, The furniture domain, Dale and Reiter, 1995, Cognitive Science,19(2):233--263. p226 "valueIn fvariablesg target sequence distractors canUseLoc eqA ifte set empty for
   set add and, or, not set remove fattributeERCg add fattributeListERCg remove Functions supplied to the GP algorithm" "The realizer was the simple template-based realizer
   written by Irene Langkilde-Geary, Brighton University for the ASGRE 2007 challenge." "the evolved programs have plenty of room for improvement" INLG2008 held immediately
   prior to ACL-08:HLT Conference http://linguistics.osu.edu/inlg2008/

%A Ross D. King
%A Kenneth E. Whelan
%A Ffion M. Jones
%A Philip G. K. Reiser
%A Christopher H. Bryant
%A Stephen H. Muggleton
%A Douglas B. Kell
%A Stephen G. Oliver
%T Functional genomic hypothesis generation and experimentation by a robot scientist
%J Nature
%V 427
%D 2004
%P 247--252
%I 
%K AI, ILP, QSAR, prolog, qsar, ase, aaa, robot scientist, KEGG, yeast
%U http://dbk.ch.umist.ac.uk/Papers/robot_sci_nature_published.pdf
%X The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many
   scientific areas, data are being generated much faster than they can be effectively analysed. We describe a physically implemented robotic system that applies techniques
   from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises experiments
   to test these hypotheses, physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses inconsistent with the data, and then
   repeats the cycle. Here we apply the system to the determination of gene function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth
   experiments. We built and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid synthesis pathway. In biological
   experiments that automatically reconstruct parts of this model, we show that an intelligent experiment selection strategy is competitive with human performance and
   significantly outperforms, with a cost decrease of 3-fold and 100-fold (respectively), both cheapest and random-experiment selection.
%8 15  January
%Z ase_prolog code online? graphs need colour printer

%A Ross D. King
%A Jem Rowland
%A Stephen G. Oliver
%A Michael Young
%A Wayne Aubrey
%A Emma Byrne
%A Maria Liakata
%A Magdalena Markham
%A Pinar Pir
%A Larisa N. Soldatova
%A Andrew Sparkes
%A Kenneth E. Whelan
%A Amanda Clare
%T The Automation of Science
%J Science
%V 324
%D 2009
%P 85-
%I 
%X The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot
   Scientist Adam, which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and
   experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have
   developed an ontology and logical language. The resulting formalisation involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that
   relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge.
%8 3  April
%Z The robot scientist, cf \citereiser:2002:robotTR \citeking:2004:nature

%A Kenneth E. {Kinnear, Jr.}
%T Fitness Landscapes and Difficulty in Genetic Programming
%B Proceedings of the 1994 IEEE World Conference on Computational Intelligence
%V 1
%D 1994
%P 142--147
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, algorithm theory, search problems, learning (artificial intelligence), fitness landscapes, landscape measures, autocorrelation,
   random walks, landscape basin depths, adaptive walks
%U http://ieeexplore.ieee.org/iel2/1125/8059/00350026.pdf?isNumber=8059
%X The structure of the fitness landscape on which genetic programming operates is examined. The landscapes of a range of problems of known difficulty are analyzed in an
   attempt to determine which landscape measures correlate with the difficulty of the problem. The autocorrelation of the fitness values of random walks, a measure which has
   been shown to be related to perceived difficulty using other techniques, is only a weak indicator of the difficulty as perceived by genetic programming. All of these
   problems show unusually low autocorrelation. Comparison of the range of landscape basin depths at the end of adaptive walks on the landscapes shows good correlation with
   problem difficulty, over the entire range of problems examined.
%8 27-29  June
%Z Defines difficulty as number of fitness cases/1000. Considers a few parity and sort problems. Fitness landscape investigated by using GP operators (without selection) on
   gen=0 to give a number of random walks. Look at autocorrelation of fitness along these walks. Essentially none (<0.5) very much worse than published GA. Also little
   correlation between this and difficulty measure.
%@ 0-7803-1899-4

%A Kenneth E. {Kinnear, Jr.}
%T Generality and Difficulty in Genetic Programming: Evolving a Sort
%B Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93
%E Stephanie Forrest
%D 1993
%P 287--294
%I Morgan Kaufmann
%C University of Illinois at Urbana-Champaign
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/kinnear.icga93.ps.Z
%X application of GP to evolving sorting algorithms and the lessons learned from this. Plus the discovery of a connection between size and generality.
%8 17-21  July
%Z Adding inverse prog size decreases size of progs and makes them more general. Ref \citeBickel:1989:tsrGA Tree structured rules in GAs Kinnear IEEE Press, ICNN U M O'Reilly
   and F. Oppacher 'An experimental Perspective on Genetic Programming' in 'Parallel Problem solving from nature' R.Manner and B. Manderick (eds) Holland:Elsevier. Loads of
   references on Steady State v generational (also see kim's own reference) Various fiddling to find balance of size parameters. Various terminal/function sets tried (less
   powerful -> more difficult) Smaller successful programs more general. David R. White says reproduced without problem May 2009

%A Kenneth E. {Kinnear, Jr.}
%T Alternatives in Automatic Function Definition: A Comparison Of Performance
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 119--141
%I MIT Press
%I Adaptive Computing Technology
%K genetic algorithms, genetic programming, Hoist (shrink) mutation, ADF, MA, GLib
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%O 6

%A Kenneth E. {Kinnear, Jr.}
%T Evolving a Sort: Lessons in Genetic Programming
%B Proceedings of the 1993 International Conference on Neural Networks
%V 2
%D 1993
%P 881--888
%I IEEE Press Piscataway, NJ, USA
%C San Francisco, USA
%K genetic algorithms, genetic programming, iterative sorting algorithms, steady state genetic algorithm, genetic operator, nonfitness single cross-over, iterative methods
%U http://citeseer.ist.psu.edu/kinnear93evolving.html
%X In applying the genetic programming paradigm to the task of evolving iterative sorting algorithms, a variety of lessons are learned. With proper selection of the
   primitives, sorting algorithms are evolved that are both general and non-trivial. The sorting problem is used as a testbed to evaluate the value of several alternative
   parameters, with some small gains shown. The value of applying steady state genetic algorithm techniques to genetic programming, called steady state genetic programming, is
   demonstrated. One unusual genetic operator is created, i.e., nonfitness single cross-over. It shows promise in at least this environment.
%8 28  March -1  April
%Z dobl (INDEX), SSGP, hoist, mutation, create, non-fitness single crossover, permuation, inversion Nov 2005 Ghostview inside browser barfs at kinnear.icnn93.ps but ok if file
   is down loaded, decompressed and ghostview is run normally
%@ 0-7803-0999-5

%A Kenneth E. {Kinnear, Jr.}
%T A perspective on the Work in this Book
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 3--19
%I MIT Press
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%O 1
%Z A whole load of good advice to try when your GP don't work

%T Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%I MIT Press
%C Cambridge, MA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%Z Hardback 24 chapters, most have entries in this bibliography

%A Kenneth E. {Kinnear, Jr.}
%T Genetic Programming
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%P B1.5:1--6
%I Oxford University Press
%K genetic algorithms, genetic programming
%O section B1.5.1
%@ 0-7503-0392-1

%A Kenneth E. {Kinnear, Jr.}
%T Derivative methods in genetic programming
%B Evolutionary Computation 1 Basic Algorithms and Operators
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 2000
%P 103--113
%I Institute of Physics Publishing
%C Bristol
%K genetic algorithms, genetic programming
%O 11
%Z http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=IP274
%@ 0-7503-0664-5

%A Witold Kinsner
%T Towards cognitive analysis of DNA
%B 9th IEEE International Conference on Cognitive Informatics (ICCI 2010)
%D 2010
%P 6--7
%I 
%K bioinformatics, biological process
%X Summary form only given. Deoxyribonucleic acid (DNA) has become one of the most examined molecules on the planet. Scientist around
%O Keynote Speaker
%8 July
%Z Also known as \cite5599728

%A David Kinzett
%A Mengjie Zhang
%A Mark Johnston
%T Using Numerical Simplification to Control Bloat in Genetic Programming
%B Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)
%S Lecture Notes in Computer Science
%E Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb
   and Kay Chen Tan and J\"urgen Branke and Yuhui Shi
%V 5361
%D 2008
%P 493--502
%I Springer
%C Melbourne, Australia
%K genetic algorithms, genetic programming
%X In tree based genetic programming there is a tendency for the size of the programs to increase from generation to generation, a process known as bloat. It is standard
   practice to place some form of control on program size either by limiting the number of nodes or the depth of the tree, or by adding a component to the fitness function
   that rewards smaller programs (parsimony pressure). Others have proposed directly simplifying individual programs using algebraic methods. In this paper, we add node-based
   numerical simplification as a tree pruning criterion to control program size. We show that simplification results in reductions in expected program size, memory use and
   computation time. We further show that numerical simplification performs at least as well as algebraic simplification alone, and in some cases will outperform algebraic
   simplification.
%8 Decemeber  7-10

%A David Kinzett
%A Mark Johnston
%A Mengjie Zhang
%T How online simplification affects building blocks in genetic programming
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 979--986
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X This paper investigates the effect on building blocks during evolution of two online program simplification methods in genetic programming. The two simplification methods
   considered are algebraic simplification and numerical simplification. The building blocks considered are of a more general form (two and three level subtrees) than numeric
   constants only. Unlike most of the existing work which often uses simple symbolic regression tasks, this work considers classification tasks as examples. We develop a new
   method for encoding possible building blocks for the analysis. The results show that the two online program simplification methods can generate new diverse building blocks
   during evolution although they also destroy existing ones and that many of the existing building blocks are retained during evolution. Compared with the canonical genetic
   programming method, the two simplification methods can generate much smaller programs, use much shorter evolutionary training time and achieve comparable effectiveness
   performance.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A David Kinzett
%A Mark Johnston
%A Mengjie Zhang
%T Numerical simplification for bloat control and analysis of building blocks in genetic programming
%J Evolutionary Intelligence
%V 2
%N 4
%D 2009
%P 151--168
%I 
%K genetic algorithms, genetic programming, Program simplification, Code bloat, Building blocks
%X In tree-based genetic programming, there is a tendency for the size of the programs to increase from generation to generation, a phenomenon known as bloat. It is standard
   practise to place some form of control on program size either by limiting the number of nodes or the depth of the program trees, or by adding a component to the fitness
   function that rewards smaller programs (parsimony pressure). Others have proposed directly simplifying individual programs using algebraic methods. In this paper, we add
   node-based numerical simplification as a tree pruning criterion to control program size. We investigate the effect of on-line program simplification, both algebraic and
   numerical, on program size and resource usage. We also investigate the distribution of building blocks within a genetic programming population and how this is changed by
   using simplification. We show that simplification results in reductions in expected program size, memory use and computation time. We also show that numerical
   simplification performs at least as well as algebraic simplification, and in some cases will outperform algebraic simplification. We further show that although the two
   on-line simplification methods destroy some existing building blocks, they effectively generate new more diverse building blocks during evolution, which compensates for the
   negative effect of disruption of building blocks.
%O Special Issue
%8 Decemeber
%Z School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand

%A David Kinzett
%A Mengjie Zhang
%A Mark Johnston
%T Analysis of Building Blocks with Numerical Simplification in Genetic Programming
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 289--300
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X This paper investigates the effect of numerical simplification on building blocks during evolution in genetic programming. The building blocks considered are three level
   subtrees. We develop a method for encoding building blocks for the analysis. Compared with the canonical genetic programming method, numerical simplification can generate
   much smaller programs, use much shorter evolutionary training time and achieve comparable effectiveness performance.
%8 7-9  April
%Z Might be better to call these fragments of trees schemata rather than building blocks -- they have nothing to do with fitness (footnote 2, p290). Part of
   \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A David Kinzett
%A Mengjie Zhang
%A Mark Johnston
%T Investigation of simplification threshold and noise level of input data in numerical simplification of genetic programs
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X In tree based Genetic Programming (GP) there is a tendency for program sizes to increase as the run proceeds without a corresponding improvement in fitness. This increases
   resource usage, both memory and CPU time, and may result in over-fitting the training data. Numerical simplification is a method for removing redundant code from the
   program trees as the run proceeds. Compared with the canonical genetic programming method, numerical simplification can generate much smaller programs, use much shorter
   evolutionary training times and achieve comparable effectiveness performance. A key parameter of this method is the simplification threshold. This paper examines whether
   there exists any relationship between the noise level in the input data and the optimum value for the simplification threshold and, if it exists, what that relationship is.
   Our results suggest that there is a relationship between the optimum value of the simplification threshold and the level of noise in the input data and that a lower bound
   for the optimum simplification threshold is equal to the noise level and an upper bound is five times the noise level.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586181

%A I. M. A. Kirkwood
%A S. H. Shami
%A M. C. Sinclair
%T Discovering Simple Fault-Tolerant Routing Rules using Genetic Programming
%B Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference, ICANNGA97
%E George D. Smith and Nigel C. Steele and Rudolf F. Albrecht
%D 1997
%I Springer-Verlag
%C University of East Anglia, Norwich, UK
%K genetic algorithms, genetic programming, telecommunication networks, routing
%U http://uk.geocities.com/markcsinclair/ps/icannga97_kir.ps.gz
%X A novel approach to solving network routing and restoration problems using the genetic programming (GP) paradigm is presented, in which a single robust and fault-tolerant
   program is evolved which determines the near-shortest paths through a network subject to link failures. The approach is then applied to five different test networks. In
   addition, two multi-population GP techniques are tried and the results compared to simple GP.
%O published in 1998
%Z http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html
%@ 3-211-83087-1

%A Evan Kirshenbaum
%T Genetic Programming with Statically Scoped Local Variables
%R Technical Report HPL-2000-106
%D 2000
%P 10
%I 
%I Hewlett Packard Laboratories
%C Palo Alto
%K genetic algorithms, genetic programming
%U http://www.hpl.hp.com/techreports/2000/HPL-2000-106.html
%X This paper presents an extension to genetic programming to allow the evolution of programs containing local variables with static scope which obey the invariant that all
   variables are bound at time of use. An algorithm is presented for generating trees which obey this invariant, and an extension to the crossover operator is presented which
   preserves it. New genetic operators are described which abstract subexpressions to variables and delete variables. Finally, extensions of this work to iteration and
   functional constructs are discussed.
%8 11  August
%Z see also \citeKirshenbaum:2000:GECCO

%A Evan Kirshenbaum
%T Genetic Programming with Statically Scoped Local Variables
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 459--468
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP148.pdf
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO See also \citeHPL-2000-106
%@ 1-55860-708-0

%A Evan Kirshenbaum
%T Iteration Over Vectors in Genetic Programming
%R Technical Report HPL-2001-327
%D 2001
%I 
%I HP Laboratories
%K genetic algorithms, genetic programming
%U http://www.kirshenbaum.net/evan/publications/GECCO-2001-tr.pdf
%X genetic programming with bounded iteration constructs, which allow the computational complexity of the solution to be an emergent property. It is shown that such operators
   render the even-6-parity problem trivial, and the results of experiments with other, harder, problems that require O(n) complexity are shown. This method is contrasted with
   Automatically Defined Iterators.
%8 Decemeber  17

%A Evan Kirshenbaum
%T Modeling Disk Arrays Using Genetic Programming
%R Technical Report HPL-2002-20
%D 2002
%I 
%I HP Laboratories
%K genetic algorithms, genetic programming
%U http://www.kirshenbaum.net/evan/publications/HPL-2002-20.pdf
%X genetic programming to evolve models that predict the throughput in disk arrays. The results are compared to previous hand-crafted analytical and automatically-generated
   interpolation-based device models. An analysis is performed to investigate the optimality of the run parameters chosen as well as to discover whether the approach has the
   tendency to overfit its training data. The process is shown to find models that outperform both recently published and currently used models and to be sensitive to
   population size but not run length.
%8 January  29
%Z our printer barfs with HPL-2002-20.pdf 19/3/2003

%A Evan Kirshenbaum
%A Henri J. Suermondt
%T Using Genetic Programming to Obtain a Closed-Form Approximation to a Recursive Function
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 543--556
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030543.htm
%X We demonstrate a fully automated method for obtaining a closed form approximation of a recursive function. This method resulted from a real world problem in which we had a
   detector that monitors a time series and where we needed an indication of the total number of false positives expected over a fixed amount of time. The problem, because of
   the constraints on the available measurements on the detector, was formulated as a recursion, and conventional methods for solving the recursion failed to yield a closed
   form or a closed-form approximation. We demonstrate the use of genetic programming to rapidly obtain a high-accuracy approximation with minimal assumptions about the
   expected solution and without a need to specify problem-specific parameterisations. We analyse both the solution and the evolutionary process. This novel application shows
   a promising way of using genetic programming to solve recurrences in practical settings.
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004) GPlab
%@ 3-540-22343-6

%A Mikhail V. Kiselev
%A Sergei M. Ananyan
%A Sergei B. Arseniev
%T Regression-Based Classification Methods and Their Comparison with Decision Tree Algorithms
%B Proceedings of the 1st European Symposium on Principles of Data Mining and Knowledge Discovery
%S Lecture Notes in Artificial Intelligence
%E Jan Komorowski and Jan Zytkow
%V 1263
%D 1997
%P 134--144
%I Springer-Verlag Berlin
%8 24--27  June
%Z PolyAnalyst
%@ 3-540-63223-9

%A Mikhail V. Kiselev
%A Sergei M. Ananyan
%A Sergei B. Arseniev
%T PolyAnalyst Data Analysis Technique and Its Specialization for Processing Data Organized as a Set of Attribute Values
%B Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD-98)
%S Lecture Notes in Artificial Intelligence
%E Jan M. \.Zytkow and Mohamed Quafafou
%V 1510
%D 1998
%P 352--360
%I Springer-Verlag Berlin
%8 23-26  September
%Z PolyAnalyst uses a strongly typed functional language with the expressive power of a universal programming language as its respresentation. It searches this with a
   combination of enumeration and beam search. http://www.megaputer.com/html/skat.html More Information at:
   http://www.primenet.com/pcai/New_Home_Page/issues/pcai_13_issue_details.html#Data_Mining Theme: Data Mining/Genetic Algorithms - Vol 13 Issue 5 (Sept/Oct 1999) Available
   Aug 25 Future Issue Featuring Articles: AI @ Work Editorial Secret Agent Man The Intelligence File The Book Zone Buyers Guide:Data Mining, Genetic Algorithms, Modeling,
   Training, Consultingl http://www.nautilus-systems.com/datamine/msg00793.html From: Sergei Ananyan Date: Thu, 21 Jan 1999 11:40:44 -0500 (EST) PC AI Magazine, January issue,
   page 48
%@ 3-540-65068-7

%A Nand Kishor
%A Madhusudan Singh
%A A. S. Raghuvanshi
%T Genetic Programming Approach for Model Structure Determination of Hydro Turbine in Closed Loop Operation
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 2751--2757
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X This paper addresses the appropriate structure selection of linear-in parameter model for speed identification of hydro turbine. The genetic programming (GP) approach with
   parameters determined by orthogonal least square (OLS) is adopted in the study. The simulation of second order H infinity turbine penstock dynamic transfer function in
   closed loop with random load variation is performed to generate data for model structure determination. The data generated from among the available PID variants, forward
   rectangular controller in conjunction with online identification algorithm, LSMADF provided the best model structure.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A J. K. Kishore
%A L. M. Patnaik
%A V. Mani
%A V. K. Agrawal
%T Application of genetic programming for multicategory pattern classification
%J IEEE Transactions on Evolutionary Computation
%V 4
%N 3
%D 2000
%P 242--258
%I 
%K genetic algorithms, genetic programming, pattern classification, multicategory pattern classification, GP, distribution-free methods, statistical distribution, two-category
   classification, discriminant function, association strength measure, SA measure, heuristic rules, training sets, incremental learning, function set choice, conflict
   resolution
%U http://ieeexplore.ieee.org/iel5/4235/18897/00873237.pdf
%X Explores the feasibility of applying genetic programming (GP) to multicategory pattern classification problem. GP can discover relationships and express them
   mathematically. GP-based techniques have an advantage over statistical methods because they are distribution-free, i.e., no prior knowledge is needed about the statistical
   distribution of the data. GP also automatically discovers the discriminant features for a class. GP has been applied for two-category classification. A methodology for
   GP-based n-class classification is developed. The problem is modeled as n two-class problems, and a genetic programming classifier expression (GPCE) is evolved as a
   discriminant function for each class. The GPCE is trained to recognize samples belonging to its own class and reject others. A strength of association (SA) measure is
   computed for each GPCE to indicate the degree to which it can recognize samples of its own class. SA is used for uniquely assigning a class to an input feature vector.
   Heuristic rules are used to prevent a GPCE with a higher SA from swamping one with a lower SA. Experimental results are presented to demonstrate the applicability of GP for
   multicategory classification, and they are found to be satisfactory. We also discuss the various issues that arise in our approach to GP-based classification, such as the
   creation of training sets, the role of incremental learning, and the choice of function set in the evolution of GPCE, as well as conflict resolution for uniquely assigning
   a class.
%8 September
%Z comparison in \citeyu:2004:ECDM

%A J. K. Kishore
%A L. M. Patnaik
%A V. Mani
%A V. K. Agrawal
%T Genetic programming based pattern classification with feature space partitioning
%J Information Sciences
%V 131
%N 1-4
%D 2001
%P 65--86
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V0C-42MFDYH-4/2/de6209d53f2e0ec0addf0e2cfa62fd91
%X Genetic programming (GP) is an evolutionary technique and is gaining attention for its ability to learn the underlying data relationships and express them in a mathematical
   manner. Although GP uses the same principles as genetic algorithms, it is a symbolic approach to program induction; i.e., it involves the discovery of a highly fit computer
   program from the space of computer programs that produces a desired output when presented with a particular input. We have successfully applied the GP paradigm for the
   <i>n</i>-category pattern classification problem. The ability of the GP classifier to learn the data distributions depends upon the number of classes and the spatial spread
   of data. As the number of classes increases, it increases the difficulty for the GP classifier to resolve between classes. So, there is a need to partition the feature
   space and identify sub-spaces with reduced number of classes. The basic objective is to divide the feature space into sub-spaces and hence the data set that contains
   representative samples of n classes into sub-data sets corresponding to the sub-spaces of the feature space, so that some of the sub-data sets/spaces can have data
   belonging to only p-classes (p<n). The GP classifier is then evolved independently for the sub-data sets/spaces of the feature space. The GP classifier becomes simpler for
   some of the sub-data sets/spaces as only p classes are present. It also results in localized learning as the GP classifier has to learn the data distribution in only a
   sub-space of the feature space rather than in the entire feature space. In this paper, we are integrating the GP classifier with feature space partitioning (FSP) for
   localized learning to improve pattern classification.
%8 January
%Z Information Sciences http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt GPQUICK

%A Ozgur Kisi
%A Aytac Guven
%T A machine code-based genetic programming for suspended sediment concentration estimation
%J Advances in Engineering Software
%V 41
%N 7-8
%D 2010
%P 939--945
%I 
%K genetic algorithms, genetic programming, Suspended sediment concentration, Modelling, Neuro-fuzzy, Neural networks, Rating curve
%U http://www.sciencedirect.com/science/article/B6V1P-50G0DYF-1/2/c917b48e2cfced4167ad3ab9ee02e797
%X Correct estimation of suspended sediment concentration carried by a river is very important for many water resources projects. The application of linear genetic programming
   (LGP), which is an extension to genetic programming (GP) technique, for suspended sediment concentration estimation is proposed in this paper. The LGP is compared with
   those of the adaptive neuro-fuzzy, neural networks and rating curve models. The daily streamflow and suspended sediment concentration data from two stations, Rio Valenciano
   Station and Quebrada Blanca Station, operated by the US Geological Survey (USGS) are used as case studies. The root mean square errors (RMSE) and determination coefficient
   (R2) statistics are used for evaluating the accuracy of the models. Comparison of the results indicated that the LGP performs better than the neuro-fuzzy, neural networks
   and rating curve models. For the Rio Valenciano and Quebrada Blanca Stations, it is found that the LGP models with RMSE = 44.4 mg/l, R2 = 0.910 and RMSE = 13.9 mg/l, R2 =
   0.952 in test period is superior in estimating daily suspended sediment concentrations than the best accurate neuro-fuzzy model with RMSE = 52.0 mg/l, R2 = 0.876 and RMSE =
   17.9 mg/l, R2 = 0.929, respectively.
%O Advances in Structural Optimization

%A Ozgur Kisi
%A Aytac Guven
%T Evapotranspiration Modeling Using Linear Genetic Programming Technique
%J Journal of Irrigation and Drainage Engineering
%V 136
%N 10
%D 2010
%P 715--723
%I American Society of Civil Engineers
%K genetic algorithms, genetic programming, Evapotranspiration, Computer programming, ANN, Neural networks, Evapotranspiration modelling, Linear genetic programming, SVM,
   Support vector regression
%X The study investigates the accuracy of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily reference evapotranspiration
   (ET0) modelling. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from three stations, Windsor, Oakville, and Santa Rosa, in
   central California, are used as inputs to the LGP to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. The accuracy of the LGP is compared with those of the
   support vector regression (SVR), artificial neural network (ANN), and those of the following empirical models: the California irrigation management system Penman,
   Hargreaves, Ritchie, and Turc methods. The root-mean-square errors, mean-absolute errors, and determination coefficient (R2) statistics are used for evaluating the accuracy
   of the models. Based on the comparison results, the LGP is found to be superior alternative to the SVR and ANN techniques.
%8 October
%Z 1Dept. of Civil Engineering, Hydraulics Div., Erciyes Univ., 38039 Kayseri, Turkey (corresponding author). E-mail: kisi@erciyes.edu.tr 2Dept. of Civil Engineering,
   Hydraulics Div., Gaziantep Univ., 27310 Gaziantep, Turkey.

%A Stefan Klahold
%A Steffen Frank
%A Robert E. Keller
%A Wolfgang Banzhaf
%T Exploring the Possibilites and Restrictions of Genetic Programming in Java Bytecode
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%P 120--124
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, JAPHET
%X \citeFrankKlahold:masters
%8 22-25  July
%Z GP-98LB p121 all crossover and mutation operations produce correct syntax byte code. All initial, random code, is legal (use of user supplied semantically legal class file)
   insertion of small fragments of random code which leave virtual machine stack at same depth (and state?). Random genetic and initialisation operation operate on whole
   instructions. Ie respect their variable length. p122 function regression, image reconstruction, seven segment LED digit. No run time errors. p123 'better chance for the
   evolution of a valid stack when the number of parameters is low.' Problem of method calls becoming extinct. p124 'an evolved bytecode program may not pass a specific
   [implementation of a] virtual machine's class file verifier.'

%A Andreas Klappenecker
%A Frank U. May
%T Evolving Better Wavelet Compression Schemes
%B Wavelet Applications in Signal and Image Processing III
%E Andrew F. Laine and Michael A. Unser and Mladen V. Wickerhauser
%V 2569
%D 1995
%I SPIE
%C San Diego, CA, USA
%K genetic algorithms, genetic programming, Wavelets, compression, optimization
%U http://citeseer.ist.psu.edu/334994.html
%X Wavelet based compression schemes belong to the general class of transform coding schemes. We show how the genetic programming approach can be used to optimise such a
   compression scheme in the sense of rate-distortion. The results of optimized wavelet based compression schemes are compared with the JPEG compression standard. A prototype
   implementation of the method is realized as a distributed, parallel implementation on a heterogeneous Unix network.
%O The Pennsylvania State University CiteSeer Archives
%8 9-14  July
%Z http://www.spie.org/web/abstracts/2500/2569.html Reprinted in: Selected SPIE Papers on CD-ROM series, Vol 8: Mathematical Imaging and Vision. G. Ritter (Ed.), SPIE, 2000.
%@ 0-8194-1928-1

%A Tim J. Klassen
%A Malcolm I. Heywood
%T Towards the On-line Recognition of Arabic Characters
%B Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN'02
%D 2002
%P 1900--1905
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE
%C Hilton Hawaiian Village Hotel, Honolulu, Hawaii
%K genetic algorithms, genetic programming, Arabic cursive handwriting, on-line character recognition, SOM, perceptron, MLP
%U http://users.cs.dal.ca/~mheywood/X-files/Publications/IEEEarabic.pdf
%X A generic system is proposed for the recognition of on-line handwritten Arabic characters. Automatic extraction of features from on-line data using SOMs avoids heuristic
   extraction of features. Performance of a perceptron classifier is competitive with MLP and Genetic Programming based approaches and a better fit for handheld computing
   devices.
%8 12-17  May
%Z IJCNN 2002 Held in connection with the World Congress on Computational Intelligence (WCCI 2002)
%@ 0-7803-7278-6

%A Mark Klausner
%T Taking Advantage of Diversity in Genetic Algorithms
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 64--72
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford
   University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Jon Klein
%A Lee Spector
%T Unwitting distributed genetic programming via asynchronous JavaScript and XML
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1628--1635
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, AJAX, JavaScript, networking, push, PushGP, stack based genetic programming, XML
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1628.pdf
%X The success of a genetic programming system in solving a problem is often a function of the available computational resources. For many problems, the larger the population
   size and the longer the genetic programming run the more likely the system is to find a solution. In order to increase the probability of success on difficult problems,
   designers and users of genetic programming systems often desire access to distributed computation, either locally or across the Internet, to evaluate fitness cases more
   quickly. Most systems for internet-scale distributed computation require a user's explicit participation and the installation of client side software. We present a
   proof-of-concept system for distributed computation of genetic programming via asynchronous JavaScript and XML (AJAX) techniques which requires no explicit user interaction
   and no installation of client side software. Clients automatically and possibly even unknowingly participate in a distributed genetic programming system simply by visiting
   a webpage, thereby allowing for the solution of genetic programming problems without running a single local fitness evaluation. The system can be easily introduced into
   existing webpages to exploit unused client-side computation for the solution of genetic programming and other problems.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Jon Klein
%A Lee Spector
%T Genetic Programming with Historically Assessed Hardness
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 61--75
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%O 5
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008

%A Peer Kleinau
%A Christian Steffens
%T Dynamisches Modell zur Gleichteileverwendung
%B Wirtschafts-wissenschaftliches Studium Zeitschrift f\"ur Ausbildung und Hochschulkontakt
%D 2002
%P 228
%I 
%K genetic algorithms, genetic programming
%8 4  April
%Z WiSt case study -- Application of Genetic Programming to Finance and Operations Management. In german
   http://vahlen.becksche.de/asp/zeitschriften/z_anzeigen.asp?zheft=4&zeitschrift=WiSt&jahr=2002

%A Peer Kleinau
%T Solving an Inventory-Control problem with Genetic Programming
%B GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference
%E Alwyn M. Barry
%D 2003
%P 300--303
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C Chigaco
%K genetic algorithms, genetic programming
%8 11  July
%Z Bird-of-a-feather Workshops, GECCO-2003. A joint meeting of the twelth International Conference on Genetic Algorithms (ICGA-2003) and the eigth Annual Genetic Programming
   Conference (GP-2003) part of barry:2003:GECCO:workshop

%A Peer Kleinau
%T Deriving simple (R,T)-policies with Genetic Programming
%B Fifth metaheuristics conference, MIC2003
%E Toshihide Ibaraki
%D 2003
%I 
%C Kyoto, Japan
%K genetic algorithms, genetic programming, inventory-control, (R,T)-policy
%X In the US, inventories are estimated to be worth of more than 1 trillion. To manage these inventories, numerous inventory-control policies have been developed in the last
   decades. These inventory-control policies are typically derived analytically, which is often complicated and time consuming. For many relevant settings, such as complex
   multi-echelon models, there exist no closed-form formulae to describe the optimal solution. Optimal solutions for those problems are determined by complex algorithms that
   require several iteration steps. With Genetic Programming (GP) however, inventory-control policies can be derived in a simple manner. GP is an algorithm related to Genetic
   Algorithms. It applies the principles of natural evolution to solve optimization problems. In this paper, we show how a simple closed-form heuristics for a common
   inventory-control setting can be derived with GP. We focus on a simple (R,T)-policy to demonstrate the capabilities of GP.
%8 25-28  August
%Z published on CD-ROM. Selected papers in special issue? mic03-office@amp.i.kyoto-u.ac.jp http://www-or.amp.i.kyoto-u.ac.jp/mic2003/index.html

%A Peer Kleinau
%A Ulrich Thonemann
%T Deriving Inventory Control Policies for Periodic Review with Genetic Programming
%B International Conference on Operations Research, OR 2003
%D 2003
%P 71
%I 
%C Heidelberg
%K genetic algorithms, genetic programming
%X In Germany, inventories are estimated to be worth of more than 500 billion. To manage these inventories, numerous inventory-control policies have been developed in the last
   decades. These inventory-control policies are typically derived analytically, which is often complicated and time consuming. For many relevant settings, such as complex
   multi-echelon models, there exist no closed-form formulae to describe the optimal solution. Optimal solutions for those problems are determined by complex algorithms that
   require several iteration steps. With Genetic Programming (GP) however, inventory-control policies can be derived in a simple manner. GP is an algorithm related to Genetic
   Algorithms. It applies the principles of natural evolution to solve optimization problems. In this paper, we show how closed-form heuristics for a common inventory-control
   setting with periodic review can be found with GP.
%8 3-5  September
%Z Wednesday, Sep. 3, 2003, 16:45 - 18:45, Room: NU 05 (see p. 41 for session)

%A Peer Kleinau
%T Application of Genetic Programming to Finance and Operations Management
%R Ph.D. Thesis
%D 2003
%I 
%I University of Muenster, Germany
%K genetic algorithms, genetic programming, inventory control, option pricing, credit risk
%X In this work the application of Genetic Programming to three common problems arising in the context of Business Administration is analysed. Genetic Programming is based on
   the basic principles of natural evolution. It adapts some common features of Genetic Algorithms, such as selection, crossover, and mutation. In contrast to Genetic
   Algorithms which use strings of fixed length, Genetic Programming operates on variable tree structures that make the algorithm capable to solve a great variety of problems.
   Numerous instructions are given how researchers can develop useful applications in Business Administration with Genetic Programming. In the context of option pricing,
   Genetic Programming can develop formulae that provide better results than the famous Black and Scholes model. In rating experiments, rating classification simulators are
   found which give meaningful insights into Standard and Poor s rating assignments. In studies related to the topic of inventory control, Genetic Programming finds both
   optimal inventory control policies for problems with a low complexity and specialised heuristics for complex problems which are comparable or even better than
   state-of-the-art heuristics.
%Z published as \citekleinau:thesis_book

%A Peer Kleinau
%T Application of Genetic Programming to Finance and Operations Management
%D 2004
%I Logos
%C Berlin
%K genetic algorithms, genetic programming, inventory control, option pricing, credit risk Genetische Programmierung, Optionsbewertung, Rating, Lagerhaltung
%U http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=0458&lng=deu&id=
%Z See \citekleinau:thesis
%@ 3-8325-0458-3

%A Peer Kleinau
%A Ulrich W. Thonemann
%T Deriving inventory-control policies with genetic programming
%J OR Spectrum
%V 26
%N 4
%D 2004
%P 521--546
%I 
%K genetic algorithms, genetic programming, Inventory control, Supply chain management
%X One of the key areas of operations and supply chain management is inventory control. Inventory control determines which quantity of a product should be ordered when to
   achieve some objective, such as minimising cost. Inventory-control policies are typically derived analytically, and this requires advanced mathematical skills and can be
   quite time consuming. In this paper, we present an alternative approach for solving inventory-control problems that is based on Genetic Programming. Genetic Programming is
   an optimisation method that applies the principles of natural evolution to optimization problems. One of the key characteristics of Genetic Programming is that it does not
   require the specification of how a problem should be solved, but only the specification of what needs to be solved. After the user has specified the problem, GP searches
   for a solution without significant human involvement. The solutions generated by GP can be simple algorithms or closed-form expressions that represent the decision
   variables, i.e., the order point and the order quantity as a function of the problem parameters. However, expert knowledge in inventory control is still essential for
   building the inventory models and determining the parameters of Genetic Programming. Genetic Programming searches for both the structure and the parameters of the optimal
   solution. For simple settings, the structure and the parameters of the optimal solution can be found. For complex settings, near-optimal solutions that outperform
   traditional heuristics can be found if the structure of the optimal solution is known.
%8 October
%Z http://www.or-spectrum.de/

%A Peer Kleinau
%T Option Valuation with Genetic Programming
%D 2004
%I 
%K genetic algorithms, genetic programming
%X ? Applying Genetic Programming to derive S&P s rating methology ?

%A Jeffrey A. Kline
%A Andrew J. Novobilski
%A Christopher Kabrhel
%A Peter B. Richman
%A D. Mark Courtney
%T Derivation and Validation of a Bayesian Network to Predict Pretest Probability of Venous Thromboembolism
%J Annals of Emergency Medicine
%V 45
%N 3
%D 2005
%P 282--290
%I 
%K genetic algorithms, genetic programming, Bayesian Networks, datamining
%X Study objective A Bayesian network can estimate a numeric pretest probability of venous thromboembolism on the basis of values of clinical variables. We determine the
   accuracy with which a Bayesian network can identify patients with a low pretest probability of venous thromboembolism, defined as less than or equal to 2percent. Methods
   Using commercial software, we derived a population of Bayesian networks from 25 input variables collected on 3,145 emergency department (ED) patients with suspected venous
   thromboembolism who underwent standardised testing, including pulmonary vascular imaging, and 90-day follow-up (11.0percent of patients were venous thromboembolism
   positive). The best-fit Bayesian network was selected using a genetic algorithm. The selected Bayesian network was tested in a validation population of 1,423 ED patients
   prospectively evaluated for venous thromboembolism, including 90-day follow-up (8.0percent were venous thromboembolism positive). The Bayesian network probability estimate
   was normalised to a score of 0percent to 100percent. Results Of 1,423 patients in the validation cohort, 711 (50percent; 95percent confidence interval [CI] 47percent to
   52percent) had a score less than or equal to 2percent that predicted a low pretest probability. Of these 711 patients, 700 (98.5percent; 95percent CI 97.2percent to
   99.2percent) had no venous thromboembolism at follow-up. Conclusion A Bayesian network, derived and independently validated in ED populations, identified half of the
   validation cohort as having a low pretest probability (le 2percent); 98.5percent of these patients were correctly classified by the network.
%8 March
%Z SEE EDITORIAL, P. 291.

%A Miroslav Klucik
%A Jana Juriova
%A Marian Klucik
%T Time Series Modeling with Genetic Programming Relative to ARIMA Models
%B New Techniques and Technologies in Statistics, NTTS 2009
%D 2009
%I 
%I EUROSTAT and European Commission
%C Brussels, Belgium
%K genetic algorithms, genetic programming, symbolic regression, ARIMA
%U http://epp.eurostat.ec.europa.eu/portal/page/portal/research_methodology/documents/POSTER_4P_TIME_SERIES_MODELLING_KLUCIK.pdf
%X INFOSTAT, the research institution of the Statistical Office of the Slovak Republic, is intending to supplement its model tools (ECM, ARIMA) with modern heuristic methods
   for analyzing and forecasting the macroeconomic reality. The initial research is concentrated on time series modelling using genetic programming and comparing the results
   with a more conventional ARIMA model. Genetic programming tool based on evolutionary computation technique can find not only optimal parameters of a searched function but
   also its structure. Our experiments deal with modeling and forecasting of the industrial production for Slovakia and European Monetary Union. For our purpose the genetic
   programming tool is kept as simple as possible. The predicted variables are estimated by the concept of symbolic regression. The solutions of symbolic regression are
   expressed in a tree-type structure. Concerning the ARIMA approach, we have used seasonal ARIMA models that satisfied all the quality model conditions. Both methods'
   performance was tested in a twelve-month forecasting. The second experiment involves the simulation of shocks for each model. The GP model manages to compete with ARIMA
   models in all cases. Finally we show a way to depict a complicated nonlinear solution in a simply understandable form. The continually changing and hardly predictable
   environment of contemporary and future global economy will require a multidisciplinary approach to approximate the complex reality. The GP instrument with its flexibility
   and efficiency manages to confront these challenges with promising results.
%8 18-20  February

%A Brig Klyce
%T In real or artificial life, Is Evolutionary Progress in a Closed System Possible?
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1444
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-021.pdf
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Joshua Knowles
%A David Corne
%A Martin Oates
%T A New Evolutionary Approach to the Degree Constrained Minimum Spanning Tree Problem
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 794
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://iridia.ulb.ac.be/~jknowles/KnowlesCorneGECCO99.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Eun-Joung Ko
%A Oscar N. Garcia
%T Adaptive Control of Crossover Rate in Genetic Programming
%B Intelligent Engineering Systems Through Artificial Neural Networks (ANNIE 2001)
%E Cihan H. Dagli
%D 2001
%P 171--176
%I 
%I University of Missouri-Rolla, Smart Engineering Systems Laboratory Department of Engineering Management In Cooperation with IEEE Neural Networks Council
%C St. Louis, Missouri, USA
%K genetic algorithms, genetic programming
%8 4-7  November
%Z cf http://web.umr.edu/~annie/annie01/ ANNIE01 session TP2.2 George Washington University, Washington, DC, Wright State University, Dayton, OH

%A Jill Kobashigawa
%A Hyoung-sun Youn
%A Magdy Iskander
%A Zhengqing Yun
%T Comparative study of genetic programming vs. neural networks for the classification of buried objects
%B IEEE Antennas and Propagation Society International Symposium, APSURSI '09
%D 2009
%P 1--4
%I 
%K genetic algorithms, genetic programming, buried objects classification, character classification problems, neural network structure optimization, untrained data robustness,
   buried object detection, image classification, neural nets
%X A comparative study of neural networks and genetic programming was conducted on six character classification problems. Based on the obtained results of the six problems,
   genetic programming showed better performance than neural networks in the various levels of problem difficulty. Genetic programming also showed robustness to untrained
   data, which caused difficulties for the neural networks. The optimization of the neural network structure was observed to be integral in obtaining both convergence and
   acceptable performance. A clear trend for structure optimization is not evident in the case of neural networks, and a global optimal solution may not be practical. On the
   other hand, because of the global searching nature of genetic programming, these problems with neural networks could be solved by using genetic programming.
%8 June
%Z Also known as \cite5172386

%A Jill Kobashigawa
%A Hyoung-sun Youn
%A Magdy Iskander
%A Zhengqing Yun
%T Comparison between genetic programming and Neural Network in classification of buried unexploded ordnance (UXO) targets
%B 2010 IEEE Antennas and Propagation Society International Symposium (APSURSI)
%D 2010
%I 
%K genetic algorithms, genetic programming, buried unexploded ordnance targets, dimensional scattering images, ground penetrating radar, neural network, numerical simulation,
   electrical engineering computing, ground penetrating radar, neural nets, numerical analysis
%X In this paper, we present the results of our next step effort in comparison of classification performances between the NN and the GP techniques based on the simulated
   scattering patterns of UXO-like object and non-UXO objects. For this comparative study, 2 dimensional scattering images from one UXO target and four non-UXO objects were
   generated by numerical simulation tool (FEKO). For non-UXO objects, the most challenging targets to discriminate from UXO, since all these objects produce resonance signal
   as UXO-like targets do [6], were selected. Classification performances of both techniques (NN vs. GP) in different level of noise and in the case of presence of untrained
   data were examined and the results and observations are discussed.
%8 11-17  July
%Z 'we confirmed that GP provided better performance than neural networks'. Hawaii Center for Advanced Communications College of Engineering, University of Hawaii at Manoa,
   USA 96822 Also known as \cite5561278

%A Yoshiyuki Kobayashi
%T Automatic Generation of Musical Instrument Detector by Using Evolutionary Learning Method
%B 10th International Society for Music Information Retrieval Conference
%E Keiji Hirata and George Tzanetakis
%D 2009
%P 93--98
%I 
%C Kobe, Japan
%K genetic algorithms, genetic programming
%U http://ismir2009.ismir.net/proceedings/PS1-7.pdf
%X This paper presents a novel way of generating information extractors that obtain high-level information from recorded music such as the presence of a certain musical
   instrument. Our information extractor is comprised of a feature set and a discrimination or regression formula. We introduce a scheme to generate the entire information
   extractor given only a large amount of labeled dataset. For example, data could be waveform, and label could be the presence of musical instruments in them. We propose a
   very flexible description of features that allows various kinds of data other than waveform. Our proposal also includes a modified evolutionary learning method to optimize
   the feature set. We applied our scheme to automatically generate musical instrument detectors for mixed-down music in stereo. The experiment showed that our scheme could
   find a suitable set of features for the objective and could generate good detectors.
%8 26-30  October

%A Mykel J. Kochenderfer
%T Evolving Teleo-Reactive Programs for Block Stacking using Indexicals through Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 111--118
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming, stgp
%U http://www.genetic-programming.org/sp2002/Kochenderfer.pdf
%8 June
%Z part of \citekoza:2002:gagp

%A Mykel J. Kochenderfer
%T Evolving Hierarchical and Recursive Teleo-Reactive Programs through Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 83--92
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=83
%X Teleo-reactive programs and the triple tower architecture have been proposed as a framework for linking perception and action in agents. The triple tower architecture
   continually updates the agent's knowledge of the world and evokes actions according to teleo-reactive control structures. This paper uses block stacking problems to
   demonstrate how genetic programming may be used to evolve hierarchical and recursive teleo-reactive programs.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003 p88 "maximum recursion depth that varied with the number of blocks in the world." p89 popsize=60000. up to 3
   subprograms (ie 4 trees), which can call each other and the main program. "a few days" on a Sun UltraSPARC III+ 900Mhz, evolved 2 "completely general solutions for block
   stacking". Hierarchical Infinite recursion 2 subroutines.
%@ 3-540-00971-X

%A Gary J. Koehler
%T Computing Simple GA Expected Waiting Times
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 795
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cba.ufl.edu/dis/papers/9706100.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Mario Koeppen
%A Xiufen Liu
%T Texture Detection by Genetic Programming
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 867--872
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, texture analysis, texture detection, evolutionary algorithms, 2D-Lookup
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =
%@ 0-7803-6658-1

%A Timo Koetzing
%A Frank Neumann
%A Reto Spoehel
%T PAC Learning and Genetic Programming
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 2091--2096
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, Theory
%X Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand from a theoretical point of view. With this paper we contribute to the
   computational complexity analysis of genetic programming that has been started recently. We analyse GP in the well-known PAC learning framework and point out how it can
   observe quality changes in the the evolution of functions by random sampling. This leads to computational complexity bounds for a simple tree GP algorithm for perfectly
   learning any member of a simple class of linear pseudo-Boolean functions. Furthermore, we show that the same algorithm on the functions from the same class finds good
   approximations of the target function in less time.
%8 12-16  July
%Z Not a linear GP system. No population. No crossover. Vandermonde identity. Chernoff bounds. Cites Feldman 2008/2009 and Valiant 1984 and 2009. Also known as \cite2001857
   GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)

%A Monika Kofler
%A Andreas Beham
%A Stefan Wagner
%A Michael Affenzeller
%T Evaluation of Dispatching Strategies for the Optimization of a Real-World Production Plant
%B 2nd Symposium on International Logistics and Industrial Informatics, LINDI 2009
%D 2009
%P 1--6
%I 
%K genetic algorithms, genetic programming, dispatching strategies, long-term stability, optimization, production control system, production planning, real-world production
   plant, reusability, dispatching, industrial plants, optimisation, production planning
%X In this paper we present three optimization approaches for a real-world production planning problem, which are based on dispatching. In particular, we employ genetic
   algorithms to select one rule per machine or machine group from a set of simple dispatching rules and compare the results with a global, composite rule as generated with
   genetic programming. Reusability and long-term stability of these rules is also investigated on four snapshots extracted from the production planning and control system of
   our project partner over a time frame of several months.
%8 September
%Z Also known as \cite5258765

%A Anders Kofod-Petersen
%T Adaptive Behaviour Based Robotics using On-Board Genetic Programming
%R M.S. Thesis Cand. Scient.
%D 2002
%I 
%I Norwegian University of Science and Technology
%C Norway
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.3070
%X This thesis investigates the use of Genetic Programming (GP) to evolve controllers for an autonomous robot. GP is a type of Genetic Algorithm (GA) using the Darwinian idea
   of natural selection and genetic recombination, where the individuals most often is represented as a tree-structure. The GP is used to evolve a population of possible
   solutions over many generations to solve problems. The most common approach used today, to develop controllers for autonomous robots, is to employ a GA to evolve an
   Artificial Neural Network (ANN). This approach is most often used in simulation only or in conjunction with online evolution; where simulation still covers the largest part
   of the process. The GP has been largely neglected in Behaviour Based Robotics (BBR). The is primarily due to the problem of speed, which is the biggest curse of any
   standard GP. The main contribution of this thesis is the approach of using a linear representation of the GP in online evolution, and to establish whether or not the GP is
   feasible in this situation. Since this is not a comparison with other methods, only a demonstration of the possibilities with GP, there is no need for testing the
   particular test cases with other methods. The work in this thesis builds upon the work by Wolfgang Banzhaf and Peter Nordin, and therefor a comparison with their work will
   be done.

%A Fumio Kojima
%A Naoyuki Kubota
%A Setsuo Hashimoto
%T Identification of crack profiles using genetic programming and fuzzy inference
%J Journal of Materials Processing Technology
%V 108
%N 2
%D 2001
%P 263--267
%I 
%K genetic algorithms, genetic programming, Fuzzy inference, Eddy current
%U http://www.sciencedirect.com/science/article/B6TGJ-41WSC52-10/1/dab41d59bda7ac05c11a7560f11f27ba
%X This paper deals with a quantitative nondestructive evaluation in eddy current testing for steam generator tubes of nuclear power plants by using genetic programming (GP)
   and fuzzy inference system. Defects can be detected as a probe impedance trajectory by scanning a pancake type probe coil. An inference system is proposed for identifying
   the defect shape inside and/or outside tubes. GP is applied to extract and select effective features from a probe impedance trajectory. Using the extracted features, a
   fuzzy inference system detects presence, position, and size of a defect of test sample. The effectiveness of the proposed method is demonstrated through computer simulation
   studies.
%8 4  January

%A Gabriella K\'okai
%A Zolt\'an T\'oth
%A Robert V\'anyi
%T Application of Genetic Algorithms with more Populations for Lindenmayer Systems
%B Proceedings of the International Symposium on Engineering of Intelligent Systems, EIS'98
%E E. Alpaydin and Colin Fyfe
%D 1998
%P 324--331
%I 
%K genetic algorithms, genetic programming, lindenmayer L-systems
%U http://citeseer.ist.psu.edu/515449.html
%X The paper describes a solution of the inverse problem for Lindenmayer systems with genetic programming. Inverse problem means that we try to evolve Lindenmayer grammars to
   describe fractal images. Genetic algorithm is used to evolve the rewriting rules of the system. As a starting point the solutions of Koza [7] and Jacob [6] is applied but
   these solutions were extended: for different types of fractal images, and we executed some modifications: more populations are processed parallel during the evolution
   process, where the selection of the individuals after the meeting of the populations can happen on the basis of either the best or the median fitness value or the user
   himself can select from the individuals. A further improvement we applied is an adaptation scheme for the application probability of genetic operators. With the help of
   this method, operators which produce better individuals can be used more frequently, so better individuals are more likely produced. Thus, with the help of them the speed
   of convergence can be significantly increased.

%A Gabriella K\'okai
%A Zolt\'an T\'oth
%A Robert V\'anyi
%T Evolving Artificial Trees Described by Parametric L-Systems
%B Proceedings of the First Canadian Workshop on Soft Computing
%D 1999
%P 1722--1728
%I 
%C Edmonton, Alberta, Canada
%K genetic algorithms, genetic programming, lindenmayer L-systems
%U http://citeseer.ist.psu.edu/464865.html
%X Describes a solution of the inverse problem for parametric Lindenmayer systems (L-systems) with genetic algorithms. The inverse problem studied is that of evolving
   Lindenmayer grammars to describe plants. A genetic algorithm is used to evolve the rewriting rules and the set of system parameters. In this paper, we present on extension
   of the solution of G. Ochoa (1998) and K.J. Mock (1998), by which genetic algorithms are applied to simulate the evolution of artificial plant morphologies. Three main
   improvements are described: (1) more populations are processed in parallel; (2) an adaptation scheme for the application probability of genetic operators is applied; (3)
   the turtle graphics system is modified so that 3D structures such as trees can be evolved and drawn
%8 9  May
%@ 0-7803-5579-2

%A Gabriella K\'okai
%A Zolt\'an T\'oth
%A Robert V\'anyi
%T Modelling Blood Vessel of the Eye with Parametric L-Systems Using Evolutionary Algorithms
%B Artificial Intelligence in Medicine, Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM'99
%S Lecture Notes of Computer Science
%E W. Horn and Y. Shahar and G. Lindberg and S. Andreassen and Jeremy C. Wyatt
%V 1620
%D 1999
%P 433--443
%I Springer-Verlag
%C Aalborg, Denmark
%K genetic algorithms, genetic programming, lindenmayer L-systems, Computer vision, image and signal interpretation
%U http://link.springer.de/link/service/series/0558/papers/1620/16200433.pdf
%X In this paper the GREDEA system is presented. The main idea behind it is that with the help of evolutionary algorithms a grammatical description of the blood circulation of
   the human retina can be inferred. The system uses parametric Lindenmayer systems as description language. It can be applied on patients with diabetes who need to be
   monitored over long periods.
%8 20-24  June
%@ 3-540-66162-X

%A Gabriella Kokai
%A Zoltan Toth
%A Robert Vanyi
%T Generic Evolution Algorithms Programming Library
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1867
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, methodology, pedagogy and philosophy, poster paper, lindenmayer L-systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/MP-601b.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Gabriella Kokai
%A Robert Vanyi
%A Zoltan Toth
%T Parametric L-System Description of the Retina with Combined Evolutionary Operators
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1588--1595
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, real world applications, evolution strategies, lindenmayer systems, multi-population
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-769.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99) GREDA. "Blood
   vessels can be described by a binary tree", diabetic, parametric D0L, turtle graphics, medical. Regulated evolution. Fitness = distance between 2 images. Fitness
   calculating turtle, ie compare L-system images _as it is being drawn_ with target,
%@ 1-55860-611-4

%A Gabriella K{\'o}kai
%A Zolt{\'a}n T{\'o}th
%A Szilvia Zvada
%T An Experimental Comparison of Genetic and Classical Concept Learning Methods
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 287--294
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%U http://www2.informatik.uni-erlangen.de/Forschung/Publikationen/download/gecco2002.ps.gz
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp GeLog = GA + Inductive Logic Programming. Prolog. Foil. C4.5. GEA.

%A Gabriella Kokai
%T Expansion of the GeLog System by Automatic Adaptation of Parameters
%B 7th WSEAS International Multiconference on Circuits, Systems, Communications and Computers (WSEAS CSCC)
%D 2002
%I 
%I The World Scientific and Engineering Academy and Society (WSEAS)
%C Rethymno, Crete Island, Greece
%K genetic algorithms, genetic programming, Hybrid Intelligent Systems, Inductive Logic Programming
%X Different techniques have been introduced for the improvement of Evolutionary Algorithms (EA) to increase their performance as well as to achieve better results. Adaptive
   methods are commonly used. In the scope of this work these extensions are applied to
%8 July ~7-14

%A Marisa E. Kolodny
%T Computers, Art and Smart Rooms: A Smart Picture Frame that Senses the Weather and Genetically Evolves Images
%R Technical Report PCS-TR99-354
%D 1999
%I 
%I Dartmouth College, Computer Science
%C Hanover, NH, USA
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.dartmouth.edu/TR/TR99-354.ps.Z
%X By using sensors to sense the environment and genetic programming to evolve images, this thesis explores two methods for developing smart pictures that can be integrated
   with a living space. The system presented senses the weather and indoor conditions, displays current weather and forecast information retrieved from the web, and displays
   genetically evolved images. Sensing the weather not only provides the user with information they might find useful, but also allows the computer to gain a better
   understanding of the user which in turn allows the computer to respond more accurately. Genetic programming allows the computer to better respond to its environment by
   evolving fitter programs.
%8 June
%Z Undergraduate Honors Thesis. Advisor: Daniela Rus.

%A Tomasz Kolodziejczyk
%A Rosario Toscano
%A Cyril Fillon
%A Siegfried Fouvry
%A Carlo Poloni
%A Guillermo Morales-Espejel
%A Patrick Lyonnet
%T A methodological approach ball bearing damage prediction under fretting wear conditions
%B 4th International IEEE Conference Intelligent Systems, IS '08
%V 2
%D 2008
%P 10--53--10--59
%I 
%K genetic algorithms, genetic programming, artificial neural network model, ball bearing damage prediction, damage mechanisms, feature extraction, flywheels, fretting wear
   conditions, mechanical engineering computing, neural nets, optimisation, reliability, wear
%X The industrial demand for higher reliability of various components is one of the main flywheels of the research and development in the field of modelling of complex
   phenomena. There is a need to characterize the wear behaviour of the interface under fretting wear conditions in ball bearing application. Pre-treated experimental data was
   used to determine the wear of contacting surfaces as a criterion of damage that can be useful for a life-time prediction. The benefit of acquired knowledge can be crucial
   for the industrial expert systems and the scientific feature extraction that cannot be underestimated. Wear is a very complex and partially-formalized phenomenon involving
   numerous parameters and damage mechanisms. To correlate the working conditions with the state of contacting bodies and to define damage mechanisms different techniques are
   used. The use of our approaches in the prediction of the response of the system to different test conditions is validated. Two physical models, based on Archard and
   Energetic approach, are compared with artificial neural network model and genetic programming. Decisive factors for a comparison of used AI techniques are their:
   performance, generalization capabilities, complexity and time-consumption. Optimization of the structure of the model is done to reach high robustness of field
   applications. Finally, application of the wear level information to forecast a probability of damage is presented.
%8 September
%Z Also known as \cite4670497

%A T. Kolodziejczyk
%A R. Toscano
%A S. Fouvry
%A G. Morales-Espejel
%T Artificial intelligence as efficient technique for ball bearing fretting wear damage prediction
%J Wear
%V 268
%N 1-2
%D 2010
%P 309--315
%I 
%K genetic algorithms, genetic programming, Fretting, Wear, Friction, Modelling, Artificial intelligence, Artificial neural networks
%U http://www.sciencedirect.com/science/article/B6V5B-4X0XF4J-1/2/3c189b35f0992af14970b9a1f6061dc1
%X Broadening functionality of artificial intelligence and machine learning techniques shows that they are very useful computational intelligence methods. In the present study
   the potential of various artificial intelligence techniques to predict and analyse the damage is investigated. Pre-treated experimental data was used to determine the wear
   of contacting surfaces as a criterion of damage that can be useful for a life-time prediction. The benefit of acquired knowledge can be crucial for the industrial expert
   systems and the scientific feature extraction that cannot be underestimated. Wear is a very complex and partially formalised phenomenon involving numerous parameters and
   damage mechanisms. To correlate the working conditions with the state of contacting bodies and to define damage mechanisms different techniques are used. Neural network
   structures are implemented to learn from experimental data, genetic programming to find a formula describing the wear volume and fuzzy inference system to impose physically
   meaningful rules. To gain data for the creation and verification of the model, experiments were conducted on commonly used chromium steel under dry and base oil
   bath-lubricated fretting test apparatus. Decisive factors for a comparison of used AI techniques are their: performance, generalisation capabilities, complexity and
   time-consumption. Optimisation of the structure of the model is done to reach high robustness of field applications.

%A Marcus Komann
%A Andreas Mainka
%A Dietmar Fey
%T Comparison of Evolving Uniform, Non-uniform Cellular Automaton, and Genetic Programming for Centroid Detection with Hardware Agents
%B Proceedings of the 9th International Conference Parallel Computing Technologies, PaCT 2007
%S Lecture Notes in Computer Science
%E Victor E. Malyshkin
%V 4671
%D 2007
%P 432--441
%I Springer
%C Pereslavl-Zalessky, Russia
%K genetic algorithms, genetic programming, Cellular Automaton
%X Current industrial applications require fast and robust image processing in systems with low size and power dissipation. One of the main tasks in industrial vision is fast
   detection of centroids of objects. This paper compares three different approaches for finding geometric algorithms for centroid detection which are appropriate for a
   fine-grained parallel hardware architecture in an embedded vision chip. The algorithms shall comprise emergent capabilities and high problem-specific functionality without
   requiring large amounts of states or memory. For that problem, we consider uniform and non-uniform cellular automata (CA) as well as Genetic Programming. Due to the
   inherent complexity of the problem, an evolutionary approach is applied. The appropriateness of these approaches for centroid detection is discussed.
%8 September  3-7

%A Marcus Komann
%A Patrick Ediger
%A Dietmar Fey
%A Rolf Hoffmann
%T On the Effectivity of Genetic Programming Compared to the Time-Consuming Full Search of Optimal 6-State Automata
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 280--291
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming, poster
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Marcus Komann
%A Dietmar Fey
%T Revising the Trade-off Between the Number of Agents and Agent Intelligence
%B EvoCOMPLEX
%S LNCS
%E Cecilia Di Chio and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and
   Mike Preuss and Julian Togelius and Georgios N. Yannakakis
%V 6024
%D 2010
%P 31--40
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X Emergent agents are a promising approach to handle complex systems. Agent intelligence is thereby either defined by the number of states and the state transition function
   or the length of their steering programs. Evolution has shown to be successful in creating desired behaviors for such agents. Genetic algorithms have been used to find
   agents with fixed numbers of states and genetic programming is able to balance between the steering program length and the costs for longer programs. This paper extends
   previous work by further discussing the relationship between either using more agents with less intelligence or using fewer agents with higher intelligence. Therefore, the
   Creatures' Exploration Problem with a complex input set is solved by evolving emergent agents. It shows that neither a sole increase in intelligence nor amount is the best
   solution. Instead, a cautious balance creates best results.
%8 7-9  April
%Z EvoCOMPLEX'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Marcus Komann
%T Manual Engineering and Evolution of Emergent Algorithms for Agents on Two-dimensional Grids
%R Ph.D. Thesis Doktor-Ingenieur
%D 2010
%I 
%I Erlangen
%C Germany
%K genetic algorithms, genetic programming, EHW, FPGA, agent, evolution, emergence, deterministic finite state machine, field programmable gate array, feature extraction,
   smart camera
%U http://www.opus.ub.uni-erlangen.de/opus/volltexte/2011/2315/pdf/MarcusKomannDissertation.pdf
%X In this thesis, the problem of detecting the attributes of multiple objects in binary images in realtime is solved. It is a common problem in industrial machine vision. For
   the solution, the usage of emergent algorithms on a smart camera with a fine-grained massively-parallel processor is proposed. Combining both is promising since such
   processors can exploit the abilities of emergent algorithms. Therefore, so-called Marching Pixels are introduced. These are local agents that traverse the pixel grid of an
   image in a certain way in order to accumulate global data about the image objects. At first, manually engineered Marching Pixels algorithms for different object classes are
   presented and compared. Afterwards, their realisations in hardware are shown. These realizations are able to fulfil the real time requirements and are small enough for
   application in real industrial scenarios. They can further be used to execute other emergent algorithms that are based on 2-D grids. However, the absolute quality of the
   manually engineered algorithms is unknown. In the thesis, emergent agent algorithms for 2-D grids are thus also evolved. First, the effectiveness of evolution for finding
   good emergent agent algorithms is shown. Then, it is argued that improving agent abilities has to be done by cautiously balancing increased agent amounts and higher agent
   intelligence. The focus on the initial machine vision problem is thereby expanded to emergent agents on 2-D grids in general since many complex systems today comprise such
   entities and controlling their emergent phenomena is difficult but worthwhile.
%8 22  Decemeber
%Z Some consideration of GP in later chapters. Advisor: Fey, Dietmar (Prof. Dr.-Ing.)

%A Kyoko Komatsu
%A Tomomi Yamanaka
%A Masami Takata
%A Joe Kazuki
%T A Music Composition Model with Genetic Programming
%B Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, (PDPTA 2010)
%E Hamid R. Arabnia and Steve C. Chiu and George A. Gravvanis and Minoru Ito and Kazuki Joe and Hiroaki Nishikawa and Ashu M. G. Solo
%D 2010
%P 686--692
%I CSREA Press
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%8 July  12-15
%Z PDPTA

%A Michael Kommenda
%A Gabriel Kronberger
%A Stephan Winkler
%A Michael Affenzeller
%A Stefan Wagner
%A Leonhard Schickmair
%A Benjamin Lindner
%T Application of Genetic Programming on Temper Mill Datasets
%B 2nd International Conference on Logistics and Industrial Informatics, LINDI 2009
%D 2009
%P 1--5
%I 
%K genetic algorithms, genetic programming, evolutionary inspired based modeling technique, linear regression analysis, mathematical model, mechanical properties, population
   based modeling technique, steel sheet quality, strip tension, support vector machines, temper mill dataset, temper rolling, data handling, hot rolling, production
   engineering computing, rolling mills, sheet metal processing, tempering
%X Temper rolling is essential for the quality of steel sheets. The degree of temper rolling determines the mechanical properties of the steel sheet and is highly influenced
   by the rolling force or strip tension. Since mathematical models generate unsatisfactory results for the calculation of these two process parameters, other methods for the
   presetting of tempers mills must be used. The parameter presetting of temper mills is of prime importance because it reduces the effort of tuning these parameters later.
   Hence, the production costs are reduced by minimizing the amount of wasted material that does not fulfill the quality requirements. Genetic programming (GP) is an
   evolutionary inspired and population based modeling technique and has been successfully applied in different contexts. In this paper we present first results of advanced
   genetic programming concepts on large datasets from a temper mill in comparison to linear regression (LR), support vector machines (SVMs) and previous analysis on the
   datasets. The use of GP shows an improvement compared to previous work, but is still inferior to models obtained by SVMs. A major advantage of GP compared to support vector
   machines is that the identified models are mathematical formulae which can be interpreted and enable knowledge generation about the temper rolling process.
%8 September
%Z Also known as \cite5258766

%A Michael Kommenda
%A Gabriel Kronberger
%A Christoph Feilmayr
%A Michael Affenzeller
%T Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset
%B Applications of Evolutionary Computing, EvoApplications 2011: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, EvoSTOC
%S LNCS
%E Cecilia Di Chio and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I Esparcia-Alcazar and Juan J. Merelo and Ferrante Neri and Mike Preuss and
   Hendrik Richter and Julian Togelius and Georgios N. Yannakakis
%V 6624
%D 2011
%P 274--283
%I Springer Verlag Berlin
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming, Variable Selection, Data Mining, Blast Furnace, steel
%X In this paper a data mining approach for variable selection and knowledge extraction from datasets is presented. The approach is based on unguided symbolic regression
   (every variable present in the dataset is treated as the target variable in multiple regression runs) and a novel variable relevance metric for genetic programming. The
   relevance of each input variable is calculated and a model approximating the target variable is created. The genetic programming configurations with different target
   variables are executed multiple times to reduce stochastic effects and the aggregated results are displayed as a variable interaction network. This interaction network
   highlights important system components and implicit relations between the variables. The whole approach is tested on a blast furnace dataset, because of the complexity of
   the blast furnace and the many interrelations between the variables. Finally the achieved results are discussed with respect to existing knowledge about the blast furnace
   process.
%8 27-29  April
%Z Part of \citeDiChio:2011:evo_a EvoApplications2011 held inconjunction with EuroGP'2011, EvoCOP2011 and EvoBIO2011

%A R. Konig
%A U. Johansson
%A L. Niklasson
%T Genetic Programming - A Tool for Flexible Rule Extraction
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 1304--1310
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Although data mining is performed to support decision making, many of the most powerful techniques, like neural networks and ensembles, produce opaque models. This lack of
   interpretability is an obvious disadvantage, since decision makers normally require some sort of explanation before taking action. To achieve comprehensibility, accuracy is
   often sacrificed by the use of simpler, transparent models, such as decision trees. Another alternative is rule extraction; i.e. to transform the opaque model into a
   comprehensible model, keeping acceptable accuracy. We have previously suggested a rule extraction algorithm named G-REX, which is based on genetic programming. One key
   property of G-REX, due to the use of genetic programming, is the possibility to use different representation languages. In this study we apply G-REX to estimation tasks.
   More specifically, three representation languages are evaluated using eight publicly available data sets. The quality of the extracted rules is compared to two standard
   techniques producing comprehensible models; multiple linear regression and the decision tree algorithm C&amp;RT. The results show that G-REX outperforms the standard
   techniques, but that the choice of representation language is important.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Rikard Konig
%A Ulf Johansson
%A Lars Niklasson
%T Using Genetic Programming to Increase Rule Quality
%B Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference
%E David Wilson and H. Chad Lane
%D 2008
%P 288--293
%I AAAI Press
%C Coconut Grove, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Papers/FLAIRS/2008/FLAIRS08-071.pdf
%X Rule extraction is a technique aimed at transforming highly accurate opaque models like neural networks into comprehensible models without losing accuracy. G-REX is a rule
   extraction technique based on Genetic Programming that previously has performed well in several studies. This study has two objectives, to evaluate two new fitness
   functions for G-REX and to show how G-REX can be used as a rule inducer. The fitness functions are designed to optimize two alternative quality measures, area under ROC
   curves and a new comprehensibility measure called brevity. Rules with good brevity classifies typical instances with few and simple tests and use complex conditions only
   for atypical examples. Experiments using thirteen publicly available data sets show that the two novel fitness functions succeeded in increasing brevity and area under the
   ROC curve without sacrificing accuracy. When compared to a standard decision tree algorithm, G-REX achieved slightly higher accuracy, but also added additional quality to
   the rules by increasing their AUC or brevity significantly.
%8 May  15-17
%Z BNF grammar Fig 2.

%A Rikard Konig
%A Ulf Johansson
%A Lars Niklasson
%T G-REX: A Versatile Framework for Evolutionary Data Mining
%B IEEE International Conference on Data Mining Workshops, ICDMW '08
%D 2008
%P 971--974
%I 
%K genetic algorithms, genetic programming, G-REX, Weka, decision lists, decision trees, evolutionary data mining, fuzzy-rules, hybrid kNN-rules, regression models, data
   mining, decision trees, fuzzy set theory, regression analysis
%X This paper presents G-REX, a versatile data mining framework based on genetic programming. What differs G-REX from other GP frameworks is that it doesn't strive to be a
   general purpose framework. This allows G-REX to include more functionality specific to data mining like preprocessing, evaluation- and optimization methods, but also a
   multitude of predefined classification and regression models. Examples of predefined models are decision trees, decision lists, k-NN with attribute weights, hybrid
   kNN-rules, fuzzy-rules and several different regression models. The main strength is, however, the flexibility, making it easy to modify, extend and combine all of the
   predefined functionality. G-REX is, in addition, available in a special Weka package adding useful evolutionary functionality to the standard data mining tool Weka.
%8 Decemeber
%Z Also known as \cite4734030

%A Rikard Konig
%A Ulf Johansson
%A Tuve Lofstrom
%A Lars Niklasson
%T Improving GP classification performance by injection of decision trees
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X This paper presents a novel hybrid method combining genetic programming and decision tree learning. The method starts by estimating a benchmark level of reasonable
   accuracy, based on decision tree performance on bootstrap samples of the training set. Next, a normal GP evolution is started with the aim of producing an accurate GP. At
   even intervals, the best GP in the population is evaluated against the accuracy benchmark. If the GP has higher accuracy than the benchmark, the evolution continues
   normally until the maximum number of generations is reached. If the accuracy is lower than the benchmark, two things happen. First, the fitness function is modified to
   allow larger GPs, able to represent more complex models. Secondly, a decision tree with increased size and trained on a bootstrap of the training data is injected into the
   population. The experiments show that the hybrid solution of injecting decision trees into a GP population gives synergetic effects producing results that are better than
   using either technique separately. The results, from 18 UCI data sets, show that the proposed method clearly outperforms normal GP, and is significantly better than the
   standard decision tree algorithm.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5585988

%A Rikard Konig
%A Ulf Johansson
%A Lars Niklasson
%T Finding the tree in the Forest
%B Proceedings of the IADIS International Conference Applied Computing
%E Hans Weghorn and Pedro Isaias and Radu Vasiu
%D 2010
%P 135--142
%I 
%I IADIS
%C Timisoara, Romania
%K genetic algorithms, genetic programming, decision support, decision trees, alternative solutions, inconsistency
%U http://www.iadisportal.org/applied-computing-2010-proceedings
%X Decision trees are often used for decision support since they are fast to train, easy to understand and deterministic; i.e., always create identical trees from the same
   training data. This property is, however, only inherent in the actual decision tree algorithm, nondeterministic techniques such as genetic programming could very well
   produce different trees with similar accuracy and complexity for each execution. Clearly, if more than one solution exists, it would be misleading to present a single tree
   to a decision maker. On the other hand, too many alternatives could not be handled manually, and would only lead to confusion. Hence, we argue for a method aimed at
   generating a suitable number of alternative decision trees with comparable accuracy and complexity. When too many alternative trees exist, they are grouped and
   representative accurate solutions are selected from each group. Using domain knowledge, a decision maker could then select a single best tree and, if required, be presented
   with a small set of similar solutions, in order to further improve his decisions. In this paper, a method for generating alternative decision trees is suggested and
   evaluated. All in all,four different techniques for selecting accurate representative trees from groups of similar solutions are presented. Experiments on 19 UCI data sets
   show that it often exist dozens of alternative trees, and that one of the evaluated techniques clearly outperforms all others for selecting accurate and representative
   models.
%8 14-16  October

%A Aaron Konstam
%T Group Classification Using a Mix of Genetic Programming and Genetic Algorithms
%B 1998 ACM Symposium on Applied Computing
%E K. M. George
%D 1998
%I 
%C Marriott Marquis, Atlanta, Georgia, U.S.A.
%K genetic algorithms, genetic programming
%U http://www.cogsci.ed.ac.uk/~ceilidh/SAC-Papers/Paper34/index.html
%8 27  February -1  March
%Z SAC'98

%A Andreas Konstantinidis
%A Haris Haralambous
%A Alexandros Agapitos
%A Harris Papadopoulos
%T GP-MOEA/D Approach for Modelling Total Electron Content over Cyprus
%J International Journal of Engineering Intelligent Systems
%V 18
%N 3/4
%D 2010
%I 
%K genetic algorithms, genetic programming, time-series prediction, multi-objective evolutionary algorithms
%O Special issue Artificial Intelligence Applications and Innovations 2010 Lazaros Iliadis, Andreas Andreou and Harris Papadopoulos
%8 September /  Decemeber
%Z http://www.crlpublishing.co.uk/journal.asp?j=eis&s=Vol%2018%202010

%A Behrooz Koohestani
%A Riccardo Poli
%T A Genetic Programming Approach to the Matrix Bandwidth-Minimization Problem
%B PPSN 2010 11th International Conference on Parallel Problem Solving From Nature
%S Lecture Notes in Computer Science
%E Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph
%V 6239
%D 2010
%P 482--491
%I Springer
%C Krakow, Poland
%K genetic algorithms, genetic programming, Bandwidth Minimization Problem, BMP, Graph Labelling, Sparse Matrices, Combinatorial Optimisation
%X The bandwidth of a sparse matrix is the distance from the main diagonal beyond which all elements of the matrix are zero. The bandwidth minimisation problem for a matrix
   consists of finding the permutation of rows and columns of the matrix which ensures that the non-zero elements are located in as narrow a band as possible along the main
   diagonal. This problem, which is known to be NP-complete, can also be formulated as a vertex labelling problem for a graph whose edges represent the non-zero elements of
   the matrix. In this paper, a Genetic Programming approach is proposed and tested against two of the best-known and widely used bandwidth reduction algorithms. Results have
   been extremely encouraging.
%8 11-15  September
%Z flattened tree GP, C#, tress create permutations (row-column re-ordering) of sparse symmetric matrix adjacency graph. Function set +-*protected division, sin, abs, terminal
   set: three inputs (one or more required), 100 floating erc. Subtree crossover, point mutation. Each tree run n(graph size) times. Three inputs based on eigenvectors, level
   structure from other BMP solvers. fitness biased to better areas of search space. math.nist.gov Harwell-Boeing, Compared with Matlab RCMM and Spectral

%A Behrooz Koohestani
%A Riccardo Poli
%T A Hyper-Heuristic Approach to Evolving Algorithms for Bandwidth Reduction Based on Genetic Programming
%B Proceedings of the 31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI-2011
%E Max Bramer and Miltos Petridis and Lars Nolle
%D 2011
%P 93--106
%I Springer London
%I BCS special interest group on Artificial Intelligence
%C Cambridge, England
%K genetic algorithms, genetic programming
%X The bandwidth reduction problem is a well-known NP-complete graph layout problem that consists of labelling the vertices of a graph with integer labels in such a way as to
   minimise the maximum absolute difference between the labels of adjacent vertices. The problem is isomorphic to the important problem of reordering the rows and columns of a
   symmetric matrix so that its non-zero entries are maximally close to the main diagonal, a problem which presents itself in a large number of domains in science and
   engineering. A considerable number of methods have been developed to reduce the bandwidth, among which graph-theoretic approaches are typically faster and more effective. a
   hyper-heuristic approach based on genetic programming is presented for evolving graph-theoretic bandwidth reduction algorithms. The algorithms generated from our
   hyper-heuristic are extremely effective. We test the best of such evolved algorithms on a large set of standard benchmarks from the Harwell-Boeing sparse matrix collection
   against two state-of-the-art algorithms from the literature. Our algorithm outperforms both algorithms by a significant margin, clearly indicating the promise of the
   approach.
%O Research and Development in Intelligent Systems XXVIII, Incorporating Applications and Innovations in Intelligent Systems XIX
%8 Decemeber

%A Sylvain Koos
%A Jean-Baptiste Mouret
%A Stephane Doncieux
%T Automatic System Identification Based on Coevolution of Models and Tests
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P -
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming
%X In evolutionary robotics, controllers are often designed in simulation, then transferred onto the real system. Nevertheless, when no accurate model is available, controller
   transfer from simulation to reality means potential performance loss. It is the reality gap problem. Unmanned aerial vehicles are typical systems where it may arise. Their
   locomotion dynamics may be hard to model because of a limited knowledge about the underlying physics. Moreover, a batch identification approach is difficult to use due to
   costly and time consuming experiments. An automatic identification method is then needed that builds a relevant local model of the system concerning a target issue. This
   paper deals with such an approach that is based on coevolution of models and tests. It aims at improving both modeling and control of a given system with a limited number
   of manipulations carried out on it. Experiments conducted with a simulated quad rotor helicopter show promising initial results about test learning and control improvement.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Stephan Kopp
%A Henning S. Mortveit
%A Christian M. Reidys
%T Sequential Dynamical Systems and Simulation
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1445
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-009.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A M. Koppen
%A B. Nickolay
%T Design of Image Exploring Agent using Genetic Programming
%B Proc. IIZUKA'96
%D 1996
%P 549--552
%I 
%C Iizuka, Japan
%K genetic algorithms, genetic programming, image processing, agent design, image exploring agent, image understanding, genetic optimization
%U http://citeseer.ist.psu.edu/403202.html
%X An essential goal of image processing is the detection of image structures like objects, object boundaries, shapes, regions or surface defects. This goal can be reached by
   a combination of detection algorithms and localisation algorithms. Detection algorithms are able to recognise the physical evidence of a structure according to a given
   localization in the image. Localisation algorithms cope with the position of image structures in images. Most of them are tracking algorithms. Because of the limited
   recognition abilities the main goal of the foregoing detection algorithm is to enhance an image object. The image structure must be transformed into a kind of information
   which is suitable for the localisation algorithm. However, in many fields of image processing this approach does not give satisfying results. This paper introduces a new
   approach by replacing the conventional localisation algorithm by an image exploring agent and by adapting its functional design by means of genetic programming.

%A Mario Koppen
%A Bertram Nickolay
%T Design of Image Exploring Agent using Genetic Programming
%J Fuzzy Sets and Systems
%V 2
%N 103
%D 1999
%P 303--315
%I 
%K genetic algorithms, genetic programming, Pattern recognition, Image processing, Agent design, Image understanding, Decision making
%U http://www.elsevier.com/locate/fss
%X This paper presents a new methodology for the design of image processing algorithms. The presented approach is based on the conception and design of "image exploring
   agents". Image exploring agents iteratively run a sense-compute-act loop. While all loop processing is performed within the image on a local base, the trace of the agent,
   which results from the repetition of this loop, is a global image property. Hence, a given global recognition task is represented by a corresponding task of finding a
   suitable, local-based computation. The exploitation of genetic programming for finding suitable computational parts of the agent is considered. A case study is included, in
   which a crack detector is designed and improved by means of an image exploring agent. The functionality of this agent is studied, and it is used for designing an image
   processing algorithm for crack detection. Also, further improvements of the presented agent concept are given in this paper. c 1999 Elsevier Science B.V. All rights
   reserved.
%O Special Issue on Softcomputing
%8 16  April

%A Arthur K. Kordon
%A Guido F. Smits
%T Soft Sensor Development Using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 1346--1351
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, real world applications, soft sensors, hybrid intelligent, systems, empirical modeling
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d24.pdf
%X Dow Chemical Company
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO Preliminary sensitivity analysis based on stacked artificial neural networks (MATLAB). ANN, Support vector machines (SVM), GP. Vacuum distillation
   column, continuous stirred tank reactor, twin screw extruder. Hybrid intelligent system methodology "The GP-generated soft sensor was implemented in Gensym G2"... "The
   system is in operation since November 1997 and initially it included a soft sensor for one reactor." p1350.
%@ 1-55860-774-9

%A Arthur Kordon
%A Hoang Pham
%A Clive Bosnyak
%A Mark Kotanchek
%A Guido Smits
%T Accelerating Industrial Fundamental Model Building with Symbolic Regression: A Case Study with Structure-Property Relationships
%B GECCO-2002 Presentations in the Evolutionary Computation in Industry Track
%E Lawrence ``Dave'' Davis and Rajkumar Roy
%D 2002
%P 111--116
%I 
%I ISGEC
%C New York, New York
%K genetic algorithms, genetic programming
%8 11-13  July
%Z context-free grammars, Arrhenius, G3P dimensionally consistent. large function set, parsimony pressure, little man hours. outlier detection - GP+SVM+PCA, GIGO

%A Arthur Kordon
%A Guido Smits
%A Elsa Jordaan
%A Ed Rightor
%T Robust Soft Sensors Based On Integration of Genetic Programming, Analytical Neural Networks, and Support Vector Machines
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 896--901
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming, ANN, SVM
%X A novel approach for development of inferential sensors based on integration of three key computational intelligence approaches (genetic programming, analytical neural
   networks, and support vector machines) is proposed. The advantages of this type of soft sensors are their good generalization capabilities, increased robustness, explicit
   input/output relationships, self-assessment capabilities, and low implementation and maintenance cost.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Arthur K. Kordon
%A Guido F. Smits
%A Alex N. Kalos
%A Elsa M. Jordaan
%T Robust soft sensor development using genetic programming
%B Nature-insprired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks
%S Data Handling in Science and Technology
%E Riccardo Leardi
%V 23
%D 2003
%P 69--108
%I Elsevier
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B7RNT-4HRP42S-3/2/32d573f0a1b35132e0643d2aa5e7c76d
%O 3

%A Arthur Kordon
%A Ching-Tai Lue
%T Symbolic Regression Modeling of Blown Film Process Effects
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 561--568
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Evolutionary Computing in the Process Industry
%X The potential of symbolic regression for automatic generation of process effects empirical models has been explored on a real industrial case study. A novel methodology
   based on nonlinear variable selection and model derivation by Genetic Programming has been defined and successfully applied for blown film process effects modeling. The
   derived nonlinear models are simple, have better performance than the linear models, and predicted behavior in accordance with the process physics.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Arthur Kordon
%A Elsa Jordaan
%A Lawrence Chew
%A Guido Smits
%A Torben Bruck
%A Keith Haney
%A Annika Jenings
%T Biomass Inferential Sensor Based on Ensemble of Models Generated by Genetic Programming
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 1078--1089
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31031078.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Arthur Kordon
%A Flor Castillo
%A Guido Smits
%A Mark Kotanchek
%T Application Issues of Genetic Programming in Industry
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 241--258
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, symbolic regression, industrial applications, design of experiments, real world problems, parameter selection
%X The chapter gives a systematic view, based on the experience from The Dow Chemical Company, of the key issues for applying symbolic regression with Genetic Programming (GP)
   in industrial problems. The competitive advantages of GP are defined and several industrial problems appropriate for GP are recommended and referenced with specific
   applications in the chemical industry. A systematic method for selecting the key GP parameters, based on statistical design of experiments, is proposed. The most
   significant technical and non-technical issues for delivering a successful GP industrial application are discussed briefly.
%O 16
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Arthur K. Kordon
%T Future Trends in Soft Computing Industrial Applications
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 7854--7861
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X The paper summarises the current state of the art of applying soft computing solutions in the chemical industry, based on the experience of The Dow Chemical Company and
   projects the future trends in the field, based on the expected future industrial needs. Several examples of successful industrial applications of different soft computing
   techniques are given: automated operating discipline, based on fuzzy logic; empirical emulators of fundamental models, based on neural networks; accelerated new product
   development, based on genetic programming, and advanced process optimisation, based on swarm intelligence. The impact of these applications is a significant improvement in
   process operation and in much faster modelling of new products. The expected future needs of industry are defined as: predictive marketing, accelerated new products
   diffusion, high throughput modelling, manufacturing at economic optimum, predictive optimal supply-chain, intelligent security, emerging simplicity, and handling the curse
   of decentralisation. The future trends that are expected from the soft computing technologies, which may satisfy these needs, are as follows: perception-based modelling,
   integrated systems, universal role of intelligent agents, models/rules with evolved structure, and swarm intelligence-based process control.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Arthur Kordon
%T Evolutionary Computation at Dow Chemical
%J SIGEVOlution
%V 1
%N 3
%D 2006
%P 4--9
%I 
%K genetic algorithms, genetic programming
%U http://www.sigevolution.org/2006/03/issue.pdf
%8 September

%A Arthur Kordon
%T Evolutionary Computation in the Chemical Industry
%B Evolutionary Computation in Practice
%S Studies in Computational Intelligence
%E Tina Yu and David Davis and Cem Baydar and Rajkumar Roy
%V 88
%D 2008
%P 245--262
%I Springer
%K genetic algorithms, genetic programming, competitive advantage
%O 11
%Z Part of \citeTinaYu:2008:book

%A Arthur Kordon
%T Computational Intelligence Marketing
%J SIGEVOlution
%V 4
%N 1
%D 2009
%P 2--11
%I 
%K genetic algorithms, genetic programming
%U http://www.sigevolution.org/issues/pdf/SIGEVOlution200901.pdf
%Z The Dow Chemical Company

%A Arthur K. Kordon
%T Applying Computational Intelligence How to Create Value
%D 2010
%I Springer
%K genetic algorithms, genetic programming, Artificial intelligence, Business intelligence, Computational intelligence, Data mining, Empirical modeling, Evolutionary
   computing, Fuzzy systems, Industrial applications, Machine learning, Natural computing, Optimization, Process monitoring, Six Sigma, Swarm intelligence, Technology
   management
%U http://www.springer.com/engineering/book/978-3-540-69910-1
%Z Reviewed by \citeLeguizamon:2011:GPEM

%A Arthur Kordon
%T Issues in Applying Computational Intelligence
%J SIGEvolution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation
%V 4
%N 3
%D 2009
%P 2--14
%I 
%K genetic algorithms, genetic programming
%U http://www.sigevolution.org/issues/pdf/SIGEVOlution200903.pdf
%X Applying any emerging technology is not trivial and requires some level of risk-taking even when the competitive advantage is clear. In the case of computational
   intelligence the process is even harder due to the different nature of the comprising methods, the lack of marketing, affordable professional tools and application
   methodology. Another important factor slowing down computational intelligence applications is the wrong perception of the technology. To many potential users it looks like
   it's either too expensive or it's rocket science. The pendulum of expectations also swings from one extreme of anticipating a silver bullet to all problems to the other
   extreme of awaiting the next technology fiasco. This article focuses on the most important application issues of computational intelligence, shown in the mind-map in Figure
   1. Their understanding and resolution is critical for the success of applied computational intelligence.
%O The Dow Chemical Company
%Z Features cover of \citeKordon:book

%A Arthur Kordon
%T Marketing computational intelligence in industry
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X The paper gives guidelines for a systematic marketing approach for promoting computational intelligence in industry. The proposed research marketing strategy includes three
   key steps. The first step identifies the target market (industry, government, or academia). The second step defines and protects the product. The third step implements the
   promotional strategy by publishing, conference presentations, and technical and business advertising. Different mechanisms of marketing computational intelligence through
   interactions between academia and industry, such as protecting intellectual property, publishing, conference advertising, technology development, and interactions with
   vendors are discussed. Specific directions for targeting technical and nontechnical audience are given as well.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586356

%A Michael Korkin
%A Hugo {de Garis}
%A Felix Gers
%A Hitoshi Hemmi
%T ``CBM (CAM-Brain Machine)'': A Hardware Tool which Evolves a Neural Net Module in a Fraction of a Second and Runs a Million Neuron Artificial Brain in Real Time
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 498--503
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Evolvable Hardware
%8 13-16  July
%Z GP-97

%A Emin Erkan Korkmaz
%A Gokturk Ucoluk
%T Controlling the Genetic Programming Search
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 180
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster, Grammar Induction, Context-free
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Emin Erkan Korkmaz
%A Gokturk Ucoluk
%T Genetic Programming for Grammar Induction
%B 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Erik D. Goodman
%D 2001
%P 245--251
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, context free grammar induction, CFG, English NLP, C4.5
%U http://citeseer.ist.psu.edu/451812.html
%X There has been a big interest in inducing classes of grammars in the area of machine learning. Various attempts have been carried out for automatically inferring different
   grammar classes. The symbolic nature of the grammar induction problem makes it suitable for the GP-approach. However the straightforward application of the GP method on
   Context Free Grammar Induction problem fails to generate a satisfactory solution. The interdependency among subparts of a CFG is high and it seems to be the reason that
   prevents the GP method from finding out effective building blocks during the search. In this paper a new approach is presented where the aim is to formalize a control
   module for the genetic search which can use the interdependency information existing in CFGs and hence can direct the search only among well-fit grammars in the search
   space.
%8 9-11  July
%Z GECCO-2001LB

%A Emin Erkan Korkmaz
%A G{\"o}kt{\"u}rk {\"U}{\c{c}}oluk
%T Controlling The Genetic Programming Search
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 891
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, poster paper
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Emin Erkan Korkmaz
%A G{\"o}kt{\"u}rk {\"U}{\c{c}}oluk
%T Controlled Genetic Programming Search for Solving Deceptive Problems
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 295--300
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp Context free grammar induction. N-parity (N=5). Uses global information (ie outside population).

%A Emin Erkan Korkmaz
%T Controlled Genetic Programming Search for Solving Deceptive Problems
%R Ph.D. Thesis
%D 2003
%I 
%I Department of Computer Engineering, Middle East Technical University
%C Turkey
%K genetic algorithms, genetic programming, deception, epistasis
%U http://www.cpsc.ucalgary.ca/~korkmaze/publication/tezphd.ps.gz
%X Traditional Genetic Programming randomly combines subtrees by applying crossover. There is a growing interest in methods that can control such recombination operations. In
   this thesis, a new approach is presented for guiding the recombination process for Genetic Programming.The method is based on extracting the global information of the
   promising solutions that appear during the genetic search. The aim isto use this information to control the crossover operation afterwards. A separate control module is
   used to process the collected information.This module guides the search process by sending feedback to the genetic engine about the consequences of possible recombination
   alternatives.
%8 March
%Z 317K

%A Emin Erkan Korkmaz
%A Gokturk Ucoluk
%T Design and Usage of a New Benchmark Problem for Genetic Programming
%B Lecture Notes in Computer Science
%V 2869
%D 2003
%P 561--567
%I Springer-Verlag Heidelberg
%I ISCIS XVIII - Eighteenth International Symposium on Computer and Information Sciences, 2003 Antalya, Turkey
%K genetic algorithms, genetic programming, benchmark
%U http://cse.yeditepe.edu.tr/~ekorkmaz/publication/benchmark.ps
%X Not so many benchmark problems have been proposed in the area of Genetic Programming (GP). In this study, a new artificial benchmark problem is designed for GP. The
   different parameters that can be used to tune the difficulty of the problem are analyzed. Also, the initial experimental results obtained on different instances of the
   problem are presented.
%8 November  3-5

%A Emin Erkan Korkmaz
%A Gokturk Ucoluk
%T A Controlled Genetic Programming Approach for the Deceptive Domain
%J IEEE Transactions on Systems, Man and Cybernetics, Part B
%V 34
%N 4
%D 2004
%P 1730--1742
%I 
%K genetic algorithms, genetic programming, decision trees, genetic algorithms, tree searching, deceptive domain problem, genetic programming, genetic search, global
   information extraction, recombination process, Crossover, GP, deceptive problems, epistasis
%U http://www.ceng.metu.edu.tr/~ucoluk/research/publications/controledGP.pdf
%X Traditional genetic programming (GP) randomly combines subtrees by applying crossover. There is a growing interest in methods that can control such recombination operations
   in order to achieve faster convergence. In this paper, a new approach is presented for guiding the recombination process for genetic programming. The method is based on
   extracting the global information of the promising solutions that appear during the genetic search. The aim is to use this information to control the crossover operation
   afterwards. A separate control module is used to process the collected information. This module guides the search process by sending feedback to the genetic engine about
   the consequences of possible recombination alternatives.
%8 August
%Z INSPEC Accession Number:8111580 PMID: 15462440

%A S. Kornienko
%A O. Kornienko
%A A. Nagarathinam
%A P. Levi
%T From Real Robot Swarm to Evolutionary Multi-Robot Organism
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 1483--1490
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Collective working allows microrobots to achieve more functionality, better performance and higher reliability on the macroscopic level. In this paper we demonstrate the
   on-going work in developing novel collective systems, where swarm robots work not only collectively, but are also capable of autonomous aggregation and disaggregation into
   a higher multi-robot organism. The main issues of such an organism, as well as its genome-based control, are discussed. We show the developed docking approach and
   investigate topological transformations in a prototype of self-assembling robots.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C Is it a GP? Executable content?
%@ 1-4244-1340-0

%A Michael F. Korns
%T Large-Scale, Time-Constrained Symbolic Regression
%B Genetic Programming Theory and Practice IV
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%V 5
%D 2006
%P -
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, artificial intelligence, portfolio selection, data mining, formal grammars, quantitative portfolio management
%X narrative of the problems we encountered using genetic programming to build a symbolic regression tool for large-scale, time constrained regression problems. It describes
   in detail the problems encountered, the commonly held beliefs challenged, and the techniques required to achieve reasonable performance with large-scale, time-constrained
   regression. We discuss in some detail the selection of the compilation tools, the construction of the fitness function, the chosen system grammar (including internal
   functions and operators), and the chosen system architecture (including multiple island populations). Furthermore in order to achieve the level of performance reported
   here, of necessity, we borrowed a number of ideas from disparate schools of genetic programming and recombined them in ways not normally seen in the published literature.
%O 16
%8 11-13  May
%Z part of \citeRiolo:2006:GPTP Published Jan 2007 after the workshop. Investment Science Corporation, 1 Plum Hollow, Henderson, Nevada 89052 USA
%@ 0-387-33375-4

%A Michael F. Korns
%T Large-Scale, Time-Constrained Symbolic Regression-Classification
%B Genetic Programming Theory and Practice V
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2007
%P 53--68
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%X This chapter demonstrates a novel method combining particle swarm, differential evolution, and genetic programming to build a symbolic regression tool for large-scale,
   time-constrained regression-classification problems. In a previous paper we experimented with large scale symbolic regression. Here we describe in detail the enhancements
   and techniques employed to support large scale, timeôconstrained regression and classification. In order to achieve the level of performance reported here, of necessity, we
   borrowed a number of ideas from disparate schools of genetic programming and recombined them in ways not normally seen in the published literature. We discuss in some
   detail the construction of the fitness function, the use of abstract grammars to combine genetic programming with differential evolution and particle swarm agents, and the
   use of context-aware crossover.
%O 4
%8 17-19  May
%Z part of \citeRiolo:2007:GPTP published 2008

%A Michael F. Korns
%A Loryfel Nunez
%T Profiling Symbolic Regression-Classification
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 215--229
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%O 14
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008

%A Michael F. Korns
%T Symbolic regression using abstract expression grammars
%B GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
%E Lihong Xu and Erik D. Goodman and Guoliang Chen and Darrell Whitley and Yongsheng Ding
%D 2009
%P 859--862
%I ACM New York, NY, USA
%I SigEvo
%C Shanghai, China
%K genetic algorithms, genetic programming, Poster, PSO, DE
%X Expression Grammars have the potential to integrate Genetic Algorithms, Genetic Programming, Swarm Intelligence, and Differential Evolution into a seamlessly unified array
   of tools for use in symbolic regression. The features of abstract expression grammars are explored, examples of implementations are provided, and the beneficial effects of
   abstract expression grammars are tested with several published nonlinear regression problems.
%8 June  12-14
%Z p 860 "Abstract constants represent placeholders for real numbers which are to be optimized". Abstract features inputs again optimised by PSO, DE, etc. Abstract functions
   chosen by GA etc from predefined list of functions. User uses grammar to decide how much freedom or how constrained system is to be. 10000 training examples. Also known as
   \citeDBLP:conf/gecco/Korns09. Part of \citeDBLP:conf/gec/2009

%A Michael F. Korns
%T Mutation and crossover with abstract expression grammars
%B GECCO-2009 Symbolic regression and modeling workshop (SRM)
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 2219--2224
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X Simple enhancements to the standard population operators of mutation and crossover, using Abstract Expression Grammars, are investigated. In previous works, Abstract
   Expression Grammars have been used to integrate Genetic Algorithms, Genetic Programming, Swarm Intelligence, and Differential Evolution, into a seamlessly unified approach
   to symbolic regression. In this work, the potential for Abstract Expression Grammars to have a direct impact on the classic Genetic Programming mutation and crossover
   operators is demonstrated. The features of abstract expression grammars are explored, details of abstract mutation and crossover are provided, and the beneficial effects of
   abstract mutation and crossover are tested with several published nonlinear regression problems.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Michael F. Korns
%T Symbolic Regression of Conditional Target Expressions
%B Genetic Programming Theory and Practice VII
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Una-May O'Reilly and Trent McConaghy
%D 2009
%P 211--228
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Abstract Expression Grammars, Differential Evolution, Particle Swarm, Symbolic Regression
%O 13
%8 14-16  May
%Z part of \citeRiolo:2009:GPTP

%A Michael F. Korns
%T Abstract Expression Grammar Symbolic Regression
%B Genetic Programming Theory and Practice VIII
%S Genetic and Evolutionary Computation
%E Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva
%V 8
%D 2010
%P 109--128
%I Springer
%C Ann Arbor, USA
%K genetic algorithms, genetic programming
%U http://www.springer.com/computer/ai/book/978-1-4419-7746-5
%O 7
%8 20-22  May
%Z part of \citeRiolo:2010:GPTP

%A Michael F. Korns
%T Accuracy in Symbolic Regression
%B Genetic Programming Theory and Practice IX
%S Genetic and Evolutionary Computation
%E Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore
%D 2011
%P 129--151
%I Springer
%C Ann Arbor, USA
%K genetic algorithms, genetic programming, Abstract Expression Grammars, Differential Evolution, Grammar Template, Particle Swarm, Symbolic Regression
%X This chapter asserts that, in current state-of-the-art symbolic regression engines, accuracy is poor. That is to say that state-of-the-art symbolic regression engines
   return a champion with good fitness; however, obtaining a champion with the correct formula is not forthcoming even in cases of only one basis function with minimally
   complex grammar depth. Ideally, users expect that for test problems created with no noise, using only functions in the specified grammar, with only one basis function and
   some minimal grammar depth, that state-of-the-art symbolic regression systems should return the exact formula (or at least an isomorph) used to create the test data.
   Unfortunately, this expectation cannot currently be achieved using published state-of-the-art symbolic regression techniques. Several classes of test formulas, which prove
   intractable, are examined and an understanding of why they are intractable is developed. Techniques in Abstract Expression Grammars are employed to render these problems
   tractable, including manipulation of the epigenome during the evolutionary process, together with breeding of multiple targeted epigenomes in separate population islands. A
   selected set of currently intractable problems are shown to be solvable, using these techniques, and a proposal is put forward for a discipline-wide program of improving
   accuracy in state-of-the-art symbolic regression systems.
%O 8
%8 12-14  May
%Z part of \citeRiolo:2011:GPTP

%A L. N. Korolev
%T On Evolutionary Algorithms, Neural-Network Computations, and Genetic Programming. Mathematical Problems
%J Automation and Remote Control
%V 68
%N 5
%D 2007
%P 811--821
%I Pleiades Publishing, Ltd.
%K genetic algorithms, genetic programming
%X Some problems related to evolutionary and genetic algorithms, genetic programming, and neural-network computations on solving applied problems that are reduced to analysis
   of functions prescribed at permutations are roughly studied. Natural parallelism of these algorithms and possibility of their realization on modern computers are noted.
%Z Original Russian Text L.N. Korolev, 2007, published in Avtomatika i Telemekhanika, 2007, No. 5, pp. 71-83. Moscow State University, Moscow, Russia PACS number: 02.60.Pn

%A Kostyantyn Korovkin
%A Robert Richards
%T Visual auction: A classifier system pedagogical and researcher tool
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 159--163
%I 
%C Orlando, Florida, USA
%8 13  July
%Z GECCO-99LB

%A Jyothi Korra
%A Susheela Devi
%T Object Categorization Using Genetic Programming
%B Fourth International Conference on Genetic and Evolutionary Computing (ICGEC 2010)
%D 2010
%P 297--300
%I 
%C Shenzhen, China
%K genetic algorithms, genetic programming, SVM, MKL method, computer vision, linear combination, linear combination, multiple kernel learning framework, real world object
   categorisation, vision feature, computer vision, image classification, object detection, support vector machines
%X In Computer Vision, problem of identifying or classifying the objects present in an image is called Object Categorisation. It is challenging problem, especially when the
   images have clutter background, occlusions or different lighting conditions. Many vision features have been proposed which aid object categorization even in such adverse
   conditions. Past research has shown that, employing multiple features rather than any single features leads to better recognition. Multiple Kernel Learning (MKL) framework
   has been developed for learning an optimal combination of features for object categorization. Existing MKL methods use linear combination of base kernels which may not be
   optimal for object categorization. Real-world object categorisation may need to consider complex combination of kernels(non-linear) and not only linear combination.
   Evolving non-linear functions of base kernels using Genetic Programming is proposed in this paper. Experiment results show that non-kernel generated using genetic
   programming gives good accuracy as compared to linear combination of kernels.
%8 13-15  Decemeber
%Z Caltech-5 Also known as \cite5715428

%A Mehmet M. Kose
%A Cafer Kayadelen
%T Modeling of transfer length of prestressing strands using genetic programming and neuro-fuzzy
%J Advances in Engineering Software
%V 41
%N 2
%D 2010
%P 315--322
%I 
%K genetic algorithms, genetic programming, Neuro-fuzzy, Genetic expression, Prestressed concrete, Transfer length
%U http://www.sciencedirect.com/science/article/B6V1P-4WW7NKX-3/2/cf26e3a1767d18f4b3a93895ab93d7e8
%X In this study, the efficiency of neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the transfer length of prestressing strands in
   prestressed concrete beams was investigated. Many models suggested for the transfer length of prestressing strands usually consider one or two parameters and do not provide
   consistent accurate prediction. The alternative approaches such as GEP and ANFIS have been recently used to model spatially complex systems. The transfer length data from
   various researches have been collected to use in training and testing ANFIS and GEP models. Six basic parameters affecting the transfer length of strands were selected as
   input parameters. These parameters are ratio of strand cross-sectional area to concrete area, surface condition of strands, diameter of strands, percentage of debonded
   strands, effective prestress and concrete strength at the time of measurement. Results showed that the ANFIS and GEP models are capable of accurately predicting the
   transfer lengths used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.

%A Konstantinos Kostikas
%A Charalambos Fragakis
%T Genetic Programming Applied to Mixed Integer Programming
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 113--124
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=113
%X We present the application of Genetic Programming (GP) in Branch and Bound (B&B) based Mixed Integer Linear Programming (MIP). The hybrid architecture introduced employs GP
   as a node selection expression generator: a GP run, embedded into the B&B process, exploits the characteristics of the particular MIP problem being solved, evolving a
   problem-specific node selection method. The evolved method replaces the default one for the rest of the B&B. The hybrid approach outperforms depth-first and breadth-first
   search, and compares well with the advanced Best Projection method.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Konstantinos Kostikas
%A Charalambos Fragakis
%T Genetic Programming for Guiding Branch and Bound Search
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP045.pdf
%X We propose how Genetic Programming (GP) can be used for developing, in real time, problem-specific heuristics for Branch and Bound (B&B) search. A GP run, embedded into the
   B&B process, exploits the characteristics of the particular problem being solved, evolving a problem-specific heuristic expression. The evolved heuristic replaces the
   default one for the rest of the B&B search. The application of our method to node selection for B&B based Mixed Integer Programming is illustrated by incorporating the GP
   node selection heuristic generator into a B&B MIP solver. The hybrid system compares well with the unmodified solver using DFS, BFS, or even the advanced Best Projection
   heuristic when confronted with hard MIP problems from the MIPLIB3 benchmarking suite.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Mark Kotanchek
%A Arthur Kordon
%A Guido Smits
%A Flor Castillo
%A R. Pell
%A M. B. Seasholtz
%A L. Chiang
%A P. Margl
%A P. K. Mercure
%A A. Kalos
%T Evolutionary Computing in Dow Chemical
%B GECCO-2002 Presentations in the Evolutionary Computation in Industry Track
%E Lawrence ``Dave'' Davis and Rajkumar Roy
%D 2002
%P 101--110
%I 
%I ISGEC
%C New York, New York
%K genetic algorithms, genetic programming, particle swarm, PSO, neural networks, support vector machines, SVM
%8 11-13  July
%Z powerpoint slides? Diverse subsets from chemical libraries. Soft sensors in intelligent alarm processing. Polypropylene structure-property relationships. non-linear DOE
   (design of experiments) using GP/GENPRO See also \citekordon:2002:gecco

%A Mark Kotanchek
%A Guido Smits
%A Arthur Kordon
%T Industrial Strength Genetic Programming
%B Genetic Programming Theory and Practice
%E Rick L. Riolo and Bill Worzel
%D 2003
%P 239--256
%I Kluwer
%K genetic algorithms, genetic programming, Empirical Modeling, Symbolic Regression, Support Vector Machines, SVM, ANN, Neural Networks
%X Since the mid-1990's, symbolic regression via genetic programming (GP) has become a core component of a multi-disciplinary approach to empirical modeling at Dow Chemical.
   Herein we review the role of symbolic regression within an integrated empirical modeling methodology, discuss symbolic regression system design issues, best practices and
   lessons learned from industrial application, and present future directions for research and application
%O 15
%Z In theory, there is no difference between theory and practice. In practice, there is. -- Jan L.A. van de Snepscheut Dowchemical Part of \citeRioloWorzel:2003
%@ 1-4020-7581-2

%A Mark Kotanchek
%A Guido Smits
%A Ekaterina Vladislavleva
%T Pursuing the Pareto Paradigm Tournaments, Algorithm Variations \& Ordinal Optimization
%B Genetic Programming Theory and Practice IV
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%V 5
%D 2006
%P 167--186
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%X The ParetoGP algorithm, which adopts a multi-objective optimisation approach to balancing expression complexity and accuracy, has proven to have significant impact on
   symbolic regression of industrial data due to its improvement in speed and quality of model development as well as user model selection. In this chapter, we explore a range
   of topics related to exploiting the Pareto paradigm. First we describe and explore the strengths and weaknesses of the ClassicGP and ParetoGP variants for symbolic
   regression as well as touch on related algorithms. Next, we show a derivation for the selection intensity of tournament selection with multiple winners (albeit, in a
   single-objective case). We then extend classical tournament and elite selection strategies into a multi-objective framework which allows classical GP schemes to be readily
   Pareto-aware. Finally, we introduce the latest extension of the Pareto paradigm which is the melding with ordinal optimization. It appears that ordinal optimisation will
   provide a theoretical foundation to guide algorithm design. Application of these insights has already produced at least a four-fold improvement in the performance for a
   suite of test problems.
%O 12
%8 11-13  May
%Z part of \citeRiolo:2006:GPTP Published Jan 2007 after the workshop
%@ 0-387-33375-4

%A Mark Kotanchek
%A Guido Smits
%A Ekaterina Vladislavleva
%T Trustable symbolic regression models: using ensembles, interval arithmetic and Pareto fronts to develop robust and trust-aware models
%B Genetic Programming Theory and Practice V
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2007
%P 201--220
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%X Trust is a major issue with deploying empirical models in the real world since changes in the underlying system or use of the model in new regions of parameter space can
   produce (potentially dangerous) incorrect predictions. The trepidation involved with model usage can be mitigated by assembling ensembles of diverse models and using their
   consensus as a trust metric, since these models will be constrained to agree in the data region used for model development and also constrained to disagree outside that
   region. The problem is to define an appropriate model complexity (since the ensemble should consist of models of similar complexity), as well as to identify diverse models
   from the candidate model set. In this chapter we discuss strategies for the development and selection of robust models and model ensembles and demonstrate those strategies
   against industrial data sets. An important benefit of this approach is that all available data may be used in the model development rather than a partition into training,
   test and validation subsets. The result is constituent models are more accurate without risk of over-fitting, the ensemble predictions are more accurate and the ensemble
   predictions have a meaningful trust metric.
%O 12
%8 17-19  May
%Z part of \citeRiolo:2007:GPTP published 2008

%A Mark Kotanchek
%A Guido Smits
%A Ekaterina Vladislavleva
%T Exploiting Trustable Models via Pareto GP for Targeted Data Collection
%B Genetic Programming Theory and Practice VI
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%D 2008
%P 145--163
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%O 10
%8 15-17  May
%Z part of \citeRiolo:2008:GPTP To be published late 2008

%A Mark E. Kotanchek
%A Ekaterina Y. Vladislavleva
%A Guido F. Smits
%T Symbolic Regression via GP as a Discovery Engine: Insights on Outliers and Prototypes
%B Genetic Programming Theory and Practice VII
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Una-May O'Reilly and Trent McConaghy
%D 2009
%P 55--72
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, symbolic regression, data modeling, system identification, research assistant, discovery engine, outlier detection, outliers,
   prototypes, data balancing
%X In this chapter we illustrate a framework based on symbolic regression to generate and sharpen the questions about the nature of the data-generating system and provide
   additional context and understanding based on the multi-variate numeric data. We emphasize the necessity to perform data modeling in a global approach iteratively applying
   data analysis & adaptation, model building, and problem reduction procedures. We demonstrate it for the problem of detecting outliers and extracting significant features
   from the CountryData http://reference.wolfram.com/mathematica/ref/CountryData.html -- a data set of economic, political, social and geographic data collected. We present
   two complementary ways of extracting outliers from the data -the content-based and the model-based approach. The content-based approach studies the geometrical structure of
   the multi-variate data, and uses data-balancing algorithms to sort the data records in the order of decreasing typicalness, and identifies the outliers as the least typical
   records before the modeling is applied to a data set. The model-based outlier detection approach uses symbolic regression via Pareto genetic programming to identify records
   which are systematically under- or over-predicted by diverse ensembles of (thousands of) global non-linear symbolic regression models. Both approaches applied to the
   CountryData produce insights into outlier vs. prototypes division among world countries and about driving economic properties predicting gross domestic product (GDP) per
   capita.
%O 4
%8 14-16  May
%Z part of \citeRiolo:2009:GPTP

%A Manabu Kotani
%A Daisuke Kato
%T Feature Extraction Using Coevolutionary Genetic Programming
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 614--619
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Coevolution \& collective behavior, Combinatorial \& numerical optimization, Poster Session
%X We propose a new feature extraction method using evolutionary computations. The extracted features are defined as polynomial expressions, which are composed of the original
   input pattern. These polynomial expressions are searched by coevolutionary genetic programming. We introduce a new fitness function based on competition between
   individuals. Experiments are performed for some databases of UCI repository using the proposed method and k-Nearest Neighbor rule. Experimental results show that the
   proposed method can preserve the diversity of populations and improve recognition accuracy on most databases.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Peyman Kouchakpour
%A Anthony Zaknich
%A Thomas Braunl
%T Population variation in genetic programming
%J Information Sciences
%V 177
%N 17
%D 2007
%P 3438--3452
%I 
%K genetic algorithms, genetic programming, Computational effort, Average number of evaluations, Convergence, Population variation
%X A new population variation approach is proposed, whereby the size of the population is systematically varied during the execution of the genetic programming process with
   the aim of reducing the computational effort compared with standard genetic programming (SGP). Various schemes for altering population size under this proposal are
   investigated using a comprehensive range of standard problems to determine whether the nature of the population variation, i.e. the way the population is varied during the
   search, has any significant impact on GP performance. The initial population size is varied in relation to the initial population size of the SGP such that the worst case
   computational effort is never greater than that of the SGP. It is subsequently shown that the proposed population variation schemes do have the capacity to provide
   solutions at a lower computational cost compared with the SGP.
%8 1  September

%A Peyman Kouchakpour
%T Population Variation in Canonical Tree-based Genetic Programming
%R Ph.D. Thesis
%D 2008
%I 
%I School of Electrical, Electronic and Computer Engineering, University of Western Australia
%C Perth
%K genetic algorithms, genetic programming
%U http://robotics.ee.uwa.edu.au/theses/2008-Genetic-Kouchakpour-PhD.pdf
%X The Genetic Programming paradigm, which applies the Darwinian principle of evolution to hierarchical computer programs, has produced promising breakthroughs in various
   scientific and engineering applications. However, one of the main drawbacks of Genetic Programming has been the often large amount of computational effort required to solve
   complex problems. There have been various amounts of research conducted to devise innovative methods to improve the efficiency of Genetic Programming. This thesis has three
   main contributions. It firstly provides a comprehensive overview of the related work to improve the performance of Genetic Programming and classifies these various proposed
   approaches into categories. Secondly, a new static population variation scheme (PV) is proposed, whereby the size of the population is varied according to a predetermined
   schedule during the execution of the Genetic Programming system with the aim of reducing the computational effort with respect to that of Standard Genetic Programming.
   Within this new static scheme the initial population size is made to be different from the initial size of the Standard Genetic Programming such that the worst case
   computational effort is never greater than that of the Standard Genetic Programming. Various static schemes for altering population size under this proposal are
   investigated using a comprehensive range of standard problems to determine whether the nature of the 'population variation', i.e. the way the population is varied during
   the search, has any significant impact on Genetic Programming performance. It is shown that these population variation schemes do have the capacity to provide solutions at
   a lower computational cost compared with the Standard Genetic Programming. Thirdly, three innovations for dynamically varying the population size during the run of the
   Genetic Programming system are proposed. These are related to what is called Dynamic Population Variation (DPV), where the size of the population is dynamically varied
   using a heuristic feedback mechanism during the execution of the Genetic Programming with the aim of reducing the computational effort. The efficacy of these innovations is
   examined using the same comprehensive range of standard representative problems. It is shown that these new ideas do have the capacity to provide solutions at a lower
   computational cost compared with standard genetic programming and previously reported algorithms. Finally, further interesting research potentials for population variation
   are identified together with some of the open areas of research within the Genetic Programming and also possible future trends in this discipline.
%8 May

%A Peyman Kouchakpour
%A Anthony Zaknich
%A Thomas Braunl
%T Dynamic Population Variation in Genetic Programming
%J Information Sciences
%V 179
%N 8
%D 2009
%P 1078--1091
%I 
%K genetic algorithms, genetic programming, Computational Effort, Average Number of Evaluations, Convergence, Diversity, Population Variation, Dynamic PopulationVariation
%U http://www.sciencedirect.com/science/article/B6V0C-4V7647M-2/2/7260de43f34e6b9e878cb068373a639f
%X Three innovations are proposed for dynamically varying the population size during the run of the genetic programming (GP) system. These are related to what is called
   Dynamic Population Variation (DPV), where the size of the population is dynamically varied using a heuristic feedback mechanism during the execution of the GP with the aim
   of reducing the computational effort compared with Standard Genetic Programming (SGP). Firstly, previously developed population variation pivot functions are controlled by
   four newly proposed characteristic measures. Secondly, a new gradient based pivot function is added to this dynamic population variation method in conjunction with the four
   proposed measures. Thirdly, a formula for population variations that is independent of special constants is introduced and evaluated. The efficacy of these innovations is
   examined using a comprehensive range of standard representative problems. It is shown that the new ideas do have the capacity to provide solutions at a lower computational
   cost compared with standard genetic programming and previously reported algorithms such as the plague operator and the static population variation schemes previously
   introduced by the authors.
%8 29  March

%A Peyman Kouchakpour
%A Anthony Zaknich
%A Thomas Braunl
%T A survey and taxonomy of performance improvement of canonical genetic programming
%J Knowledge and Information Systems
%V 21
%N 1
%D 2009
%P 1--39
%I 
%K genetic algorithms, genetic programming, Computational effort, Efficiency, Performance improvement, Taxonomy
%X The genetic programming (GP) paradigm, which applies the Darwinian principle of evolution to hierarchical computer programs, has been applied with breakthrough success in
   various scientific and engineering applications. However, one of the main drawbacks of GP has been the often large amount of computational effort required to solve complex
   problems. Much disparate research has been conducted over the past 25 years to devise innovative methods to improve the efficiency and performance of GP. This paper
   attempts to provide a comprehensive overview of this work related to Canonical Genetic Programming based on parse trees and originally championed by Koza (Genetic
   programming: on the programming of computers by means of natural selection. MIT, Cambridge, 1992). Existing approaches that address various techniques for performance
   improvement are identified and discussed with the aim to classify them into logical categories that may assist with advancing further research in this area. Finally,
   possible future trends in this discipline and some of the open areas of research are also addressed.

%A Jan Koutnik
%A Jiri Kubalik
%T Structure Aware Two Point Crossover for Grammatical Evolution
%B 15th International Conference on Soft Computing, Mendel'09
%E R. Matousek and L. Nolle
%D 2009
%P 49--54
%I 
%C Brno, Czech Republic
%K genetic algorithms, genetic programming, grammatical evolution
%8 24-26  June
%Z ID09035 http://www.mendel-conference.org/tmp/ScheduleMendel2009b.pdf Also in electronic form ISSN 1803-3814

%A Miha Kovacic
%A Miran Brezocnik
%T Evolutionary transport system
%B Annals of DAAAM for 2001 and Proceedings of the 12th International DAAAM Symposium "Intelligent Manufacturing and Automation: Focus on Precision Engineering
%E Branko Katalinic
%D 2001
%P 253--254
%I DAAAM Publishing Vienna
%I Jena University of Applied Sciences
%C Jena, Germany
%K genetic algorithms, genetic programming
%8 October
%Z http://www.daaam.com/daaam/IMS%20Seite/Vedran%20Kordic/Start%20Page.asp
%@ 3-901509-19-4

%A Miha Kovacic
%A Joze Balic
%T Genetic algorithm method for cutting optimisation of Monel alloy
%B Proceedings of the 10th Jubilee International Scientific Conference Achievements in the Mechanical and Materials Engineering, AMME'2001
%E Leszek A. Dobrzanski
%D 2001
%P 305--316
%I 
%I Gliwice: Silesian University of Technology, Institute of Engineering Materials and Biomaterials
%C Gliwice-Krakow-Zakopane, Poland
%K genetic algorithms, genetic programming
%8 9-13  Decemeber
%Z http://www.wamme.org/index.php?id=37&PHPSESSID=8b9ce9355f0dbdaebee40f5d6ddec320
%@ 83-914458-4-4

%A Miha Kovacic
%A Miran Brezocnik
%T Genetic approximation method for surface quality prediction
%B Annals of DAAAM for 2002 and Proceedings of the 13th International DAAAM Symposium "Intelligent Manufacturing and Automation: Learning from nature
%S DAAAM International Scientific Book
%E Branko Katalinic
%V 1
%D 2002
%P 285--286
%I DAAAM International Vienna
%I Wiena, University of Technology
%C Slavonski Brod, Croatia
%K genetic algorithms, genetic programming
%8 September  12-14
%Z http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm http://www.sfsb.hr/atdc2002/atdc2002.html "Annals" Given in preference to "Proceedings of
   the 1st DAAAM international conference on Advanced technologies for developing countries" pages 405-410.
%@ 3-901509-29-1

%A Miha Kovacic
%A Miran Brezocnik
%T Model of evolutionary transport system
%B DAAAM International Scientific Book 2002
%E B. Katalinic
%V 1
%D 2002
%P 323--328
%I DAAAM International Vienna
%C Vienna
%K genetic algorithms, genetic programming
%X In this paper the model of an evolutionary transport system is introduced. In the concept, planning, optimisation and realization phases are joint into intelligent
   transport vehicles capable of efficient adaptation to a dynamic production environment. The genetic algorithm method is used. In this research the transport system is
   represented as a weighted complete graph, where the vertices represent machine tools and weights of the edges represent the production environment circumstances (e.g.
   length of supply paths, number of loads and obstacles). Weights of the edges can change with regard to production circumstances and they influence the fitness function
   value and thus determine the quality of the solutions. The research clearly shows that the genetic algorithm method is a successful optimization tool for modelling dynamic
   system.
%8 September
%Z publication@daaam.com http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm
%@ 3-901509-30-5

%A Miha Kovacic
%A Joze Balic
%T Evolutionary programming of a CNC cutting machine
%J International journal for advanced manufacturing technology
%V 22
%N 1-2
%D 2003
%P 118--124
%I 
%K genetic algorithms, genetic programming, CNC laser cutting, Path planning
%U http://www.springerlink.com/openurl.asp?genre=article&eissn=1433-3015&volume=22&issue=1&spage=118
%X The scope of the paper is to construct an autonomous, intelligent CAD/CAM programming system for the cutting device controller (for instance a CNC laser cutting machine
   tool) based on evolutionary methods. The CNC cutting device should be able to optimise paths autonomously between cutting trajectories, determined by the product's CAD
   model. An evolutionary GA was used for this purpose.
%8 September
%Z The International Journal of Advanced Manufacturing Technology
   http://www.springerlink.com/app/home/journal.asp?wasp=7d5206d9e1f345ff99193df3925456a9&referrer=parent&backto=linkingpublicationresults,1:102823,1

%A Miha Kovacic
%A Miran Brezocnik
%T Genetic programming approach for cutting forces prediction in milling
%B Annals of DAAAM for 2003 and proceedings of the 14th International DAAAM symposium "Intelligent manufacturing and Automation: focus on reconstruction and development
%E Branko Katalinic
%V 2
%D 2003
%P 234--235
%I DAAAM International Vienna
%C Vienna
%K genetic algorithms, genetic programming
%8 July
%Z publication@daaam.com http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm
%@ 3-901509-30-5

%A Miha Kovacic
%A Miran Brezocnik
%T Evolutionary method for surface quality prediction
%B DAAAM International Scientific Book 2003
%E B. Katalinic
%V 2
%D 2003
%P 331--338
%I DAAAM International Vienna
%C Vienna
%K genetic algorithms, genetic programming
%X In this paper the genetic algorithm based method which we named genetic approximation is used to optimise floating-point constants of the presumed mathematical model for
   surface quality prediction. In addition, a practical example of the use of genetic approximation method for predicting surface quality of Monel alloy is presented. Cutting
   depth, feeding, and cutting speed were parameters monitored during cutting process. For each combination of cutting parameters average roughness of the machined surface was
   measured. Two sets of experimental data were used: first set of 8 experimental data to determine prediction model and second set of 10 experimental measurements to verify
   the model. Research shows that developed model fits to experimental data.
%8 Juli
%Z publication@daaam.com http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm
%@ 3-901509-36-4

%A Miha Kovacic
%A Miran Brezocnik
%T Genetic programming approach for surface quality prediction
%J Tehnicki vjesnik
%V 10
%N 1
%D 2003
%P 19--24
%I 
%K genetic algorithms, genetic programming
%X The evolutionary computation is a common name for the optimisation methods imitating Darwinian findings about evolution of the biological species and the survival of the
   fittest. The common characteristic of the evolutionary computation is that the optimum or near optimum solution of the problem is formed gradually, from generation to
   generation, similarly as nature acts during the improvement and evolution of living beings. We use genetic programming to predict the surface quality of aluminium alloy.
   Two sets of experimental data were used: first set to determine prediction model by genetic programming for surface roughness and second set to verify the model. The
   researches show very good agreement between the experimental data and the predicted values obtained by the model developed genetically.
%Z http://www.gradst.hr/library/journals/catalogue_T/teh-vjes.htm TECHNICAL GAZETTE Scientific-professional journal of technical faculties of the University of Osijek

%A Miha Kovacic
%A Miran Brezocnik
%A Joze Balic
%T Genetic programming approach for surface quality prediction of monel alloy
%B 4th International Conference on Industrial Tools, ICIT 2003
%E Karl Kuzman
%D 2003
%P 385--388
%I 
%C Bled
%K genetic algorithms, genetic programming
%8 8-12  April
%Z http://www.tecos.si/icit2003/enter.htm
%@ 961-90-4017-1

%A Miha Kovacic
%A Miran Brezocnik
%T Evolutionary computation approach for autonomous vehicles
%B Proceedings of the 2nd DAAAM International conference on Advanced technologies for developing countries
%E Branko Katalinic and Dzemo Tufkecic and Ramiz Selo
%D 2003
%P 339--344
%I DAAAM
%C Tuzla, Bosnia and Herzegovina
%K genetic algorithms, genetic programming, inteligentni proizvodni sistemi, avtonomna vozila, transport, genetsko programiranje, genetski algoritem, programiranje poti,
   intelligent manufacturing systems, autonomous vehicles, transport, path programming
%X This paper discusses about using genetic algorithm and genetic programming for intelligent path planning of autonomous vehicles. The concept imitates the natural evolution
   of living organisms, where in the struggle for natural resources the successful individuals gradually become more and more dominant, and adaptable to the environment in
   which they live, whereas the less successful ones are present in the next generations rarely. In the proposed concept the travelling strategies leading the robot to the
   goal location undergo adaptation. During the simulated evolution more and more successful organisms (paths) emerge on the basis of given environment. For the paper to be
   self-contained the problem is stated in the beginning of the section 2. In Section 3 the idea of the proposed concept is presented. Section 4 and Section 5 show functioning
   of the proposed concept. The examples of path planning with discussion of the results obtained are also given. Section 6 summarizes the main contributions of the performed
   research and also gives guidelines for further researches.
%8 June  25-28
%Z http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=8007958
%@ 9958-609-14-2

%A Miha Kovacic
%A Miran Brezocnik
%T Genetic programming planner for mobile robots
%B Proceedings A of the 6th International Multi-Conference Information Society
%D 2003
%I 
%I ACM Slovenia
%C "Jozef Stefan" Institute, Jamova 39, Ljubljana, Slovenija
%K genetic algorithms, genetic programming
%8 15-16  October
%Z http://is.ijs.si/is/is2003/program_SubConferences/program_ICS03.pdf#search=%22Miha%20site%3Ais.ijs.si%2Fis%2Fis2003%22 Proceedings of the 6th International Multi-Conference
   Information Society IS 2003; Intelligent and Computer Systems
%@ 961-63-0351-1

%A Miha Kovacic
%A Miran Brezocnik
%T Evolution and transport systems
%B Computers in technical systems. Intelligent systems : proceedings of the joint conferences CTS+CIS
%D 2004
%P 41--44
%I 
%C Opatija, Croatia
%K genetic algorithms, genetic programming
%8 May  24-28
%@ 953-23-3003-8

%A Miha Kovacic
%A Joze Balic
%A Miran Brezocnik
%T Evolutionary approach to manufacturing control
%B Proceedings of the 14th International conference on flexible automation \& intelligent manufacturing
%E Lihui Wang and Jeff Xi and William G. Sullivan and Munir Ahmad
%D 2004
%P 915--921
%I NRC Research Press
%I Ryerson University \& National Research Council Canada
%C Toronto, Canada
%K genetic algorithms, genetic programming, proizvodni sistemi, inteligentni obdelovalni sistemi, genetsko programiranje, umetna inteligenca, evolucijske metode, manufacturing
   systems, intelligent manufacturing system, artificial intelligence, simulation , evolutionary methods
%X The paper presents the use of evolutionary method for improving efficiency of the production process. The purpose of research was to select the parameters of turning the
   monel alloy in order to increase productivity with required tool wearing resistance and surface quality. Three independent variables, cutting speed, feed rate, and cutting
   depth were monitored between cutting process. Those variables influence the dependent variables, i.e., tool life and surface roughness. On the basis of experimental data
   set, different models for tool life and surface roughness were developed by genetic programming method. The two models for tool life and surface roughness predict if the
   specified tool life and surface roughness are reached. The limit for high quality machining of the surface was the quality class N6 (Ra=0.8 m), whereas for tool life the
   time of 13 minutes and more was expected. After process modelling the parameter selection plan was developed according to predicted tool life and surface roughness. The
   proposed concept robustly and simply ensures monitoring and optimization of the turning process. The results of researches were transferred into practice.
%8 July  12-14
%Z http://www.faim2008.org/faimhistory.html
%@ 0-662-37217-4

%A Miha Kovacic
%A Miran Brezocnik
%A Joze Balic
%T Evolutionary cutting forces prediction
%B DAAAM International Scientific Book 2004
%E B. Katalinic
%V 3
%D 2004
%P 313--322
%I DAAAM International Vienna
%C Vienna
%K genetic algorithms, genetic programming
%X We propose the modelling of cutting forces with genetic programming. In the proposed concept, the mathematical expressions (models) for cutting forces prediction undergo
   adaptation. On the basis of experimental data different models for cutting forces prediction were obtained. Research results show that genetically developed models fit the
   experimental data.
%8 October
%Z publication@daaam.com http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm
%@ 3-901509-38-0

%A Miha Kovacic
%A Miran Brezocnik
%T Programming of CNC lathe with GA
%B publish or perish!
%E Martin Klanjsek
%D 2004
%P 265--274
%I Drustvo mladih raziskovalcev Slovenije
%C Ljubljana, Slovenia
%K genetic algorithms, genetic programming
%X The paper proposes a new concept for programming of CNC lathes. The concept based on genetic algorithms assures evolutionary generation and optimisation of NC programs on
   the basis of CAD models of blank and product. The structure,undergoing simulated evolution, is the population of NC programs presented as weighted graphs. The NC programs
   control the tool which performs simple elementary motions in the discretised machining area. During the evolution the tool movement becomes more and more complex and
   intelligent solutions emerge gradually as a result of the low level interaction between the simple tool movements and the workpiece dynamic environment. The examples of
   evolutionary programming of CNC lathes for different complexities of the blanks and products are presented. The proposed concept showed a high degree of universality,
   efficiency, and reliability and it can be simply adopted to other CNC machines.
%O CD-ROM
%8 Decemeber
%Z http://www.nuk.uni-lj.si/publikacije/2004/knjige_dec04.html 18. ZNANSTVENO delo podiplomskih ?tudentov v Sloveniji - "publish or perish!" [Elektronski vir] : e-knjiga /
   uredil Martin Klanj?ek ... [et al.]. - Ljubljana : Dru?tvo mladih raziskovalcev Slovenije, 2004. - 1 el. opticni disk (CD-ROM) ; 12 cm
   378.046.4:001.891(497.4)(082)(086.034.4) COBISS.SI-ID 217169408 http://www.nuk.uni-lj.si/vstop.cgi?jezik=eng
%@ 961-90-6697-9

%A Miha Kovacic
%T Programming of CNC machine tools using evolutionary methods
%R Ph.D. Thesis Doktorska disertacija
%D 2004
%I 
%I Faculty of Mechanical Engineering, University of Maribor
%C Maribor, Slovenia
%K genetic algorithms, genetic programming, NC-machine tool, NC-programming, evolutionary computation, genetic algorithm
%X The concept imitates the natural evolution of living organisms, where in the struggle for natural resources the successful individuals gradually become more and more
   dominant, and adaptable to the environment in which they live, whereas the less successful ones are present in the next generations rarely. In the proposed concept the
   NC-programs undergo adaptation. During the simulated evolution more and more successful organisms (the NC-programs) emerge on the basis of given data on tool, blank, and
   desired product. Concept was used for NC-programming of turning, milling and metal sheet cutting. The proposed concept shows a high degree of universality, efficiency, and
   reliability and it can be adopted to other NC-machines. Presented systems and results can be compared with commercially available software packages. Results can be
   implemented in practice
%Z Supervisor: Prof. Joze BALIC Mentor: redni prof. dr. Jo?e Balic Univerza v Mariboru, Fakulteta za strojni?tvo Somentor: prof. dr. Branko Katalinic, TU Wien, Avstrija

%A M. Kovacic
%A J. Balic
%A M. Brezocnik
%T Evolutionary approach for cutting forces prediction in milling
%J Journal of Materials Processing Technology
%V 155-156
%D 2004
%P 1647--1652
%I 
%K genetic algorithms, genetic programming, Milling cutting forces prediction
%U http://www.sciencedirect.com/science/article/B6TGJ-4CHS1T2-9/2/ca7fc5b9431830386384db78e7ee233a
%X Knowing cutting forces is important for choosing cutting parameters for milling. Traditionally, cutting forces are calculated by equation which includes empirically
   measured specific cutting forces. In the article modelling of cutting forces with genetic programming is proposed, which imitates principles of living beings. Measurements
   have been made for two materials (aluminium alloy AlMgSi1 and steel 1.2343) and two different types of milling (conventional milling and STEP milling). For each material
   and type of milling parameters, tensile strength and hardness of workpiece, tool diameter, cutting depth, spindle speed, feeding and type of milling were monitored, and for
   each combination of milling parameters cutting forces were measured. On the basis of the experimental data, different models for cutting forces prediction were obtained by
   genetic programming. Research shows that genetically developed models fit the experimental data.
%8 30  November
%Z Journal article given in preference to conference "Proceedings of the International Conference on Advances in Materials and Processing Technologies" AMPT 2003 Part 2 ISBN
   1-8723-2739-7, 2003, Dublin City University, pages 852-855. 8-11 July

%A Miha Kovacic
%A Bostjan Vaupotic
%A Joze Balic
%T NC Machine tools and genetics
%B Annals of DAAAM for 2005 and proceedings of the 16th International DAAAM symposium "Intelligent manufacturing and Automation: "Focus on young researchers and scientists"
%E Branko Katalinic
%V 4
%D 2005
%P 199--200
%I DAAAM International Vienna
%I University of Rijeka
%C Croatia
%K genetic algorithms, genetic programming
%8 Decemeber
%Z publication@daaam.com http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm
%@ 3-901509-43-7

%A Miha Kovacic
%A Miran Brezocnik
%A Ivo Pahole
%A Joze Balic
%A Borut Kecelj
%T Evolutionary programming of CNC machines
%J Journal of Materials Processing Technology
%V 164-165
%D 2005
%P 1379--1387
%I 
%K genetic algorithms, genetic programming, Manufacturing systems, NC programming, CNC machines
%X We propose a new concept for programming of CNC machines. The concept based on genetic algorithms assures evolutionary generation and optimization of NC programs on the
   basis of CAD models of manufacturing environment. The structure, undergoing simulated evolution, is the population of NC programs. The NC programs control the machine which
   performs simple elementary motions. During the evolution the machine movement becomes more and more complex and intelligent solutions emerge gradually as a result of the
   interaction between machine movements and manufacturing environment. The examples of evolutionary programming of CNC lathe and CNC milling machine tool for different
   complexities of the blanks and products are presented. The proposed concept showed a high degree of universality, efficiency, and reliability and it can be also simply
   adopted to other CNC machines.
%8 15  May
%Z AMPT/AMME05 Part 2 University of Maribor, Smetanova ulica 17, Maribor, Slovenia

%A Miha Kovacic
%A Miran Brezocnik
%A Radomir Turk
%T Modeling of Hot Yield Stress Curves for Carbon Silicon Steel by Genetic Programming
%J Materials and Manufacturing Processes
%V 20
%N 3
%D 2005
%P 543--551
%I 
%K genetic algorithms, genetic programming, Carbon silicon steel, Hot yield stress curves
%U http://taylorandfrancis.metapress.com/openurl.asp?genre=article&eissn=1532-2475&volume=20&issue=3&spage=543
%X The shape of hot yield stress curves (surfaces) is complicated because of the microstructural changes and phase transformations that occur during the hot working process.
   Such dependencies are difficult to describe mathematically. In this article, modelling of hot yield stress curves for carbon silicon steel (electric steel, 1.87%Si,
   0.05?0.08%C) by genetic programming was carried out. Strain rate, strain, and temperature were the parameters monitored experimentally. On the basis of the experimental
   data, a model for prediction of hot yield stress curves was obtained by genetic programming. The study showed that genetic programming is a powerful prediction tool.
%8 May
%Z A1 Faculty of Mechanical Engineering, University of Maribor, Slovenia, Maribor A2 Faculty of Natural Sciences and Engineering, University of Ljubljana, Ljubljana, Slovenia

%A Miha Kovacic
%A Peter Uratnik
%A Miran Brezocnik
%A Radomir Turk
%T Prediction of the Bending Capability of Rolled Metal Sheet by Genetic Programming
%J Materials and Manufacturing Processes
%V 22
%N 5
%D 2007
%P 634--640
%I 
%K genetic algorithms, genetic programming, Bending capability, Metal sheet, Rolling, Titanzinc
%X The paper proposes genetic programming (GP) to predict the bending capability of rolled titanzinc metal sheet. In this study ZnTiCu alloy with 0.1percent Cu and 0.1percent
   Ti was used for production of metal sheet. Three groups of independent input variables were measured: (1) chemical composition of the ZnTiCu alloy during casting
   (percentage of Cu, Ti, and Fe), (2) parameters of hot rolling (temperature of ingot before rolling, time of rolling, temperature of plate after rolling, time of cooling),
   and (3) parameters of cold rolling (temperature of plate before rolling, temperature of sheet after rolling). Therefore, nine input variables (parameters) influence the
   bending capability of the sheet metal. On the basis of the experimental data, several models for prediction of the bending capability of titanzinc metal sheet were
   developed by the simulated evolution. The influence of individual input variables on bending capability was also studied. The most accurate model was verified with an
   independent testing data set. The results showed that GP is a powerful tool for predicting the bending capability of metal sheet.
%8 June

%A Miha Kovacic
%T Genetic Programming and Jominy Test
%B 2nd International Conference on the Heat Treatment and Surface Engineering of Tools and Dies
%E Jana Jamar
%D 2008
%P 149--150
%I Institute of Metals and Technology Ljubljana, Solvenian
%C Bled, Slovenia
%K genetic algorithms, genetic programming, hardenability, Jominy test, modelling
%U http://ctklj.ctk.uni-lj.si/kovine/mit/izvlecki-bled.pdf
%X The paper presents the use of genetic programming method for Jominy test modelling for 51CrV4 (DIN) spring steel. The experimental data on chemical composition, distance
   from the specimen face and Jominy test results of 75 batches were collected. On the basis of experimental data set, mathematical model for Jominy test was developed by
   genetic programming method. The model developed during the simulated evolution was tested also on the basis of experimental data on 871 batches. The results show that the
   proposed concept can be easily used also in practice.
%O Extended Abstract
%8 25-28  May
%Z ICHT & SETD 2008

%A Miha Kovacic
%A Bozidar Sarler
%T Application of the Genetic Programming for Increasing the Soft Annealing Productivity in Steel Industry
%J Materials and Manufacturing Processes
%V 24
%N 3
%D 2009
%P 369--374
%I 
%K genetic algorithms, genetic programming, Furnace productivity, Hardness, Modeling, Soft annealing, Steel
%X The optimal thermomechanical processing in steel industry is difficult because of the multi constituent and multiphase character of the commercial steels, variety of the
   possible processing paths, and plant specific equipment characteristics. This article shows successful implementation of the genetic programming approach for increasing the
   furnace conveyor speed and consequently productivity of the heat treatment furnace in the soft annealing process. The data (222 samples covering 24 different steel grades)
   on a furnace conveyor speed, chemical composition of steel (weight percent of C, Cr, Mo, Ni, and V) and Brinell hardness, before and after the soft annealing, were
   collected during daily production. On the basis of the monitored data a mathematical model for the hardness after the soft annealing was developed by genetic programming.
   According to the modeled influences on the hardness, the higher furnace conveyor speed was attempted in practice. The experimental results of the hardness after the soft
   annealing with the increased conveyor speed and the predictions of the mathematical model were compared within the agreement of 3.24percent. The genetic model was also
   compared and verified with linear regression model. The productivity of the soft annealing process increased (from the furnace conveyor speed 3.2 m/h to 7 m/h) as a
   consequence of the used computational intelligence approach.
%Z Research Department, Scarontore Steel d.o.o., Scarontore, Slovenia

%A Miha Kovacic
%A Katja Mackosek
%A Andrej Mihevc
%A Tomaz Marolt
%T Crack presence modeling after rolling by genetic programming
%J RMZ - MATERIALS AND GEOENVIRONMENT
%V 56
%N 1
%D 2009
%P 24--29
%I 
%K genetic algorithms, genetic programming, steel, cracks, modeling, jeklo, razpoke, modeliranje, genetsko programiranje
%U http://www.rmz-mg.com/letniki/rmz56/RMZ56_0024-0029.pdf
%X Optimalna obdelava materiala v jeklarstvu je v splo\u0161nem ote\u017eena zaradi raznolikosti komercialnih jekel, mo\u017enih obdelovalnih procesov in specifi\u010dne
   obdelovalne in procesne opreme. V \u010dlanku je opisana uporaba metode genetskega programiranja za modeliranje prisotnosti razpok v jeklu po valjanju. Podatki (110
   primerov, 7 razli\u010dnih kvalitet) so zbrani med dnevno proizvodnjo: kemijska sestava (masni procenti Mn, Cr, Mo in V), \u0161irina in debelina palice ter \u010das
   ogrevanja prve in zadnje gredice. Prisotnost napak smo ugotavljali z ro\u010dnim ultrazvokom. Na podlagi monitoringa podatkov je bil razvit matemati\u010dni model za
   ugotavljanje prisotnosti razpok v jeklu. Pri modeliranju smo predpostavili, da je bil postopek po valjanju (ohlajevanje) za ves material enak. Rezultati modeliranja
   ka\u017eejo na to, da je rokovanje po valjanju izredno pomembno za pojav razpok.
%8 March
%Z Store Steel Ltd.

%A Miha Kovacic
%T Genetic Programming and Jominy Test Modeling
%J Materials and Manufacturing Processes
%V 24
%N 7 \& 8
%D 2009
%P 806--808
%I 
%K genetic algorithms, genetic programming, Hardenability, Jominy test, Modeling
%U http://www.informaworld.com/smpp/content~db=all?content=10.1080/10426910902841050
%X The article presents the use of genetic programming for Jominy test modeling for 51CrV4 (DIN) spring steel. The experimental data on chemical composition, distance from the
   specimen face, and Jominy test results of 74 batches were collected. On the basis of the experimental data set, a mathematical model for the Jominy test was developed by
   genetic programming. The model developed during the simulated evolution was also tested on the basis of experimental data on 871 batches. The results show that the proposed
   concept can be easily used also in practice.
%8 July
%Z Affiliation: a STORE STEEL d.o.o., Store, Slovenia and Laboratory for Multiphase Processes, University of Nova Gorica, Slovenia

%A Miha Kovacic
%A Matej Psenicnik
%T Extra Machinability Quality Prediction
%B QUALITY 2009, 6th Research/Expert Conference with International Participations
%E Safet Brdarevic
%D 2009
%P 465--470
%I 
%C Neum, Bosnia and Herzegovina
%K genetic algorithms, genetic programming
%8 June  4-7
%Z QUALITY 2009 http://quality.unze.ba/final%20program%202009.pdf

%A Miha Kovacic
%A Matej Psenicnik
%T Extra machinability modeling
%J RMZ - Materials and Geoenvironment (Materiali in geookolje)
%V 56
%N 3
%D 2009
%P 338--345
%I 
%K genetic algorithms, genetic programming
%U http://www.rmz-mg.com/letniki/rmz56/RMZ56_3.pdf
%X The steels with extra machinability are made according to special technological process. It depends on several parameters, particularly on the steel chemical composition,
   whether the steel will meet the criterion of extra machinability. By special test it is established whether the steel has extra machinability or not. In our researches the
   prediction of machinability of steels, depending on input parameters, was performed by logistic regression and genetic programming. The research shows that genetic
   programming model performs better. The best model developed during the simulated evolution was practically verified.
%8 September

%A Miha Kovacic
%A Sandra Sencic
%T Critical inclusion size in spring steel and genetic programming
%J RMZ - Materials and Geoenvironment (RMZ -Materiali in Geookolje)
%V 57
%N 1
%D 2010
%P 17--23
%I 
%K genetic algorithms, genetic programming, spring steel, inclusions, modeling
%U http://www.rmz-mg.com/letniki/rmz57/RMZ57_1.pdf
%X In the paper the genetic programming method was used for critical inclusion size determination. At first the mathematical model according to dynamically testing results of
   the seven broken 51CrV4 springs has been obtained and after the optimization with the model was performed. For the modeling of the spring life the inclusion size of the
   inclusion found at the breakage surface and the distance of the inclusion from the spring tensile surface were used. The results show that the critical inclusion (the
   inclusion at the spring tensile surface) size in our case is 0.14 mm. The results of the proposed concept can be used in practice.
%O Kriticna velikost vkljucka v vzmetnem jeklu in genetsko programiranje
%Z PERIODICAL FOR MINING, METALLURGY AND GEOLOGY

%A Miha Kovacic
%T Hardenability modeling
%J RMZ - Materials and Geoenvironment (Materiali in geookolje)
%V 57
%N 2
%D 2010
%P 173--180
%I 
%K genetic algorithms, genetic programming, Hardenability, Jominy test, spring steel, modeling, prekaljivost, Jominyjev preizkus, vzmetno jeklo, modeliranje, genetsko
   programiranje
%U http://www.rmz-mg.com/letniki/rmz57/RMZ57_2.pdf
%X The paper presents the use of genetic programming and linear regression method for hardenability modeling for 51CrV4 spring steel. The experimental data on chemical
   composition, distance from the specimen face and Jominy test results of 74 batches were collected. On the basis of the experimental data set, a mathematical model for the
   Jominy test was developed by genetic programming and linear regression. The models were also tested on the basis of experimental data on 871 batches. The results show that
   the genetically developed model performs better and the results can be easily used also in practice.
%8 June
%Z p180 'The results show that both approaches' [GP and Linear Regression] 'give pretty the same idea about influencing parameters and also the genetically developed model
   performs better. The results can be easily practically used for chemical composition optimization.'

%A Miha Kovacic
%A Bozidar Sarler
%T Genetic programming and soft annealing productivity
%B Program and book of abstracts of the 18th Conference on Materials and Technology
%E Monika Jenko
%D 2010
%P 74
%I Institute of metals and technology
%C Portoroz, Slovenia
%K genetic algorithms, genetic programming, Steel, soft annealing, furnace productivity, hardness, modelling
%U http://www.imt.si/portoroz/index.html
%X The optimal thermo-mechanical processing in steel industry is difficult because of the multi-constituent and multiphase character of the commercial steels, variety of the
   possible processing paths, and plant specific equipment characteristics. This paper shows successful implementation of the genetic programming approach for increasing the
   furnace conveyor speed and consequently productivity of the heat treatment furnace in the soft annealing process. The data (222 samples covering 24 different steel grades)
   on a furnace conveyor speed, chemical composition of steel (weight percent of C, Cr, Mo, Ni and V) and Brinell hardness before and after the soft annealing were collected
   during daily production. On the basis of the monitored data a mathematical model for the hardness after the soft annealing was developed by genetic programming. According
   to the modeled influences on the hardness, the higher furnace conveyor speed was attempted in practice. The experimental results of the hardness after the soft annealing
   with the increased conveyor speed and the predictions of the mathematical model were compared within the agreement of 3.24percent. The genetic model was also compared and
   verified with linear regression model. The productivity of the soft annealing process increased (from the furnace conveyor speed 3.2 m/h to 7 m/h) as a consequence of the
   used computational intelligence approach.
%8 15-17  November
%Z Laboratory for Multiphase Processes, University of Nova Gorica, Vipavska 13, SI-5000, Nova Gorica, Slovenia

%A Miha Kovacic
%A Bozidar Sarler
%T Genetic programming modeling of the critical size of inclusions
%B KomPlasTech 2011, 18th Conference Computer Methods in Materials Technology
%E Danuta Szeliga and Lukasz Rauch and Barbara Barabasz and Lukasz Sztangret
%D 2011
%I 
%C Zakopane, Poland
%K genetic algorithms, genetic programming
%U http://www.komplastech.agh.edu.pl/utils.php?op=abstract_list
%8 16-19  January

%A Miha Kovacic
%A Bozidar Sarler
%T Soft annealing productivity optimization
%J RMZ -- Materials and Geoenvironment
%V 58
%N 1
%D 2011
%P 29--38
%I 
%K genetic algorithms, genetic programming, steel, soft annealing, productivity, hardness, modeling jeklo, mehko zarjenje, produktivnost, trdota, genetsko programiranje,
   modeliranje
%U http://www.rmz-mg.com/letniki/rmz58/RMZ58_0029-0038.pdf
%X Izvlecek: Zaradi tezko dolocljivih lastnosti komercialnih jekel, raznolikosti tehnoloskih poti in specificne opreme je optimalno termomehansko procesiranje v jeklarstvu
   izredno problematicno. V clanku je predstavljena uporaba genetskega pogramiranja z namenom povecati hitrost transportnega traku zarilne peci in posledicno produktivnost
   zarilne peci in procesa zarjenja samega. Med tipicno proizvodnjo so bili zbrani podatki (222 vzorcev, 24 kvalitet jekla) o hitrosti peci, kemijski sestavi jekla (masni
   delezi C, Cr, Mo, Ni in V) ter trdota po Brinellu pred mehkim zarjenju in po njem. Na podlagi zbranih podatkov je bil izdelan matematicni model trdote po mehkem zarjenju z
   metodo genetskega programiranja. Glede na izracunane vplive na trdoto po mehkem zarjenju smo povecali hitrost zarjenja. Po povecanju hitrosti zarjenja se izmerjene trdote
   materiala ujemajo z izracunanimi v povprecju 3,24-odstotno. Produktivnost mehkega zarjenja se je povecala iz 3,2 m/h na 7 m/h kot posledica umetne inteligence.
%8 March
%Z In English STORE STEEL, d. o. o., Store, Slovenia University of Nova Gorica, Laboratory for Multiphase Processes, Nova Gorica, Slovenia Published in Ljubljana, Slovenija
   http://www.rmz-mg.com/contents.htm

%A Miha Kovacic
%A Katja Mackosek
%A Andrej Mihevc
%A Tomaz Marolt
%T Modeling of Cracks Presence in Steel After Hot Rolling
%B 7th Research/Expert Conference with International Participations, QUALITY 2011
%E Safet Brdarevic
%D 2011
%I 
%C Neum, Bosnia and Herzegovina
%K genetic algorithms, genetic programming
%8 1-4  June
%Z http://quality.unze.ba/

%A Miha Kovacic
%A Sandra Sencic
%T Modeling of PM10 Emission near the Steel Plant area in Slovenia with Genetic Programming
%B 19th Conference on Materials and Technology
%E Matjaz Godec
%D 2011
%P 37
%I Institute of Metals and Technology Ljubljana, Solvenia
%I Institute of Metals and Technology - IMT
%C Portoroz, Solvenia
%K genetic algorithms, genetic programming, pollution
%U http://konferenca2011.imt.si/fileadmin/dokumenti/IMT/KONFERENCA_-_abstracti/Book_of_Abstracts_Protoroz_2011.pdf
%X To implement sound air quality policies.. The genetic programming modelling results show good agreement with measured daily PM10 concentrations
%O Program and book of abstracts, Oral presentation
%8 22-23  November
%Z http://www.iom3.org/events/19th-conference-materials-and-technology

%A Tim Kovacs
%T Deletion Schemes for Classifier Systems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 329--336
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-856.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Yevgeniya Kovalchuk
%T Seller's Strategies for Predicting Winning Bid Prices in Online Auctions
%B International Conference on Computational Intelligence for Modelling Control Automation
%D 2008
%P 1--6
%I 
%K genetic algorithms, genetic programming, first price sealed bid reverse auctions, genetic programming learning techniques, neural networks, online auctions, seller
   strategies, trading agent competition supply chain management game, winning bid price prediction, electronic commerce, learning (artificial intelligence), neural nets,
   pricing, supply chain management
%X Online auctions have become extremely popular in recent years. Ability to predict winning bid prices accurately can help bidders to maximize their profit. This paper
   proposes a number of strategies and algorithms for performing such predictions for the first price sealed bid reverse auctions (FPSBRA). The neural networks (NN) and
   genetic programming (GP) learning techniques are used in the models. The algorithms are tested in the trading agent competition supply chain management (TAC SCM) game,
   where manufacture agents compete for customers' orders following the rules of the FPSBRA. Although all the proposed algorithms demonstrate the potential for predicting
   winning bid prices in competitive and dynamic environments, some of them perform more accurately than the others.
%8 Decemeber
%Z Also known as \cite5172590

%A Yevgeniya Kovalchuk
%T A Multi-Agent Decision Support System for Supply Chain Management
%R Ph.D. Thesis
%D 2009
%I 
%I School of Computer Science and Electronic Engineering, University of Essex
%C UK
%K genetic algorithms, genetic programming
%U http://privatewww.essex.ac.uk/~yvkova/Papers/thesisYKovalhuk.pdf
%X Supply Chain Management (SCM) involves a number of activities from negotiating with suppliers to competing for customer orders and scheduling the manufacturing process and
   delivery of goods. The activities are different in their nature: they work with various data, have different tasks and constraints. At the same time, they are interrelated
   to ensure the achievement of the ultimate goal of maximising the enterpriseĂąs profit. This makes the chain very difficult to manage; being successful in one of its areas
   does not necessarily guarantee the improvement of the overall performance. Designing an effective decision-support system for SCM has become crucial in recent years. With
   the advent of e-Commerce and in a global economy, SCM systems have to be able to deal with the uncertainty and volatility of modern markets. In such systems, the ability to
   learn and adapt to new conditions in the environment is of paramount importance. This thesis proposes a reusable demand-driven SCM solution. It introduces a number of
   algorithms for solving different tasks across the supply chain, including: constraint satisfaction, prediction, planning, and online adjustments. The main contribution of
   the thesis lies in exploring the problem of dynamic pricing and predicting on line auctions in the context of SCM. A number of algorithms for modelling competitorsĂą
   behaviour and predicting customer order prices are proposed and compared in the thesis. Their influence on the overall system performance is also explored. The algorithms
   are based on the Neural Networks and Genetic Programming learning techniques. The Trading Agent Competition SCM game is used as a testbed for analysing the proposed
   methods. Although only the results from testing the algorithms in this simulated environment are provided, the methods are not tied to the game rules and can successfully
   be used for predicting other financial instruments and in other competitive trading environments.
%8 July

%A Ryszard Kowalczyk
%T On Linguistic Approximation with Genetic Programming
%B Methodology and Tools in Knowledge-Based Systems
%S LNCS
%E Jose Mira and Angel Pasqual del Pobil and Moonis Ali
%V 1415
%D 1998
%P 200--224
%I Springer Verlag
%C Benicassim, Castelln, Sapin
%K genetic algorithms, genetic programming, artificial intelligence fuzzy sets, natural language processing NLP
%8 June

%A Taras Kowaliw
%A Wolfgang Banzhaf
%A Nawwaf Kharma
%A Simon Harding
%T Evolving Novel Image Features Using Genetic Programming-Based Image Transforms
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 2502--2507
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, Cartesian Genetic Programming
%X In this paper, we use Genetic Programming (GP) to define a set of transforms on the space of grey scale images. The motivation is to allow an evolutionary algorithm means
   of transforming a set of image patterns into a more classifiable form. To this end, we introduce the notion of a Transformbased Evolvable Feature (TEF), a moment value
   extracted from a GP-transformed image, used in a classification task. Unlike many previous approaches, the TEF allows the whole image space to be searched and augmented.
   TEFs are instantiated through Cartesian Genetic Programming, and applied to a medical image classification task, that of detecting muscular dystrophy-indicating inclusions
   in cell images. It is shown that the inclusion of a single TEF allows for significantly superior classification relative to predefined features alone.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Taras Kowaliw
%A Jon McCormack
%A Alan Dorin
%T Evolutionary automated recognition and characterization of an individual's artistic style
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X In this paper, we introduce a new image database, consisting of examples of artists' work. Successful classification of this database suggests the capacity to automatically
   recognise an artist's aesthetic style. We use the notion of Transform-based Evolvable Features as a means of evolving features on the space, these features are then
   evaluated through a standard classifier. We obtain recognition rates for our six artistic styles -relative to images by the other artists and images randomly downloaded
   from a search engine -of a mean true positive rate of 0.946 and a mean false positive rate of 0.017. Distance metrics designed to indicate the similarity between an
   arbitrary greyscale image and one of the artistic styles are created from the evolved features. These metrics are capable of ranking control images so that artist-drawn
   instances appear at the front of the list. We provide evidence that other images ranked as similar by the metric correspond to naive human notions of similarity as well,
   suggesting the distance metric could serve as a content-based aesthetic recommender.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5585975

%A Taras Kowaliw
%A Alan Dorin
%A Jon McCormack
%T Promoting Creative Design in Interactive Evolutionary Computation
%J IEEE Transactions on Evolutionary Computation
%I 
%K genetic algorithms, genetic programming, Artificial Ecosystem , Computational Creativity, Electronic Art, Interactive Evolution
%X We use a new measure of creativity as a guide in an interactive evolutionary art task and tie the results to natural language usage of the term creative. Following previous
   work, we explore a tractable definition of creativity, one emphasising the novelty of systems, and its addition to an interactive application. We next introduce a
   generative ecosystemic art system, EvoEco, an agent-based pixel-level means of generating images. EvoEco is used as a component of an online survey which asks users to
   evolve a pleasing image and then rank the success of the process and its output. Evolutionary search is augmented with the creativity measure, and compared with control
   groups augmented with either random search or a measure of phenotypic distance. We show that users consistently rate the creativity measure-enhanced version as more
   creative and novel than other search techniques. We further derive additional insights into appropriate forms of genetic representation and pattern space-traversal in an
   interactive evolutionary algorithm.
%O Accepted for future publication
%Z also known as \cite6151108

%A J. R. Koza
%T Hierarchical genetic algorithms operating on populations of computer programs
%B Proceedings of the Eleventh International Joint Conference on Artificial Intelligence IJCAI-89
%E N. S. Sridharan
%V 1
%D 1989
%P 768--774
%I Morgan Kaufmann San Mateo, CA, USA
%C Detroit, MI, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ijcai1989.pdf
%X Existing approaches to artificial intelligence problems such as sequence induction, automatic programming, machine learning, planning, and pattern recognition typically
   require specification in advance of the size and shape of the solution to the problem (often in a unnatural and difficult way). This paper reports on a new approach in
   which the size and shape of the solution to such problems is dynamically created using Darwinian principles of reproduction and survival of the fittest. Moreover, the
   resulting solution is inherently hierarchical. The paper describes computer experiments, using the author's 4341 line LISP program, in five areas of artificial
   intelligence, namely (1) sequence induction (e.g. inducing a computational procedure for the recursive Fibonacci sequence and inducing a computational procedure for a cubic
   polynomial sequence), (2) automatic programming (e.g. discovering a computational procedure for solving pairs of linear equations, solving quadratic equations for complex
   roots, and discovering trigonometric identities), (3) machine learning of functions (e.g. learning a Boolean multiplexer function previously studied in neural net and
   classifier system work and learning the exclusive-or and parity function), (4) planning (e.g. developing a robotic action sequence that can stack an arbitrary initial
   configuration of blocks into a specified order), and (5) pattern recognition (e.g. translation-invariant recognition of a simple one dimensional shape in a linear retina).
%8 20-25  August

%A J. Koza
%T Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems
%R Technical Report STAN-CS-90-1314
%D 1990
%I 
%I Dept. of Computer Science, Stanford University
%K genetic algorithms, genetic programming
%U ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/90/1314/CS-TR-90-1314.pdf
%X Many seemingly different problems in artificial intelligence, symbolic processing, and machine learning can be viewed as requiring discovery of a computer program that
   produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible
   computer programs for a most fit individual computer program. The new "genetic programming" paradigm described herein provides a way to search for this most fit individual
   computer program. In this new "genetic programming" paradigm, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest
   and using a genetic crossover (recombination) operator appropriate for genetically mating computer programs. In this paper, the process of formulating and solving problems
   using this new paradigm is illustrated using examples from various areas. Examples come from the areas of machine learning of a function; planning; sequence induction;
   function function identification (including symbolic regression, empirical discovery, "data to function" symbolic integration, "data to function" symbolic differentiation);
   solving equations, including differential equations, integral equations, and functional equations); concept formation; automatic programming; pattern recognition,
   time-optimal control; playing differential pursuer-evader games; neural network design; and finding a game-playing strategyfor a discrete game in extensive form.
%8 June
%Z ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/90/1314 also contains GIF and MAC versions

%A John R. Koza
%T A genetic approach to econometric modeling
%B Sixth World Congress of the Econometric Society
%D 1990
%I 
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/wces1990.pdf
%X An important problem in economics and other areas of science is finding the mathematical relationship between the empirically observed variables measuring a system. In many
   conventional modelling techniques, one necessarily begins by selecting the size and shape of the mathematical model. Because the vast majority of available mathematical
   tools only handle linear models, this choice is often simply a linear model. After making this choice, one usually then tries to find the values of certain coefficients and
   constants required by the particular model so as to achieve the best fit between the observed data and the model. But, in many cases, the most important issue is the size
   and shape of the mathematical model itself. That is, one really wants first to find the functional form of the model that best fits observed empirical data, and, only then,
   go on to find any constants and coefficients that happen to be needed. Some techniques exist for doing this. We suggest that finding the functional form of the model can
   productively be viewed as being equivalent to searching a space of possible computer programs for the particular individual computer program which produces the desired
   output for given input. That is, one is searching for the computer program whose behaviour best fits the observed data. Computer programs offer great flexibility in the
   ways that they compute their output from the given inputs. The most fit individual computer program can be found via a new 'genetic programming' paradigm originally
   developed for solving artificial intelligence problems. This new 'genetic programming' paradigm genetically breeds populations of computer programs in a Darwinian
   competition using genetic operations. The Darwinian competition is based on the principle of survival and reproduction of the fittest. The genetic crossover (sexual
   recombination) operator is designed for genetically mating computer programs so as to create potentially more fit new offspring programs. The best single individual
   computer program produced by this process after many generations can be viewed as the solution to the problem. In this paper, we illustrate the process of formulating and
   solving problems of modeling (i.e. symbolic regression, symbolic function identification) with this new 'genetic programming' paradigm using hierarchical genetic
   algorithms. In particular, the 'genetic programming' paradigm is illustrated by rediscovering the well-known multiplicative (non-linear) 'exchange equation' M=PQ/V relating
   the money supply, price level, gross national product, and velocity of money in an economy.
%Z 27 August

%A John R. Koza
%T Genetically breeding populations of computer programs to solve problems in artificial intelligence
%B Proceedings of the Second International Conference on Tools for AI
%D 1990
%P 819--827
%I IEEE Computer Society Press, Los Alamitos, CA, USA
%C Herndon, Virginia, USA
%K genetic algorithms, genetic programming, artificial ant, artificial intelligence, computer programs, evader, game tree, genetic breeding, genetic programming paradigm,
   hierarchical computer programs, independently acting pursuer, irregular trail, minimax control strategy, simple discrete two-person game, computer games, discrete systems,
   knowledge based systems, minimax techniques, problem solving
%U http://www.genetic-programming.com/jkpdf/tai1990.pdf
%X This paper describes the recently developed 'genetic programming' paradigm which genetically breeds populations of computer programs to solve problems. In genetic
   programming, the individuals in the population are hierarchical computer programs of various sizes and shapes. Three applications to problems in artificial intelligence are
   presented.
%8 6-9  November

%A John R. Koza
%T Non-Linear Genetic Algorithms for Solving Problems
%D 1990
%I 
%K genetic algorithms, genetic programming
%O United States Patent 4935877
%O filed may 20, 1988, issued june 19, 1990, 4,935,877. Australian patent 611,350 issued september 21, 1991. Canadian patent 1,311,561 issued december 15, 1992.
%8 19  June

%A John R. Koza
%T Integrating symbolic processing into genetic algorithms
%B Workshop on Integrating Symbolic and Neural Processes at AAAI-90
%D 1990
%I AAAI
%C Boston
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/aaai1990isnp.pdf
%X As will be seen, the LISP S-expression required to solve each of the problems described above will emerge from a simulated evolutionary process. This process will start
   with an initial population of randomly generated LISP S-expressions composed of functions and atoms appropriate to the problem domain. Then, a process based on the
   Darwinian model of reproduction and survival of the fittest and genetic recombination will be used to create a new population of individuals. In particular, a genetic
   process of sexual reproduction among two parental S-expressions will be used to create offspring S-expressions. At least one of two participating parental S-expressions
   will be selected in proportion to fitness. The resulting offspring S-expressions will be composed of sub-expressions ("building blocks") from their parents. Finally, the
   new population of offspring (i.e. the new generation) will replace the old population of parents and the process will continue. As will be seen, this highly parallel,
   locally controlled, and decentralized process will produce populations which, over a period of generations, tend to exhibit increasing average fitness in dealing with their
   environment, and which, in addition, can robustly (i.e. rapidly and effectively) adapt to changes in their environment.
%8 29  July
%Z Presented at the Workshop on Integrating Symbolic and Neural Processes at AAAI-90 in Boston, MA, USA, July 29, 1990.

%A John R. Koza
%T Non-Linear Genetic Algorithms for Solving Problems by Finding a Fit Composition of Functions
%D 1992
%I 
%K genetic algorithms, genetic programming
%O United States Patent 5136686
%O filed march 28, 1990, issued august 4, 1992. 5,136,686
%8 4  August

%A John R. Koza
%T Evolution and co-evolution of computer programs to control independent-acting agents
%B From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior
%E Jean-Arcady Meyer and Stewart W. Wilson
%D 1991
%P 366--375
%I MIT Press Cambridge, MA
%C Paris, France
%K genetic algorithms, genetic programming, coevolution
%U http://www.genetic-programming.com/jkpdf/sab1990.pdf
%X This SAB-90 paper describes the recently developed 'genetic programming' paradigm which genetically breeds populations of computer programs to solve problems. In genetic
   programming, the individuals in the population are hierarchical computer programs of various sizes and shapes. This paper also extends the genetic programming paradigm to a
   'co-evolution' algorithm which operates simultaneously on two populations of independently-acting hierarchical computer programs of various sizes and shapes.
%8 24-28  September
%Z SAB-1990 conference was held in 1990 but its proceedings were published in 1991. Santa Fe ant, pursuit game, game tree

%A John R. Koza
%T Genetic evolution and co-evolution of computer programs
%B Artificial Life II
%S SFI Studies in the Sciences of Complexity
%E Christopher Taylor Charles Langton and J. Doyne Farmer and Steen Rasmussen
%V X
%D 1991
%P 603--629
%I Addison-Wesley Redwood City, CA, USA
%C Santa Fe Institute, New Mexico, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/alife1990.pdf
%X Research in the field of artificial life focuses on computer programs that exhibit some of the properties of biological life (e.g. self-reproducibility, evolutionary
   adaptation to an environment, etc.). In one area of artificial life research, human programmers write intentionally simple computer programs (often incorporating observed
   features of actual biological processes) and then study the 'emergent' higher level behavior that may be exhibited by such seemingly simple programs. In this chapter, we
   consider a different problem, namely, 'How can computer programs be automatically written by the computer itself using only measurements of a given program's performance?'
   In particular, this chapter describes the recently developed 'genetic programming paradigm' which genetically breeds populations of computer programs in order to find a
   computer program that solves the given problem. In the genetic programming paradigm, the individuals in the population are hierarchical compositions of functions and
   arguments. The hierarchies are of various sizes and shapes. Increasingly fit hierarchies are then evolved in response to the problem environment using the genetic
   operations of fitness proportionate reproduction (Darwinian survival and reproduction of the fittest) and crossover (sexual recombination). In the genetic programming
   paradigm, the size and shape of the hierarchical solution to the problem is not specified in advance. Instead, the size and shape of the hierarchy, as well as the contents
   of the hierarchy, evolve in response to the Darwinian selective pressure exerted by the problem environment. This ALIFE-90 chapter also describes an extension of the
   genetic programming paradigm to the case where two (or more) populations of hierarchical computer programs simultaneously co-evolve. In co-evolution, each population acts
   as the environment for the other population. In particular, each individual of the first population is evaluated for 'relative fitness' by testing it against each
   individual in the second population, and, simultaneously, each individual in the second population is evaluated for relative fitness by testing them against each individual
   in the first population. Over a period of many generations, individuals with high 'absolute fitness' may evolve as the two populations mutually bootstrap each other to
   increasingly high levels of fitness. The genetic programming paradigm is illustrated by genetically breeding a population of hierarchical computer programs to allow an
   'artificial ant' to traverse an irregular trail. In addition, we genetically breed a computer program controlling the behavior of an individual ant in an ant colony which,
   when simultaneously executed by a large number of ants, causes the emergence of interesting collective behavior for the colony as a whole. Co-evolution is illustrated with
   a problem involving finding an optimal strategy for playing a simple discrete two-person competitive game represented by a game tree in extensive form.
%8 February  1990
%Z This conference was held in 1990 but its proceedings were published in 1991.

%A John R. Koza
%T Evolving a computer program to generate random numbers using the genetic programming paradigm
%B Proceedings of the Fourth International Conference on Genetic Algorithms
%E Richard K. Belew and Lashon B. Booker
%D 1991
%P 37--44
%I Morgan Kaufmann San Mateo, CA, USA
%C University of California - San Diego, La Jolla, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/icga1991.pdf
%X This paper demonstrates that it is possible to genetically breed a computer program that is considered difficult to write, namely, a randomiser that converts a sequence of
   consecutive integers into pseudo-random bits with near maximal entropy.
%8 13-16  July
%Z comparison of Park-Miller, IBM RANDU, Shift-register, SHUFFLE, TI RANDOM, GP, chi-squared.

%A John R. Koza
%T A genetic approach to econometric modeling
%B Economics and Cognitive Science
%E Paul Bourgine and Bernard Walliser
%D 1991
%P 57--75
%I Pergamon Press
%C Oxford, UK
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ecs1991cecoia1990.pdf
%X An important problem in economics is finding the mathematical relationship between the empirically observed variables measuring a system. In many conventional modeling
   techniques, one necessarily begins by selecting the size and shape of the model. After making this choice, one usually then tries to find the values of certain coefficients
   required by the particular model so as to achieve the best fit between the observed data and the model. But, in many cases, the most important issue is the size and shape
   of the model itself. Finding the functional form of the model can be viewed as searching a space of possible computer programs for the particular computer program which
   produces the desired output for given inputs. This most fit computer program can be found via a recently developed genetic programming paradigm originally developed for
   solving artificial intelligence problems. This new genetic programming paradigm genetically breeds populations of computer programs in a Darwinian competition using genetic
   operations, such as crossover (sexual recombination). In this paper, we illustrate the process of discovering a model by rediscovering the well-known multiplicative
   (non-linear) "exchange equation" P=F(MV,Q) relating the money supply, price level, gross national product, and velocity of money in an economy.
%Z 'This paper was submitted in 1989 to the CECOIA-1990 conference held in Paris in 1990. http://www.genetic-programming.com/jkpdf/cecoia1990.pdf '

%A John R. Koza
%T The genetic programming paradigm: Genetically breeding populations of computer programs to solve problems
%B Dynamic, Genetic, and Chaotic Programming
%E Branko Soucek and the IRIS Group
%D 1992
%P 203--321
%I John Wiley
%C New York
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/soucek1992.pdf
%X Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that
   produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible
   computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of
   possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields.
   In the genetic programming paradigm, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic
   crossover (sexual recombination) operator appropriate for genetically mating computer programs. This chapter shows how to reformulate seemingly different problems into a
   common form (i.e. a problem requiring discovery of a computer program) and, then, to show how the genetic programming paradigm can serve as a single, unified approach for
   solving problems formulated in this common way.

%A John R. Koza
%T A genetic approach to finding a controller to back up a tractor-trailer truck
%B Proceedings of the 1992 American Control Conference
%V III
%D 1992
%P 2307--2311
%I American Automatic Control Council
%C Evanston, IL, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/acc1992.pdf
%X Problems of control can be viewed as requiring the discovery of a computer program (i.e. control strategy) that takes the state variables of a problem as its inputs and
   produces the values of the control variables as its output. This paper describes the recently developed genetic programming paradigm which genetically breeds a population
   of computer programs to solve problems. Genetic programming begins with a population of hundreds or thousands of random computer programs and improves them from generation
   to generation using the Darwinian operation of fitness proportionate reproduction and the genetic operation of sexual recombination. The sexual recombination operation
   combines parts of two computer programs, each selected proportional to their fitness, to produce new offspring programs. The paper shows, step by step, how to apply genetic
   programming to the four dimensional control problem of backing up a tractor-trailer truck to a loading dock. The genetic programming paradigm breeds an approximately
   correct computer program (i.e. control strategy) that successfully performs the required task.

%A John R. Koza
%T Genetic evolution and co-evolution of game strategies
%B The International Conference on Game Theory and Its Applications
%D 1992
%I 
%C Stony Brook, New York
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/icgt1992.pdf
%X The problem of discovering a strategy for playing a game is an important problem in game theory. This problem can be viewed as requiring discovery of a computer program.
   The desired computer program takes either the entire history of past moves in the game or the current state of the game as its input and produces the next move as its
   output. This paper describes the recently developed genetic programming paradigm which genetically breeds populations of computer programs to solve problems. In genetic
   programming, the individuals in the population are independently acting hierarchical compositions of functions and arguments of various sizes and shapes. Each of these
   individual computer programs is evaluated for its fitness in playing the game. A simulated evolutionary process driven by this measure of fitness then uses the Darwinian
   principle of reproduction and survival of the fittest and the genetic operation of crossover (sexual recombination) to solve the problem. Genetic programming can also
   operate simultaneously on two (or more) populations of programs. In such "co-evolution," each population acts as the environment for the other population. In particular,
   each individual of the first population is evaluated for "relative fitness" by testing it against each individual in the second population, and, simultaneously, each
   individual in the second population is evaluated for "relative fitness" by testing it against each individual in the first population. In this paper, genetic programming is
   illustrated with three different problems. The first problem involves genetically breeding a population of computer programs to find an optimal strategy for a player of a
   discrete two-person 32-outcome game represented by a game tree in extensive form. In this problem, the entire history of past moves of both players is used as input to the
   computer program. The second problem involves genetically breeding a minimax control strategy in a differential game with an independently-acting pursuer and evader. In
   this problem, the state of the game is used as input to the computer program. The third problem illustrates the "co-evolution" and involves genetically breeding an optimal
   strategy for a player of a discrete two-person 32-outcome game represented by a game tree in extensive form.
%8 July  15
%Z Paper presented at

%A John R. Koza
%T Evolution of a subsumption architecture that performs a wall following task for an autonomous mobile robot via genetic programming
%B Computational Learning Theory and Natural Learning Systems
%E Stephen Jose Hanson and Thomas Petsche and Ronald L. Rivest and Michael Kearns
%V 2
%D 1994
%P 321--346
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/cltnls1994.pdf
%X The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done, rather than by explicitly programming it. This paper considers
   the task of automatically generating a computer program to enable an autonomous mobile robot to perform the task of following the wall of an irregular shaped room. A human
   programmer has written such a program in the style of the subsumption architecture. The solution produced by genetic programming emerges as a result of Darwinian natural
   selection and genetic crossover (sexual recombination) in a population of computer programs. This evolutionary process is driven by a fitness measure which communicates the
   nature of the task to the computer.
%8 June
%@ 0-262-58133-7

%A John R. Koza
%T Simultaneous discovery of detectors and a way of using the detectors via genetic programming
%B 1993 IEEE International Conference on Neural Networks
%V III
%D 1993
%P 1794--1801
%I IEEE Piscataway, NJ, USA
%C San Francisco, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/icnn1993pattern.pdf
%X Conventional approaches to problems of pattern recognition and machine learning usually require that the user hand-craft detectors for key features in the problem
   environment. Conventional approaches often additionally require the user to specify in advance the size and shape of the eventual way of combining the detectors into a
   compete solution. This paper describes a general approach for simultaneously discovering detectors and a way of combining the detectors to solve a problem via genetic
   programming with automatic function definition. Genetic programming provides a way to genetically breed a computer program to solve a wide variety of problems. Automatic
   function definition automatically and dynamically enables genetic programming to define potentially useful functions dynamically during a run and to facilitate a solution
   of a problem by automatically and dynamically decomposing the problem into simpler subproblems. This approach is illustrated with a problem of letter recognition.

%A John R. Koza
%T Discovery of a main program and reusable subroutines using genetic programming
%B Proceedings of the Fifth Workshop on Neural Networks: An International Conference on Computational Intelligence: Neural Networks, Fuzzy Systems, Evolutionary Programming,
   and Virtual Reality
%D 1993
%P 109--118
%I San Diego, CA, USA
%I The Society for Computer Simulation
%K genetic algorithms, genetic programming, ADF
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.5520
%X This paper describes an approach for automatically decomposing a problem into subproblems, automatically creating reusable subroutines to solve the subproblems, and
   automatically assembling the results produced by the subroutines in order to solve the problem. The approach uses genetic programming with the recently developed additional
   facility of automatic function definition. Genetic programming provides a way to genetically breed a computer program to solve a problem and automatic function definition
   enables genetic programming to create reusable subroutines dynamically during a run. The approach is applied to an illustrative problem containing a considerable amount of
   regularity. Solutions to the problem produced using automatic function definition are considerably smaller in size and require processing of considerably fewer individuals
   than is the case without automatic function definition. Specifically, the average program size for a solution to the problem without using automatic function definition is
   3.65 times larger than the size for a solution when using automatic function definition. The number of individuals required to be processed to yield a solution with 99%
   probability without automatic function definition is 9.09 times larger than the equivalent number required with automatic function definition.

%T Artificial Life at Stanford 1993
%E John R. Koza
%D 1993
%I Stanford University Bookstore
%C Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670
%K genetic algorithms, genetic programming
%X This volume contains 16 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford
   University offered during the winter quarter 1993. The appendix contains some of the material containing basicinformation about the course.
   http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-171957-6

%T Genetic Algorithms at Stanford 1993
%E John R. Koza
%D 1993
%I Stanford University Bookstore
%C Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670
%K genetic algorithms, genetic programming
%X This volume contains 24 papers written and submitted by students describing their term projects for the course in genetic algorithms (Computer Science 426) at Stanford
   University for the spring quarter 1993. The appendix contains some of the material containing basic information about the course.
   http://www-cs-faculty.stanford.edu/~koza/cs426.html

%A John R. Koza
%T Discovery of rewrite rules in Lindenmayer systems and state transition rules in cellular automata via genetic programming
%B Symposium on Pattern Formation (SPF-93)
%D 1993
%I 
%C Claremont, California, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/spf1993.pdf
%X It is difficult to write programs for both Lindenmayer systems and cellular automata. This paper demonstrates the possibility of discovering the rewrite rule for
   Lindenmayer systems and the state transition rules for cellular automata by means of genetic programming. Genetic programming is an extension of the genetic algorithm in
   which computer programs are genetically bred to solve problems. We demonstrate the use of genetic programming to discover the rewrite rules for a Lindenmayer system for the
   quadratic Koch island using a pattern matching measure as the driving force for the evolutionary process. We also demonstrate the use of genetic programming to discover the
   state transition rules for a one-dimensional and two-dimensional cellular automata using entropy as the driving force for the evolutionary process.
%8 February
%Z Presented at

%A John R. Koza
%T Scalable learning in genetic programming using automatic function definition
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 99--117
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/aigp1994lawn.pdf
%X This chapter uses three differently sized versions of an illustrative problem that has considerable regularity, symmetry, and homogeneity in its problem environment to
   compare genetic programming with and without the newly developed mechanism of automatic function definition. Genetic programming with automatic function definition can
   automatically decompose a problem into simpler subproblems, solve the subproblems, and assemble the solutions to the subproblems into a solution to the original overall
   problem. The solutions to the problem produced by genetic programming with automatic function definition are more parsimonious than those produced without it. Genetic
   programming requires fewer fitness evaluations to yield a solution to the problem with 99percent probability with automatic function definition than without it. When we
   consider the three differently sized versions of the problem we find that the size of the solutions produced without automatic function definition can be expressed as a
   direct multiple of problem size. In contrast, the average size of solutions with automatic function definition is expressed as a certain minimum size representing the
   overhead associated with automatic function definition; however, there is only a very slight increase in the average size of the solutions with problem size. Moreover, the
   number of fitness evaluations required to yield a solution to the problem with a 99percent probability grows very rapidly with problem size without automatic function
   definition, but this same measure grows only linearly with problem size with automatic function definition.
%O 6

%A John R. Koza
%T Introduction to genetic programming
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 21--42
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/aigp1994intro.pdf
%X This chapter provides an introduction to genetic algorithms, the LISP programming language, genetic programming, and automatic function definition. This chapter also
   outlines additional sources of information about genetic algorithms and genetic programming.
%O 2

%A John R. Koza
%T Spontaneous emergence of self-replicating and evolutionarily self-improving computer programs
%B Artificial Life III
%S SFI Studies in the Sciences of Complexity
%E Christopher G. Langton
%V XVII
%D 1994
%P 225--262
%I Addison-Wesley Redwood City, CA, USA
%C Santa Fe, New Mexico, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/alife1994.pdf
%X This ALIFE-92 paper reports on the spontaneous emergence, from a primordial ooze of primitive computational elements that are only capable of agglomerating themselves to
   one another, of computer programs exhibiting the ability to asexually reproduce, to reproduce by combining parts from two parents, and to improve their performance through
   evolution.
%8 15-19  June  1992
%Z Held June 1992 in Santa Fe, New Mexico, USA, published in 1994

%A J. R. Koza
%T Recognizing patterns in protein sequences using iteration-performing calculations in genetic programming
%B 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 244--249
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, memory
%U http://www.genetic-programming.com/jkpdf/icec1994.pdf
%X This paper uses genetic programming with automatically defined functions (ADFs) for the dynamic creation of a pattern-recognising computer program consisting of
   initially-unknown detectors, an initially-unknown iterative calculation incorporating the as-yet-undiscovered detectors, and an initially-unspecified final calculation
   incorporating the results of the as-yet-unspecified iteration. The program's goal is to recognise a given protein segment as being a transmembrane domain or
   non-transmembrane area of the protein. Genetic programming with automatic function definition is given a training set of differently-sized mouse protein segments and their
   correct classification. Correlation is used as the fitness measure. Automatic function definition enables genetic programming to dynamically create subroutines (detectors).
   A restricted form of iteration is introduced to enable genetic programming to perform calculations on the values returned by the detectors. When cross-validated, the best
   genetically-evolved recogniser for transmembrane domains achieves an out-of-sample correlation of 0.968 and an out-of-sample error rate of 1.6percent. This error rate is
   better than that recently reported for five other methods.
%8 27-29  June

%A John R. Koza
%T Automated discovery of detectors and iteration-performing calculations to recognize patterns in protein sequences using genetic Programming
%B Proceedings of the Conference on Computer Vision and Pattern Recognition
%D 1994
%P 684--689
%I IEEE Computer Society Press
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/cvpr1994.pdf
%X This paper describes an automated process for the dynamic creation of a pattern-recognizing computer program consisting of initially-unknown detectors, an initially-unknown
   iterative calculation incorporating the as-yet-uncreated detectors, and an initially-unspecified final calculation incorporating the results of the as-yet-uncreated
   iteration. The program's goal is to recognize a given protein segment as being a transmembrane domain or non-transmembrane area. The recognizing program to solve this
   problem will be evolved using the recently-developed genetic programming paradigm. Genetic programming starts with a primordial ooze of randomly generated computer programs
   composed of available programmatic ingredients and then genetically breeds the population using the Darwinian principle of survival of the fittest and the genetic crossover
   (sexual recombination) operation. Automatic function definition enables genetic programming to dynamically create subroutines (detectors). When cross-validated, the best
   genetically-evolved recognizer achieves an out-of-sample correlation of 0.968 and an out-of-sample error rate of 1.6%. This error rate is better than that recently reported
   for five other methods.

%A John R. Koza
%T Architecture-Altering Operations for Evolving the Architecture of a Multipart Program in Genetic Programming
%R Technical Report STAN-CS-94-1528
%D 1994
%I 
%I Dept. of Computer Science, Stanford University
%C Stanford, California 94305, USA
%K genetic algorithms, genetic programming
%U http://www-db.stanford.edu/TR/CS-TR-94-1528.html
%X Previous work described a way to evolutionarily select the architecture of a multi-part computer program From among preexisting alternatives in the population while
   concurrently solving a problem during a run of genetic programming. This report describes six new architecture-altering operations that provide a way to evolve the
   architecture of a multi-part program in the sense of actually changing the architecture of programs dynamically during the run. The new architecture-altering operations are
   motivated by the naturally occurring operation of gene duplication as described in Susumu Ohno's provocative 1970 book Evolution by Means of Gene Duplication as well as the
   naturally occurring operation of gene deletion. The six new architecture-altering operations are branch duplication, argument duplication, branch creation, argument
   creation, branch deletion and argument deletion. A connection is made between genetic programming and other techniques of automated problem solving by interpreting the
   architecture-altering operations as providing an automated way to specialize and generalize programs. The report demonstrates that a hierarchical architecture can be
   evolved to solve an illustrative symbolic regression problem using the architecture- altering operations. Future work will study the amount of additional computational
   effort required to employ the architecture-altering operations.
%8 October
%Z See also \citekoza:1995:ea ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/94/1528/ also contains GIF and MAC versions

%A John R. Koza
%T Genetic Programming: On the programming of computers by means of natural selection
%J Statistics and Computing
%V 4
%N 2
%D 1994
%P 87--112
%I Chapman \& Hall, London
%K genetic algorithms, genetic programming
%X Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that
   produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible
   computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of
   possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields.
   In genetic programming, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (sexual
   recombination) operator appropriate for genetically mating computer programs. Genetic programming is illustrated via an example of machine learning of the Boolean
   11-multiplexer function, symbolic regression of the econometric exchange equation from noisy empirical data, the control problem of backing up a tractor-trailer truck, the
   classification problem of distinguishing between two intertwined spirals., and the robotics problem of controlling an autonomous mobile robot to find a box in the middle of
   an irregular room and move the box to the wall. Hierarchical automatic function definition enables genetic programming to define potentially useful functions automatically
   and dynamically during a run - much as a human programmer writing a complex computer program creates subroutines (procedures, functions) to perform groups of steps which
   must be performed with different instantiations of the dummy variables (formal parameters) in more than one place in the main program. Hierarchical automatic function
   definition is illustrated via the machine learning of the Boolean 11-parity function.
%8 June
%Z Special issue on Evolutionary Programming, Guest editor Zbigniew Michalewicz

%A John R. Koza
%A Martin A. Keane
%T Cart centering and broom balancing by genetically breeding populations of control strategy programs
%B Proceedings of International Joint Conference on Neural Networks
%V I
%D 1990
%P 198--201
%I Lawrence Erlbaum Hillsdale, NJ, USA
%C Washington
%K genetic algorithms, genetic programming
%X The paper describes a search for the time-optimal "bang bang" control strategy for the cart centering problem and a version of the broom balancing problem by genetically
   breeding populations of control strategy programs using a recently developed new genetic algorithm paradigm. The output of the new genetic algorithm paradigm comes in the
   form of a computer program composed of arithmetic operations, conditional logical operations, and mathematical functions which take the state variables of the problem as
   input and which produce commands specifying how to apply the 'bang bang' force as output.
%8 15-19  January

%A John R. Koza
%A Martin A. Keane
%T Genetic breeding of non-linear optimal control strategies for broom balancing
%B Proceedings of the Ninth International Conference on Analysis and Optimization of Systems. 1990
%S Lecture Notes in Control and Information Sciences
%E Alain Bensoussan and Jacques Louis Lions
%D 1990
%P 47--56
%I Springer-Verlag Berlin, Germany
%C Antibes, France
%K genetic algorithms, genetic programming
%X This paper describes a search for the time-optimal bang bang control strategy for the three dimensional broom balancing (inverted pendulum) problem by genetically breeding
   populations of control strategies using a recently developed new 'genetic computing' paradigm. The new paradigm produces results in the form of a control strategy
   consisting of a composition of functions, including arithmetic operations, conditional logical operations, and mathematical functions. This control strategy takes the
   problem's state variables as its input and generates the direction from which to apply the bang bang force as its output.
%8 June
%@ 0-387-52630-7

%A John R. Koza
%A Martin A. Keane
%A James P. Rice
%T Performance improvement of machine learning via automatic discovery of facilitating functions as applied to a problem of symbolic system identification
%B 1993 IEEE International Conference on Neural Networks
%V I
%D 1993
%P 191--198
%I IEEE Piscataway, NJ, USA
%C San Francisco, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/icnn1993impulse.pdf
%X The recently developed genetic programming paradigm provides a way to genetically breed a population of computer programs to solve problems. Automatic function definition
   enables genetic programming to define potentially useful functions dynamically during a run - much as a human programmer writing a complex computer program creates
   subroutines to perform certain groups of steps which must be performed in more than one place in the main program. This paper illustrates the value of automatic function
   definition in enabling genetic programming to accelerate the finding of an approximation to the impulse response function, in symbolic form, for a linear time-invariant
   system.

%A John R. Koza
%A James P. Rice
%T A Non-Linear Genetic Process for Use with Plural Co-Evolving Populations
%D 1992
%I 
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpatents/US5148513.htm
%O U.S. Patent 5148513
%O filed september 18, 1990, issued september 15, 1992, 5,148,513
%8 September

%A John R. Koza
%A James P. Rice
%T A Non-Linear Genetic Process for Problem Solving Using Spontaneously Emergent Self-Replicating and Self- Improving Entities
%D 1992
%I 
%K genetic algorithms, genetic programming
%O U.S. Patent 5390282
%O application filed june 16, 1992, issued february 14, 1995, 5,390,282
%8 June

%A John R. Koza
%A James P. Rice
%T A Non-Linear Genetic Process for Data Encoding and for Solving Problems Using Automatically Defined Functions
%D 1992
%I 
%K genetic algorithms, genetic programming
%O U.S. Patent 5343554
%O application filed May 11, 1992, issued August 30, 1994, 5,343,554
%8 May

%A John R. Koza
%A David Andre
%A Walter Alden Tackett
%T Evolution of the Architecture of a Multi-Part Program to Solve a Problem Using Architecture Altering Operations
%D 1994
%I 
%K genetic algorithms, genetic programming
%O United States Patent 5742738
%O filed August 4, 1994. Issued April 21, 1998. 5,742,738
%8 August
%Z NOTE: This patent has same title as 6,058,385

%A John R. Koza
%A James P. Rice
%A Jonathan Roughgarden
%T Evolution of Food Foraging Strategies for the Caribbean Anolis Lizard Using Genetic Programming
%R Working Paper 92-06-028
%D 1992
%I 
%I Santa Fe Institute
%C 1399 Hyde Park Road Santa Fe, New Mexico 87501-8943 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/sfi1992.pdf
%X This paper describes the recently developed genetic programming paradigm which genetically breeds a population of computer programs to solve problems. The paper then shows,
   step by step, how to apply genetic programming to a problem of behavioural ecology in biology - specifically, two versions of the problem of finding an optimal food
   foraging strategy for the Caribbean Anolis lizard. A simulation of the adaptive behavior of the lizard is required to evaluate each possible adaptive control strategy
   considered for the lizard. The foraging strategy produced by genetic programming is close to the mathematical solution for the one version for which the solution is known
   and appears to be a reasonable approximation to the solution for the second version of the problem.
%8 June
%Z 'This paper has been submitted to the Simulation of Adaptive Behavior journal published by The MIT Press.' See \citeKoza:1992:lizrd.

%A John R. Koza
%A James P. Rice
%A Jonathan Roughgarden
%T Evolution of food foraging strategies for the Caribbean Anolis lizard using genetic programming
%J Adaptive Behavior
%V 1
%N 2
%D 1992
%P 171--199
%I 
%K genetic algorithms, genetic programming, optimal foraging strategy, lizards, crossover
%U http://www.genetic-programming.com/jkpdf/sab1992journal.pdf
%X This article describes the recently developed genetic programming paradigm that genetically breeds a population of computer programs to solve problems. The article then
   demonstrates, step by step, how to apply genetic programming to a problem of behavioral ecology in biology--specifically, two versions of the problem of finding an optimal
   food- foraging strategy for the Caribbean Anolis lizard. A simulation of the adaptive behavior of the lizard is required to evaluate each possible adaptive control strategy
   considered for the lizard. The foraging strategy produced by genetic programming is close to the mathematical solution for the one version for which the solution is known
   and appears to be a reasonable approximation of the solution for the second version of the problem.
%Z http://www.isab.org/journal/adap1_2.php#Pages%20171-199

%A John R. Koza
%A James P. Rice
%T A genetic approach to artificial intelligence
%B Artificial Life II Video Proceedings
%E Christopher G. Langton
%D 1991
%I Addison-Wesley Redwood City, CA, USA
%C Santa Fe, New Mexico, USA
%K genetic algorithms, genetic programming
%X Genetic programming is described in an approximately 10-minute segment of this videotape from the 1990 Artificial Life II conference. This ALIFE-1990 videotape is available
   from Addison-Wesley.
%8 February  1990
%@ 0-201-55492-5

%A John R. Koza
%A James P. Rice
%T Automatic programming of robots using genetic programming
%B Proceedings of Tenth National Conference on Artificial Intelligence
%D 1992
%P 194--201
%I AAAI Press/MIT Press Menlo Park, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/aaai1992.pdf
%X The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done, rather than by explicitly programming it. This paper considers
   the task of automatically generating a computer program to enable an autonomous mobile robot to perform the task of moving a box from the middle of an irregular shaped room
   to the wall. We compare the ability of the recently developed genetic programming paradigm to produce such a program to the reported ability of reinforcement learning
   techniques, such as Q learning, to produce such a program in the style of the subsumption architecture. The computational requirements of reinforcement learning
   necessitates considerable human knowledge and intervention, whereas genetic programming comes much closer to achieving the goal of getting the computer to perform the task
   without explicitly programming it. The solution produced by genetic programming emerges as a result of Darwinian natural selection and genetic crossover (sexual
   recombination) in a population of computer programs. The process is driven by a fitness measure which communicates the nature of the task to the computer and its learning
   paradigm.

%A John R. Koza
%A James P. Rice
%T Genetic Generation of Both the Weights and Architecture for a Neural Network
%B International Joint Conference on Neural Networks, IJCNN-91
%V II
%D 1991
%P 397--404
%I IEEE Computer Society Press 1109 Spring Street, Suite 300, Silver Spring, MD 20910, USA
%C Washington State Convention and Trade Center, Seattle, WA, USA
%K genetic algorithms, genetic programming, connectionism, one-bit adder, cogann ref, LISP symbolic expressions, connectivity, genetic programming, layers, neural net
   architecture, neural net weights, one-bit adder, performance, processing elements, LISP, digital arithmetic, neural nets,
%U http://www.genetic-programming.com/jkpdf/ijcnn1991.pdf
%X This paper shows how to find both the weights and architecture for a neural network (including the number of layers, the number of processing elements per layer, and the
   connectivity between processing elements). This is accomplished using a recently developed extension to the genetic algorithm which genetically breeds a population of LISP
   symbolic expressions (S-expressions) of varying size and shape until the desired performance by the network is successfully evolved. The new genetic programming paradigm is
   applied to the problem of generating a neural network for the one-bit adder.
%8 8-12  July
%Z IJCNN-91
%@ 0-7803-0164-1

%A John R. Koza
%T Concept formation and decision tree induction using the genetic programming paradigm
%B Parallel Problem Solving from Nature - Proceedings of 1st Workshop, PPSN 1
%S Lecture Notes in Computer Science
%E H.-P. Schwefel and R. M\"anner
%V 496
%D 1991
%P 124--128
%I Springer-Verlag Berlin, Germany
%C Dortmund, Germany
%K genetic algorithms, genetic programming, genetic programming paradigm, concept formation, decision trees, ID3, hierarchies, LISP programming language
%U http://www.genetic-programming.com/jkpdf/ppsn1990.pdf
%X This PPSN-90 paper describes the application of the recently developed 'genetic programming' paradigm to the problem of concept formation and decision tree induction.
%8 1-3  October
%Z PPSN-1990 conference was held in 1990 but its proceedings were published in 1991.

%A J. R. Koza
%T Evolution of subsumption using genetic programming
%B Proceedings of the First European Conference on Artificial Life. Towards a Practice of Autonomous Systems
%E F. J. Varela and P. Bourgine
%D 1992
%P 110--119
%I MIT Press Cambridge, MA, USA
%C Paris, France
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ecal1991.pdf
%X The recently developed genetic programming paradigm is used to evolve emergent wall following behaviour in an irregularly shaped room for an autonomous mobile robot using
   the subsumption architecture. The evolutionary process is driven only by the fitness of the programs in solving the problem. Emergent functionality means that overall
   functionality is not achieved in the conventional tightly coupled, centrally controlled way, but, instead, indirectly by the interaction of relatively primitive components
   with the world and among themselves.
%8 11-13  Decemeber
%Z ECAL-91. ECAL-1991 conference was held in 1991 but its proceedings were published in 1992. Evolution of sonar guided wall following robot.

%A John R. Koza
%T A Genetic Approach to the Truck Backer Upper Problem and the Inter-Twined Spiral Problem
%B Proceedings of IJCNN International Joint Conference on Neural Networks
%V IV
%D 1992
%P 310--318
%I IEEE Press Piscataway, NJ, USA
%K genetic algorithms, genetic programming, connectionism
%U http://www.genetic-programming.com/jkpdf/ijcnn1992.pdf
%X ABSTRACT Neural networks are a biologically motivated problem-solving paradigm that has proven successful in robustly solving a variety of problems. This paper describes
   another biologically motivated paradigm, namely genetic programming, which can also solve a variety of problems. This paper explains genetic programming and applies it to
   two well-know benchmark problems from the field of neural networks. The truck backer upper problem is a multi-dimensional control problem and the inter-twined spirals
   problem is a challenging classification problem.
%Z IJCNN-92

%A John R. Koza
%T Hierarchical automatic function definition in genetic programming
%B Foundations of Genetic Algorithms 2
%E L. Darrell Whitley
%D 1992
%P 297--318
%I Morgan Kaufmann
%C Vail, Colorado, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/foga1992.pdf
%X A key goal in machine learning and artificial intelligence is to automatically and dynamically decompose problems into simpler problems in order to facilitate their
   solution. This paper describes two extensions to genetic programming, called "automatic" function definition and "hierarchical automatic" function definition, wherein
   functions that might be useful in solving a problem are automatically and dynamically defined during a run in terms of dummy variables. The defined functions are then
   repeatedly called from the automatically discovered "main" result-producing part of the program with different instantiations of the dummy variables. In the "hierarchical"
   version of automatic function definition, automatically defined functions may call other automatically defined functions, thus creating a hierarchy of dependencies among
   the automatically defined functions. The even-11-parity problem was solved using hierarchical automatic function definition.
%8 24--29  July
%Z Email: Koza@Sunburn.Stanford.edu

%A John R. Koza
%T A hierarchical approach to learning the boolean multiplexer function
%B Foundations of genetic algorithms
%E Gregory J. E. Rawlins
%D 1991
%P 171--192
%I Morgan Kaufmann San Mateo, California, USA
%C Indiana University
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/foga1990.pdf
%X This paper desribes the recently developed genetic programming paradigm, which genetically breeds populations of computer programs to solve problems. In genetic
   programming, the individuals in the population are hierarchical compositions of functions and arguments. Each of these individual computer programs is evaluated for its
   fitness in handling the problemenvironment. The size and shape of the computer program needed to solve the problem is not predetermined by the user, but instead emerges
   from the simulated evolutionary process driven by fitness. In this paper, the operation of the genetic programming paradigm is illustrated with the problem of learning the
   boolean 11-multiplexer function.
%8 15-18  July  1990
%Z FOGA-90 Published in 1991

%A John R. Koza
%T Genetic Programming: On the Programming of Computers by Means of Natural Selection
%D 1992
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming, text book
%@ 0-262-11170-5

%A John R. Koza
%T Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming
%B Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93
%E Stephanie Forrest
%D 1993
%P 295--302
%I Morgan Kaufmann San Mateo, CA, USA
%C University of Illinois at Urbana-Champaign
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/icga1993.pdf
%X This paper describes an approach for automatically decomposing a problem into subproblems and then automatically discovering reusable subroutines, and a way of assembling
   the results produced by these subroutines in order to solve a problem. The approach uses genetic programming with automatic function definition. Genetic programming
   provides a way to genetically breed a computer program to solve a problem. Automatic function definition enables genetic programming to define potentially useful
   subroutines dynamically during a run. The approach is applied to an illustrative problem. Genetic programming with automatic function definition reduced the computational
   effort required to learn a solution to the problem by a factor of 2.0 as compared to genetic programming without automatic function definition. Similarly, the average
   structural complexity of the solution was reduced by about 21percent.
%8 17-21  July
%Z Comparison of GP and GP+Automatic Function definition for San Mateo trail ants, finds improvement of 1:2 in number of fitness cases required and 21% reduction is size of
   eventual s-expressions. NO CASE made that either cases are using optimal parameters.

%A John R. Koza
%A James P. Rice
%T Genetic Programming:The Movie
%D 1992
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%Z Accompanies \citekoza:book VHS NTSC format (KOZGVV) (ISBN 0-262-61084-1) VHS PAL format (KOZGPV) (ISBN 0-262-61087-6) VHS SECAM format (KOZGSV) (ISBN 0-262-61088-4)

%A John R. Koza
%T Genetic Programming II: Automatic Discovery of Reusable Programs
%D 1994
%I MIT Press
%C Cambridge Massachusetts
%K genetic algorithms, genetic programming
%U http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8307
%X Genetic Programming II extends the results of John Koza's ground-breaking work on programming by means of natural selection, described in his first book, Genetic
   Programming. Using a hierarchical approach, Koza shows that complex problems can be solved by breaking them down into smaller, simpler problems using the recently developed
   technique of automatic function definition in the context of genetic programming. Where conventional techniques of machine learning and artificial intelligence fail to
   provide an effective means for automatically handling the process of decomposing complex problems into smaller subsets, reassembling the solutions to these subsets, and
   applying an overall solution to the original problem, automatic function definition enables genetic programming to define useful and reusable subroutines dynamically. Koza
   illustrates this new technique by showing how it solves (or approximately solves) a variety of problems in Boolean function learning, symbolic regression, control, pattern
   recognition, robotics, classification, and molecular biology. In each example, the problem is automatically decomposed into subproblems; the subproblems are automatically
   solved; and the solutions to the subproblems are automatically assembled into a solution to the original problem. Koza shows that leverage accrues because genetic
   programming with automatic function definition repeatedly uses the solutions to the subproblems in the assembly of the solution to the overall problem. Moreover, genetic
   programming with automatic function definition produces solutions that are simpler and smaller than the solution obtained without automatic function definition.
%8 May
%@ 0-262-11189-6

%A John R. Koza
%T Genetic Programming II Videotape: The next generation
%D 1994
%I MIT Press
%C 55 Hayward Street, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/gpvideo2.html
%X The 1994 book \citekoza:gp2 Genetic Programming II: Automatic Discovery of Reusable Programs extends the results of John Koza's groundbreaking work on programming computers
   by means of natural selection, described in this first book, Genetic Programming. This videotape provides an explanation of automatically defined functions, the
   hierarchical approach to problem solving by means of genetic programming with automatically defined functions, and a visualisation of computer runs for many of the problems
   discussed in Genetic Programming II. These problems include symbolic regression, the parity problem, the lawnmower problem, the bumblebee problem, the artificial ant, the
   impulse response problem, the minesweeper problem. the letter recognition problem, the transmembrane problem, and the omega loop problem.
%Z Accompanies \citekoza:gp2 VHS NTSC Format (KOZGV2) VHS PAL Format (KOZGP2) VHS SECAM Format (KOZGS2)

%A John R. Koza
%T Evolution of a computer program for classifying protein segments as transmembrane domains using genetic programming
%B Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology
%E Russ Altman and Douglas Brutlag and Peter Karp and Richard Lathrop and David Searls
%D 1994
%P 244--252
%I AAAI Press Menlo Park, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ismb1994.pdf
%X The recently-developed genetic programming paradigm is used to evolve a computer program to classify a given protein segment as being a transmembrane domain or
   non-transmembrane area of the protein. Genetic programming starts with a primordial ooze of randomly generated computer programs composed of available programmatic
   ingredients and then genetically breeds the population of programs using the Darwinian principle of survival of the fittest and an analog of the naturally occurring genetic
   operation of crossover (sexual recombination). Automatic function definition enables genetic programming to dynamically create subroutines dynamically during the run.
   Genetic programming is given a training set of differently-sized protein segments and their correct classification (but no biochemical knowledge, such as hydrophobicity
   values). Correlation is used as the fitness measure to drive the evolutionary process. The best genetically-evolved program achieves an out-of-sample correlation of 0.968
   and an out-of-sample error rate of 1.6percent. This error rate is better than that reported for four other algorithms reported at the First International Conference on
   Intelligent Systems for Molecular Biology. Our genetically evolved program is an instance of an algorithm discovered by an automated learning paradigm that is superior to
   that written by human investigators.

%A John R. Koza
%T Evolution of emergent cooperative behavior using genetic programming
%B Computing with Biological Metaphors
%E Ray Paton
%D 1994
%P 280--297
%I Chapman \& Hall
%C London, UK
%K genetic algorithms, genetic programming, emergent behavior, painted desert problem, independent agents, artificial ants, cooperative behavior, crossover, recombination
%U http://www.genetic-programming.com/jkpdf/paton1994.pdf
%X The recently developed genetic programming paradigm provides a way to genetically breed a computer program to solve a wide variety of problems. Genetic programming employs
   Darwinian survival and reproduction of the fittest and the genetic crossover (sexual recombination) operation to evolve progressively better solutions to problems. In this
   chapter, a computer program that governs the behaviour of a group of independently acting agents is genetically evolved to solve the painted desert problem. The evolved
   program exhibits emergent and cooperative behavior.

%T Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%I Stanford University Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670
%K genetic algorithms, genetic programming, artificial life
%U http://www.genetic-programming.org/studentpapers1994alife.html
%X This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford
   University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%8 June
%@ 0-18-182105-2

%T Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%I Stanford University Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/studentpapers1994.html
%X This volume contains 20 papers written and submitted by students describing their term projects for the course 'Genetic Algorithms and Genetic Programming' (Computer
   Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%8 Decemeber
%@ 0-18-187263-3

%A John R. Koza
%A David Andre
%T Parallel Genetic Programming on a Network of Transputers
%R Technical Report CS-TR-95-1542
%D 1995
%I 
%I Stanford University, Department of Computer Science
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/tr1542parallelsuversion.pdf
%X This report describes the parallel implementation of genetic programming in the C programming language using a PC 486 type computer (running Windows) acting as a host and a
   network of transputers acting as processing nodes. Using this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is
   intermediate between the power of currently available workstations and that of supercomputers at a cost that is intermediate between the two. A comparison is made of the
   computational effort required to solve the problem of symbolic regression of the Boolean even-5-parity function with different migration rates. Genetic programming required
   the least computational effort with an 8% migration rate. Moreover, this computational effort was less than that required for solving the problem with a serial computer and
   a panmictic population of the same size. That is, apart from the nearly linear speed-up in executing a fixed amount of code inherent in the parallel implementation of
   genetic programming, parallelization delivered more than linear speed-up in solving the problem using genetic programming.
%8 January
%Z Our printers barfed about halfway through. See also \citeandre:1995:parallel

%A John R. Koza
%T Evolving the Architecture of a Multi-Part Program in Genetic Programming Using Architecture-Altering Operations
%B Evolutionary Programming IV Proceedings of the Fourth Annual Conference on Evolutionary Programming
%E John Robert McDonnell and Robert G. Reynolds and David B. Fogel
%D 1995
%P 695--717
%I MIT Press
%C San Diego, CA, USA
%K genetic algorithms, genetic programming, ADF
%U http://www.genetic-programming.com/jkpdf/ep1995.pdf
%X This paper describes six new architecture-altering operations that provide a way to dynamically determine the architecture of a multi-part program during a run of genetic
   programming. The new operations are patterned after the naturally occurring operations of gene duplication and gene deletion and are motivated by Ohno's provocative book
   Evolution by Means of Gene Duplication. The new operations are branch duplication, argument duplication, branch creation, argument creation, branch deletion, and argument
   deletion. These operations dynamically change the architecture of various programs during a run of genetic programming. The new operations can also be interpreted as
   providing an automated way to specialise and generalise programs. The paper demonstrates that problems can be solved while the architecture is being evolved.
%8 1-3  March
%Z EP-95, Like \citekoza:1994:aao
%@ 0-262-13317-2

%A John R. Koza
%T A Response to the ML-95 paper entitled ``Hill climbing beats genetic search on a Boolean circuit synthesis of Koza's''
%D 1995
%I 
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jktahoe24page.html#anchor5865650
%O Distributed 11 July 1995 at the 1995 International Machine Learning Conference in Tahoe City, California, USA
%8 11  July
%Z See \citelang:1995:hcbgs and http://www.genetic-programming.com/jktahoemain.html

%A John R. Koza
%T Two Ways of Discovering the Size and Shape of a Computer Program to Solve a Problem
%B Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)
%E Larry J. Eshelman
%D 1995
%P 287--294
%I Morgan Kaufmann San Francisco, CA, USA
%C Pittsburgh, PA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/icga1995.pdf
%X The requirement that the user predetermine the size and shape of the ultimate solution to a problem has been a bane of automated machine learning from the earliest times.
   This paper compares two techniques for automatically discovering, during a run of genetic programming, the architecture of a multi-part computer program while concurrently
   solving the problem. In the first technique, called evolutionary selection, the initial random population is architecturally diverse and there is a competition during the
   run among the various architectures while they are trying to solve the problem. The second technique, called evolution of architecture, employs six new
   architecture-altering operations that provide a way to evolve the architecture of a multi-part program in the sense of actually changing the architecture of the program
   dynamically during the run. The new architecture-altering operations are motivated by the naturally occurring operation of gene duplication, as described in Susumu Ohno's
   provocative book Evolution by Means of Gene Duplication.
%8 15-19  July
%@ 1-55860-370-0

%A John R. Koza
%A David Andre
%T Automatic Discovery Using Genetic Programming of an Unknown-Sized Detector of Protein Motifs Containing Repeatedly-used Subexpressions
%B Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications
%E Justinian P. Rosca
%D 1995
%P 89--97
%I 
%C Tahoe City, California, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ml1995motif.pdf
%X Automated methods of machine learning may be useful in discovering biologically meaningful patterns that are hidden in the rapidly growing databases of genomic and protein
   sequences. However, almost all existing methods of automated discovery require that the user specify, in advance, the size and shape of the pattern that is to be
   discovered. Moreover, existing methods do not have a workable analog of the idea of a reusable subroutine to exploit the recurring sub-patterns of a problem environment.
   Genetic programming can evolve complicated problem-solving expressions of unspecified size and shape. When automatically defined functions are added to genetic programming,
   genetic programming becomes capable of efficiently capturing and exploiting recurring sub-patterns. This paper describes how genetic programming with automatically defined
   functions successfully evolved motifs for detecting the D-E-A-D box family of proteins and for detecting the manganese superoxide dismutase family. Both motifs were evolved
   without prespecifying their length. Both evolved motifs employed automatically defined functions to capture the repeated use of common subexpressions. When tested against
   the SWISS-PROT database of proteins, the two genetically evolved consensus motifs detect the two families either as well, or slightly better than, the comparable
   human-written motifs found in the PROSITE database.
%8 9  July
%Z part of \citerosca:1995:ml

%A John R. Koza
%T Gene Duplication to Enable Genetic Programming to Concurrently Evolve Both the Architecture and Work-Performing Steps of a Computer Program
%B IJCAI-95 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence
%V 1
%D 1995
%P 734--740
%I Morgan Kaufmann San Francisco, CA, USA
%I IJCAII,AAAI,CSCSI
%C Montreal, Quebec, Canada
%K genetic algorithms, genetic programming, ADF
%U http://www.genetic-programming.com/jkpdf/ijcai1995.pdf
%X Susumu Ohno's provocative book Evolution by Gene Duplication proposed that the creation of new proteins in nature (and hence new structures and new behaviours in living
   things) begins with a gene duplication and that gene duplication is "the major force of evolution." This paper describes six new architecture-altering operations for
   genetic programming that are patterned after the naturally-occurring chromosomal operations of gene duplication and gene deletion. When these new operations are included in
   a run of genetic programming, genetic programming can dynamically change, during the run, the architecture of a multi-part program consisting of a main program and a set of
   hierarchically-called subprograms. These on-the-fly architectural changes occur while genetic programming is concurrently evolving the work-performing steps of the main
   program and the hierarchically-called subprograms. The new operations can be interpreted as an automated way to change the representation of a problem while solving the
   problem. Equivalently, these operations can be viewed as an automated way to decompose a problem into an non-pre-specified number of subproblems of non-pre-specified
   dimensionality; solve the subproblems; and assemble the solutions of the subproblems into a solution of the overall problem. These operations can also be interpreted as
   providing an automated way to specialise and generalise.
%8 20-25  August
%@ 1-55860-363-8

%A John R. Koza
%A David Andre
%T Automated discovery of protein motifs with genetic programming
%B Working Notes for the AAAI Symposium on Genetic Programming
%E E. V. Siegel and J. R. Koza
%D 1995
%P 38--49
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025, USA
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Library/Symposia/Fall/fs95-01.php
%X Automated methods of machine learning may prove to be useful in discovering biologically meaningful information hidden in the rapidly growing databases of DNA sequences and
   protein sequences. Genetic programming is an extension of the genetic algorithm in which a population of computer programs is bred, over a series of generations, in order
   to solve a problem. Genetic programming is capable of evolving complicated problem-solving expressions of unspecified size and shape. Moreover, when automatically defined
   functions are added to genetic programming, genetic programming becomes capable of efficiently capturing and exploiting recurring sub-patterns. This chapter describes how
   genetic programming with automatically defined functions successfully evolved motifs for detecting the D-E-A-D box family of proteins and for detecting the manganese
   superoxide dismutase family. Both motifs were evolved without prespecifying their length. Both evolved motifs employed automatically defined functions to capture the
   repeated use of common subexpressions. When tested against the SWISS-PROT database of proteins, the two genetically evolved consensus motifs detect the two families either
   as well, or slightly better than, the comparable human-written motifs found in the PROSITE database.
%8 10--12  November
%Z AAAI-95f GP. Part of \citesiegel:1995:aaai-fgp \em Telephone: 415-328-3123 \em Fax: 415-321-4457 \em email info@aaai.org \em URL: http://www.aaai.org/

%A John R. Koza
%A David Andre
%T Evolution of Both the Architecture and the Sequence of Work-Performing Steps of a Computer Program Using Genetic Programming with Architecture-Altering Operations
%B Working Notes for the AAAI Symposium on Genetic Programming
%E E. V. Siegel and J. R. Koza
%D 1995
%P 50--60
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025, USA
%C MIT, Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Library/Symposia/Fall/fs95-01.php
%X The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve a problem. Ideally, an automatic programming system
   should require that the user pre-specify as little as possible about the problem environment. In particular, it is desirable that the user not be required to prespecify the
   architecture of the ultimate solution to his problem. The question of how to automatically create the architecture of the overall program in an evolutionary approach to
   automatic programming, such as genetic programming, has a parallel in the biological world: how new structures and behaviors are created in living things. This corresponds
   to the question of how new DNA that encodes for a new protein is created in more complex organisms. This chapter describes how the biological theory of gene duplication
   described in Susumu Ohno's provocative book, Evolution by Means of Gene Duplication, was brought to bear on the problem of architecture discovery in genetic programming.
   The resulting biologically-motivated approach uses six new architecture-altering operations to enable genetic programming to automatically discover the architecture of the
   solution at the same time as genetic programming is evolving a solution to the problem. Genetic programming with the architecture-altering operations is used to evolve a
   computer program to classify a given protein segment as being a transmembrane domain or non-transmembrane area of the protein (without biochemical knowledge, such as the
   hydrophobicity values used in human-written algorithms for this task). The best genetically-evolved program achieved an out-of-sample error rate that was better than that
   reported for other previously reported human-written algorithms. This is an instance of an automated machine learning algorithm matching human performance on a non-trivial
   problem.
%8 10--12  November
%Z AAAI-95f GP. Part of \citesiegel:1995:aaai-fgp \em Telephone: 415-328-3123 \em Fax: 415-321-4457 \em email info@aaai.org \em URL: http://www.aaai.org/ Transmembrane protien
   classification 'out of sample error rate that was better than that previously reported for other previously repored human-written algorithms' [p50]

%T Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%I Stanford University Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/gpstanfordpapers.html
%8 Decemeber
%Z This volume (ISBN 0-18-195720-5) contains 34 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic
   Programming" (Computer Science 426) at Stanford University offered during the fall quarter 1995 (both on campus and on SITN TV). The appendix to this volume contains
   material providing basic information about the course, including schedules, reading lists, project instructions, and the take-home final. In the take-home final examination
   in this course, each student "peer reviews" 4 papers written by other students in the class. Copies of the 1995 volume (ISBN 0-18-195720-5) are available DIRECTLY from
   Stanford University Bookstore for $14.96 plus $6.00 shipping and handling (in the USA) by calling 415-329-1217 or 800-533-2670 or by writing Stanford Bookstore Stanford
   University Stanford, California 94305-3079 USA The E-Mail address of the bookstore for e-mail orders is mailorder@bookstore.stanford.edu. Be sure to mention the ISBN
   number, exact title, refer to "Custom Publishing" and "CSD 000" when ordering to avoid confusion with course readers, collections of student papers, and other materials
   associated with my courses at Stanford. A course reader entitled Course Reader for Computer Science 426 (Genetic Algorithms) for Fall Quarter 1995 (ISBN 0-18-192183-9)
   contains 10 selected papers from the current genetic algorithms and genetic programming literature to supplement the two textbooks used in the CS 426 course.
   http://www-cs-faculty.stanford.edu/~koza/cs425.html http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-195720-5

%A John Koza
%T Genetic Programming for Economic Modelling
%B Intelligent Systems for Finance and Business
%E Suran Goonatilake and Philip Treleaven
%D 1995
%P 251--269
%I John Wiley \& Sons
%C 605 Third Avenue, New York, NY 10158-0012, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/treleaven1995.pdf
%X The problem of finding an econometric model expressing the mathematical relationship between empirically observed econometric variables can be viewed as a search of the
   space of possible computer programs for a program that produces the desired output for given inputs. This computer program can be found via the recently developed genetic
   programming paradigm which breeds populations of computer programs in a Darwinian competition using genetic operations such as fitness proportionate reproduction and
   crossover (sexual recombination). In this paper, we rediscover the well-known non-linear "exchange equation" P=F(MV,Q) relating the money supply, price level, gross
   national product, and velocity of money in an economy.
%O 14
%Z http://www.cs.ucl.ac.uk/staff/S.Goonatilake/busbook.html contains info on book
%@ 0-471-94404-1

%A John R. Koza
%T Survey of genetic algorithms and genetic programming
%B Proceedings of 1995 WESCON Conference
%D 1995
%P 589--594
%I IEEE
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/wescon1995.pdf
%X This paper provides an introduction to genetic algorithms and genetic programming and lists sources of additional information, including books and conferences as well as
   e-mail lists and software that is available over the Internet.
%Z survey, tutorial

%A John R. Koza
%A David Andre
%T Classifying Protein Segments as Transmembrane Domains Using Architecture-Altering Operations in Genetic Programming
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 155--176
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/172
%X The biological theory of gene duplication, concerning how new structures and new behaviors are created in living things, is brought to bear on the problem of automated
   architecture discovery in genetic programming. Using architecture-altering operations patterned after naturally-occurring gene duplication, genetic programming is used to
   evolve a computer program to classify a given protein segment as being a transmembrane domain or non-transmembrane area of the protein. The out-of-sample error rate for the
   best genetically-evolved program achieved was slightly better than that of previously-reported human-written algorithms for this problem. This is an instance of an
   automated machine learning algorithm rivaling a human-written algorithm for a problem.
%O 8
%@ 0-262-01158-1

%A John R. Koza
%A David Andre
%T Evolution of iteration in genetic programming
%B Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming
%E Lawrence J. Fogel and Peter J. Angeline and Thomas Baeck
%D 1996
%I MIT Press Cambridge, MA, USA
%C San Diego
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/153818.html
%X The solution to many problems requires, or is facilitated by, the use of iteration. Moreover, because iterative steps are repeatedly executed, they must have some degree of
   generality. An automatic programming system should require that the user make as few problem-specific decisions as possible concerning the size, shape, and character of the
   ultimate solution to the problem. Work first presented at the Fourth Annual Conference on Evolutionary Programming in 1995 (EP-95) demonstrated that six then-new
   architecture-altering operations made it possible to automate the decision about the architecture of an overall program dynamically during a run of genetic programming. The
   question arises as to whether it is also possible to automate the decision about whether to employ iteration, how much iteration to employ, and the particular sequence of
   iterative steps. This paper introduces the new operation of restricted iteration creation that automatically creates a restricted iteration-performing branch out of a
   portion of an existing computer program during a run a genetic programming. Genetic programming with the new operation is then used (in conjunction with the other
   architecture-altering operations first presented at EP-95) to evolve a computer program to solve a non-trivial problem
%8 February  29-  March  3
%Z EP-96 http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383
%@ 0-262-06190-2

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Toward evolution of electronic animals using genetic programming
%B Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems
%D 1996
%I MIT Press Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/alife1996circuits.pdf
%X This paper describes an automated process for designing an optimal food-foraging controller for a lizard. The controller consists of an analog electrical circuit that is
   evolved using the principles of natural selection, sexual recombination, and developmental biology. Genetic programming creates both the topology of the controller circuit
   and the numerical values for each electrical component.
%Z ALIFE-V

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Automated WYWIWYG Design of Both the Topology and Component Values of Electrical Circuits Using Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 123--131
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X This paper describes an automated process for designing electrical circuits in which "What You Want Is What You Get" ("WYWIWYG" - pronounced "wow-eee-wig"). The design
   process uses genetic programming to produce both the topology of the desired circuit and the sizing (numerical values) for all the components of a circuit. Genetic
   programming successfully evolves both the topology and the sizing for an asymmetric bandpass filter that was described as being difficult-to-design in a leading electrical
   engineering journal. This evolved circuit is another instance in which a genetically evolved solution to a non- trivial problem is competitive with human performance.
%8 28--31  July
%Z GP-96

%A John R. Koza
%A David Andre
%A Forrest H {Bennett III}
%A Martin A. Keane
%T Use of Automatically Defined Functions and Architecture-Altering Operations in Automated Circuit Synthesis Using Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 132--149
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X This paper demonstrates the usefulness of automatically defined functions and architecture-altering operations in designing analog electrical circuits using genetic
   programming. A design for a lowpass filter is genetically evolved in which an automatically defined function is profitably reused in the 100% compliant circuit. The
   symmetric reuse of an evolved substructure directly enhances the performance of the circuit. Genetic programming rediscovered the classical ladder topology used in
   Butterworth and Chebychev filters as well as the more complex topology used in Cauer (elliptic) filters. A design for a double-passband filter is genetically evolved in
   which the architecture- altering operations discover a suitable program architecture dynamically during the run. Two automatically defined functions are profitably reused
   in the genetically evolved 100% complaint circuit.
%8 28--31  July
%Z GP-96

%T Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Four problems for which a computer program evolved by genetic programming is competitive with human performance
%B Proceedings of the 1996 IEEE International Conference on Evolutionary Computation
%V 1
%D 1996
%P 1--10
%I IEEE Press
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/icec1996.pdf
%X It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a
   domain-independent automatic programming technique in which "What You Want Is What You Get" ("WYWIWYG" pronounced "wow-eee-wig"). Genetic programming is such a technique.
   This paper surveys three recent examples of problems (from the fields of cellular automata and molecular biology) in which genetic programming evolved a computer program
   that produced results that were slightly better than human performance for the same problem. This paper then discusses the problem of electronic circuit synthesis in
   greater detail. It shows how genetic programming can evolve both the topology of a desired electrical circuit and the sizing (numerical values) for each component in a
   crossover (woofer and tweeter) filter. Genetic programming has also evolved the design for a lowpass filter, the design of an amplifier, and the design for an asymmetric
   bandpass filter that was described as being difficult-to-design in an article in a leading electrical engineering journal.
%Z ICEC-96 Population 640,000 on filter problem, 1% mutation. 64 demes, torrodally connected at end of each generation. 4 emigrant boats of 2% per generation.

%T Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/gp96latebreaking.html
%8 28--31  July
%Z GP-96LB In order to provide conference attendees at the Genetic Programming 1996 Conference (GP-96) held at Stanford University on July 28 P 31, 1996 (Sunday P Wednesday)
   with information about research that was initiated, enhanced, improved, or completed after the original paper submission deadline of January 10, 1996, this rapidly-printed
   book of 27 late-breaking papers is being distributed to all attendees (in addition to the official conference proceedings published by the MIT Press). The deadline for
   late-breaking papers was July 3, 1996. Late-breaking papers were briefly examined for minimum standards of acceptability and relevance, but were not peer reviewed or
   evaluated by the conference organizers. This volume (ISBN 0-18-201-031-7) may be purchased directly from the Custom Publishing Department of the Stanford University
   Bookstore by calling 415-329-1217 or 800-533-2670 or by writing Custom Publishing Department, Stanford Bookstore, Stanford University, Stanford, California 94305-3079 USA.
   The E-Mail address of the bookstore for mail orders is mailorder@bookstore.stanford.edu. The price is $9.54 plus $6.00 shipping and handling (in the USA).
%@ 0-18-201031-7

%A John R. Koza
%A David Andre
%A Forrest H {Bennett III}
%A Martin A. Keane
%T Evolution of a Low-Distortion, Low-Bias 60 Decibel Op Amp with Good Frequency Generalization using Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 94--100
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/gp1996lbpamplifier.pdf
%X Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a low-distortion, low-bias 60 decibel (1000-to-1) amplifier
   circuit with good frequency generalization. The evolved circuit was composed of two types of transistors (active elements) as well as resistors and capacitors.
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-201031-7

%A John R. Koza
%A David Andre
%A Forrest H {Bennett III}
%A Martin A. Keane
%T Design of a 96 Decibel operational amplifier and other problems for which a computer program evolved by genetic programming is competitive with human performance.
%B Proceedings of l996 Japan-China Joint International Workshop on Information Systems
%E Mitsuo Gen and Weixuan Zu
%D 1996
%P 30--49
%I 
%I Ashikaga Institute of Technology
%C Ashikaga
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ashikaga1996.pdf
%X It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a
   domain-independent automatic programming technique in which "What You Want Is What You Get" ("WYWIWYG" -p; pronounced "wow-eee-wig"). Genetic programming is such a
   technique. This paper surveys three recent examples of problems (one from the field of cellular automata and two from the fields of molecular biology) in which genetic
   programming evolved a computer program that produced results that were slightly better than human performance for the same problem. This paper then discusses a fourth
   problem in greater detail and demonstrates that a design for a low-distortion 96 decibel op amp (including both topology and component sizing) can be evolved using genetic
   programming. The information that the user must supply to genetic programming consists of the parts bin (transistors, resistors, and capacitors) and the fitness measure for
   the major operating characteristics of an op amp.
%8 4-16  October
%Z Invited paper

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A Keane
%T Reuse, parameterized reuse, and hierarchical reuse of substructures in evolving electrical circuits using genetic programming
%B Proceedings of International Conference on Evolvable Systems: From Biology to Hardware (ICES-96)
%S Lecture Notes in Computer Science
%E Tetsuya Higuchi and Iwata Masaya and Weixin Liu
%V 1259
%D 1996
%I Springer-Verlag Berlin, Germany
%C Tsukuba, Japan
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ices1996jkreuse.pdf
%X Most practical electrical circuits contain modular substructures that are repeatedly used to create the overall circuit. Genetic programming with automatically defined
   functions and architecture-altering operations successfully evolved a design for a two-band crossover (woofer and tweeter) filter with a crossover frequency of 2,512 Hz.
   Both the topology and the sizing (numerical values) for each component of a the circuit were evolved. In the evolved circuit, three different electrical substructures were
   used; one was invoked five times; and one was invoked as part of a hierarchy; and one substructure was invoked with different numerical arguments so that different
   numerical component values were assigned to the substructure's components.
%8 7-8  October
%Z URL=version 1 as presented at the conference http://www.etl.go.jp:8080/etl/kikou/ICES96/
%@ 3-540-63173-9

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A Keane
%T Automated design of both the topology and sizing of analog electrical circuits using genetic programming
%B Artificial Intelligence in Design '96
%E John S. Gero and Fay Sudweeks
%D 1996
%P 151--170
%I Kluwer Academic
%C Dordrecht
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/aid1996.pdf
%X This paper describes an automated process for designing analog electrical circuits based on the principles of natural selection, sexual recombination, and developmental
   biology. The design process starts with the random creation of a large population of program trees composed of circuit-constructing functions. Each program tree specifies
   the steps by which a fully developed circuit is to be progressively developed from a common embryonic circuit appropriate for the type of circuit that the user wishes to
   design. Each fully developed circuit is translated into a netlist, simulated using a modified version of SPICE, and evaluated as to how well it satisfies the user's design
   requirements. The fitness measure is a user-written computer program that may incorporate any calculable characteristic or combination of characteristics of the circuit,
   including the circuit's behavior in the time domain, its behavior in the frequency domain, its power consumption, the number of components, cost of components, or surface
   area occupied by its components. The population of program trees is genetically bred over a series of many generations using genetic programming. Genetic programming is
   driven by a fitness measure and employs genetic operations such as Darwinian reproduction, sexual recombination (crossover), and occasional mutation to create offspring.
   This automated evolutionary process produces both the topology of the circuit and the numerical values for each component. This paper describes how genetic programming can
   evolve the circuit for a difficult-to-design low-pass filter.

%A John R. Koza
%A David Andre
%T Automatic discovery of protein motifs using genetic programming
%B Evolutionary Computation: Theory and Applications
%E Xin Yao
%D 1999
%P 171--197
%I World Scientific
%C Singapore
%K genetic algorithms, genetic programming, DEAD box, SWISSPROT, PROSITE
%U http://www.genetic-programming.com/jkpdf/ecta1999.pdf
%X Automated methods of machine learning may prove to be useful in discovering biologically meaningful information hidden in the rapidly growing databases of DNA sequences and
   protein sequences. Genetic programming is an extension of the genetic algorithm in which a population of computer programs is bred, over a series of generations, in order
   to solve a problem. Genetic programming is capable of evolving complicated problem-solving expressions of unspecified size and shape. Moreover, when automatically defined
   functions are added to genetic programming, genetic programming becomes capable of efficiently capturing and exploiting recurring sub-patterns. This chapter describes how
   genetic programming with automatically defined functions successfully evolved motifs for detecting the D-E-A-D box family of proteins and for detecting the manganese
   superoxide dismutase family. Both motifs were evolved without prespecifying their length. Both evolved motifs employed automatically defined functions to capture the
   repeated use of common subexpressions. When tested against the SWISS-PROT database of proteins, the two genetically evolved consensus motifs detect the two families either
   as well, or slightly better than, the comparable human-written motifs found in the PROSITE database.
%O 5
%Z ECTA, two ADFs each has OR in function set (ie combination of 2 alternative amino acids at this point). Result producing branch has AND (ie two adjacent (along backbone)
   amino-acids (or sets of aacids)). Covariance fitness, formula in terms of number true positives etc. Jury method: 12 motives evolved by separate GP runs combined into one
   by requiring unanimous jury decision. (Combined by hand or automatically?) Parallel GP system, 64 transputer nodes.
%@ 981-02-2306-4

%A John R. Koza
%A David Andre
%T A case study where biology inspired a solution to a computer science problem
%B Pacific Symposium on Biocomputing '96
%E Lawrence Hunter and Teri E. Klein
%D 1996
%P 500--511
%I World Scientific Singapore
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/psb1996.pdf
%X This paper describes how the biological theory of gene duplication described in Susumu Ohno's provocative book, Evolution by Means of Gene Duplication, was brought to bear
   on a vexatious problem from the domain of automated machine learning, namely the problem of architecture discovery. An automatic programming system should require that the
   user make as few problem-specific decisions as possible concerning the size, shape, and character of the ultimate solution to the problem. Six new architecture-altering
   operations enable genetic programming to automatically discover an appropriate architecture for solving the problem concurrently with its efforts to solve the problem.
   These architecture-altering operations were motivated by the way that new biological structures, functions, and behaviors arise in nature using gene duplication. Genetic
   programming with the new architecture-altering operations was then applied to the transmembrane protein segment identification problem. The out-of-sample error rate for the
   best genetically-evolved program achieved was slightly better than that of previously-reported human-written algorithms for this problem.
%Z PSB 96

%A John R. Koza
%T Genetic Programming: Automatic Programming of Computers
%J EvoNews
%V 1
%N 3
%D 1997
%P 4--7
%I 
%K genetic algorithms, genetic programming
%X Introduction and survey of GP
%8 March
%Z EvoNews - The Newsletter of EvoNet at http://www.dcs.napier.ac.uk/evonet/evonews.htm

%A John R. Koza
%A Forrest H {Bennett III}
%A Jason Lohn
%A Frank Dunlap
%A Martin A. Keane
%A David Andre
%T Evolution of tri-state Frequency Discriminator for the Source Identification Problem using Genetic Programming
%B International Conference of Information Sciences
%E Paul P. Wang
%V 1
%D 1997
%P 95--99
%I 
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/fea1997.pdf
%X Automated synthesis of analog electronic circuits is recognized as a difficult problem. Genetic programming was used to evolve both the topology and the sizing (numerical
   values) for each component of a circuit that can perform source identification by correctly classify an incoming signal into categories.
%Z FEA-97

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Evolution Using Genetic Programming of a Low-Distortion 96 Decibel Operational Amplifier
%B Proceedings of the 1997 ACM Symposium on Applied Computing
%E Barrett Bryant and Janice Carroll and Dave Oppenheim and Jim Hightower and K. M. George
%D 1997
%P 207--216
%I New York
%I Association for Computing Machinery
%C Hyatt Sainte Claire Hotel, San Jose, California, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/sac1997.pdf
%X There is no known general technique for automatically designing an analog electrical circuit that satisfies design specifications. Genetic programming was used to evolve
   both the topology and the sizing (numerical values) for each component of a low-distortion 96 decibel (64,860-to-1) amplifier circuit.
%8 28  February -2  March
%Z ACM SAC-97

%A John R. Koza
%A Forrest H {Bennett III}
%A Jason Lohn
%A Frank Dunlap
%A Martin A. Keane
%A David Andre
%T Automated Synthesis of Computational Circuits Using Genetic Programming
%B Proceedings of the 1997 IEEE International Conference on Evolutionary Computation
%D 1997
%P 447--452
%I IEEE Press Piscataway, NJ, USA
%C Indianapolis
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/524150.html
%X Analog electrical circuits that perform mathematical functions (e.g., cube root, square) are called computational circuits. Computational circuits are of special practical
   importance when the small number of required mathematical functions does not warrant converting an analog signal into a digital signal, performing the mathematical function
   in the digital domain, and then converting the result back to the analog domain. The design of computational circuits is difficult even for mundane mathematical functions
   and often relies on the clever exploitation of some aspect of the underlying device physics of the components. Moreover, implementation of each different mathematical
   function typically requires an entirely different clever insight. This paper demonstrates that computational circuits can be designed without such problem-specific insights
   using a single uniform approach involving genetic programming. Both the circuit topology and the sizing of all circuit components are created by genetic programming. This
   uniform approach to the automated synthesis of computational circuits is illustrated by evolving circuits that perform the cube root function (for which no circuit was
   found in the published literature) as well as for the square root, square, and cube functions.
%8 13-16  April
%Z ICEC-97, cube root, square, embryonic circuit

%A John R. Koza
%A David Andre
%A Forrest H {Bennett III}
%A Martin A. Keane
%T Design of a high-gain operational amplifier and other circuits by means of genetic programming
%B Evolutionary Programming VI. 6th International Conference, EP97
%S Lecture Notes in Computer Science
%E Peter J. Angeline and Robert G. Reynolds and John R. McDonnell and Russ Eberhart
%V 1213
%D 1997
%P 125--136
%I Springer-Verlag Berlin
%C Indianapolis, Indiana, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ep1997.pdf
%X This paper demonstrates that a design for a low-distortion high-gain 96 decibel (64,860-to-1) operational amplifier (including both circuit topology and component sizing)
   can be evolved using genetic programming.
%Z EP-97

%T Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/gpstanfordpapers.html
%8 17  March
%Z Thu, 27 Mar 1997 07:03:18 PST This volume (ISBN 0-18-205981-2) contains 24 papers written and submitted by students describing their term projects for the course "Genetic
   Algorithms and Genetic Programming" (Computer Science 426) at Stanford University offered during the winter quarter 1997 (both on campus and on SITN TV). The appendix to
   this volume contains material providing basic information about the course, including schedules, reading lists, project instructions, and the take-home final. In the
   take-home final examination in this course, each student "peer reviews" 4 papers written by other students in the class. Copies of the 1997 volume (ISBN 0-18-205981-2) are
   available DIRECTLY from Stanford University Bookstore for $12.54 plus $6.00 shipping and handling (in the USA) by calling 415-329-1217 or 800-533-2670 or by writing
   Stanford Bookstore Stanford University Stanford, California 94305-3079 USA The E-Mail address of the bookstore for e-mail orders is mailorder@bookstore.stanford.edu. The
   WWW URL for the Stanford Bookstore is http://bookstore.stanford.edu/. Be sure to mention the ISBN number, exact title, refer to "Custom Publishing" and "CSD 000" when
   ordering to avoid confusion with course readers, collections of student papers, and other materials associated with my courses at Stanford.
%@ 0-18-205981-2

%A John R. Koza
%A Forrest H {Bennett III}
%A Martin A. Keane
%A David Andre
%T Evolution of a Time-Optimal Fly-To Controller Circuit using Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 207--212
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/gp1997flyto.pdf
%X Most problem-solving techniques used by engineers involve the introduction of analytical and mathematical representations and techniques that are entirely foreign to the
   problem at hand. Genetic programming offers the possibility of solving problems in a more direct way using the given ingredients of the problem. This idea is explored by
   considering the problem of designing an electrical controller to implement a solution to the time-optimal fly-to control problem.
%8 13-16  July
%Z GP-97

%A John R. Koza
%A Forrest H {Bennett III}
%A Martin A. Keane
%A David Andre
%T Automatic programming of a time-optimal robot controller and an analog electrical circuit to implement the robot controller by means of genetic programming
%B Proceedings of 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
%D 1997
%P 340--346
%I Computer Society Press
%C Los Alamitos, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/cira1997.pdf
%X Genetic programming is an automatic programming technique that evolves computer programs to solve, or approximately solve, problems. This paper presents two examples in
   which genetic programming creates a computer program for controlling a robot so that the robot moves to a specified destination point in minimal time. In the first
   approach, genetic programming evolves a computer program composed of ordinary arithmetic operations and conditional operations to implement a time-optimal control strategy.
   In the second approach, genetic programming evolves the design of an analog electrical circuit consisting of transistors, diodes, resistors, and power supplies to implement
   a near-optimal control strategy.
%Z IEEE CIRA-97

%A John R. Koza
%A Forrest H {Bennett III}
%A Jason Lohn
%A Frank Dunlap
%A Martin A. Keane
%A David Andre
%T Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate a New Source
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 213--221
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/gp1997fourway.pdf
%X The problem of source identification involves correctly classifying an incoming signal into a category that identifies the signal's source. The problem is difficult because
   information is not provided concerning each source's distinguishing characteristics and because successive signals from the same source differ. The source identification
   problem can be made more difficult by dynamically changing the repertoire of sources while the problem is being solved. We used genetic programming to evolve both the
   topology and the sizing (numerical values) for each component of an analog electrical circuit that can correctly classify an incoming analog electrical signal into three
   categories. Then, the repertoire of sources was dynamically changed by adding a new source during the run. The paper describes how the architecture-altering operations
   enabled genetic programming to adapt, during the run, to the changed environment. Specifically, a three-way source identification circuit was evolved and then adapted into
   a four-way classifier, during the run, thereby successfully handling the additional new source.
%8 13-16  July
%Z GP-97

%T Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.amazon.com/Genetic-Programming-2nd-Conference-Author/dp/1558604839
%8 13-16  July
%Z GP-97
%@ 1-55860-483-9

%T Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/gp97latebreaking.html
%X This book containing 38 late-breaking papers and 16 one-page summaries of PhD thesis work in progress was distributed to all attendees of the Genetic Programming 1997
   Conference (GP-97) held at Stanford University on July 13 - 16, 1997 (Sunday &shyp; Wednesday). This rapidly-printed book was distributed in addition to the peer-reviewed
   conference proceedings book published by Morgan Kaufmann Publishers of San Francisco. The 38 late-breaking papers describe research that was initiated, enhanced, improved,
   or completed after the conference's original paper submission deadline of January 8, 1997. The deadline for late-breaking papers was June 11, 1997. Late-breaking papers
   were briefly examined for minimum standards of acceptability and relevance, but were not peer reviewed or evaluated by the conference organizers. The statements and
   opinions contained in these papers are solely those of the authors and not those of the editor or Genetic Programming Conferences, Inc. (a California not-for-profit
   corporation), the AAAI, or the Stanford Bookstore. Late-breaking papers were presented as part of the poster session on the evening of Monday July 14, 1997. The 16 one-page
   summaries of PhD thesis work in progress were provided by the 16 students who presented their work at the PhD Student Workshop held on Saturday July 12, 1997 (the day
   before the start of the conference).
%8 13--16  July
%Z GP-97LB
%@ 0-18-206995-8

%A John R. Koza
%A Forrest H {Bennett III}
%A Jeffrey L. Hutchings
%A Stephen L. Bade
%A Martin A. Keane
%A David Andre
%T Rapidly Reconfigurable Field-Programmable Gate Arrays for Accelerating Fitness Evaluation in Genetic Programming
%B Late Breaking Papers at the 1997 Genetic Programming Conference
%E John R. Koza
%D 1997
%P 121--131
%I Stanford Bookstore Stanford University, Stanford, California, 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/gp1997lbpfpqa.pdf
%X The dominant component of the computational burden of solving non-trivial problems with evolutionary algorithms is the task of measuring the fitness of each individual in
   each generation of the evolving population. The advent of rapidly reconfigurable field-programmable gate arrays (FPGAs) and the idea of evolvable hardware opens the
   possiblity of embodying each individual of the evolving population into hardware for the purpose of accelerating the time-consuming fitness evaluation task This paper
   demonstrates how the massive parallelism of the rapidly reconfigurable Xilinx XC6216 FPGA can be exploited to accelerate the computationally burdensome fitness evaluation
   task of genetic programming. The work was done on Virtual Computing Corporation's low-cost HOTS expansion board for PC type computers. A 16-step 7-sorter was evolved that
   has two fewer steps than the sorting network described in the 1962 O'Connor and Nelson patent on sorting networks and that has the same number of steps as the minimal
   7-sorter that was devised by Floyd and Knuth subsequent to the patent.
%8 13--16  July
%Z GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A John R. Koza
%T Future work and practical applications of genetic programming
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%P H1.1--1--6
%I Oxford University Press
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/hecfuture1997.pdf
%X Genetic programming is a relatively new domain-independent method for evolving computer programs to solve problems. This chapter suggests avenues for possible future
   research on genetic programming, opportunities to extend the technique, and areas for possible practical applications.
%O section H1.1
%Z Survey
%@ 0-7503-0392-1

%A John R. Koza
%T Classifying protein segments as transmembrane domains using genetic programming and architecture-altering operations
%B Handbook of Evolutionary Computation
%E Thomas Baeck and David B. Fogel and Zbigniew Michalewicz
%D 1997
%P G6.1:1--5
%I Oxford University Press
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/hectransmembrane1997.pdf
%X The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve a problem. Ideally, an automatic programming system
   should require that the user pre-specify as little as possible about the problem. In particular, it is desirable that the user not be required to specify the size and shape
   (i.e., the architecture) of the ultimate solution to the problem before applying the technique. This paper describes how the biological theory of gene duplication described
   in Susumu Ohno's provocative book, Evolution by Means of Gene Duplication, was brought to bear on a vexatious problem from the domain of automated machine learning in the
   computer science field. The resulting biologically-motivated approach using six new architecture-altering operations enables genetic programming to automatically discover
   the size and shape of the solution at the same time as it is evolving a solution to the problem Genetic programming with the architecture-altering operations was used to
   evolve a computer program to classify a given protein segment as being a transmembrane domain or non-transmembrane area of the protein (without biochemical knowledge, such
   as hydrophobicity values). The best genetically-evolved program achieved an out-of-sample error rate that was better than that reported for other previously reported
   human-constructed algorithms. This is an instance of an automated machine learning algorithm that is competitive with human performance on a non-trivial problem.
%O section G6.1
%Z memory cell M0
%@ 0-7503-0392-1

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%A Frank Dunlap
%T Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming
%J IEEE Transactions on Evolutionary Computation
%V 1
%N 2
%D 1997
%P 109--128
%I 
%K genetic algorithms, genetic programming, analogue circuit syntehesis, design automation, electrical cicuits
%U http://www.genetic-programming.com/jkpdf/ieeetecjournal1997.pdf
%X The design (synthesis) of analog electrical circuits starts with a high-level statement of the circuit's desired behavior and requires creating a circuit that satisfies the
   specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. The
   difficulty of the problem of analog circuit synthesis is well known and there is no previously known general automated technique for synthesizing an analog circuit from a
   high-level statement of the circuit's desired behavior. This paper presents a single uniform approach using genetic programming for the automatic synthesis of both the
   topology and sizing of a suite of eight different prototypical analog circuits, including a lowpass filter, a crossover (woofer and tweeter) filter, a source identification
   circuit, an amplifier, a computational circuit, a time-optimal controller circuit, a temperature-sensing circuit, and a voltage reference circuit. The problem-specific
   information required for each of the eight problems is minimal and consists primarily of the number of inputs and outputs of the desired circuit, the types of available
   components, and a fitness measure that restates the high-level statement of the circuit's desired behavior as a measurable mathematical quantity. The eight genetically
   evolved circuits constitute an instance of an evolutionary computation technique producing results on a task that is usually thought of as requiring human intelligence. The
   fact that a single uniform approach yielded a satisfactory design for each of the eight circuits as well as the fact that a satisfactory design was created on the first or
   second run of each problem are evidence for the general applicability of genetic programming for solving the problem of automatic synthesis of analog electrical circuits.
%8 July

%A John R. Koza
%A Forrest H {Bennett III}
%A Jeffrey L. Hutchings
%A Stephen L. Bade
%A Martin A. Keane
%A David Andre
%T Evolving sorting networks using genetic programming and the rapidly reconfigurable Xilinx 6216 field-programmable gate array
%B Proceedings of the 31st Asilomar Conference on Signals, Systems, and Computers
%V 1
%D 1997
%P 404--410
%I IEEE Press
%C Pacific Grove, CA , USA
%K genetic algorithms, genetic programming, HOT Works board, Virtual Computing Corporation, field-programmable gate array, fitness measurement task, massive parallelism,
   patent, population, reconfigurable Xilinx 6216 FPGA, sorting networks, digital signal processing chips, field programmable gate arrays, parallel architectures, parallel
   programming, reconfigurable architectures, sorting
%U http://www.genetic-programming.com/jkpdf/ieeeasilmoar1997.pdf
%X This paper describes how the massive parallelism of the rapidly reconfigurable Xilinx XC6216 FPGA (in conjunction with Virtual Computing Corporation's H.O.T. Works board)
   can be exploited to accelerate the computationally burdensome fitness measurement task of genetic algorithms and genetic programming. This acceleration is accomplished by
   embodying each individual of the evolving population into hardware in order to perform this time-consuming fitness measurement task. A 16-step sorting network for seven
   items was evolved that has two fewer steps than the sorting network described in the 1962 O'Connor and Nelson patent on sorting networks (and the same number of steps as a
   7-sorter that was devised by Floyd and Knuth subsequent to the patent and that is now known to be minimal).
%8 2-5  November
%@ 0-8186-8316-3

%A John R. Koza
%A Forrest H {Bennett III}
%A Jeffrey L. Hutchings
%A Stephen L. Bade
%A Martin A. Keane
%A David Andre
%T Evolving Sorting Networks using Genetic Programming and Rapidly Reconfigurable Field-Programmable Gate Arrays
%B Workshop on Evolvable Systems. International Joint Conference on Artificial Intelligence
%E Tetsuya Higuchi
%D 1997
%P 27--32
%I 
%C Nagoya
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ijcai1997wseh.pdf
%X This paper describes ongoing work involving the use of the Xilinx XC6216 rapidly reconfigurable field-programmable gate array to evolve sorting networks using genetic
   programming. We successfully evolved a network for sorting seven items that employs two fewer steps than the sorting network described in a l962 patent and that has the
   same number of steps as the seven-sorter devised by Floyd and Knuth subsequent to the patent.
%Z IJCAI-97

%A John R. Koza
%A Forrest H {Bennett III}
%A Jeffrey L. Hutchings
%A Stephen L. Bade
%A Martin A. Keane
%A David Andre
%T Evolving Computer Programs using Rapidly Reconfigurable FPGAs and Genetic Programming
%B FPGA'98 Sixth International Symposium on Field Programmable Gate Arrays
%E Jason Cong
%D 1998
%P 209--219
%I ACM Press New York, NY, USA
%I ACM/SIGDA
%C Doubletree Hotel, Monterey, California, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/fpga1998.pdf
%X This paper describes how the massive parallelism of the rapidly reconfigurable Xilinx XC6216 FPGA (in conjunction with Virtual Computing's H.O.T. Works board) can be
   exploited to accelerate the time-consuming fitness measurement task of genetic algorithms and genetic programming. This acceleration is accomplished by embodying each
   individual of the evolving population into hardware in order to perform the fitness measurement task. A 16-step sorting network for seven items was evolved that has two
   fewer steps than the sorting network described in the 1962 O'Connor and Nelson patent on sorting networks (and the same number of steps as a 7-sorter that was devised by
   Floyd and Knuth subsequent to the patent and that is now known to be minimal). Other minimal sorters have been evolved.
%8 22-24  February
%Z FPGA'98 http://www.ece.nwu.edu/~hauck/fpga98

%A John Koza
%A Forrest Bennett
%A David Andre
%T Using Programmatic Motifs and Genetic Programming to Classify Protein Sequences as to Extracellular and Membrane Cellular Location
%B Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming
%S LNCS
%E V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben
%V 1447
%D 1998
%I Springer-Verlag Berlin
%C Mission Valley Marriott, San Diego, California, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ep1998.pdf
%X As newly sequenced proteins are deposited into the world's ever-growing archive of protein sequences, they are typically immediately tested by various algorithms for clues
   as to their biological structure and function. One question about a new protein involves its cellular location &shyp; that is, where the protein resides in a living
   organism (extracellular, membrane, etc.). A human-created five-way algorithm for cellular location using statistical techniques with 76% accuracy was recently reported.
   This paper describes a two-way algorithm that was evolved using genetic programming with 83% accuracy for determining whether a protein is extracellular and with 89%
   accuracy for membrane proteins. Unlike the statistical calculation, the genetically evolved algorithm employs a large and varied arsenal of computational capabilities,
   including arithmetic functions, conditional operations, subroutines, iterations, memory, data structures, set-creating operations, macro definitions, recursion, etc. The
   genetically evolved classification algorithm can be viewed as an extension (which we call a programmatic motif) of the conventional notion of a protein motif.
%8 25-27  March
%Z EP-98.
%@ 3-540-64891-7

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%T Classifying Proteins as Extracellular using Programmatic Motifs and Genetic Programming
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%P 212--217
%I IEEE Press
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/icec1998.pdf
%X As newly sequenced proteins are deposited into the world' s ever-growing archive of protein sequences, they are typically immediately tested by various computerized
   algorithms for clues as to their biological structure and function. One question about a new protein involves its cellular location - that is, where the protein resides in
   a living organism (extracellular, intracellular, etc.). A 1997 paper reported a human-created five-way algorithm for cellular location created using statistical techniques
   with 76% accuracy. This paper describes a two-way classification algorithm that was evolved using genetic programming with 83% accuracy for determining whether a protein is
   extracellular. Unlike the statistical calculation, the genetically evolved algorithm employs a large and varied arsenal of computational capabilities, including arithmetic
   functions, conditional operations, subroutines, iterations, memory, data structures, set-creating operations, macro definitions, recursion, etc. The genetically evolved
   classification algorithm can be viewed as an extension (which we call a programmatic motif) of the conventional notion of a protein motif. The genetically evolved program
   constitutes an instance of an evolutionary computation technique producing a solution to a problem that is competitive with that produced using human intelligence.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
%@ 0-7803-4869-9

%T Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, WI, USA
%K genetic algorithms, genetic programming, classifier systems, evolutionary programming, evolvable hardware, DNA computing, evolutionary robotics, evolutionary strategies
%X Sat, 08 Aug 1998 20:03:37 PDT contains 120 papers and 13 poster papers that were selected by a peer review process and is available directly from Morgan Kaufmann
   Publishers. This book may be purchased for $74.95 plus shipping and handling charges and applicable sales tax from the publisher. Morgan Kaufmann Publishers 340 Pine Street
   - 6th Floor San Francisco, CA 94104 USA E-MAIL: mkp@mkp.com
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%T Late Breaking Papers at the 1998 Genetic Programming Conference
%E John R. Koza
%D 1998
%I Omni Press
%C University of Wisconsin, Madison, WI, USA
%K genetic algorithms, genetic programming, classifier systems, evolutionary programming, evolvable hardware, DNA computing
%8 22-25  July
%Z GP-98LB 53 late breaking papers and 12 summaries of PHD thesis work in progress

%T Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/gpstanfordpapers.html
%X This volume contains 19 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426) at Stanford University offered during the winter quarter 1998.
%Z These books contain the papers written and submitted by students describing their term projects for the courses involved. They also contain information on the structure of
   the courses involved. These books are available from the Custom Publishing Department of the Stanford Bookstore. These volumes are in the Mathematics and Computer Science
   Library in the Main Quad at Stanford University. These volumes are available directly from the Stanford Bookstore by calling 650-329-1217 or 800-533-2670 or by writing
   Stanford Bookstore, Stanford University, Stanford, California 94305-3079 USA. The E-Mail address of the bookstore for mail orders is mailorder@bookstore.stanford.edu. Be
   sure to mention the ISBN number (or Stanford Bookstore order number), the exact title, and refer to "Custom Publishing" when ordering these items to avoid confusion with
   the numerous other course readers, collections of student papers, and other materials at the Stanford Bookstore.
%@ 0-18-212568-8

%A John R. Koza
%T Using biology to solve a problem in automated machine learning
%B Models of Action: Mechanisms for Adaptive Behavior
%E Clive D. L. Wynne and John E. R. Staddon
%D 1998
%P 157--199
%I Lawrence Erlbaum Associates
%C Hillsdale, NJ, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/wynnestaddon1998.pdf
%X This chapter describes how the biological theory of gene duplication described in Susumu Ohno's provocative book, Evolution by Means of Gene Duplication, was brought to
   bear on a vexatious problem from the domain of automated machine learning. The goal of automatic programming is to create, in an automated way, a computer program that
   enables a computer to solve a problem. Ideally, an automatic programming system should require that the user pre-specify little about the problem environment. Genetic
   programming is a domain-independent approach to automated machine learning that attempts to evolve a computer program that solves, or approximately solves, problems.
   Starting with a primordial ooze of randomly generated computer programs composed of the available programmatic ingredients, genetic programming applies the principles of
   animal husbandry (including Darwinian selection and sexual recombination) to breed new (and often improved) populations of computer programs. One of the undesirable aspects
   of many techniques of automated machine learning is that the user of the technique may be required to specify the size and shape (i.e., the architecture) of the ultimate
   solution to his problem before he can begin to apply the technique to his problem. Specification of the size and shape of the solution often corresponds to discovering a
   way to decompose the problem into useful subspaces (usually of lower dimensionality) or to discovering a congenial representation of the problem that facilitates solution
   of the problem. Thus, in practice, for many problems of interest, determining the size and shape of the solution may be the problem (or at least a substantial part of the
   problem). This chapter describes how biology motivated a solution to the problem of architecture discovery for genetic programming. The resulting biologically-motivated
   approach enables genetic programming to automatically discover the size and shape of the solution at the same time as genetic programming is evolving a solution to the
   problem. This is accomplished using six new architecture-altering operations that provide a way to automatically discover, during a run of genetic programming, both the
   architecture and the sequence of steps of a multi-part computer program that will solve the given problem.
%O 5
%Z http://www.bookdepository.co.uk/Models-Action-Clive-DL-Wynne/9780805815979?b=-3&t=-26#Bibliographicdata-26
%@ 0-8058-1597-X

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Evolutionary Design of Analog Electrical Circuits using Genetic Programming
%B Proceedings of Adaptive Computing in Design and Manufacture Conference
%D 1998
%I 
%C Plymouth, England
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/acdm1998.pdf
%X The design (synthesis) of analog electrical circuits entails the creation of both the topology and sizing (numerical values) of all of the circuit's components. There has
   previously been no general automated technique for automatically designing an analog electrical circuit from a high-level statement of the circuit's desired behavior. This
   paper shows how genetic programming can be used to automate the design of both the topology and sizing of a suite of five prototypical analog circuits, including a lowpass
   filter, a tri-state frequency discriminator circuit, a 60 dB amplifier, a computational circuit for the square root, and a time-optimal robot controller circuit. All five
   of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence.
%8 21-23  April
%Z ACDM-98

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Automatic creation of computer programs for designing electrical circuits using genetic programming
%B Computational Intelligence in Software Engineering.
%E Witold Pedrycz and James F. Peters
%D 1998
%P 127--150
%I World Scientific
%C Singapore
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/cise1998.pdf
%X One of the central goals of computer science is to get computers to solve problems starting from only a high-level statement of the problem. The goal of automating the
   design process bears many similarities to the goal of automatically creating computer programs. The design process entails creation of a complex structure to satisfy
   user-defined requirements. The design process is usually viewed as requiring human intelligence. Indeed, design is a major activity of practicing engineers. For these
   reasons, the design process offers a practical yardstick for evaluating automated programming (program synthesis) techniques. In particular, the design (synthesis) of
   analog electrical circuits entails the creation of both the topology and sizing (numerical values) of all of a circuit's components. There has previously been no general
   automated technique for automatically designing an analog electrical circuit from a high-level statement of the circuit's desired behavior. This paper shows how genetic
   programming can be used to automate the design of both the topology and sizing of a suite of five prototypical analog circuits, including a lowpass filter, a tri-state
   frequency discriminator circuit, a 60 dB amplifier, a computational circuit for the square root, and a time-optimal robot controller circuit. All five of these genetically
   evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence.
%@ 981-02-3503-8

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Fourteen instances where genetic programming has produced results that are competitive with results produced by humans
%B Evolutionary Robotics: From Intelligent Robots to Artificial Life (ER'98)
%E Takeshi Gomi
%D 1998
%P 37--76
%I AAI Press
%C Kanata, Canada
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/er1998.pdf

%A John R. Koza
%T Genetic programming
%B Encyclopedia of Computer Science and Technology
%E James G. Williams and Allen Kent
%V 39
%D 1998
%P 29--43
%I Marcel-Dekker
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/encyclopedia1998.pdf
%O Supplement 24
%Z This is summary of genetic programming for Encyclopedia of Computer Science and Technology edited by Allen Kent and James G. Williams. Good quick survey of GP (koza and
   others work).

%A Mij Kelly
%T Genetic pragmatism - an exclusive interview with John Koza
%J EvoWeb
%V 8
%D 1998
%I 
%K genetic algorithms, genetic programming
%U http://evonet.lri.fr/evoweb/news_events/news_features/article.php?id=15
%8 14  August
%Z Source:EvoNews 8 Posted:14 Aug 98)

%A John Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T The Design of Analog Circuits by Means of Genetic Programming
%B Evolutionary Design by Computers
%E Peter Bentley
%D 1999
%P 365--385
%I Morgan Kaufmann
%C San Francisco, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/P.Bentley/evdes.html
%X In this chapter, genetic programming succeeded in evolving both the topology and sizing of six different prototypical analog electrical circuits, including a lowpass
   filter, a highpass filter, a tri-state frequency discriminator circuit, a 60 dB amplifier, a computational circuit for the square root, and a time-optimal robot controller
   circuit. All six of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require
   human intelligence. There has previously been no general automated technique for synthesizing an analog electrical circuit from a high-level statement of the circuit's
   desired behavior. The approach using genetic programming to the problem of analog circuit synthesis is general; it can be directly applied to other problems of analog
   circuit synthesis. Each of the problems in this chapter illustrates the automatic creation of a satisfactory way of "how to do it" from a high-level statement of "what
   needs to be done."
%O 16
%@ 1-55860-605-X

%A John R. Koza
%A Forrest H {Bennett III}
%T Automatic Synthesis, Placement, and Routing of Electrical Circuits by Means of Genetic Programming
%B Advances in Genetic Programming 3
%E Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline
%D 1999
%P 105--134
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/aigp3/ch16.pdf
%X The design of an electrical circuit entails creation of the circuit's topology, sizing, placement, and routing. Each of these tasks is either vexatious or computationally
   intractable. Design engineers typically perform these tasks sequentially - thus forcing the engineer to grapple with one vexatious or intractable problem after another. We
   describe a holistic approach to the automatic creation of a circuit's topology, sizing, placement, and routing. This approach starts with a high level statement of the
   requirements for the desired circuit and uses genetic programming to automatically and simultaneously create the circuit's topology, sizing, placement, and routing. The
   approach is illustrated with the problem of designing an analog lowpass filter circuit. The fitness measure for a candidate circuit considers the area of the fully laid-out
   circuit as well as whether the circuit passes or suppresses the appropriate frequencies. Genetic programming requires only about 11/2orders of magnitude more computer time
   to create the circuit's topology, sizing, placement, and routing than to create the topology and sizing for this illustrative problem.
%O 6
%8 June
%Z AiGP3
%@ 0-262-19423-6

%A John R. Koza
%A Forrest H {Bennett III}
%A Oscar Stiffelman
%T Genetic Programming as a Darwinian Invention Machine
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 93--108
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=93
%X Genetic programming is known to be capable of creating designs that satisfy prespecified high-level design requirements for analog electrical circuits and other complex
   structures. However, in the real world, it is often important that a design satisfy various non-technical requirements. One such requirement is that a design not possess
   the key characteristics of any previously known design. This paper shows that genetic programming can be used to generate novel solutions to a design problem so that
   genetic programming may be potentially used as an invention machine. This paper turns the clock back to the period just before the time (1917) when George Campbell of
   American Telephone and Telegraph invented and patented the design for an electrical circuit that is now known as the ladder filter. Genetic programming is used to reinvent
   the Campbell filter. The paper then turns the clock back to the period just before the time (1928) when Wilhelm Cauer invented and patented the elliptic filter. Genetic
   programming is then used to reinvent a technically equivalent filter that avoids the key characteristics of then-preexisting Campbell filter. Genetic programming can be
   used as an invention machine by employing a two-part fitness measure that incorporates both the degree to which an individual in the population satisfies the given
   technical requirements and the degree to which the individual does not possess the key characteristics of preexisting technology.
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP Evolving novel (patentable) circuits. Add fitness penalty for being like existing circuits. Ghostview barfs with EUROGP99.ps 27/11/99
%@ 3-540-65899-8

%A John R. Koza
%A David Andre
%A Forrest H {Bennett III}
%A Martin Keane
%T Genetic Programming 3: Darwinian Invention and Problem Solving
%D 1999
%I Morgan Kaufman
%K genetic algorithms, genetic programming
%U http://www.mkp.com/books_catalog/1-55860-543-6.asp
%8 April
%Z Genetic programming is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. The authors present genetically
   evolved solutions to dozens of problems of design, optimal control, classification, system identification, function learning, and computational molecular biology. Among the
   solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions. Researchers in artificial intelligence,
   machine learning, evolutionary computation, and genetic algorithms will find this an essential reference to the most recent and most important results in the rapidly
   growing field of genetic programming. FEATURES -- Explains how the success of genetic programming arises from seven fundamental differences distinguishing it from
   conventional approaches to artificial intelligence and machine learning -- Describes how genetic programming uses architecture-altering operations to make on-the-fly
   decisions on whether to use subroutines, loops, recursions, and memory -- Demonstrates that genetic programming possesses 16 attributes that can reasonably be expected of a
   system for automatically creating computer programs -- Presents the general-purpose Genetic Programming Problem Solver -- Includes an introduction to genetic programming
   for the uninitiated CONTENTS Introduction -- Background -- Architecture-Altering Operations -- Genetic Programming Problem Solver (GPPS) -- Automated Synthesis of Analog
   Electrical Circuits -- Evolvable Hardware -- Discovery of Cellular Automata Rules -- Discovery of Motifs and Programmatic Motifs for Molecular Biology -- Parallelization
   and Implementation Issues -- Conclusion
%@ 1-55860-543-6

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%A Scott Brave
%T Genetic Programming III Videotape: Human Competitive Machine Intelligence
%D 1999
%I Morgan Kaufmann
%C 340 Pine Street - 6th Floor, San Francisco, CA 94104, USA
%K genetic algorithms, genetic programming
%U http://www.mkp.com/books_catalog/1-55860-616-5.asp
%X This 45-minute videotape surveys the new book Genetic Programming III: Darwinian Invention and Problem Solving. \citekoza:gp3 The book shows how genetic programming can
   automatically create a computer program to solve a problem. Fourteen of the results are competitive with human-produced results. Ten infringe on previously issued patents
   or duplicate the functionality of previous patents in novel and creative ways.
%Z Videotapes available in VHS NTSC or VHS PAL format ISBN for VHS NTSC: 1-55860-617-3 ISBN for VHS PAL: 1-55860-616-5

%A J. R. Koza
%A F. Bennett
%A D. Andre
%A M. A. Keane
%T Genetic Programming: Turing's Third Way to Achieve Machine Intelligence
%B Evolutionary Algorithms in Engineering and Computer Science
%E Kaisa Miettinen and Marko M. Makela and Pekka Neittaanmaki and Jacques Periaux
%D 1999
%P 185--197
%I John Wiley \& Sons Chichester, UK
%C Jyvaskyla, Finland
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/eurogen1999turing.pdf
%X This paper is about genetic programming - a way to implement Turing's third way to achieve machine intelligence. Genetic programming is a "genetical or evolutionary"
   technique that automatically creates a computer program from a high-level statement of a problem's requirements.
%8 30  May  - 3  June
%Z EUROGEN'99 http://www.wiley.com/Corporate/Website/Objects/Products/0,9049,91449,00.html
%@ 0-471-99902-4

%T Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/gpstanfordpapers.html
%X This volume contains 30 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426 . Medical Information Sciences 226) at Stanford University offered during the winter quarter 1999 (both on campus and on SITN TV).
%Z Stanford Bookstore order number 00000-1216B These books contain the papers written and submitted by students describing their term projects for the courses involved. They
   also contain information on the structure of the courses involved. These books are available from the Custom Publishing Department of the Stanford Bookstore. These volumes
   are in the Mathematics and Computer Science Library in the Main Quad at Stanford University. These volumes are available directly from the Stanford Bookstore by calling
   650-329-1217 or 800-533-2670 or by writing Stanford Bookstore, Stanford University, Stanford, California 94305-3079 USA. The E-Mail address of the bookstore for mail orders
   is mailorder@bookstore.stanford.edu. Be sure to mention the ISBN number (or Stanford Bookstore order number), the exact title, and refer to "Custom Publishing" when
   ordering these items to avoid confusion with the numerous other course readers, collections of student papers, and other materials at the Stanford Bookstore.

%A John R. Koza
%T Human-Competitive Machine Intelligence by Means of Genetic Algorithms
%B Festschrift in honor of John H. Holland
%E Lashon Booker and Stephanie Forrest and Melanie Mitchell and Rick Riolo
%D 1999
%P 15--22
%I 
%C Ann Arbor, MI: Center for the Study of Complex Systems
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/262782.html
%X This paper demonstrates the correctness of John Holland's expectation that the genetic algorithm would have 'applications to ... artificial intelligence' by showing
   examples of the automatic creation of human-competitive computer programs from a high-level statement of a problem's requirements. The paper argues that the field of design
   is a useful testbed for determining whether an automated technique can produce results that are competitive with human-produced results and then presents several results
   that are competitive with the products of human creativity and inventiveness.
%8 May  15 - 18
%Z see also \citekoza:2000:IS

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Genetic Programming: Biologically Inspired Computation that Creatively Solves Non-Trivial Problems
%B Evolution as Computation, DIMACS Workshop, Princeton, January 1999
%S Natural Computing Series
%E Laura F. Landweber and Erik Winfree
%D 2001
%P 95--124
%I Springer-Verlag
%C Princeton University
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/472804.html
%X This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems.
   Starting with a primordial ooze of thousands of randomly created computer programs, genetic programming progressively breeds a population of computer programs over a series
   of generations using the Darwinian principle of natural selection, recombination (crossover), mutation, gene duplication, gene deletion, and certain mechanisms of
   developmental biology. The technique is illustrated by applying it to a non-trivial problem involving the automatic synthesis (design) of a lowpass filter circuit. The
   evolved results are competitive with human-produced solutions to the problem. In fact, four of the automatically created circuits exhibit human-level creativity and
   inventiveness, as evidenced by the fact that they correspond to four inventions that were patented between 1917 and 1936
%8 11-12  January
%Z http://dimacs.rutgers.edu/Workshops/Evolution/ Published Jan 2001 http://www.amazon.com/exec/obidos/ASIN/3540667091/dominantsystems/107-7663466-9560554 see also
   \citekoza:2001:CES
%@ 3-540-66709-1

%A John R. Koza
%A Martin A. Keane
%A Forrest H {Bennett III}
%A Jessen Yu
%A William Mydlowec
%A Oscar Stiffelman
%T Automatic creation of both the topology and parameters for a robust controller by means of genetic programming
%B Proceedings of the 1999 IEEE International Symposium on Intelligent Control, Intelligent Systems, and Semiotics
%D 1999
%P 344--352
%I Piscataway, NJ, USA
%I IEEE
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ieeeisic1999.pdf
%X The paper describes a general automated method for synthesizing the design of both the topology and parameter values for controllers. The automated method automatically
   makes decisions concerning the total number of processing blocks to be employed in the controller, the type of each block, the topological interconnections between the
   blocks, the values of all parameters for the blocks, and the existence, if any, of internal feedback between the blocks of the overall controller. Incorporation of
   time-domain, frequency-domain, and other constraints on the control or state variables (often analytically intractable using conventional methods) can be readily
   accommodated. The automatic method described in the paper (genetic programming) is applied to a problem of synthesizing the design of a robust controller for a plant with a
   second-order lag. A textbook PID compensator preceded by a lowpass pre-filter delivers credible performance on this problem. However, the automatically created controller
   employs a second derivative processing block (in addition to proportional, integrative, and derivative blocks and a pre-filter). It is better than twice as effective as the
   textbook controller as measured by the integral of the time-weighted absolute error, has only two-thirds of the rise time in response to the reference (command) input, and
   is 10 times better in terms of suppressing the effects of disturbance at the plant input.
%Z IEEE ISIC-99

%A John R. Koza
%A Martin A. Keane
%A Jessen Yu
%A Forrest H {Bennett III}
%A William Mydlowec
%A Oscar Stiffelman
%T Automatic synthesis of both the topology and parameters for a robust controller for a non-minimal phase plant and a three-lag plant by means of genetic programming
%B Proceedings of 1999 IEEE Conference on Decision and Control
%D 1999
%P 5292--5300
%I 
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ieeecdc1999.pdf
%X This paper describes how genetic programming can be used to automate the synthesis of the design of both the topology and parameter values for controllers. The method
   described in this paper automatically makes decisions concerning the total number of processing blocks to be employed in the controller, the type of each block, the
   topological interconnections between the blocks, the values of all parameters for the blocks, and the existence, if any, of internal feedback between the blocks of the
   overall controller. This design process can readily combine optimization of performance (e.g., by a metric such as the integral of the time-weighted error) with time-domain
   constraints and frequency-domain constraints. Genetic programming is applied to two illustrative problems of controller synthesis: the design of a robust controller for a
   non-minimal-phase plant and the design of a robust controller for a three-lag plant. A previously published PID compensator (Astrom and Hagglund 1995) for the three-lag
   plant delivers credible performance. The automatically created controller is better than 7.2 times as effective as the previous controller as measured by the integral of
   the time-weighted absolute error, has only 50% of the rise time in response to the reference input, has only 35% of the settling time, and is 92.7 dB better in terms of
   suppressing the effects of disturbance at the plant input.
%Z IEEE CDC-99

%A John Koza
%T What is Genetic Programming?
%D 1999
%I 
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/gpanimatedtutorial.html
%O www
%8 25  October
%Z Brief introduction. Several animated gifs etc

%A John R. Koza
%A Martin A. Keane
%A Forrest H {Bennett III}
%A Jessen Yu
%A William Mydlowec
%A Oscar Stiffelman
%T Searching for the Impossible using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1083--1091
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-457.ps
%X Many potential inventions are never discovered because the thought processes of scientists and engineers are channeled along well-traveled paths. In contrast, the
   evolutionary process tends to opportunistically solve problems without considering whether the evolved solution comports with human preconceptions about whether the goal is
   impossible. This paper demonstrates how genetic programming can be used to automate the process of exploring queries, conjectures, and challenges concerning the existence
   of seemingly impossible entities. The paper suggests a way by which genetic programming can be used to automate the invention process. We illustrate the concept using a
   challenge posed by a leading analog electrical engineer concerning whether it is possible to design a circuit composed of only resistors and capacitors that delivers a gain
   of greater than one. The paper contains a circuit evolved by genetic programming that satisfies the requirement of this challenge as well a related more difficult
   challenge. The original challenge was motivated by a circuit patented in 1956 for preprocessing inputs to oscilloscopes. The paper also contains an evolved circuit
   satisfying (and exceeding) the original design requirements of the circuit patented in 1956. This evolved circuit is another example of a result produced by genetic
   programming that is competitive with a human-produced result that was considered to be creative and inventive at the time it was first discovered.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%T Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%I Stanford University Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/studentpapers2000.html
%8 Decemeber
%Z This volume contains 55 papers written by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Medical Information Sciences
   226 / Computer Science 426 / Electrical Engineering 392K) at Stanford University during the winter quarter 2000 (offered on campus, on SITN TV, and on Stanford On Line).
   Note that this book of student papers is different from the course reader used in the course. Stanford Bookstore order number 002-0-00-002365-B. Stanford University
   Bookstore for $26.00 Telephone 415-329-1217 or 800-533-2670 or by writing Stanford Bookstore Stanford University Stanford, California 94305-3079 USA The E-Mail address of
   the bookstore for e-mail orders is mailorder@bookstore.stanford.edu. Stanford koza CS426 course.

%A John R. Koza
%A Martin A. Keane
%A Jessen Yu
%A William Mydlowec
%A Forrest H {Bennett III}
%T Automatic Synthesis of Both the Topology and Parameters for a Three-Lag Plant with a five-Second Delay Using Genetic Programming
%B Real-World Applications of Evolutionary Computing
%S LNCS
%E Stefano Cagnoni and Riccardo Poli and George D. Smith and David Corne and Martin Oates and Emma Hart and Pier Luca Lanzi and Egbert Jan Willem and Yun Li and Ben Paechter
   and Terence C. Fogarty
%V 1803
%D 2000
%P 168--177
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=168
%X This paper describes how the process of synthesizing the design of both the topology and the numerical parameter values (tuning) for a controller can be automated by using
   genetic programming. Genetic programming can be used to automatically make the decisions concerning the total number of signal processing blocks to be employed in a
   controller, the type of each block, the topological interconnections between the blocks, and the values of all parameters for all blocks requiring parameters. In
   synthesizing the design of controllers, genetic programming can simultaneously optimize prespecified performance metrics (such as minimizing the time required to bring the
   plant output to the desired value), satisfy time-domain constraints (such as overshoot and disturbance rejection), and satisfy frequency domain constraints. Evolutionary
   methods have the advantage of not being encumbered by preconceptions that limit its search to well-traveled paths. Genetic programming is applied to an illustrative problem
   involving the design of a controller for a three-lag plant with a significant (five-second) time delay in the external feedback from the plant to the controller. A delay in
   the feedback makes the design of an effective controller especially difficult.
%8 17  April
%Z EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April 17, 2000 Proceedings
   http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61
%@ 3-540-67353-9

%A John R. Koza
%A William Mydlowec
%A Guido Lanza
%A Jessen Yu
%A Martin A. Keane
%T Reverse Engineering and Automatic Synthesis of Metabolic Pathways from Observed Data Using Genetic Programming
%R Technical Report SMI-2000-0851
%D 2000
%I 
%I Stanford Medical Informatics
%K genetic algorithms, genetic programming, metabolic pathways, chemical reaction networks
%U http://citeseer.ist.psu.edu/525713.html
%X Recent work has demonstrated that genetic programming is capable of automatically creating complex networks (such as analog electrical circuits and controllers) whose
   behavior is modeled by continuous-time differential equations (both linear and non-linear) and whose behavior matches prespecified output values. The concentrations of
   substances participating in networks of chemical reactions are also modeled by non-linear continuous-time differential equations. This paper demonstrates that it is
   possible to automatically create (reverse engineer) a network of chemical reactions from observed time-domain data. Genetic programming starts with observed time-domain
   concentrations of input substances and automatically creates both the topology of the network of chemical reactions and the rates of each reaction within the network such
   that the concentration of the final product of the automatically created network matches the observed time-domain data. This paper describes how genetic programming
   automatically created a metabolic pathway involving four chemical reactions that takes in glycerol and fatty acid as input, uses ATP as a cofactor, and produces
   diacyl-glycerol as its final product. In addition, this paper describes how genetic programming similarly created a metabolic pathway involving three chemical reactions for
   the synthesis and degradation of ketone bodies. Both automatically created metabolic pathways contain at least one instance of three noteworthy topological features, namely
   an internal feedback loop, a bifurcation point where one substance is distributed to two different reactions, and an accumulation point where one substance is accumulated
   from two sources.
%8 November  7
%Z See also \citekoza:2000:ICSB These slide transparencies were presented at the Computation in Cells workshop on Tuesday April 18, 2000 in Hertfordshire, UK and partially at
   the tutorial on Saturday April 15, 2000 at the Euro-GP-2000 conference in Edinburgh. http://www.genetic-programming.com/jkpdf/cic2000slides.pdf

%A John R. Koza
%A Martin A. Keane
%A Jessen Yu
%A William Mydlowec
%A Forrest H {Bennett III}
%T Automatic synthesis of both the control law and parameters for a controller for a three-lagplant with five-second delay using genetic programming and simulation techniques
%B Proceedings of the 2000 American Control Conference
%D 2000
%P 453--459
%I 
%I American Automatic Control Council
%C Chicago, Illinois, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/477516.html
%X This paper describes how the process of synthesizing the design of both the topology (control law) and the numerical parameter values (tuning) for a controller can be
   automated using genetic programming. Genetic programming can be used to automatically make the decisions concerning the total number of signal processing blocks to be
   employed in a controller, the type of each block, the topological interconnections between the blocks, and the values (tuning) of all parameters for all blocks requiring
   parameters. In synthesizing the design of controllers, genetic programming can simultaneously optimize prespecified performance metrics (such as minimizing the time
   required to bring the plant output to the desired value), satisfy time-domain constraints (such as overshoot and disturbance rejection), and satisfy frequency domain
   constraints. Evolutionary methods have the advantage of not being encumbered by preconceptions that limit its search to well-traveled paths. Genetic programming is applied
   to an illustrative problem involving the design of a controller for a three-lag plant with a significant (five-second) time delay in the external feedback from the plant to
   the controller. The delay in the feedback makes the design of an effective controller difficult.
%8 June  28 - 30

%A John R. Koza
%T CS 426 / MIS 226 / EE 392K: Genetic Algorithms and Genetic Programming Winter 2000 Course Reader
%R Technical Report 2810000021091
%D 2000
%I 
%I Stanford University Bookstore
%C Stanford, CA, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/gpstanfordreaders.html
%Z This course reader contains slide transparencies and course materials used in CS 426 / MIS 226 / EE 392K course on genetic algorithms and genetic programming in the Winter
   quarter 2000 at Stanford University.

%A John R. Koza
%A Jessen Yu
%A Martin A. Keane
%A William Mydlowec
%T Use of conditional developmental operators and free variables in automatically synthesizing generalized circuits using genetic programming
%B Proceedings of the Second NASA / DoD Workshop on Evolvable Hardware
%D 2000
%P 5--15
%I 
%C Palo Alto, California
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/eh2000parameterizedfilter.pdf
%X Genetic programming can be used to create a circuit-constructing computer program that contains both conditional operations and inputs (free variables). The conditional
   operations and free variables enable a single genetically evolved program to yield functionally and topologically different electrical circuits. The conditional operations
   can trigger the execution of alternative sequences of steps based on the particular values of the free variables. The particular values of the free variables can also
   determine the component value of the circuit's components. Thus, a single evolved computer program can represent the solution to many instances of a problem. This principle
   is illustrated by evolving a single computer program that yields a lowpass or a highpass filter whose passband and stopband boundaries depend on the program's inputs.
%8 July  13-15
%Z EH-2000
%@ 0-7695-0762-X

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Automatic design of analog electrical circuits using genetic programming
%B Intelligent Data Analysis in Science
%E Hugh Cartwright
%D 2000
%P 172--200
%I Oxford University Press
%C Oxford
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/idashcartwright2000.pdf
%X The design (synthesis) of analog electrical circuits entails the creation of both the topology and sizing (numerical values) of all of the circuit's components. There has
   previously been no general automated technique for automatically designing an analog electrical circuit from a high-level statement of the circuit's desired behavior. This
   chapter introduces genetic programming and shows how it can be used to automate the design of both the topology and sizing of a suite of five prototypical analog circuits,
   including a lowpass filter, a tri-state frequency discriminator circuit, a 60 dB amplifier, a computational circuit for the square root, and a time-optimal robot controller
   circuit. The problem-specific information required for each of the eight problems is minimal and consists primarily of the number of inputs and outputs of the desired
   circuit, the types of available components, and a fitness measure that restates the high-level statement of the circuit's desired behavior as a measurable mathematical
   quantity. All five of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require
   human intelligence.
%O 8

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Synthesis of topology and sizing of analog electrical circuits by means of genetic programming
%J Computer Methods in Applied Mechanics and Engineering
%V 186
%N 2-4
%D 2000
%P 459--482
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V29-40CRYKF-M/1/5360f0a76976701c046d5032c98fdcee
%X The design (synthesis) of an analog electrical circuit entails the creation of both the topology and sizing (numerical values) of all of the circuit's components. There has
   previously been no general automated technique for automatically creating the design for an analog electrical circuit from a high-level statement of the circuit's desired
   behavior. This paper shows how genetic programming can be used to automate the design of eight prototypical analog circuits, including a lowpass filter, a highpass filter,
   a bandstop filter, a tri-state frequency discriminator circuit, a frequency-measuring circuit, a 60 dB amplifier, a computational circuit for the square root function, and
   a time-optimal robot controller circuit.
%8 June

%A William Mydlowec
%A John R. Koza
%T Use of Time-Domain Simulations in Automatic Synthesis of Computational Circuits Using GP
%B Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference
%E Darrell Whitley
%D 2000
%P 187--197
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/gecco2000computational.pdf
%X Previously reported applications of genetic programming to the automatic synthesis of computational circuits have employed simulations based on DC sweeps. DC sweeps have
   the advantage of being considerably less time-consuming than time-domain simulations. However, this type of simulation does not necessarily lead to robust circuits that
   correctly perform the desired mathematical function over time. This paper addresses the problem of automatically synthesizing computational circuits using multiple
   time-domain simulations and presents results involving the synthesis of both the topology and sizing for a squaring, square root, and multiplier computational circuit and a
   lag circuit (from the field of control).
%8 8  July
%Z Part of \citewhitley:2000:GECCOlb

%A William Comisky
%A Jessen Yu
%A John R. Koza
%T Automatic Synthesis of a Wire Antenna Using Genetic Programming
%B Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference
%E Darrell Whitley
%D 2000
%P 179--186
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/gecco2000antenna.pdf
%X This paper demonstrates the use of genetic programming to automatically synthesize the design of a wire antenna for an illustrative problem that has been previously solved
   by both conventional antenna design techniques and the genetic algorithm operating on fixed-length character strings. When the genetic algorithm was used, the human user
   prespecified many characteristics of the size and shape of the solution. The run of genetic programming also produced a satisfactory result for the illustrative problem.
   However, it did not require the human user to prespecify the size and shape of the solution. Functions from the Logo programming language and Lindenmayer systems enable
   genetic programming to draw the antenna. The solution evolved by genetic programming possesses the essential characteristics of the Yagi-Uda type of antenna. The
   rediscovery by genetic programming of the essential characteristics of the Yagi-Uda antenna is an instance where genetic programming has produced a result that is
   competitive with a result produced by creative and inventive humans.
%8 8  July
%Z Part of \citewhitley:2000:GECCOlb

%A John R. Koza
%A Jessen Yu
%A Martin A. Keane
%A William Mydlowec
%T Evolution of a Controller with a Free Variable using Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 91--105
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=91
%X A mathematical formula containing one or more free variables is "general" in the sense that it provides a solution to an entire category of problems. For example, the
   familiar formula for solving a quadratic equation contains free variables representing the equation's coefficients. Previous work has demonstrated that genetic programming
   can automatically synthesize the design for a controller consisting of a topological arrangement of signal processing blocks (such as integrators, differentiators, leads,
   lags, gains, adders, inverters, and multipliers), where each block is further specified ("tuned") by a numerical component value, and where the evolved controller satisfies
   user-specified requirements. The question arises as to whether it is possible to use genetic programming to automatically create a "generalized" controller for an entire
   category of such controller design problems instead of a single instance of the problem. This paper shows, for an illustrative problem, how genetic programming can be used
   to create the design for both the topology and tuning of controller, where the controller contains a free variable.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A John R. Koza
%T Human-competitive machine intelligence by means of genetic programming
%J IEEE Intelligent Systems
%V 15
%N 3
%D 2000
%P 76--78
%I 
%K genetic algorithms, genetic programming
%U http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf
%8 May -  June
%Z part of \citehirsh:2000:GP

%A John R. Koza
%A Martin A. Keane
%A Jessen Yu
%A Forrest H {Bennett III}
%A William Mydlowec
%T Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 1
%N 1/2
%D 2000
%P 121--164
%I 
%K genetic algorithms, genetic programming, control, network synthesis, human competitive results
%U http://www.genetic-programming.com/jkpdf/gpemcontrol2000.pdf
%X Genetic programming is an automatic method for creating a computer program or other complex structure to solve a problem. This paper first reviews various instances where
   genetic programming has previously produced human-competitive results. It then presents new human-competitive results involving the automatic synthesis of the design of
   both the parameter values (i.e., tuning) and the topology of controllers for two illustrative problems. Both genetically evolved controllers are better than controllers
   designed and published by experts in the field of control using the criteria established by the experts. One of these two controllers infringes on a previously issued
   patent. Other evolved controllers duplicate the functionality of other previously patented controllers. The results in this paper, in conjunction with previous results,
   reinforce the prediction that genetic programming is on the threshold of routinely producing human-competitive results and that genetic programming can potentially be used
   as an ?invention machine? to produce patentable new inventions.
%8 April
%Z Article ID: 253707

%A John R. Koza
%A Martin A. Keane
%A Jessen Yu
%A William Mydlowec
%T Automatic Synthesis of Electrical Circuits Containing a Free Variable using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 477--484
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/495857.html
%X A mathematical formula containing one or more free variables is "general" in the sense that it represents the solution to all instances of a problem (instead of just the
   solution of a single instance of the problem). For example, the familiar formula for solving a quadratic equation contains free variables representing the coefficients of
   the to-be-solved equation. This paper demonstrates, using an illustrative problem, that genetic programming can automatically create the design for both the topology and
   component values for an analog electrical circuit in which the value of each component in the evolved circuit is specified by a mathematical expression containing a free
   variable. That is, genetic programming is used to evolve a general parameterized circuit that satisfies the problem's high-level requirements. The evolved circuit has been
   cross-validated on unseen values of the free variable.
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A John R. Koza
%A William Mydlowec
%A Guido Lanza
%A Jessen Yu
%A Martin A. Keane
%T Reverse engineering of metabolic pathways from observed data by means of genetic programming
%B First International Conference on Systems Biology (ICSB)
%D 2000
%I 
%I Japan Society for Bioinformatics
%C Tokyo
%K genetic algorithms, genetic programming, Biology, metabolic pathways, reverse engineering
%U http://www.genetic-programming.com/jkpdf/icsb2000metabolic.pdf
%X Recent work has demonstrated that genetic programming is capable of automatically creating complex networks and structures (e.g., analog electrical circuits, controllers,
   and antennas) whose behavior is governed by linear and non-linear differential equations and whose behavior matches prespecified data values. The concentrations of
   substances (substrates, products, and catalysts) participating in networks of chemical reactions are described by non-linear continuous-time differential equations (e.g.,
   Michaelis-Menten equations). This paper demonstrates that it is possible to automatically create (reverse engineer) a network of chemical reactions from observed
   time-domain data. Genetic programming starts with observed time-domain concentrations of substances and automatically creates both the topology and sizing (i.e., the rates
   of each reaction) of a network whose behavior matches observed time-domain data. Specifically, genetic programming automatically created a metabolic pathway involving four
   chemical reactions that consume glycerol and fatty acids as input, used ATP as a cofactor, and produced diacyl-glycerol as the final product. The metabolic pathway was
   created from 270 data points. The automatically created metabolic pathway contains three key topological features, including an internal feedback loop, a bifurcation point
   where one substance is distributed to two different reactions, and an accumulation point where one substance is accumulated from two sources. The topology and sizing of the
   entire metabolic pathway was automatically created using only the time-domain concentration values of diacyl-glycerol (the final product).
%8 14-16  November
%Z ICSB-2000 E-CELL. Population size 100000. This paper was subsequently published in 2001 as chapter in book (edited by Kitano) \citekoza:2001:FSB

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Genetic Programming: Biologically Inspired Computation that Exhibits Creativity in Producing Human-Competitive Results
%B Creative Evolutionary Systems
%E Peter J. Bentley and David W. Corne
%D 2001
%P 275--298
%I Morgan Kaufmann
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B85XH-4P615HB-14/2/f83cb57fb76136659b9bdac031960bf2
%X This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems.
   Starting with a primordial ooze of thousands of randomly created computer programs, genetic programming progressively breeds a population of computer programs over a series
   of generations using the Darwinian principle of natural selection, recombination (crossover), mutation, gene duplication, gene deletion, and certain mechanisms of
   developmental biology. The technique is illustrated by applying it to a non-trivial problem involving the automatic synthesis (design) of a lowpass filter circuit. The
   evolved results are competitive with human-produced solutions to the problem. In fact, four of the automatically created circuits exhibit human-level creativity and
   inventiveness, as evidenced by the fact that they correspond to four inventions that were patented between 1917 and 1936.
%O 10
%8 July
%Z 'version 2001 book chapter consisting of our paper from the 1999 DIMACS conference.' \citekoza:1999:DIMACS filter circuit Campbell's patent, Zobel, Cauer, two-lag plant.
   Part of \citeBentley:2002:bookCES
%@ 1-55860-673-4

%A J. R. Koza
%A W. Mydlowec
%A G. Lanza
%A J. Yu
%A M. A. Keane
%T Reverse Engineering of Metabolic Pathways from Observed Data Using Genetic Programming
%B Pacific Symposium on Biocomputing 6
%D 2001
%P 434--445
%I World Scientific press
%C Hawaii
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/384424.html
%X Recent work has demonstrated that genetic programming is capable of automatically creating complex networks (such as analog electrical circuits and controllers) whose
   behavior is modeled by linear and non-linear continuous-time differential equations and whose behavior matches prespecified output values. The concentrations of substances
   participating in networks of chemical reactions are also modeled by non-linear continuous-time differential equations. This paper demonstrates that it is possible to
   automatically create (reverse engineer) a network of chemical reactions from observed time-domain data. Genetic programming starts with observed time-domain concentrations
   of input substances and automatically creates both the topology of the network of chemical reactions and the rates of each reaction within the network such that the
   concentration of the final product of the automatically created network matches the observed time-domain data. Specifically, genetic programming automatically created
   metabolic pathways involved in the phospholipid cycle and the synthesis and degradation of ketone bodies.
%8 3-7  January
%Z E-CELL, SPICE3, 270 fitness cases, population size 100000

%A John R. Koza
%A William Mydlowec
%A Guido Lanza
%A Jessen Yu
%A Martin A. Keane
%T Automated reverse engineering of metabolic pathways from observed data using genetic programming
%B Foundations of Systems Biology
%E Hiroaki Kitano
%D 2001
%P 95--117
%I MIT Press
%C Cambridge, MA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/kitanochaptericsb2000.pdf
%X Recent work has demonstrated that genetic programming is capable of automatically creating complex networks (e.g., analog electrical circuits, controllers) whose behaviour
   is modelled by linear and non-linear continuous-time differential equations and whose behaviour matches prespecified output values. The concentrations of substances
   participating in networks of chemical reactions are modelled by non-linear continuous-time differential equations. This chapter demonstrates that it is possible to
   automatically create (reverse engineer) a network of chemical reactions from observed time-domain data. Genetic programming starts with observed time-domain concentrations
   of substances and automatically creates both the topology of the network of chemical reactions and the rates of each reaction of a network such that the behaviour of the
   automatically created network matches the observed time-domain data. Specifically, genetic programming automatically created a metabolic pathway involving four chemical
   reactions that consume glycerol and fatty acid as input, use ATP as a cofactor, and produce diacyl-glycerol as the final product. The metabolic pathway was created from 270
   data points. The automatically created metabolic pathway contain three key topological features, including an internal feedback loop, a bifurcation point where one
   substance is distributed to two different reactions, and an accumulation point where one substance is accumulated from two sources. The topology and sizing of the entire
   metabolic pathway was automatically created using only the time-domain concentration values of diacyl-glycerol (the final product).

%A John R. Koza
%A William Mydlowec
%A Guido Lanza
%A Jessen Yu
%A Martin A. Keane
%T Automatic Synthesis of Both the Topology and Sizing of Metabolic Pathways using Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 57--65
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, chemical reactions, metabolic pathways, microarrays, reaction networks, automated synthesis
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/koza_gecco2001.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%T Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%I Stanford University Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/studentpapers2002.html
%8 June
%Z This volume contains 30 papers written by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Biomedical Informatics 226 /
   Computer Science 426 / Electrical Engineering 392K) at Stanford University during the spring quarter 2002. The course was offered on campus, by SCPD on TV, and on the web
   as Stanford On Line. http://scpd.stanford.edu/scpd/courses/academic/crseDesc.asp?crseID=208&sdID=3 These volumes are all in the Mathematics and Computer Science Library at
   Stanford University and are available for purchase directly from the Stanford University Bookstore by calling 650-329-1217 or 800-533-2670 or by writing Stanford Bookstore,
   Stanford University, Stanford, California 94305-3079 USA. The E-Mail address of the bookstore for mail orders is mailorder@bookstore.stanford.edu. The WWW URL for the
   Stanford Bookstore is http://bookstore.stanford.edu. Course NOT run in 2000-2001 All 30 papers are available on-line at
   http://www.genetic-programming.org/studentpapers2002.html

%A John R. Koza
%T Automatic synthesis of both the topology and numerical parameters for complex structures using genetic programming
%B Engineering Design Synthesis
%E Amaresh Chakrabarti
%D 2002
%P 319--337
%I Springer
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/eds2003chapter.pdf
%X This chapter demonstrates that genetic programming can automatically create complex structures from a high-level statement of the structure's purpose. The chapter presents
   results produced by genetic programming that are from problem areas where there is no known general mathematical technique for automatically creating a satisfactory
   structure. The results include automatically synthesising (designing) both the topology (graphical arrangement of components) and sizing (component values) for two
   illustrative analog electrical circuits and automatically synthesizing both the topology and tuning (component values) for a controller. Genetic programming not only
   succeeds in producing the required structure, but the structure is competitive with that produced by creative human designers. The claim that genetic programming has
   produced human-competitive results is supported by the fact that the automatically created results infringe on previously issued patents, improve on previously patented
   inventions, or duplicate the functionality of previously patented inventions.

%A John R. Koza
%A Martin A. Keane
%A Matthew J. Streeter
%T Evolving Inventions
%J Scientific American
%D 2003
%I 
%K genetic algorithms, genetic programming
%U http://www.sciam.com/article.cfm?articleID=00073FCE-F36F-1E19-8B3B809EC588EEDF
%X Computer programs that function via Darwinian evolution are creating inventions that are novel and useful enough to be patented. Evolution is an immensely powerful creative
   process. From the intricate biochemistry of individual cells to the elaborate structure of the human brain, it has produced wonders of unimaginable complexity. Evolution
   achieves these feats with a few simple processes--mutation, sexual recombination and natural selection--which it iterates for many generations. Now computer programmers are
   harnessing software versions of these same processes to achieve machine intelligence. Called genetic programming, this technique has designed computer programs and
   electronic circuits that perform specified functions. .....
%8 February
%Z PMID: 12561458

%A John R. Koza
%A Forrest H {Bennett III}
%A David Andre
%A Martin A. Keane
%T Genetic programming problem solver with automatically defined stores loops and recursions
%D 2003
%I 
%K genetic algorithms, genetic programming
%O United States Patent 6532453
%8 11  March
%Z U.S. patent 6,532,453. Filed April 12, 1999. Issued March 11, 2003.

%A John R. Koza
%A Matthew J. Streeter
%A Martin A. Keane
%T Automated Synthesis by Means of Genetic Programming of Complex Structures Incorporating Reuse, Hierarchies, Development, and Parameterized Toplogies
%B Genetic Programming Theory and Practice
%E Rick L. Riolo and Bill Worzel
%D 2003
%P 221--237
%I Kluwer
%K genetic algorithms, genetic programming, Hierarchy, reuse, development, parameterized topologies, architecture-altering operations, automatically functions, automatically
   defined iterations, automatically defined loops, automatically defined recursions, automatically defined stores, circuits, controllers
%U http://www.genetic-programming.com/jkpdf/gptp2003.pdf
%X Genetic programming can be used as an automated invention machine to synthesise designs for complex structures. In particular, genetic programming has automatically
   synthesized complex structures that infringe, improve upon, or duplicate the functionality of 21 previously patented inventions (including analog electrical circuits,
   controllers, and mathematical algorithms). Genetic programming has also generated two patentable new inventions (involving controllers). Genetic programming has also
   generated numerous additional human-competitive results involving the design of quantum computing circuits as well as other substantial results involving antennae, networks
   of chemical reactions (metabolic pathways), and genetic networks. We believe that these results are the direct consequence of a group of techniques, many unique to genetic
   programming, that facilitate the automatic synthesis of complex structures. These techniques include automatic reuse, parameterised reuse, parameterised topologies, and
   developmental genetic programming. The paper describes these techniques and how they contribute to automated design.
%O 14
%Z Part of \citeRioloWorzel:2003
%@ 1-4020-7581-2

%A John R. Koza
%A Martin A. Keane
%A Matthew J. Streeter
%A William Mydlowec
%A Jessen Yu
%A Guido Lanza
%T Genetic Programming IV: Routine Human-Competitive Machine Intelligence
%D 2003
%I Kluwer Academic Publishers
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/gpbook4toc.html
%X Genetic programming (GP) is method for automatically creating computer programs. It starts from a high-level statement of what needs to be done and uses the Darwinian
   principle of natural selection to breed a population of improving programs over many generations. Genetic Programming IV: Routine Human-Competitive Machine Intelligence
   presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The
   books describes 15 instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, 6
   instances where it has done the same with respect to post-2000 patented inventions, 2 instances where GP has created a patentable new invention, and 13 other
   human-competitive results. The book additionally establishes: GP now delivers routine human-competitive machine intelligence. GP is an automated invention machine. GP can
   create general solutions to problems in the form of parameterised topologies. GP has delivered qualitatively more substantial results in synchrony with the relentless
   iteration of Moore's Law.
%Z PDF of chapter 1 available. 42-minute DVD included in Jaws-4 -------------------- Comments on the Book -------------------- The research reported in this book is a tour de
   force. For the first time since the idea was bandied about in the 1940s and the early 1950s, we have a set of examples of human-competitive automatic programming. John H.
   Holland, University of Michigan In 1992, John Koza published his first book on genetic programming and forever changed the world of computation. At the time, many
   researchers, myself included, were skeptical about whether the idea of using genetic algorithms directly to evolve programs would ever amount to much. But scores of
   conquered problems and three additional books makes the case utterly persuasive. The latest contribution, Genetic Programming IV: Routine Human-Competitive Machine
   Intelligence, demonstrates the everyday solution of such holy grail problems as the automatic synthesis of analog circuits, the design of automatic controllers, and the
   automated programming of computers. This would be impressive enough, but the book also shows how to evolve whole families of solutions to entire classes of problems in a
   single run. Such parametric GP is a significant achievement, and I believe it foreshadows generalised evolution of complex contingencies as an everyday matter. To
   artificial evolutionaries of all stripes, I recommend that you read this book and breath in its thoughtful mechanism and careful empirical method. To specialists in any of
   the fields covered by this books sample problem areas, I say read this book and discover the computer-augmented inventions that are your destiny. To remaining skeptics who
   doubt the inventive competence of genetics and evolution, I say read this book and change your mind or risk the strong possibility that your doubts will soon cause you
   significant intellectual embarrassment. David E. Goldberg, University of Illinois The adaptive filters and neural networks that I have worked with over many years are
   self-optimising systems where the relationship between performance (usually mean-square-error) and parameter settings (weights) is continuous. Optimization by gradient
   methods works well for these systems. Now, this book describes a wider class of optimisation problems where the relationship between performance (fitness) and parameters is
   highly disjoint, and self-optimization is achieved by nature-inspired genetic algorithms involving random search (mutation) and crossover (sexual reproduction). John Koza
   and his colleagues have done remarkable work in advancing the development of genetic programming and applying this to practical problems such as electric circuit design and
   control system design. What is ingenious about their work is that they have found ways to approach design problems by parameterizing both physical and topological variables
   into a common code that can be subjected to genetic programming for optimisation. It is amazing how this approach finds optimised solutions that are not obvious to the best
   human experts. This fine book gives an accounting of the latest work in genetic programming, and it is must reading for those interested in adaptive and learning systems,
   neural networks, fuzzy systems, artificial intelligence, and neurobiology. I strongly recommend it. Bernard Widrow, Electrical Engineering Department, Stanford University
   John Koza's genetic programming approach to machine discovery can invent solutions to more complex specifications than any other I have seen. John McCarthy, Computer
   Science Department, Stanford University
%@ 1-4020-7446-8

%T Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%I Stanford University Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/studentpapers2003.html
%8 June
%Z This volume contains 27 papers written by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Biomedical Informatics 226 /
   Computer Science 426) at Stanford University during the fall quarter 2003. The course was offered on campus, by SCPD on TV, and on the web as Stanford On Line. This book is
   the 11th of a series of similar books covering various courses at Stanford university. The earlier collections include: \citeKoza:1993:alife, \citeKoza:1993:GA,
   \citekoza:1994:alife, \citekoza:1994:gas, \citekoza:1995:gagp, \citekoza:1997:GAGPs, \citekoza:1998:GAGPs, \citekoza:1999:GAGPs, \citekoza:2000:gagp, \citekoza:2002:gagp,
   These volumes are all in the Mathematics and Computer Science Library at Stanford University and are available for purchase directly from the Stanford University Bookstore
   by calling 650-329-1217 or 800-533-2670 or by writing Stanford Bookstore, Stanford University, Stanford, California 94305-3079 USA. The E-Mail address of the bookstore for
   mail orders is mailorder@bookstore.stanford.edu. The WWW URL for the Stanford Bookstore is http://bookstore.stanford.edu.

%A John R. Koza
%A Riccardo Poli
%T A Genetic Programming Tutorial
%B Introductory Tutorials in Optimization, Search and Decision Support
%E Edmond Burke
%D 2003
%I 
%K genetic algorithms, genetic programming, human-competitive machine intelligence, machine learning, schema theory
%U http://www.genetic-programming.com/jkpdf/burke2003tutorial.pdf
%X Genetic programming is a technique to automatically discover computer programs using principles of Darwinian evolution. This chapter introduces the basics of genetic
   programming. To make the material more suitable for beginners, these are illustrated with an extensive example. In addition, the chapter touches upon some of the more
   advanced variants of genetic programming as well as its theoretical foundations. Numerous pointers to further reading, software tools and Web sites are also provided.
%O www
%O 8
%Z Replaced by \citekoza:2005:gpt http://www.mistaconference.org/intros03/ INtroductory TutoRials in Optimization, Search and decision support methodologies (INTROS'03)
   Tuesday 12th August 2003, Nottingham, UK

%A John R. Koza
%A Lee W. Jones
%A Martin A. Keane
%A Matthew J. Streeter
%T Towards Industrial Strength Automated Design of Analog Electrical Circuits by Means of Genetic Programming
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 121--142
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, Automated design, automated circuit synthesis, analog circuits, amplifier, evolvable hardware, developmental process
%U http://www.genetic-programming.com/gptp2004.pdf
%X It has been previously established that genetic programming can be used as an automated invention machine to synthesise designs for complex structures. In particular,
   genetic programming has automatically synthesized structures that infringe, improve upon, or duplicate the functionality of 21 previously patented inventions (including six
   21st-century patented analog electrical circuits) and has also generated two patentable new inventions (controllers). There are seven promising factors suggesting that
   these previous results can be extended to deliver industrial-strength automated design of analog circuits, but two countervailing factors. This chapter explores the
   question of whether the seven promising factors can overcome the two countervailing factors by reviewing progress on an ongoing project in which we are employing genetic
   programming to synthesise an amplifier circuit. The work involves a multiobjective fitness measure consisting of 16 different elements measured by five different test
   fixtures. The chapter describes five ways of using general domain knowledge applicable to all analog circuits, two ways for employing problem-specific knowledge, four ways
   of improving on previously published genetic programming techniques, and four ways of grappling with the multiobjective fitness measures associated with real-world design
   problems.
%O 8
%O pages missing?
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A John R. Koza
%T Routine high-return human-competitive machine learning
%B Proceedings of the International Conference on Machine Learning and Applications
%E M. Arif Wani and K. Cois and K. Hafeez
%D 2003
%P 6--12
%I 
%C Los Angeles
%K genetic algorithms, genetic programming, machine learning, automated invention, parameterized topology, human-competitive
%U http://www.genetic-programming.com/jkpdf/icmla2003.pdf
%X Genetic programming is a systematic method for getting computers to automatically solve a problem. Genetic programming starts from a high-level statement of what needs to
   be done and automatically creates a computer program to solve the problem. The paper makes the points that (1) genetic programming now routinely delivers high-return
   human-competitive machine intelligence; (2) it is an automated invention machine; (3) it can automatically create a general solution to a problem in the form of a
   parameterised topology; and (4) it has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases
   in the expenditure of computer time.

%A John R. Koza
%A Martin A. Keane
%A Matthew J. Streeter
%T What's AI done for me lately? Genetic programming's human-competitive results
%J IEEE Intelligent Systems
%V 18
%N 3
%D 2003
%P 25--31
%I 
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/ieee2003intelligent.pdf
%X The automated problem-solving technique of genetic programming has generated at least 36 human-competitive results (21 involving previously patented inventions). Because
   patents represent current research and development efforts of the engineering and scientific communities, this article focuses on six cases where genetic programming
   automatically duplicated the functionality of inventions patented after 1 January 2000. It also covers two automatically synthesised controllers for which the authors have
   applied for a patent and includes examples of an automatically synthesized antenna, classifier program, and mathematical algorithm. As computer time becomes ever more
   inexpensive, researchers will start to routinely use genetic programming to produce useful new designs, generate patentable new inventions, and engineer around existing
   patents.
%8 May /  June  2003

%A John R. Koza
%T Automatic synthesis of topologies and numerical parameters
%B Handbook of Metaheuristics
%S International Series in Operations Research \& Management Science
%E Fred Glover and Gary A. Kochenberger
%N 57
%D 2003
%P 83--104
%I Kluwer Academic Publishers
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/glover2003.pdf
%X Many mathematical algorithms are capable of solving problems by producing optimal (or near-optimal) numerical values for a prespecified set of parameters. However, for many
   practical problems, one cannot begin a search for the set of numerical values until one first ascertains the number of numerical values that one is seeking. In fact, many
   practical problems of design and optimisation entail first discovering an entire graphical structure (that is, a topology). After the topology is identified, optimal (or
   near-optimal) numerical values can be sought for the elements of the structure. In this chapter, we will demonstrate that a biologically motivated algorithm (genetic
   programming) can automatically synthesise both a graphical structure (the topology) and a set of optimal or near-optimal numerical values for each element of analog
   electrical circuits, controllers, antennas, and networks of chemical reactions (metabolic pathways).
%O 4
%@ 1402072635

%A John R. Koza
%T Human-competitive applications of genetic programming
%B Advances in Evolutionary Computing: Theory and Applications
%E Ashish Ghosh and Shigeyeoshi Tsutsui
%D 2003
%P 663--682
%I Springer
%C Berlin
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/aec2003.pdf
%X Genetic programming is an automatic technique for producing a computer program that solves, or approximately solves, a problem. This chapter reviews several recent examples
   of human-competitive results produced by genetic programming. The examples all involve the automatic synthesis of a complex structure from a high-level statement of the
   requirements for the structure. The illustrative results include examples of automatic synthesis of both the topology and sizing (component values) for analog electrical
   circuits, automatic synthesis of placement and routing (as well as topology and sizing) for circuits, and automatic synthesis of both the topology and tuning (parameter
   values) of controllers.

%A John R. Koza
%A Matthew J. Streeter
%A Martin A. Keane
%T Automated synthesis by means of genetic programming of human-competitive designs employing reuse, hierarchies, modularities, development, and parameterized topologies
%B Computational Synthesis: From Basic Building Blocks to High Level Functionality: Papers from the 2003 AAAI Spring Symposium
%S AAAI technical report SS-03-02
%E Hod Lipson and Erik K. Antonsson and John R. Koza
%D 2003
%P 138--145
%I AAAI Press
%C Stanford, California, USA
%K genetic algorithms, genetic programming
%U http://www.aaai.org/Press/Reports/Symposia/Spring/ss-03-02.html
%X Genetic programming can be used as an automated invention machine to create designs. Genetic programming has automatically created designs that infringe, improve upon, or
   duplicate the functionality (in a novel way) of 16 previously patented inventions involving circuits, controllers, and mathematical algorithms. Genetic programming has also
   generated two patentable new inventions for which patent applications have been filed. Genetic programming has also generated numerous other human-competitive results,
   including the design of quantum computing circuits that are superior to those designed by human designers. Genetic programming has also designed antennae, networks of
   chemical reactions (metabolic pathways), and genetic networks. Genetic programming can automatically create hierarchies, automatically identify and reuse modularities,
   automatically determine program architecture, and automatically create parameterised topologies. When genetic programming is used to design complex structures, it is often
   advantageous to use a developmental process that enables syntactic validity and locality to be preserved under crossover.
%@ 1-57735-179-7

%A John R. Koza
%A Matthew J. Streeter
%A Martin A. Keane
%T Routine human-competitive machine intelligence by means of genetic programming. SPIE conference
%B Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI
%S Proceedings of SPIE
%E Bruno Bosacchi and David B. Fogel and James C. Bezdek
%V 5200
%D 2003
%P 1--15
%I SPIE
%C San Diego, California
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/jkpdf/spie2003.pdf
%X Genetic programming is a systematic method for getting computers to automatically solve a problem. Genetic programming starts from a high-level statement of what needs to
   be done and automatically creates a computer program to solve the problem. The paper demonstrates that genetic programming (1) now routinely delivers high-return
   human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem in the form of a parameterised
   topology; and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure
   of computer time. Recent results involving the automatic synthesis of the topology and sizing of analog electrical circuits and controllers demonstrate these points.

%A J. R. Koza
%A M. A. Keane
%A M. J. Streeter
%T Routine automated synthesis of five patented analog circuits using genetic programming
%J Soft Computing - A Fusion of Foundations, Methodologies and Applications
%V 8
%N 5
%D 2004
%P 318--324
%I 
%K genetic algorithms, genetic programming, Automatic circuit synthesis, Evolvable hardware, Automated design, Artificial intelligence
%X This article reports on a project in which we browsed patents issued after January 1, 2000 to commercial enterprises or university research institutions for analog
   electrical circuits. We then employed genetic programming to automatically design (synthesise) entities that duplicated the functionality of five post-2000 issued patents.
   The automated method works from a high-level statement of the circuit's intended function. The article addresses the question of what is actually delivered by the operation
   of the artificial problem-solving method in relation to the amount of intelligence that is supplied by the humans employing the method (something we refer to as the yield
   of an automated method). The article also addresses the question of the routineness of the artificial problem-solving method ? that is, the amount of effort required to
   make the transition from problem to problem within a particular domain. The conclusion is that the artificial method routinely delivers high-yield, human-competitive (i.e.,
   previously patented) results.
%8 April

%A John R. Koza
%A Martin A. Keane
%A Matthew J. Streeter
%T Routine high-return human-competitive evolvable hardware
%B Proceedings of the 2004 NASA/DoD Conference on Evolvable Hardware
%E Ricardo S. Zebulum and David Gwaltney and Gregory Hornby and Didier Keymeulen and Jason Lohn and Adrian Stoica
%D 2004
%P 3--17
%I IEEE Press
%C Seattle
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.com/eh2004.pdf
%X This paper reviews the use of genetic programming as an automated invention machine for the synthesis of both the topology and sizing of analog electrical circuits. The
   paper focuses on the importance of the developmental representation in this process. The paper makes the point that genetic programming now routinely delivers high-return
   human-competitive machine intelligence. It also makes the point that genetic programming has delivered a progression of qualitatively more substantial results in synchrony
   with five approximately order-of-magnitude increases in the expenditure of computer time. The paper shows six examples where genetic programming has synthesized a circuit
   that duplicates the functionality or infringes a 21st-century patented electrical circuit. Finally, the paper discusses how genetic programming can be enhanced in order to
   potentially enable it to deliver more complex industrial-strength results.
%8 24-26  June
%Z EH2004

%A J. R. Koza
%T The role of Hierarchies and Reuse in Genetic Programming in Achieving Scalable Automatic Programming
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WMOD008.pdf
%8 26-30  June
%Z GECCO-2004WKS Distributed on CD-ROM at GECCO-2004

%A John R. Koza
%A Matthew J. Streeter
%A Martin A. Keane
%T The Challenge of Producing Human-Competitive results by means of Genetic and Evolutionary Computation
%B Frontiers of Evolutionary Computation
%S Genetic Algorithms And Evolutionary Computation Series
%E Anil Menon
%V 11
%D 2004
%P 73--99
%I Kluwer Academic Publishers
%C Boston, MA, USA
%K genetic algorithms, genetic programming, human-competitive results
%X Human-competitive results include those equivalent to new scientific results published in peer-reviewed scientific journals, solutions to long-standing or indisputably
   difficult problems, patented inventions, and results that tie or beat human contestants in regulated competitions. We argue that the pursuit of human competitive results is
   not only a worthy goal in itself, but a useful compass for guiding the future growth of the field. We say this for reasons of utility, objectivity, complexity, and
   interminability. We believe that the continuing generation of evermore important human-competitive results relies on progress in three areas of research: multiobjective
   optimisation, parallel computing, and the development and perfection of competent genetic and evolutionary search methods. Addressing the characteristics of
   human-competitive problems is one way to expand the theoretical underpinnings of the field of genetic and evolutionary computation.
%O 9
%@ 1-4020-7524-3

%A John R. Koza
%A Sameer H. Al-Sakran
%A Lee W. Jones
%T Cross-Domain Features of Runs of Genetic Programming Used to Evolve Designs for Analog Circuits, Optical Lens Systems, Controllers, Antennas, Mechanical Systems, and
   Quantum Computing Circuits
%B Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
%E Jason Lohn and David Gwaltney and Gregory Hornby and Ricardo Zebulum and Didier Keymeulen and Adrian Stoica
%D 2005
%P 205--214
%I IEEE Press IEEE Service Center 445 Hoes Lane Asia P.O. Box 1331 Piscataway, NJ 08855-1331
%I NASA, DoD
%C Washington, DC, USA
%K genetic algorithms, genetic programming, EHW
%X Genetic programming has now been successfully used to automatically synthesise human-competitive designs in a number of fields, including analog electrical circuits,
   optical lens systems, antennas, controllers, mechanical systems, and quantum computing circuits. Despite considerable variation in representation and technique, the above
   results share a number of common features. This paper identifies five common features and suggests possible explanations and interpretations for them. Some of these
   cross-domain observations clearly could not have been anticipated prior to the work being done.
%8 29  June -1  July
%Z EH2005 IEEE Computer Society Order Number P2399
%@ 0-7695-2399-4

%A John R. Koza
%A Sameer H. Al-Sakran
%A Lee W. Jones
%T Automated re-invention of six patented optical lens systems using genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1953--1960
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Real World Applications, automated design, design, human-competitive results, invention machine, optical lens system, patents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1953.pdf
%X This paper describes how genetic programming was used as an invention machine to automatically synthesise complete designs for six optical lens systems that duplicated the
   functionality of previously patented lens systems. The automatic synthesis was done from scratch that is, without starting from a pre-existing good design and without
   pre-specifying the number of lenses, the physical layout of the lenses, the numerical parameters of the lenses, or the non-numerical parameters of the lenses. One of the
   six genetically evolved lens systems infringed a previously issued patent; three contained many of the essential features of the patents, without infringing; and the others
   were non-infringing novel designs that duplicated (or improved upon) the performance specifications contained in the patents. One of the six patents was issued in the
   21st-century. The six designs were created in a substantially similar and routine way, suggesting that the approach used may have widespread utility. The genetically
   evolved designs are instances of human-competitive results produced by genetic programming in the field of optical design.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052. Lindenmayer, LOGO. KOJAC, OSLO. population 75000. multiple types of domain specific mutation. Ludewig, Koizumi-Wantanabe, Nagler, Scidmore, Konig and
   Tackaberry-Muller eyepiece,
%@ 1-59593-010-8

%A John Koza
%T Routine Human-Competitive Machine Intelligence By Means Of Genetic Programming
%B Proceedings of the 8th Joint Conference in Information Systems (JCIS 2005)
%E Heng-Da Cheng
%D 2005
%I 
%C Salt Lake City, USA
%K genetic algorithms, genetic programming
%8 21-25  July
%Z http://www.jcis.org/jcis_program/master_schedule.pdf

%A John R. Koza
%A Riccardo Poli
%T Genetic Programming
%B Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques
%E Edmund K. Burke and Graham Kendall
%D 2005
%P 127--164
%I Springer
%K genetic algorithms, genetic programming, human-competitive machine intelligence, machine learning, schema theory
%U http://www.springer.com/sgw/cda/frontpage/0,11855,4-10045-22-67933962-0,00.html
%X INTRODUCTION The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what
   Turing called machine intelligence (Turing, 1948, 1950). In his talk entitled AI: Where It Has Been and Where It Is Going, machine learning pioneer Arthur Samuel stated the
   main goal of the fields of machine learning and artificial intelligence: [T]he aim [is] . . . to get machines to exhibit behavior, which if done by humans, would be assumed
   to involve the use of intelligence. (Samuel, 1983) Genetic programming is a systematic method for getting computers to automatically solve a problem starting from a
   high-level statement of what needs to be done. Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a
   problem. Specifically, genetic programming iteratively transforms a population of computer programs into a new generation of programs by applying analogs of naturally
   occurring genetic operations.
%O 5
%Z Replaces \citekoza:2003:gpt. Conference in Nottingham 2003, published in book 2005. KozaPoli2005.pdf pagination not identical to published version
%@ 0-387-23460-8

%A John R. Koza
%A Martin A. Keane
%A Matthew J. Streeter
%T Human-Competitive Automated Engineering Design and Optimization By Means Of Genetic Programming
%B Evolutionary Algorithms And Intelligent Tools In Engineering Optimization
%E William Annicchiarico and Jacques Periaux and Miguel Cerrolaza and Gabriel Winter
%D 2005
%I Wit Pr/Computational Mechanics
%K genetic algorithms, genetic programming
%U http://www.amazon.ca/Evolutionary-Algorithms-Intelligent-Engineering-Optimization/dp/1845640381
%8 August  30

%A John Koza
%A Sameer H. Al-Sakran
%A Lee W. Jones
%T Multi-Domain Observations Concerning Automatic Synthesis of Human-Competitive Designs Using Genetic Programming for Analog Circuits, Optical Lens Systems, Controllers,
   Antennas, Mechanical Systems, and Quantum Computing Circuits
%B Genetic Programming Theory and Practice IV
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%V 5
%D 2006
%P -
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, automated design, analogue circuits, controllers, optical lens systems, antennas, mechanical system, quantum computing circuits,
   developmental process, replication of previously patented invention, human-competitive result, NODE, NODE_INCREASED_SCOPE, PID, QGAME, SPICE, amplifiers, analog, antenna,
   automated, automatic, automate, circuit, control, controller, design, development, developmental, filter, hardware, human-competitive, lens, lenses, mechanical, native,
   optic, optical, optics, patent, quantum, qubit, reasonableness, representation, simulatable, simulate, simulation, simulator, structure, topology, transistor, turtle,
   unsimulatable
%X This paper reviews the recent use of genetic programming to automatically synthesise human-competitive designs of complex structures in six engineering domains, namely
   analogue electrical circuits, optical lens systems, controllers, antennas, mechanical systems, and quantum computing circuits. First, the paper identifies common features
   observed in the human-competitive results produced by genetic programming in the six domains and suggests possible explanations for the observed similarities. Second, the
   paper identifies the characteristics that make a particular domain amenable to the application of genetic programming for the automatic synthesis of designs. Third, the
   paper discusses certain domain-specific adjustments in technique that may increase the efficiency of the automated process in a particular domain. Fourth, the paper
   discusses several technique issues that have arisen in more than one domain.
%O 4
%8 11-13  May
%Z part of \citeRiolo:2006:GPTP Published Jan 2007 after the workshop
%@ 0-387-33375-4

%A John R. Koza
%A Martin A. Keane
%A Matthew J. Streeter
%A Sameer H. Al-Sakran
%A Lee W. Jones
%T Human-Competitive Evolvable Hardware Created by Means of Genetic Programming
%B Evolvable Hardware
%S Genetic and Evolutionary Computation
%E Tetsuya Higuchi and Yong Liu and Xin Yao
%N VIII?
%D 2006
%P 173--198?
%I Springer
%K genetic algorithms, genetic programming
%O 10
%8 August
%Z http://www.springer.com/east/home/computer/hardware?SGWID=5-150-22-46626015-0
%@ 0-387-24386-0

%A John R. Koza
%T Introduction to genetic programming: tutorial
%B GECCO-2008 tutorials
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 2299--2338
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2299.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1389057

%A John R. Koza
%A Sameer H. Al-Sakran
%A Lee W. Jones
%A Greg Manassero
%T Automated synthesis of a fixed-length loaded symmetric dipole antenna whose gain exceeds that of a commercial antenna and matches the theoretical maximum
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 2074--2081
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Real-World Applications, antenna, automated design, human competitive result, invention machine
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2074.pdf
%X This paper describes the use of genetic programming automatically synthesise the design for a fixed-length symmetric dipole antenna whose gain at a specific wavelength
   exceeds that of a commercially-marketed human-designed antenna and that reaches the theoretical maximum value antenna of its type. The run of genetic programming from
   scratch that is, without starting from a pre-existing human-created design; did not employ any knowledge base human design techniques or principles from the field of
   antenna design; and did not benefit from any human intervention the run. The run produced a human-competitive result.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A John R. Koza
%A Matthew J. Streeter
%A Martin A. Keane
%T Routine high-return human-competitive automated problem-solving by means of genetic programming
%J Information Sciences
%V 178
%N 23
%D 2008
%P 4434--4452
%I 
%K genetic algorithms, genetic programming
%X Genetic programming is a systematic method for getting computers to automatically solve problems. Genetic programming starts from a high-level statement of what needs to be
   done and automatically creates a computer program to solve the problem by means of a simulated evolutionary process. The paper demonstrates that genetic programming (1) now
   routinely delivers high-return human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem
   in the form of a parameterised topology and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately
   order-of-magnitude increases in the expenditure of computer time. These points are illustrated by a group of recent results involving the automatic synthesis of the
   topology and sizing of analog electrical circuits, the automatic synthesis of placement and routing of circuits, and the automatic synthesis of controllers as well as
   references to work involving the automatic synthesis of antennas, networks of chemical reactions (metabolic pathways), genetic networks, mathematical algorithms, and
   protein classifiers.
%O Special Section: Genetic and Evolutionary Computing
%8 1  Decemeber

%A John R. Koza
%T Human-competitive machine invention by means of genetic programming
%J Artificial Intelligence for Engineering Design, Analysis and Manufacturing
%V 22
%N 3
%D 2008
%P 185--193
%I 
%K genetic algorithms, genetic programming, Automated Design, Computer-Aided Design, Developmental Genetic Programming, Human-Competitive Results
%X Genetic programming is a systematic method for getting computers to automatically solve problems. Genetic programming uses the Darwinian principle of natural selection and
   analogs of recombination (crossover), mutation, gene duplication, gene deletion, and certain mechanisms of developmental biology to progressively breed, over a series of
   many generations, an improved population of candidate solutions to a problem. This paper makes the points that genetic programming now routinely delivers human-competitive
   machine intelligence for problems of automated design and can serve as an automated invention machine.
%Z Twenty-eight previously patented inventions reinvented by genetic programming

%A John R. Koza
%A Sameer H. Al-Sakran
%A Lee W. Jones
%T Automated ab initio synthesis of complete designs of four patented optical lens systems by means of genetic programming
%J Artificial Intelligence for Engineering Design, Analysis and Manufacturing
%V 22
%N 3
%D 2008
%P 249--273
%I 
%K genetic algorithms, genetic programming, Automated Design, Computer-Aided Design, Developmental Genetic Programming, Human-Competitive Results, Optical Lens Systems
%X This paper describes how genetic programming has been used as an invention machine to automatically synthesize complete designs for four optical lens systems that
   duplicated the functionality of previously patented lens systems. The automatic synthesis of the complete design is done ab initio, that is, without starting from a
   preexisting good design and without prespecifying the number of lenses, the topological arrangement of the lenses, or the numerical or nonnumerical parameters associated
   with any lens. One of the genetically evolved lens systems infringed a previously issued patent, whereas the others were noninfringing novel designs that duplicated (or
   improved upon) the performance specifications contained in the patents. One of the patents was issued in the 21st century. The designs were created in a substantially
   similar and routine way, suggesting that the approach described in the paper can be readily applied to other similar problems in the field of optical design. The
   genetically evolved designs are instances of human-competitive results produced by genetic programming in the field of optical design.
%Z Moore's law and GP, eg by 2035

%A John R. Koza
%T Human-competitive results produced by genetic programming
%J Genetic Programming and Evolvable Machines
%V 11
%N 3/4
%D 2010
%P 251--284
%I 
%K genetic algorithms, genetic programming, Human-competitive results, Developmental genetic programming, Automated design, Parallel computing, Patented inventions, Moore's
   law
%X Genetic programming has now been used to produce at least 76 instances of results that are competitive with human-produced results. These human-competitive results come
   from a wide variety of fields, including quantum computing circuits, analog electrical circuits, antennas, mechanical systems, controllers, game playing, finite algebras,
   photonic systems, image recognition, optical lens systems, mathematical algorithms, cellular automata rules, bioinformatics, sorting networks, robotics, assembly code
   generation, software repair, scheduling, communication protocols, symbolic regression, reverse engineering, and empirical model discovery. This paper observes that, despite
   considerable variation in the techniques employed by the various researchers and research groups that produced these human-competitive results, many of the results share
   several common features. Many of the results were achieved by using a developmental process and by using native representations regularly used by engineers in the fields
   involved. The best individual in the initial generation of the run of genetic programming often contains only a small number of operative parts. Most of the results that
   duplicated the functionality of previously issued patents were novel solutions, not infringing solutions. In addition, the production of human-competitive results, as well
   as the increased intricacy of the results, are broadly correlated to increased availability of computing power tracked by Moore's law. The paper ends by predicting that the
   increased availability of computing power (through both parallel computing and Moores law) should result in the production, in the future, of an increasing flow of
   human-competitive results, as well as more intricate and impressive results.
%O Tenth Anniversary Issue: Progress in Genetic Programming and Evolvable Machines
%8 September
%Z Open Access

%A John R. Koza
%T Introduction to genetic programming tutorial: from the basics to human-competitive results
%B GECCO 2010 Introductory tutorials
%E Una-May O'Reilly
%D 2010
%P 2137--2262
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%8 7-11  July
%Z Also known as \cite1830894 Distributed on CD-ROM at GECCO-2010. ACM Order Number 910102.

%A Donald H. Kraft
%A Frederick E. Petry
%A Bill P. Buckles
%A Thyagarajan Sadasivan
%T The use of genetic programming to build queries for information retrieval
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 468--473
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, Boolean query formulation, fitness function, index term space, information retrieval system, parse tree, relevance feedback,
   topicality, user defined measures, Boolean functions, information retrieval systems, query processing, search problems, trees (mathematics), vocabulary
%X Genetic programming is applied to an information retrieval system in order to improve Boolean query formulation via relevance feedback. This approach brings together the
   concepts of information retrieval and genetic programming. Documents are viewed as vectors in index term space. A Boolean query, viewed as a parse tree, is an organism in
   the genetic programming sense. Through the mechanisms of genetic programming, the query is modified in order to improve precision and recall. Relevance feedback is
   incorporated, in part, via user defined measures over a trial set of documents. The fitness of a candidate query can be expressed directly as a function of the relevance of
   the retrieved set. Preliminary results based on a testbed are given. The form of the fitness function has a significant effect upon performance and the proper fitness
   functions take into account relevance based on topicality (and perhaps other factors)
%8 27-29  June
%Z Syntax of programs restricted to Boolean disjunctive normal form. Has 4923 terms (=terminals?), seeds initial population so 80% of terms are predetermined to be relevant,
   rest chosen at random. 20% mutation rate (three kinds). Fitness ~ relevance of documents retrieved.

%A Michael Kramer
%T GAPS: the genetic algorithm programming system
%R M.S. Thesis
%D 1996
%I 
%I Department of Computer Science, California State University, Sacramento
%C CA, USA
%K genetic algorithms, genetic programming
%X See also \citeGlaholt:2004:ICTAI
%8 Spring

%A Michael D. Kramer
%A Du Zhang
%T GAPS: A Genetic Programming System
%B 24th International Computer Software and Applications Conference (COMPSAC 2000)
%D 2000
%P 614--619
%I IEEE Computer Society
%C Taipei, Taiwan
%K genetic algorithms, genetic programming
%X Genetic programming tackles the issue of how to automatically create a working computer program for a given problem from some initial problem statement. The goal is
   accomplished in genetic programming by genetically breeding a population of computer programs in terms of the principles of Darwinian natural selection of the fittest and
   genetic operations. We describe a genetic programming system called GAPS. GAPS has the following features: it implements the prototypical generational algorithm for genetic
   programming with three improvements (the honour roll, improved termination criteria and the tree techniques for fitness evaluation); it includes an extensible language
   tailored to the needs of genetic programming; and it is a complete, standalone system that allows for genetic programming tasks to be carried our without requiring other
   tools such as compilers. Preliminary results with GAPS have been satisfactory
%8 25-28  October
%Z replaced by \citeDBLP:journals/ijait/ZhangK03. Code? GP-Lab http://www.theglaholts.net/gplab/
%@ 0-7695-0792-1

%A Thomas Krantz
%A Oscar Lindberg
%A Gunnar Thorburn
%A Peter Nordin
%T Programmatic Compression of Natural Video
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 301--307
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%U http://thomas.krantz.com/paper.pdf
%X The use of digital video is increasing day by day. In the field of Genetic Programming a new approach called programmatic compression has been suggested for data
   compression. In this paper we describe how this technique can be applied to natural video. A programme generating intermediate frames of a video sequence is evolved where
   each frame is composed by a series of transformed regions from other frames in the sequence. The results show that the method generates a significantly better approximation
   to a frame than using a neighbouring intra-frame. Programmatic compression seems promising in the field of natural video compression.
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp fitness based on mean squared error (across time and pixels) homologous crossover, 3 mutations, parallelisation

%A Natalio Krasnogor
%A William E. Hart
%A Jim Smith
%A David A. Pelta
%T Protein Structure Prediction With Evolutionary Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1596--1601
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-733.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A N. Krasnogor
%T Self Generating Metaheuristics in Bioinformatics: The Proteins Structure Comparison Case
%J Genetic Programming and Evolvable Machines
%V 5
%N 2
%D 2004
%P 181--201
%I 
%K genetic algorithms, hill climbing, memetic algorithms, bioinformatics, MAX-CMO, self-generation, co-evolving memetic algorithms
%X the application of a so called Self-Generating Memetic Algorithm to the Maximum Contact Map Overlap problem (MAX-CMO). The maximum overlap of contact maps is emerging as a
   leading modelling technique to obtain structural alignment among pairs of protein structures. Identifying structural alignments (and hence similarity among proteins) is
   essential to the correct assessment of the relation between proteins structure and function. A robust methodology for structural comparison could have impact on the process
   of rational drug design. The Self-Generating Memetic Algorithm we present in this work evolves concurrently both the solutions (i.e. proteins alignments) and the local
   search move operators that it needs to solve the problem instance at hand. The concurrent generation of local search strategies and solutions allows the Memetic Algorithm
   to produce better results than those given by a Genetic Algorithm and a Memetic Algorithm with human-designed local searchers. The approach has been tried in four different
   data sets (1 data set composed of randomly generated proteins and the other 3 data sets with real world proteins) with encouraging results.
%8 June
%Z Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster Automated Scheduling, Optimisation and
   Planning Group, School of Computer Science & IT, University of Nottingham, NG81BB Nottingham, UK

%T GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%I New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, Invited talks, Ant colony optimization and swarm intelligence, Artificial life/robotics/evolvable hardware, Bioinformatics,
   computational, systems, and synthetic biology, Digital entertainment technologies and arts, Evolutionary combinatorial optimization and metaheuristics, Estimation of
   distribution algorithms, Evolutionary multiobjective optimization, Evolution strategies and evolutionary programming, Genetics based machine learning, Generative and
   developmental systems, Parallel evolutionary systems, Real world applications, Search-based software engineering, Self-* search, Theory
%8 12-16  July
%Z GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)

%A Oldrich Kratochvil
%A Pavel Osmera
%A Ondrej Popelka
%T Parallel Grammatical Evolution for Circuit Optimization
%B Proceedings of the World Congress on Engineering and Computer Science, WCECS '09
%E S. I. Ao and Craig Douglas and W. S. Grundfest and Jon Burgstone
%V II
%D 2009
%P 1032--1037
%I Newswood Limited
%I International Association of Engineers
%C San Francisco, USA
%K genetic algorithms, genetic programming, grammatical evolution, circuit optimization, parallel evolution
%U http://www.iaeng.org/publication/WCECS2009/WCECS2009_pp1032-1037.pdf
%X This paper describes a Parallel Grammatical Evolution (PGE) that can evolve complete circuits using a variable length of linear genome to govern the mapping of a Backus
   Naur Form grammar definition. In order to increase the efficiency of Grammatical Evolution (GE) the influence of backward processing and an influence of several fitness
   functions were tested. PGE with backward processing can also take advantage of progressive crossover and mutation operators. The algorithm is internally parallel and
   consists of three different interconnected populations. A new non-tree structure of GE was tested.
%8 October  20-22
%Z Lecture Notes in Engineering and Computer Science

%A Krzysztof Krawiec
%T Genetic Programming Using Partial Order Of Solutions For Pattern Recognition Tasks
%B Proceedings of the second National Conference on Computer Recognition Systems KOSYR-2001
%E Edward Puchala
%D 2001
%P 427--433
%I 
%C Strona palacu w Milkowie, Karpacza, Poland
%K genetic algorithms, genetic programming, machine learning, visual learning, handwritten character recognition
%U http://citeseer.ist.psu.edu/451286.html
%X This paper investigates the use of genetic programming (GP) for learning of pattern recognition programs. The central topic here is the introduction of GP incorporating
   partial order of solutions as opposed to the standard complete (linear) order imposed by the scalar fitness function. We claim that such an extension protects the
   `interesting', however worse w.r.t. the value of the fitness function, solutions from being discarded in the selection process, and thus increases the diversity of the
   population. That hypothesis is verified on a real-world case study concerning the recognition of handwritten characters.
%O The Pennsylvania State University CiteSeer Archives
%8 28-31  May
%Z http://www.zssk.pwr.wroc.pl/zssk/kosyr/kosyr2001/kosyr.html II Krajowa Konferencja Naukowo-Szkoleniowa Komputerowe Systemy Rozpoznawania KOSYR 2001 Milkow kolo Karpacza,
   28-31 maja 2001 Organizator: Politechnika Wroclawska Wydzialowy Zaklad Systemow i Sieci Komputerowych Wspolpraca: Towarzystwo Przetwarzania Obrazow Instytut Informatyki
   Politechniki Lodzkiej Instytut Techniczny Panstwowej Wyzszej Szkoly Zawodowej w Legnicy

%A Krzysztof Krawiec
%T Genetic Programming with Local Improvement for Visual Learning from Examples
%B Proceedings 9th International Conference on Computer Analysis of Images and Patterns, CAIP 2001
%S Lecture Notes in Computer Science
%E W. Skarbek
%V 2124
%D 2001
%P 209--216
%I Springer-Verlag Heidelberg
%C Warsaw, Poland
%K genetic algorithms, genetic programming, visual learning, genetic local search, learning from examples
%U http://link.springer.de/link/service/series/0558/bibs/2124/21240209.htm
%X This paper investigates the use of evolutionary programming for the search of hypothesis space in visual learning tasks. The general goal of the project is to elaborate
   human-competitive procedures for pattern discrimination by means of learning based on the training data (set of images). In particular, the topic addressed here is the
   comparison between the 'standard' genetic programming (as defined by Koza [13]) and the genetic programming extended by local optimization of solutions, so-called genetic
   local search. The hypothesis formulated in the paper is that genetic local search provides better solutions (i.e. classifiers with higher predictive accuracy) than the
   genetic search without that extension. This supposition was positively verified in an extensive comparative experiment of visual learning concerning the recognition of
   handwritten characters.
%8 September  5-7
%Z A1 Institute of Computing Science, Poznan University of Technology,Piotrowo 3A, 60965 Poznan, Poland krawiec@cs.put.poznan.pl

%A Krzysztof Krawiec
%T Constructive Induction in Picture-based Decision Support
%R Ph.D. Thesis
%D 2000
%I 
%I Institute of Computing Science, Poznan University of Technology
%C Poland
%K genetic algorithms, genetic programming
%U http://www-idss.cs.put.poznan.pl/~krawiec/pubs/phd.pdf
%8 May
%Z In Polish

%A Krzysztof Krawiec
%T Constructive Induction in Learning of Image Representation
%R Research Report RA-006/2000
%D 2000
%I 
%I Institute of Computing Science, Poznan University of Technology
%C Poland
%K genetic algorithms, genetic programming, grammar
%U http://citeseer.ist.psu.edu/451901.html
%X This report describes the results of investigations on the use of genetic programming for learning in pattern recognition problems. The general idea consists in
   evolutionary search in the space of pattern recognition programs. The solutions are expressed in terms of the Genetic Programming for Visual Learning language (GPVISL),
   described in this work. This paper continues the work initiated in [Krawiec_2000].
%O The Pennsylvania State University CiteSeer Archives
%8 October

%A Krzysztof Krawiec
%T Pairwise Comparison of Hypotheses Coverings as a Natural Mean Against Undesirable Niching in Evolutionary Inductive Learning
%R Research Report RA-005/2001
%D 2001
%I 
%I Institute of Computing Science, Poznan University of Technology
%C Poland
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/458532.html
%X This report summarizes the results of research on the use of evolutionary learning for solving pattern recognition problems. The general idea consists in evolutionary
   search in the space of pattern recognition programs. The whole body of results described here was obtained in the improved version of GPVIS environment [15]. In particular,
   this report describes the 2.0 version of the environment and is devoted in a great part to the extensions beyond the standard genetic programming introduced into GPVIS,
   including the novel method of hypothesis evaluation proposed for evolutionary learning. This work focuses on reasoning from pictorial information based on evolutionary
   computation, or, to be more precise, on the paradigm of genetic programming [12]. The outline of the method is as follows. The genetic search engine performs the search
   through the space of image processing and analysis programs. The programs have the form of expressions formulated in a specialized language called GPVISL (Genetic
   Programming for Visual Learning language). The genetic search engine realizes the selection of parent solutions (individuals), which are then crossed over and mutated to
   obtain the next generation of solutions. The selection is done w.r.t. the value of evaluation (fitness) function. A solution is evaluated by testing its behavior on a set
   of fitness cases, which are equivalent to images in this context. The fitness function is the percentage of hits[14], i.e. of the correct decisions (recognitions) made by
   the system.
%O The Pennsylvania State University CiteSeer Archives
%8 September

%A Krzysztof Krawiec
%T Evolutionary Computation Framework for Learning from Visual Examples
%J Image Processing and Communications
%V 7
%N 3-4
%D 2001
%P 85--96
%I 
%K genetic algorithms, genetic programming, visual learning, genetic local search, learning from examples
%U http://citeseer.ist.psu.edu/494563.html
%X This paper investigates the use of evolutionary programming for the search of hypothesis space in visual learning tasks. The general goal of the project is to elaborate
   human-competitive procedures for pattern discrimination by means of learning based on the training data (set of images). In particular, the topic addressed here is the
   comparison between the standard genetic programming (as defined by Koza [13]) and the genetic programming extended by local optimisation of solutions, so-called genetic
   local search. The hypothesis formulated in the paper is that genetic local search provides better solutions (i.e. classifiers with higher predictive accuracy) than the
   genetic search without that extension. This supposition was positively verified in an extensive comparative experiment of visual learning concerning the recognition of
   handwritten characters.
%Z http://wtie.atr.bydgoszcz.pl/ip&c/indexip&c.html

%A Krzysztof Krawiec
%T Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks
%J Genetic Programming and Evolvable Machines
%V 3
%N 4
%D 2002
%P 329--343
%I 
%K genetic algorithms, genetic programming, machine learning, change of representation, feature construction, feature selection
%X we use genetic programming for changing the representation of the input data for machine learners. In particular, the topic of interest here is feature construction in the
   learning-from-examples paradigm, where new features are built based on the original set of attributes. The paper first introduces the general framework for GP-based feature
   construction. Then, an extended approach is proposed where the useful components of representation (features) are preserved during an evolutionary run, as opposed to the
   standard approach where valuable features are often lost during search. Finally, we present and discuss the results of an extensive computational experiment carried out on
   several reference data sets. The outcomes show that classifiers induced using the representation enriched by the GP-constructed features provide better accuracy of
   classification on the test set. In particular, the extended approach proposed in the paper proved to be able to outperform the standard approach on some benchmark problems
   on a statistically significant level.
%8 Decemeber
%Z Article ID: 5103872

%A Krzysztof Krawiec
%T Evolutionary Feature Programming: Cooperative learning for knowledge discovery and computer vision
%S 
%N 385
%D 2004
%I Wydawnictwo Politechniki Poznanskiej
%C Poznan University of Technology, Poznan, Poland
%K genetic algorithms, genetic programming
%U http://idss.cs.put.poznan.pl/~krawiec/pubs/hab/krawiec_hab.pdf
%X This book concerns the methodology of machine learning algorithms that explicitly change the representation of their training data while learning. This process, known as
   feature construction or transformation of representation, 'rewrites' learner's input data, getting rid of useless data components, and combining the useful ones in
   synergetic way with help of background knowledge. The objective is to improve learner's predictive performance and/or to enable access to input data that is incompatible
   with learning algorithm, and could not be used directly (e.g., raster images). The new methodology elaborated in this book, termed evolutionary feature programming (EFP),
   puts the feature construction task into optimisation perspective and uses evolutionary computation to effectively search the space of solutions. Each of the evolving
   individuals represents a specific feature extraction procedure. We design a novel variant of genetic programming to encode the way the training data undergoes
   transformation prior to being fed into learning algorithm. The book provides extensive rationale for this particular design and genetic encoding of solutions. Apart from
   this canonical approach, we propose a methodology for tackling with complexity of EFP. In coevolutionary feature programming (CFP), we decompose the feature construction
   task using cooperative coevolution, a variant of evolutionary computation that allows for semi-independent elaboration of solution components. We propose and discuss four
   different decomposition strategies for breaking up the feature construction process. The practical utility of EFP and CFP is verified in two qualitatively different
   application areas: machine learning from examples given in attribute-value form, and visual learning from raw raster images. Considered real-world case studies concern
   glass type identification, diagnosing of diabetes, sonar-based object identification, 3D object recognition in visible spectrum, and vehicle identification in radar
   modality. The experimental results indicate that the proposed methodology is general and proves effective in different environments, and that it exhibits features that are
   appealing from practical viewpoint (performance, scalability, generalisation, explanatory character, to mention the most important ones).

%A Krzysztof Krawiec
%A Bir Bhanu
%T Visual Learning by Coevolutionary Feature Synthesis
%J IEEE Transactions on System, Man, and Cybernetics -- Part B
%V 35
%N 3
%D 2005
%P 409--425
%I 
%K genetic algorithms, genetic programming, Automatic programming, feature extraction, genetic algorithms, pattern recognition
%U http://ieeexplore.ieee.org/iel5/3477/30862/01430827.pdf
%X A novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition
   system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear
   genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are
   considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic
   aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions.
%8 June

%A Krzysztof Krawiec
%T Learning High-Level Visual Concepts using Attributed Primitives and Genetic Programming
%B Applications of Evolutionary Computing, EvoWorkshops2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC
%S LNCS
%E Franz Rothlauf and Jurgen Branke and Stefano Cagnoni and Ernesto Costa and Carlos Cotta and Rolf Drechsler and Evelyne Lutton and Penousal Machado and Jason H. Moore and
   Juan Romero and George D. Smith and Giovanni Squillero and Hideyuki Takagi
%V 3907
%D 2006
%P 515--519
%I Springer Verlag Berlin
%C Budapest
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3907&spage=515
%X we present a novel approach to genetic learning of high-level visual concepts that works with sets of attributed visual primitives rather than with raster images. The paper
   presents the approach in detail and verifies it in an experiment concerning locating objects in real-world 3D scenes.
%8 10-12  April
%Z part of \citeevows06 also known as \citeconf/evoW/XieZA06
%@ 3-540-33237-5

%A Krzysztof Krawiec
%A Patryk Lijewski
%T Genetic Graph Programming for Object Detection
%B Proceedings 8th International Conference on Artificial Intelligence and Soft Computing ICAISC
%S Lecture Notes on Artificial Intelligence (LNAI)
%E Leszek Rutkowski and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek Zurada
%V 4029
%D 2006
%P 804--813
%I Springer-Verlag
%C Zakopane, Poland
%K genetic algorithms, genetic programming
%X We present a novel approach to learning from visual information given in a form of raster images. The proposed learning method uses genetic programming to synthesise an
   image processing procedure that performs the desired vision task. The evolutionary algorithm maintains a population of individuals, initially populated with random
   solutions to the problem. Each individual encodes a directed acyclic graph, with graph nodes corresponding to elementary image processing operations (like image arithmetic,
   convolution filtering, morphological operations, etc.), and graph edges representing the data flow. Each graph contains a single input node to feed in the input image and
   an output node that yields the final processing result. This genetic learning process is driven by a fitness function that rewards individuals for producing output that
   conforms the task-specific objectives. This evaluation is carried out with respect to the training set of images. Thanks to such formulation, the fitness function is the
   only application-dependent component of our approach, which is thus applicable to a wide range of vision tasks (image enhancement, object detection, object tracking, etc.).
   The paper presents the approach in detail and describes the computational experiment concerning the task of object tracking in a real-world video sequence.
%8 June  25-29
%@ 3-540-35748-3

%A Krzysztof Krawiec
%T Evolutionary Learning of Primitive-Based Visual Concepts
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 4451--4458
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%U http://ieeexplore.ieee.org/iel5/11108/35623/01688460.pdf
%X The paper presents a novel method of evolutionary learning dedicated to acquisition of visual concepts. The learning process takes place in a population of Genetic
   Programming-based learners that process attributed visual primitives derived from raw raster images. The approach uses an original evaluation scheme: evolving
   individuals-learners are rewarded for being able to sketch the input visual stimulus. Recognition proceeds here as an attempt of restoring essential features of the input
   image. The approach is general by being based mostly on universal vision knowledge; only very limited amount of a priori knowledge about the particular application or
   target concept to be learned is required. We explain the method in detail and verify it experimentally on acquisition of simple visual concepts (triangle and section) from
   examples.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Krzysztof Krawiec
%A Daniel Howard
%A Mengjie Zhang
%T Overview of Object Detection and Image Analysis by Means of Genetic Programming Techniques
%B Proceedings of the 2007 International Conference Frontiers in the Convergence of Bioscience and Information Technologies (FBIT 2007)
%D 2007
%P 779--784
%I IEEE Press
%C Jeju Island, Korea
%K genetic algorithms, genetic programming, computer vision, object detection, image analysis, pattern recognition
%X This paper reviews the existing work in genetic programming for object detection and image analysis. It shortly introduces the reader into the fundamentals of evolutionary
   computation and presents the basics of the genetic programming paradigm (GP), providing a rationale for the use of GP within computer vision and pattern recognition,
   particularly when applied to object detection and image analysis. It reviews the past research on GP for vision, referring to real-world applications where possible. It
   outlines possible further research directions.
%8 October  11-13

%A Krzysztof Krawiec
%T Generative Learning of Visual Concepts using Multiobjective Genetic Programming
%J Pattern Recognition Letters
%V 28
%N 16
%D 2007
%P 2385--2400
%I 
%K genetic algorithms, genetic programming, Visual learning, Generative pattern recognition, Evolutionary synthesis of pattern recognition systems
%X This paper introduces a novel method of visual learning based on Genetic Programming, which evolves a population of individuals (image analysis programs) that process
   attributed visual primitives derived from raw raster images. The goal is to evolve an image analysis program that correctly recognises the training concept (shape). The
   approach uses generative evaluation scheme: individuals are rewarded for re-producing the shape of the object being recognised using graphical primitives and elementary
   background knowledge encoded in predefined operators. Evolutionary run is driven by a multiobjective fitness function to prevent premature convergence and enable effective
   exploration of the space of solutions. We present the method in detail and verify it experimentally on the task of learning two visual concepts from examples.
%8 1  Decemeber

%A Krzysztof Krawiec
%A Bir Bhanu
%T Visual Learning by Evolutionary and Coevolutionary Feature Synthesis
%J IEEE Transactions on Evolutionary Computation
%V 11
%N 5
%D 2007
%P 635--650
%I 
%K genetic algorithms, genetic programming, pattern recognition, visual learning, cooperative coevolution
%U http://ieeexplore.ieee.org/iel5/4235/4336114/04336120.pdf?isnumber=4336114&prod=JNL&arnumber=4336120&arSt=635&ared=650&arAuthor=Krawiec%2C+K.%3B+Bhanu%2C+B.
%X In this paper, we present a novel method for learning complex concepts/hypotheses directly from raw training data. The task addressed here concerns data-driven synthesis of
   recognition procedures for real-world object recognition. The method uses linear genetic programming to encode potential solutions expressed in terms of elementary
   operations, and handles the complexity of the learning task by applying cooperative coevolution to decompose the problem automatically at the genotype level. The training
   coevolves feature extraction procedures, each being a sequence of elementary image processing and computer vision operations applied to input images. Extensive experimental
   results show that the approach attains competitive performance for 3D object recognition in real synthetic aperture radar (SAR) imagery.
%8 October

%A Krzysztof Krawiec
%A PrzemysBaw Polewski
%T Potential fitness for genetic programming
%B GECCO-2008 Late-Breaking Papers
%E Marc Ebner and Mike Cattolico and Jano van Hemert and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank
   W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and
   Mark Hauschild and Martin Pelikan and Kumara Sastry
%D 2008
%P 2175--2180
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, context, evolutionary computation, semantics
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2175.pdf
%8 12-16  July
%Z Distributed on CD-ROM at GECCO-2008 ACM Order Number 910081. Also known as \cite1389043

%A Krzysztof Krawiec
%A Pawel Lichocki
%T Approximating geometric crossover in semantic space
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 987--994
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X We propose a crossover operator that works with genetic programming trees and is approximately geometric crossover in the semantic space. By defining semantic as program's
   evaluation profile with respect to a set of fitness cases and constraining to a specific class of metric-based fitness functions, we cause the fitness landscape in the
   semantic space to have perfect fitness-distance correlation. The proposed approximately geometric semantic crossover exploits this property of the semantic fitness
   landscape by an appropriate sampling. We demonstrate also how the proposed method may be conveniently combined with hill climbing. We discuss the properties of the methods,
   and describe an extensive computational experiment concerning logical function synthesis and symbolic regression.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Functional modularity for genetic programming
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 995--1002
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X In this paper we introduce, formalize, and experimentally validate a novel concept of functional modularity for Genetic Programming (GP). We rely on module definition that
   is most natural for GP: a piece of program code (subtree). However, as opposed to syntax-based approaches that abstract from the actual computation performed by a module,
   we analyze also its semantic using a set of fitness cases. In particular, the central notion of this approach is subgoal, an entity that embodies module's desired semantic
   and is used to evaluate module candidates. As the cardinality of the space of all subgoals is exponential with respect to the number of fitness cases, we introduce
   monotonicity to assess subgoals' potential utility for searching for good modules. For a given subgoal and a sample of modules, monotonicity measures the correlation of
   subgoal's distance from module's semantics and the fitness of the solution the module is part of. In the experimental part we demonstrate how these concepts may be used to
   describe and quantify the modularity of two simple problems of Boolean function synthesis. In particular, we conclude that monotonicity usefully differentiates two problems
   with different nature of modularity, allows us to tell apart the useful subgoals from the other ones, and may be potentially used for problem decomposition and enhance the
   efficiency of evolutionary search.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Analysis of Semantic Modularity for Genetic Programming
%J Foundations of Computing and Decision Sciences
%V 34
%N 4
%D 2009
%P 265--285
%I Poznan University of Technology
%K genetic algorithms, genetic programming, semantics, modularity, problem decomposition
%U http://fcds.cs.put.poznan.pl/FCDS2/ArticleDetails.aspx?articleId=219
%X In this paper we analyze the properties of functional modularity, a concept introduced in \citeDBLP:conf/gecco/KrawiecW09 for detecting and measuring modularity in problems
   of automatic program synthesis, in particular by means of genetic programming. The basic components of functional modularity approach are subgoals -- entities that embody
   module's semantic -- and monotonicity, a measure for assessing subgoals' potential utility for searching for good modules. For a given subgoal and a sample of solutions
   decomposed into parts and contexts according to module definition, monotonicity measures the correlation of distance between semantics of solution's part and the fitness of
   the solution. The central tenet of this approach is that highly monotonous subgoals can be used to decompose the task and improve search convergence. In the experimental
   part we investigate the properties of functional modularity using eight instances of problems of Boolean function synthesis. The results show that monotonicity varies
   depending on problem's structure of modularity and correctly identifies good subgoals, potentially enabling automatic program decomposition.

%A Krzysztof Krawiec
%A Bartosz Wieloch
%T Automatic generation and exploitation of related problems in genetic programming
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X We propose an evolutionary framework that uses the set of instructions provided with a genetic programming (GP) problem to automatically build a repertoire of related
   problems and subsequently uses them to improve the performance of search. The novel idea is to use the synthesised related problems to simultaneously exert multiple
   selection pressures on the evolving population(s). For that framework, we design two methods. In the first method, individuals optimising for particular problems dwell in
   separate populations and spawn clones which migrate to other populations, similarly to the island model. The second method operates on a single population and ranks the
   fitness values that individuals receive from particular problems to make them comparable. When applied to six symbolic regression problems of different difficulty, both
   methods perform better than the standard GP, though sometimes fail to prove superior to certain control setup.
%8 18-23  July
%Z ECJ. WCCI 2010. Also known as \cite5586120

%A Krzysztof Krawiec
%A Bartosz Kukawka
%A Tomasz Maciejewski
%T Evolving cascades of voting feature detectors for vehicle detection in satellite imagery
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%U http://www.cs.put.poznan.pl/kkrawiec/pubs/2010CECVehicleDetection.pdf
%X We propose an evolutionary method for detection of vehicles in satellite imagery which involves a large number of simple elementary features and multiple detectors trained
   by genetic programming. The complete detection system is composed of several detectors that are chained into a cascade and successively filter out the negative examples.
   Each detector is a committee of genetic programming trees that together vote over the decision concerning vehicle presence, and is trained only on the examples classified
   as positive by the previous cascade node. The individual trees use typical arithmetic transformations to aggregate features selected from a very large collections of
   Haar-like features derived from the input image. The paper presents detailed description of the proposed algorithm and reports the results of an extensive computational
   experiment carried out on real-world satellite images. The evolved detection system exhibits competitive sensitivity and relatively low false positive rate for testing
   images, despite not making use of domain-specific knowledge.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586155

%A Krzysztof Krawiec
%A Pawel Lichocki
%T Using Co-solvability to Model and Exploit Synergetic Effects in Evolution
%B PPSN 2010 11th International Conference on Parallel Problem Solving From Nature
%S Lecture Notes in Computer Science
%E Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Guenter Rudolph
%V 6239
%D 2010
%P 492--501
%I Springer
%C Krakow, Poland
%K genetic algorithms, genetic programming
%X We introduce, analyse, and experimentally examine co-solvability, an ability of a solution to solve a pair of fitness cases (tests). Based on this concept, we devise a
   co-solvability fitness function that makes solutions compete for rewards granted for solving pairs of tests, in a way analogous to implicit fitness sharing. We prove that
   co-solvability fitness function is by definition synergistic and imposes selection pressure which is qualitatively different from that of standard fitness function or
   implicit fitness sharing. The results of experimental verification on eight genetic programming tasks demonstrate that evolutionary runs driven by co-solvability fitness
   function usually converge faster to well-performing solutions and are more likely to reach global optima.
%8 11-15  September

%A Krzysztof Krawiec
%T Learnable Embeddings of Program Spaces
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 166--177
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X We consider a class of adaptive, globally-operating, semantic-based embeddings of programs into discrete multidimensional spaces termed prespaces. In the proposed
   formulation, the original space of programs and its prespace are bound with a learnable mapping, where the process of learning is aimed at improving the overall locality of
   the new representation with respect to program semantics. To learn the mapping, which is formally a permutation of program locations in the prespace, we propose two
   algorithms: simple greedy heuristics and an evolutionary algorithm. To guide the learning process, we use a new definition of semantic locality. In an experimental
   illustration concerning four symbolic regression domains, we demonstrate that an evolutionary algorithm is able to improve the embedding designed by means of greedy search,
   and that the learnt prespaces usually offer better search performance than the original program space.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Krzysztof Krawiec
%T Semantically embedded genetic programming: automated design of abstract program representations
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1379--1386
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X We propose an alternative program representation that relies on automatic semantic-based embedding of programs into discrete multidimensional spaces. An embedding imposes a
   well-structured hypercube topology on the search space, endows it with a semantic-aware neighborhood, and enables convenient search using Cartesian coordinates. The
   embedding algorithm consists in locality-driven optimization and operates in abstraction from a specific fitness function, improving locality of all possible fitness
   landscapes simultaneously. We experimentally validate the approach on a large sample of symbolic regression tasks and show that it provides better search performance than
   the original program space. We demonstrate also that semantic embedding of small programs can be exploited in a compositional manner to effectively search the space of
   compound programs.
%8 12-16  July
%Z Surjective Also known as \cite2001762 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual
   genetic programming conference (GP-2011)

%A Krzysztof Krawiec
%T Medial Crossovers for Genetic Programming
%B Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012
%S LNCS
%E Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta
%V 7244
%D 2012
%P 61--72
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Program semantic, Semantic crossover
%X We propose a class of crossover operators for genetic programming that aim at making offspring programs semantically intermediate (medial) with respect to parent programs
   by modifying short fragments of code (subprograms). The approach is applicable to problems that define fitness as a distance between program output and the desired output.
   Based on that metric, we define two measures of semantic `mediality', which we employ to design two crossover operators: one aimed at making the semantic of offsprings
   geometric with respect to the semantic of parents, and the other aimed at making them equidistant to parents' semantics. The operators act only on randomly selected
   fragments of parents' code, which makes them computationally efficient. When compared experimentally with four other crossover operators, both operators lead to success
   ratio at least as good as for the non-semantic crossovers, and the operator based on equidistance proves superior to all others.
%8 11-13  April
%Z Part of \citeMoraglio:2012:GP EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012

%A Todd Krein
%T Simple Memory Models and the Concept of Self in the Game of Concentration
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 77--86
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 Decemeber
%Z This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-187263-3

%A Marek Kretowski
%A Marek Grzes
%T Evolutionary Learning of Linear Trees with Embedded Feature Selection
%B Proceedings 8th International Conference on Artificial Intelligence and Soft Computing ICAISC
%S Lecture Notes on Artificial Intelligence (LNAI)
%E Leszek Rutkowski and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek Zurada
%V 4029
%D 2006
%P 400--409
%I Springer-Verlag
%C Zakopane, Poland
%K genetic algorithms, genetic programming
%U http://aragorn.pb.bialystok.pl/~mkret/docs/icaisc2006.pdf
%X a new evolutionary algorithm for global induction of linear trees is presented. The learning process consists of searching for both a decision tree structure and
   hyper-plane weights in all nonterminal nodes. Specialised genetic operators are developed and applied according to the node quality and location. Feature selection aimed at
   simplification of the splitting hyper-planes is embedded into the algorithm and results in elimination of noisy and redundant features. The proposed approach is verified on
   both artificial and real-life data and the obtained results are promising.
%8 June  25-29
%@ 3-540-35748-3

%A Todd Krien
%T The Effect of the Interaction Fraction on Stable Long Term Patterns in Echo Systems and Markers for Determination of Final State
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 73--80
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z John Holland's Echo SFI Echo ftp://santafe.edu/pub/Users/Terry/echo/ This volume contains 22 papers written and submitted by students describing their term projects for the
   course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Thiemo Krink
%A Brian H. Mayoh
%A Zbigniew Michalewicz
%T A PATCHWORK Model for Evolutionary Algorithms with Structured and Variable Size Populations
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1321--1328
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-031.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Klaus Krogmann
%A Michael Kuperberg
%A Ralf Reussner
%T Using Genetic Search for Reverse Engineering of Parametric Behaviour Models for Performance Prediction
%J IEEE Transactions on Software Engineering
%V 36
%N 6
%D 2010
%P 865--877
%I 
%K genetic algorithms, genetic programming, sbse, application independent bytecode benchmark, component based software engineering, genetic search, parametric behaviour model,
   reverse engineering, runtime bytecode count, static bytecode analysis, object-oriented programming, reverse engineering, search problems, software performance evaluation
%X In component-based software engineering, existing components are often reused in new applications. Correspondingly, the response time of an entire component-based
   application can be predicted from the execution durations of individual component services. These execution durations depend on the run time behaviour of a component which
   itself is influenced by three factors: the execution platform, the usage profile, and the component wiring. To cover all relevant combinations of these influencing factors,
   conventional prediction of response times requires repeated deployment and measurements of component services for all such combinations, incurring a substantial effort.
   This paper presents a novel comprehensive approach for reverse engineering and performance prediction of components. In it, genetic programming is used for reconstructing a
   behavior model from monitoring data, runtime bytecode counts, and static bytecode analysis. The resulting behavior model is parametrised over all three
   performance-influencing factors, which are specified separately. This results in significantly fewer measurements: The behaviour model is reconstructed only once per
   component service, and one application-independent bytecode benchmark run is sufficient to characterise an execution platform. To predict the execution durations for a
   concrete platform, our approach combines the behaviour model with platform-specific benchmarking results. We validate our approach by predicting the performance of a file
   sharing application.
%8 November /  Decemeber
%Z Also known as \cite5530323

%A Pavel Kromer
%A Jan Platos
%A Vaclav Snasel
%A Ajith Abraham
%T Towards intrusion detection by information retrieval and genetic programming
%B Sixth International Conference on Information Assurance and Security (IAS 2010)
%D 2010
%P 112--117
%I 
%K genetic algorithms, genetic programming, KDD Cup intrusion detection data set, data mining, fuzzy classifier, fuzzy rule, information retrieval, knowledge discovery, data
   mining, fuzzy set theory, information retrieval, pattern classification, security of data
%X Fuzzy classifiers and fuzzy rules are powerful tools in data mining and knowledge discovery. In this work, intrusion detection is approached as a data mining task and
   genetic programming is deployed to evolve fuzzy classifiers for detection of intrusion and security problems. We train the fuzzy classifier on a data set modelled as a
   fuzzy information retrieval collection and investigate its ability to detect illegitimate actions. Proposed approach is experimentally evaluated on the popular KDD Cup
   intrusion detection data set.
%8 23-25  August
%Z Also known as \cite5604063

%A Pavel Kromer
%A Jan Platos
%A Vaclav Snasel
%A Ajith Abraham
%T Evolutionary improvement of search queries and its parameters
%B 10th International Conference on Hybrid Intelligent Systems (HIS 2010)
%D 2010
%P 147--152
%I 
%K genetic algorithms, genetic programming, data collection, evolutionary algorithm, information retrieval process, optimisation tool, search queries, user queries, world wide
   web, Web sites, evolutionary computation, query processing, search engines
%X The formulation of user queries is an important part of the information retrieval process. In the complex environment of the World Wide Web and other large data
   collections, it is often not easy for the users to express their information needs in an optimal way. In this paper, we investigate evolutionary algorithms (in particular
   genetic programming) as a tool for the optimization of user queries and seek for its good settings.
%8 23-25  August
%Z Also known as \cite5600018

%A Gabriel Kronberger
%A Stephan Winkler
%A Michael Affenzeller
%A Stefan Wagner
%T On Crossover Success Rate in Genetic Programming with Offspring Selection
%B Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009
%S LNCS
%E Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner
%V 5481
%D 2009
%P 232--243
%I Springer
%I EvoStar
%C Tuebingen
%K genetic algorithms, genetic programming
%8 April  15-17
%Z Part of \citeconf/eurogp/2009 EuroGP'2009 held in conjunction with EvoCOP2009, EvoBIO2009 and EvoWorkshops2009

%A Gabriel Kronberger
%A Christoph Feilmayr
%A Michael Kommenda
%A Stephan Winkler
%A Michael Affenzeller
%A Thomas Burgler
%T System Identification of Blast Furnace Processes with Genetic Programming
%B 2nd International Symposium on Logistics and Industrial Informatics, LINDI 2009
%D 2009
%P 1--6
%I 
%C Linz, Austria
%K genetic algorithms, genetic programming, blast furnace process, burden composition, carbon content, chemical reaction, data-based modeling method, hot metal, inhomogeneous
   burden movement, injected reducing agent, iron ore reduction, linear model, linear regression, melting rate, nonlinear model, oxygen per ton, physical reaction, support
   vector regression, symbolic regression, system identification, top gas composition, blast furnaces, chemical reactions, identification, iron, melting, metallurgical
   industries, regression analysis, support vector machines
%X The blast furnace process is the most common form of iron ore reduction. The physical and chemical reactions in the blast furnace process are well understood on a high
   level of abstraction, but many more subtle inter-relationships between injected reducing agents, burden composition, heat loss in defined wall areas of the furnace,
   inhomogeneous burden movement, scaffolding, top gas composition, and the effect on the produced hot metal (molten iron) or slag are not totally understood. This paper
   details the application of data-based modeling methods: linear regression, support vector regression, and symbolic regression with genetic programming to create linear and
   non-linear models describing different aspects of the blast furnace process. Three variables of interest in the blast furnace process are modeled: the melting rate of the
   blast furnace (tons of produced hot metal per hour), the specific amount of oxygen per ton of hot metal, and the carbon content in the hot metal. The melting rate is
   largely determined by the qualities of the hot blast (in particular the amount of oxygen in the hot blast). Melting rate can be described accurately with linear models if
   data of the hot blast are available. Prediction of the required amount of oxygen per ton of hot metal and the carbon content in the hot metal is more difficult and requires
   non-linear models in order to achieve satisfactory accuracy.
%8 10-11  September
%Z http://www.fh-ooe.at/lindi2009/ Also known as \cite5258751

%A Gabriel Kronberger
%A Stephan M. Winkler
%A Michael Affenzeller
%A Andreas Beham
%A Stefan Wagner
%T On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection
%B 12th International Conference on Computer Aided Systems Theory, EUROCAST 2009
%S Lecture Notes in Computer Science
%E Roberto Moreno-D\'iaz and Franz Pichler and Alexis Quesada-Arencibia
%V 5717
%D 2009
%P 793--800
%I Springer
%C Las Palmas de Gran Canaria, Spain
%K genetic algorithms, genetic programming
%X Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve amongst others regression, classification, and
   time-series forecasting problems. A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal
   dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with
   offspring selection are largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring when applied to
   benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring
   selection and random selection of crossover operators can improve the performance of the algorithm in terms of best solution quality when no solution size constraints are
   applied.
%O Revised Selected Papers
%8 February  15-20
%Z Acknowledgment G.K. thanks the participants of EuroGP 2009 for the insightful comments and discussions. \citeKronberger:2009:eurogp

%A Gabriel K. Kronberger
%T Symbolic Regression for Knowledge Discovery - Bloat, Overfitting, and Variable Interaction Networks
%R Ph.D. Thesis
%D 2010
%I 
%I Johannes Kepler University
%C Linz, Austria
%K genetic algorithms, genetic programming
%U http://research.fh-ooe.at/en/publication/2045
%X With the growing amount of data that are collected and recorded in various application areas the need to use these data is also growing. In science, data have always played
   an important role; in recent years, however, the economic potential of data has also become increasingly important. In combination with methods for data analysis, data can
   be used to their full potential, whether in the commercial sector to optimise offers, or in the industrial sector to optimize resources and product quality based on process
   data. This work describes a new approach for the analysis of data which is based on symbolic regression with genetic programming and aims to generate an overall view of the
   interactions of various variables of a system. By this means, all potentially interesting relationships, which can be detected in a dataset, should be identified and
   represented as compact and understandable models. In the first part of this work, this approach of comprehensive symbolic regression is described in detail. Important
   issues that play a role in the process are the prevention of bloat and over-fitting, the simplification of models, and the identification of relevant input variables. In
   this context, different methods for bloat control and prevention are presented and compared. In particular, the influence of offspring selection on bloat is analysed. In
   addition, a new way to detect over-fitting is presented. On the basis of this, extensions for the reduction of over-fitting are presented and compared. Pruning of models is
   featured prominently, on the one hand to prevent over-fitting and on the other hand to simplify complex models. An important aspect is the analysis of the vast amount of
   different models that results from the proposed approach. In this context, different methods to quantify relevant factors are proposed. These methods can be used to
   identify interactions of variables of the analysed system. Visualising such interactions provides a general overview of the system in question which would not be possible
   by analysis of individual models which are concentrated on selected aspects of the problem. Additionally, the prognosis of multivariate time series with genetic programming
   is described in the first part. The second part of this work shows how the described approach can be applied to the analysis of real-world systems, and how the result of
   this data analysis process can result in the gain of new knowledge about the investigated system. The analyzed data stem from a blast furnace for the production of steel
   and an industrial chemical process. In addition the same approach is also applied on a data collection storing economic data in order to identify macro-economic
   interactions.
%Z See also \citeKronberger:book

%A Gabriel Kronberger
%T Symbolic Regression for Knowledge Discovery -- Bloat, Overfitting, and Variable Interaction Networks
%S Johannes Kepler University, Linz, Reihe C
%N 64
%D 2011
%I Trauner Verlag+Buchservice GmbH
%K genetic algorithms, genetic programming
%U http://www.trauner.at/buchdetail.aspx?artnr=20134641&unterkat=30
%X This work describes an approach for data analysis based on symbolic regression and genetic programming, that produces an overall view of the dependencies of all variables
   of a system. The identified dependencies are represented in form of a variable interaction network. In the first part of this work, this approach is described in detail.
   Important issues are the prevention of bloat and overfitting, the simplification of models, and the identification of relevant input variables. In this context, different
   methods for bloat control are presented and compared. In addition, a novel way to detect and reduce over fitting is presented and analysed. The second part of this work
   demonstrates how comprehensive symbolic regression can be applied for analysis of real-world systems. Variable interaction networks for a blast furnace process and an
   industrial chemical process are presented and discussed. Additionally, the same approach is also applied on an economic data set to identify macro-economic dependencies.
%Z Described in http://www.sigevolution.org/issues/pdf/SIGEVOlution0504.pdf on page 25

%A Gabriel Kronberger
%A Stefan Fink
%A Michael Kommenda
%A Michael Affenzeller
%T Macro-economic Time Series Modeling and Interaction Networks
%B Applications of Evolutionary Computing, EvoApplications 2011: EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, EvoTRANSLOG
%S LNCS
%E Cecilia Di Chio and Anthony Brabazon and Gianni Di Caro and Rolf Drechsler and Marc Ebner and Muddassar Farooq and Joern Grahl and Gary Greenfield and Christian Prins and
   Juan Romero and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Neil Urquhart and A. Sima Uyar
%V 6625
%D 2011
%P 101--110
%I Springer Verlag Berlin
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming, Finance, Econometrics
%X Macro-economic models describe the dynamics of economic quantities. The estimations and forecasts produced by such models play a substantial role for financial and
   political decisions. In this contribution we describe an approach based on genetic programming and symbolic regression to identify variable interactions in large datasets.
   In the proposed approach multiple symbolic regression runs are executed for each variable of the dataset to find potentially interesting models. The result is a variable
   interaction network that describes which variables are most relevant for the approximation of each variable of the dataset. This approach is applied to a macro-economic
   dataset with monthly observations of important economic indicators in order to identify potentially interesting dependencies of these indicators. The resulting interaction
   network of macro-economic indicators is briefly discussed and two of the identified models are presented in detail. The two models approximate the help wanted index and the
   CPI inflation in the US.
%8 27-29  April
%Z Part of \citeDiChio:2011:evo_b EvoApplications2011 held inconjunction with EuroGP'2011, EvoCOP2011 and EvoBIO2011

%A Gabriel Kronberger
%A Michael Kommenda
%A Michael Affenzeller
%T Overfitting detection and adaptive covariant parsimony pressure for symbolic regression
%B 3rd symbolic regression and modeling workshop for GECCO 2011
%E Steven Gustafson and Ekaterina Vladislavleva
%D 2011
%P 631--638
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X Covariant parsimony pressure is a theoretically motivated method primarily aimed to control bloat. In this contribution we describe an adaptive method to control covariant
   parsimony pressure that is aimed to reduce overfitting in symbolic regression. The method is based on the assumption that overfitting can be reduced by controlling the
   evolution of program length. Additionally, we propose an overfitting detection criterion that is based on the correlation of the fitness values on the training set and a
   validation set of all models in the population. The proposed method uses covariant parsimony pressure to decrease the average program length when over fitting occurs and
   allows an increase of the average program length in the absence of overfitting. The proposed approach is applied on two real world datasets. The experimental results show
   that the correlation of training and validation fitness can be used as an indicator for overfitting and that the proposed method of covariant parsimony pressure adaption
   alleviates overfitting in symbolic regression experiments with the two datasets.
%8 12-16  July
%Z Also known as \cite2002060 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Dan B. Kroymann
%T Dynamic Population Based Evolution using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 228--233
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A David Kruckemyer
%T An Approach to Analyzing the Immune System with Genetic Algorithms
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 81--90
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, John Holland Echo
%8 June
%Z This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford
   University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Hairong Kuang
%A Lubomir F. Bic
%A Michael B. Dillencourt
%A Adam C. Chang
%T PODC: Paradigm-oriented distributed computing
%B Proceedings. 7th IEEE Workshop on Future Trends of Distributed Computing Systems
%D 1999
%P 169--175
%I 
%C Cape Town
%K genetic algorithms, genetic programming
%U http://www.ics.uci.edu/~bic/messengers/papers/FTDCS99.ps
%X We describe an environment for distributed computing that uses the concept of well-known paradigms. The main advantage of paradigm-oriented distributed computing is that
   the user only needs to specify application-specific sequential code, while the underlying infrastructure takes care of the parallelisation and distribution. The main
   features of the proposed approach, called PODC, are the following: (1) It is intended for loosely-coupled network environments, not specialised multiprocessors; (2) it is
   based on an infrastructure of mobile agents; (3) it supports programming in C, rather than a functional or special-purpose language, and (4) it provides a Web-based
   interactive graphics interface through which programs are constructed, invoked, and monitored. The three paradigms presently supported in PODC are the bag-of-tasks,
   branch-and-bound search, and genetic programming. We demonstrate their use, implementation, and performance within the mobile agent-based PODC environment
%8 20-22  Decemeber

%A Hairong Kuang
%A Lubomir F. Bic
%A Michael B. Dillencourt
%T Paradigm-Oriented Distributed Computing Using Mobile Agents
%B Proceedings of the The 20th International Conference on Distributed Computing Systems (ICDCS 2000)
%D 2000
%P 11--19
%I IEEE Computer Society
%C Taipei, Taiwan
%K genetic algorithms, genetic programming, Programming Skeletons, Paradigm-Oriented Computing, Distributed Computing, Mobile Agents, a Bag of Task, Branch and Bound
%U http://citeseer.ist.psu.edu/486812.html
%X We describe the implementation underlying an environment for distributed computing that uses the concept of well-known paradigms. The main advantage of paradigmoriented
   distributed computing is that the user only needs to specify application-specific sequential code, while the underlying infrastructure takes care of the parallelization and
   distribution. The main features of the proposed approach, called PODC, which differentiate it from other approaches, are the following: (1) It is intended for
   looselycoupled network environments, not specialized multiprocessors; (2) it is based on an infrastructure of mobile agents; (3) it supports programming in C, rather than a
   functional or special-purpose language, and (4) it provides a Webbased interactive graphics interface through which programs are constructed, invoked, and monitored. The
   three paradigms presently supported in PODC are the bag-oftasks, the branch-and-bound, and genetic programming. We describe their implementation and performance within the
   mobile agent-based PODC environment.
%8 April  10-13
%Z PhD thesis awarded to Hairong Kuang?
%@ 0-7695-0601-1

%A Hairong Kuang
%T Paradigm-Oriented Distributed Computing Using Mobile Agents
%R Ph.D. Thesis
%D 2002
%I 
%I School of Information and Computer Sciences, University of California, Irvine
%C CA 92717-3425, USA
%K genetic algorithms, genetic programming
%U http://www.ics.uci.edu/~bic/messengers/papers/HK-DISSERT.ps
%Z PODC 6.8Mbyte

%A Hairong Kuang
%A Lubomir F. Bic
%A Michael B. Dillencourt
%T PODC: Paradigm-oriented distributed computing
%J Journal of Parallel and Distributed Computing
%V 65
%N 4
%D 2005
%P 506--518
%I 
%K genetic algorithms, genetic programming, Distributed computing, Paradigm-oriented computing, Mobile agents, Bag of tasks, Branch and bound, Finite difference,
   Individual-based simulation
%U http://www.sciencedirect.com/science/article/B6WKJ-4FFNBSD-1/2/f7a35dca806bd7431f2fdcaa99a03b67
%X We describe an environment for distributed computing that uses the concept of well-known paradigms. The main advantage of paradigm-oriented distributed computing (PODC) is
   that the user only needs to specify application-specific sequential code, while the underlying infrastructure takes care of the parallelisation and distribution. The main
   features of the proposed approach, called PODC, are the following: (1) It is intended for loosely coupled network environments, not specialised multiprocessors; (2) it is
   based on an infrastructure of mobile agents; (3) it supports programming in C, rather than a functional or special-purpose language, and (4) it provides an interactive
   graphics interface through which programs are constructed, invoked, and monitored. We discuss five paradigms presently supported in PODC: the bag-of-tasks, branch-and-bound
   search, genetic programming, finite difference, and individual-based simulation. We demonstrate their use, implementation, and performance within the mobile agent-based
   PODC environment.
%8 April

%A Jiri Kubalik
%A Leon Rothkrantz
%A Jiri Lazansky
%T Genetic Programming Fuzzy Rule Extractor Using Class Preserving Representation
%B BNAIC
%D 2001
%P 25--26 October
%I De Rode Hoed, Amsterdam, Holland
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.6332
%X This paper describes a genetic programming approach to the construction of fuzzy classification system with if-then fuzzy rules. Recently many research studies were
   focusing on evolutionary techniques for automatically extracting fuzzy rules from data. In this paper we present a method based on genetic programming with a special
   structure preserving representation and special rule base adjusting operators working on it. First results show that the new features added to standard GP extractor
   considerably improve both performance and comprehensibility of found fuzzy rules.
%Z http://hcs.science.uva.nl/bnaic01/

%A Jiri Kubalik
%A Jan Koutnik
%A Leon J. M. Rothkrantz
%T Grammatical Evolution with Bidirectional Representation
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 354--363
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming, grammatical evolution
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=354
%X Grammatical evolution is an evolutionary algorithm designed to evolve programs in any language. Grammatical evolution operates on binary strings and the mapping of the
   genotype onto the phenotype (the tree representation of the programs) is provided through the grammar described in the form of production rules. The program trees are
   constructed in a pre-order fashion, which means that as the genome is traversed first the left most branch of the tree is completed then the second from the left one etc.
   Once two individuals are crossed over by means of simple one-point crossover the tail parts of the chromosomes (originally encoding the structures on the right side of the
   program tree) may map on different program structures within the new context. Here we present a bidirectional representation which helps to equalize the survival rate of
   both the program structures appearing on the left and right side of the program parse tree.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Jiri Kubalik
%A Jan Faigl
%T Iterative Prototype Optimisation with Evolved Improvement Steps
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 154--165
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050154.pdf
%X Evolutionary algorithms have already been more or less successfully applied to a wide range of optimisation problems. Typically, they are used to evolve a population of
   complete candidate solutions to a given problem, which can be further refined by some problem-specific heuristic algorithm. In this paper, we introduce a new framework
   called \it Iterative Prototype Optimisation with Evolved Improvement Steps. This is a general optimisation framework, where an initial prototype solution is being improved
   iteration by iteration. In each iteration, a sequence of actions/operations, which improves the current prototype the most, is found by an evolutionary algorithm. The
   proposed algorithm has been tested on problems from two different optimisation problem domains - binary string optimisation and the travelling salesman problem. Results
   show that the concept can be used to solve hard problems of big size reliably achieving comparably good or better results than classical evolutionary algorithms and other
   selected methods.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Jeremy Kubica
%A Eleanor Rieffel
%T Creating a Smarter Membrane: Automatic Code Generation for Modular Self-Reconfigurable Robots
%B ICRA'02 Proceedings of the 2002 IEEE International Conference on Robotics and Automation
%V 1
%D 2002
%P 793--800
%I 
%C Washington, DC
%K genetic algorithms, genetic programming
%X This work extends previous research on developing control software for modular robotic smart membranes to a second module type with more complicated movement, to 3-D
   membranes, to the presence of gravity, and to less easily manipulatable objects. Moreover, it extends the capabilities of the membranes from simple filtering to more
   complex sorting tasks. The control software we developed is completely decentralized, and automatically generated.
%8 11-15  May
%Z ICRA2002 http://www.icra-iros.com/icra2002/
%@ 0-7803-7272-7

%A Jeremy Kubica
%A Eleanor Rieffel
%T Collaborating With A Genetic Programming System To Generate Modular Robotic Code
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 804--811
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, automatic code generation, human-computer collaboration, primitives, problem set up, robotics
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A N. Kubota
%A F. Kojima
%A S. Hashimoto
%A T. Fukuda
%T Information transformation by virus-evolutionary genetic programming
%B Proceedings 4th International Symposium on Artificial Life and Robotics
%E M. Sugisaka
%D 1999
%I 
%I Oita University
%C B-Con Plaza, Beppu, Oita, Japan
%K genetic algorithms, genetic programming
%8 19-22  January
%Z AROB'99 Details from www site etc. Updated to \citeKubota:2000:ALRob

%A Naoyuki Kubota
%A Setsuo Hashimoto
%A Fumio Kojima
%A Kazuhiko Taniguchi
%T GP-Prepocessed Fuzzy Inference for The Energy Load Prediction
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%D 2000
%P 1--6
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, hybrid systems
%X This paper deals with a prediction system based on genetic programming and fuzzy inference system. In real problems with many parameters, the prediction performance depends
   on the feature extraction and selection. These processes are performed using methods of multivariate statistical analysis by human operators. However, we should
   automatically perform feature extraction and selection from many measured data. This paper applies genetic programming for the feature extraction and selection, and further
   use fuzzy inference for the building energy load prediction. The functions generated by GP translate the measured data into the meaningful information that is used as input
   data to the fuzzy inference system. The simulation results show that the proposed method can extract meaningful information from the measured data and can predict the
   building energy load of the next day.
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Naoyuki Kubota
%A Fumio Kojima
%A Setsuo Hashimoto
%A Toshio Fukuda
%T Information transformation by virus-evolutionary genetic programming
%J Artificial Life and Robotics
%V 4
%N 3
%D 2000
%P 171--174
%I 
%K genetic algorithms, genetic programming, Computational intelligence, Classifier system
%U http://www.springerlink.com/content/k610733674621p83/fulltext.pdf
%X This paper deals with genetic programming (GP) for information translation. The GP can generate a structured computer program, but it is difficult to define recursive
   functions automatically. Therefore, we propose a virus-evolutionary genetic programming (VE-GP) composed of two populations; host and virus. Here, a virus plays the role of
   an automatically defined function. First, the VE-GP is applied to a function approximation problem, and the simulation result shows that the VE-GP can generate a function
   to approximate the given function with small errors. Next, the VE-GP is applied to the information transformation for a classification task, and the simulation result shows
   that the VE-GP can generate a function to classify a given data set.
%8 September
%Z This work was presented in part at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19-22, 1999 \citekubota:1999:itveGP (1)
   Department of Human and Artificial Intelligent Systems, Fukui University, 3-9-1 Bunkyo, 910-8507 Fukui, Japan (2) Graduate School of Science and Technology, Division of
   System Function Science, Kobe University, Kobe, Japan (3) Center for Cooperative Research in Advanced Science and Technology, Nogoya University, Nagoya, Japan

%A Tetsuji Kuboyama
%T Matching and Learning in Trees
%R Ph.D. Thesis Doctor of Engineering
%D 2007
%I 
%I Department of Advanced Interdisciplinary Studies, Graduate School of Engineering, The University of Tokyo
%K approximate pattern matching, tree edit distance, alignment of trees, machine learning, kernel methods, tree kernels, convolution kernel, glycans
%U http://tk.cc.gakushuin.ac.jp/doc/kuboyama2007phd.pdf
%X This thesis presents a unified understanding of edit-based approaches to approximate tree matching and introduces new facts on the subject. It also provides a broad view of
   kernel-based learning methods for trees, and proposes novel methods based on this view. These contributions have a wide range of applications to pattern matching,
   computational biology, and many other areas of computer science. As an example, this thesis includes an application to computational biology to demonstrate the
   effectiveness of a novel learning method developed in this work. This thesis is divided into two parts. The first part is devoted to a detailed analysis of the classes of
   edit-based approximate tree matching such as the tree edit problem and the alignment problem for trees. We focus on the notion of tree mapping in order to describe the
   semantics of approximate tree matching. We then establish an algebraic model of approximate tree matching. This algebraic model enables us to reveal unknown hidden
   relationships among a variety of edit-based approximate tree matching problems, which include the equivalence between two problems, the alignment problem and the
   less-constrained edit problem. Also, we give the tree mapping for the alignment of trees, which has been unknown for the past decade. In addition, we reveal a class
   hierarchy of edit-based approximate tree matching. The second part focuses on tree kernels for kernel-based learning of trees. We first show that existing tree kernels are
   formulated by counting functions of tree mappings from the unified view based on the class hierarchy of approximate tree matching established in the first part. In
   addition, we develop two novel tree kernels more flexible than known tree kernels, and show that the counting function for the alignment of trees does not satisfy a
   requirement for a tree kernel. The last half of the second part addresses the development of a faster tree kernel without sacrificing its learning performance. We propose
   two simple tree kernels, a spectrum tree kernel and its variant, a gram distribution kernel. The effectiveness of these methods are demonstrated by applying them to a
   problem in computational biology.

%A Felix K{\"u}hling
%A Krister Wolff
%A Peter Nordin
%T Brute-Force Approach to Automatic Induction of Machine Code on CISC Architectures
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 288--297
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming, CISC
%X The usual approach to address the brittleness of machine code in evolution is to constrain mutation and crossover to ensure syntactic closure. In the novel approach
   presented here we use no constraints on the operators, they all work blindly on the binaries in memory, but we instead encapsulate the code and trap all resulting
   exceptions. The new approach presented here for machine code evolution on CISC architectures is based on the observation that modern CPUs can cope with incorrect programmes
   and report errors to the operating system. This way it is possible to return to very simple genetic operators with the objective of increased performance. Furthermore the
   instruction set used by evolved programmes is no longer limited by the genetic programming system but only by the CPU it runs on. The mapping between evolution platform and
   execution platform becomes almost complete, ensuring correct low-level behaviour of all CPU functions.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Hans Kuhlmann
%A Mike Hollick
%T Genetic Programming in C/C++
%D 1995
%I 
%K genetic algorithms, genetic programming
%U http://www.cis.upenn.edu/~hollick/genetic/paper2.html
%X Genetic programming is a relatively new form of artificial intelligence, and is based on the ideas of Darwinian evolution and genetics. The foremost work in genetic
   programming is John Koza's Genetic Programming, which describes a set of LISP routines which modify randomly generated LISP strings. In this paper, We attempt to explain
   the paradigm of genetic programming, and its implementation in the C programming language. we shall present results obtained from various trials, and make comments on the
   viability of this new form of genetic programming.
%O CSE99/CIS899 Final Report
%8 May

%A Ekkehard Kuhn
%T Programmed synthesis of informational molecules monitored by linear and circular genetic control chains
%J Journal of Theoretical Biology
%V 95
%N 3
%D 1982
%P 423--464
%I 
%U http://www.sciencedirect.com/science/article/B6WMD-4F1J831-FM/2/39f40e0509ca0bbc2fac30af305105fe
%Z Not on GP

%A Ulrich Kuhne
%A Nicole Drechsler
%T Finding Compact BDDs Using Genetic Programming
%B Applications of Evolutionary Computing, EvoWorkshops2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC
%S LNCS
%E Franz Rothlauf and Jurgen Branke and Stefano Cagnoni and Ernesto Costa and Carlos Cotta and Rolf Drechsler and Evelyne Lutton and Penousal Machado and Jason H. Moore and
   Juan Romero and George D. Smith and Giovanni Squillero and Hideyuki Takagi
%V 3907
%D 2006
%P 308--319
%I Springer Verlag Berlin
%C Budapest
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3907&spage=308
%X Binary Decision Diagrams (BDDs) can be used to design multiplexor based circuits. Unfortunately, the most commonly used kind of BDDs ? ordered BDDs ? has exponential size
   in the number of variables for many functions. In some cases, more general forms of BDDs are more compact. In contrast to the minimisation of OBDDs, which is well
   understood, there are no heuristics for the construction of compact BDDs up to today. In this paper we show that compact BDDs can be constructed using Genetic Programming.
%8 10-12  April
%Z part of \citeevows06 also known as \citeconf/evoW/KuhneD06
%@ 3-540-33237-5

%A Ashwinikumar Kulkarni
%A B. S. C. Naveen Kumar
%A Vadlamani Ravi
%A Upadhyayula Suryanarayana Murthy
%T Colon cancer prediction with genetics profiles using evolutionary techniques
%J Expert Systems with Applications
%V 38
%N 3
%D 2011
%P 2752--2757
%I 
%K genetic algorithms, genetic programming, Microarray, Gene expression, Tumour classification, t-Statistic, Mutual information, Feature selection, Genetically Evolved
   Decision Trees
%U http://www.sciencedirect.com/science/article/B6V03-50YK82C-2/2/80df229bab9391914935d3f037d6b030
%X Microarray data provides information on gene expression levels of thousands of genes in a cell in a single experiment. DNA microarray is a powerful tool in the diagnosis of
   cancer. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In this study comparison between class prediction
   accuracy of two different classifiers, Genetic Programming and Genetically Evolved Decision Trees, was carried out using the best 10 and best 20 genes ranked by the
   t-statistic and mutual information. Genetic Programming proved out to be the better classifier for this dataset based on area under the receiver operating characteristic
   curve (AUC) and total accuracy using mutual information based feature selection. We conclude that Genetic Programming together with mutual information based feature
   selection is the most efficient alternative to the existing colon cancer prediction techniques.

%A A. Vincent Antony Kumar
%A P. Balasubramaniam
%T Optimal control for linear singular system using genetic programming
%J Applied Mathematics and Computation
%V 192
%N 1
%D 2007
%P 78--89
%I 
%K genetic algorithms, genetic programming, Optimal control, Linear singular system, Matrix Riccati differential equation, RungeKutta method, Grammatical evolution
%X In this paper, optimal control for linear singular system with quadratic performance is obtained using genetic programming (GP). The goal is to provide optimal control with
   reduced calculus effort by comparing the solutions of the matrix Riccati differential equation (MRDE), obtained from well known traditional RungeKutta (RK) method and
   genetic programming method. To obtain the optimal control, the solution of MRDE is computed based on grammatical evolution. Accuracy of the solution of the GP approach to
   the problem is qualitatively better. An illustrative numerical example is presented for the proposed method.
%8 1  September

%A Rajeev Kumar
%A Ashwin H. Joshi
%A Krishna K. Banka
%A Peter I. Rockett
%T Evolution of hyperheuristics for the biobjective 0/1 knapsack problem by multiobjective genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1227--1234
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, 0-1 knapsack problem, combinatorial optimisation, genetic algorithms (GA), heuristics, multiobjective optimisation, optimisation
   methods, Pareto front
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1227.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389335

%A Rajeev Kumar
%A Bipul Kumar Bal
%A Peter Rockett
%T Multiobjective genetic programming approach to evolving heuristics for the bounded diameter minimum spanning tree problem: MOGP for BDMST
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 309--316
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X The bounded-diameter (or diameter-constrained) minimum spanning tree (BDMST) problem is a well-studied combinatorial optimization problem for which several heuristics such
   as: one-time tree construction (OTTC), center based tree construction(CBTC), iterative refinement (IR) and randomized greedy heuristics (RGH) have been developed. Very
   little work, however, has been done on producing heuristics for BDMSTs using evolutionary algorithms. In this paper we have used multiobjective genetic programming (MOGP)
   to explore the evolution of a set of heuristics for the BDMST problem. The quality of the Pareto fronts obtained from the MOGP-evolved heuristics is comparable to, or in
   some cases better than, the Pareto fronts generated by established, hand-crafted heuristics. MOGP is thus a very promising technique for finding heuristics for BDMSTs and
   more general multiobjective combinatorial optimizations.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Sanjeev Kumar
%A Peter Bentley
%T The ABCs of evolutionary design: Investigating the evolvability of embryogenies for morphogenesis
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 164--170
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/P.Bentley/KUBEC1.pdf
%X Previous work investigated three types of embryogeny: External, Explicit, and Implicit. This paper focuses on the two most interesting embryogenies: explicit and implicit,
   investigating the evolvability and scalability of both embryogenies for morphogenesis. The problem set is that of evolving certain predefined shapes-letters of the
   alphabet. The results show that both embryogenies are good at defining different morphologies, but significantly, the implicit embryogeny incurs no increase in genotype
   size as the problem is scaled.
%8 13  July
%Z GECCO-99LB

%A Sanjeev Kumar
%A Peter J. Bentley
%T Implicit Evolvability: An Investigation into the Evolvability of an Embryogeny
%B Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference
%E Darrell Whitley
%D 2000
%P 198--204
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/ucacpjb/KUBEC2.pdf
%X This paper investigates the evolvability of an implicit embryogeny-based representation for the evolution of 3-D morphologies. Previous results using this representation
   have shown that this particular incarnation of an implicit embryogeny does not lend itself well to evolution. Two different experiments are described, the results of which
   suggest that a many-to-one genotype-to-phenotype mapping is not sufficient to ensure evolvability. The paper concludes by suggesting attributes that a better representation
   should have.
%8 8  July
%Z Part of whitley:2000:GECCOlb

%T On Growth, Form and Computers
%E Sanjeev Kumar and Peter J. Bentley
%D 2003
%I Academic Press
%K genetic algorithms, genetic programming, computer, Computers, Form, growth
%U http://www.amazon.com/Growth-Form-Computers-Sanjeev-Kumar/dp/0124287654
%@ 0-12-428765-4

%A Sanjeev Kumar
%T The Art of Nature: Evolving Mechanisms of Development for Self- Organization and Differentiation
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 1
%D 2005
%P 551--558
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X Recently, researchers have recognised the benefits of learning from biological development in order to engineer self-organising solutions to problems. This paper explores
   the application of the developmental metaphor to the problem of controlling single and multicellular development. In this paper, a summary of experiments performed using a
   multicellular test-bed model of biological development, the Evolutionary Developmental System (EDS), is presented. The EDS is shown to successfully evolve genetic
   regulatory networks that specify and control the behaviour of single cells and the construction of 3D multicellular geometric morphologies to explore self organisation and
   analogues of phenomena akin to biological cell differentiation in multicellular development.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Gireesh Kumar T
%A Sapna Sasidharan
%A N. Karthikha
%A A. P. Sherly
%A Y. Avani
%T An Efficient Embedding and Restoration Steganographic Scheme for Secure Multimedia Communication
%B 2010 International Conference on Advances in Computer Engineering (ACE)
%D 2010
%P 363--365
%I 
%K genetic algorithms, genetic programming
%X A novel steganographic scheme is proposed. For image compression, wavelets and genetic programming is used. This image compression is build upon the DWPT to shorten the
   image, and to provide better quality levels at better compression ratios. Quantising an image in the wavelet domain allows for easier evolution of genetic programs without
   sacrificing much in terms of image quality. Diamond Encoding is a new concept of data hiding used here. The Diamond Encoding provides an easy way to produce a more
   perceptible result than those yielded by other embedding methods. This method is capable of hiding more secret data while keeping the stego image quality degradation
   imperceptible. In this paper, image restoration is also done using morphological neural network to provide better performance.
%8 20-21  June
%Z Also known as \cite5532802

%A N. Kumarappan
%A M. R. Mohan
%T Lambda Based Combined Economic and Emission Dispatch Using Hybrid Genetic Algorithm for Utility System
%B Proceedings of The First Asian-Pacific Workshop on Genetic Programming
%E Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan
%D 2003
%P 45--52
%I 
%C Rydges (lakeside) Hotel, Canberra, Australia
%K genetic algorithms
%8 8  Decemeber
%Z \citeaspgp03
%@ 0-9751724-0-9

%A K. Kumarci
%A P. K. Dehkordi
%A I. Mahmodi
%T Calculation of Plate Natural Frequency by Genetic Programming
%J Journal of Applied Sciences
%V 10
%N 6
%D 2010
%P 451--461
%I Asian Network for Scientific Information
%K genetic algorithms, genetic programming, natural frequency, modal analysis, plate
%U http://docsdrive.com/pdfs/ansinet/jas/2010/451-461.pdf
%X This research emphasises the study of plate natural frequency by Genetic Programming (GP). In this study we tried to investigate the modelling of plates and modal analysis
   by finite element method and FEM software. Six parameters were used through plate modeling: length, width, thickness, Poison's ratio, modulus of elasticity and density.
   This study intends to estimate the natural frequency of seven different metals as follow: high strength low alloy steel, grey iron, aluminium by alloy 1100-H14 and 2014-T0
   and copper, cold rolling steel and malleable iron, which were different for metals. After frequency estimation by fem software, 100 samples of each metal were produced and
   GP was used for modal training to choose the best model in metal modeling. The mentioned model specifications have been produced mathematically. Afterwards, 100 extra
   samples were produced by FEM software. Using obtained models, all of results were tested. The error percentage of GP models and FEM software was compared and the results
   were produced as diagram. Considering spread of matters, all of stages are indicated only for high strength low alloy steel and the list of the best GP models for another
   metal is indicated in conclusion. Finally, due to results, modal analysis simulation in plate natural frequency was introduced by GP as a new method by GP.

%A Sourav Kundu
%T Complexity Engineering, Evolution and Optimality of Structures
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 796
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/ga-869.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Robert Kunz
%T Genetic Discovery of Solutions to the Broadside Game
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 234--243
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Chan-Sheng Kuo
%A Tzung-Pei Hong
%A Chuen-Lung Chen
%T Applying genetic programming technique in classification trees
%J Soft Computing
%V 11
%N 12
%D 2007
%P 1165--1172
%I 
%K genetic algorithms, genetic programming, Classification tree, Knowledge base, Genetic operator
%X Classification problems are often encountered in many applications. In the past, classification trees were often generated by decision-tree methods and commonly used to
   solve classification problems. In this paper, we have proposed an algorithm based on genetic programming to search for an appropriate classification tree according to some
   criteria. The classification tree obtained can be transferred into a rule set, which can then be fed into a knowledge base to support decision making and facilitate daily
   operations. Two new genetic operators, elimination and merge, are designed in the proposed approach to remove redundancy and subsumption, thus producing more accurate and
   concise decision rules than that without using them. Experimental results from the credit card data also show the feasibility of the proposed algorithm.
%8 October

%A Chan-Sheng Kuo
%A Tzung-Pei Hong
%A Chuen-Lung Chen
%T A Knowledge-Evolution Strategy Based on Genetic Programming
%B International Conference on Convergence and Hybrid Information Technology, ICHIT '08
%D 2008
%P 43--48
%I 
%K genetic algorithms, genetic programming, evolutionary process, integrated classification tree, knowledge management, knowledge-evolution strategy, learning efficiency,
   organizational need, knowledge management, organisational aspects, trees (mathematics)
%X Knowledge evolution is an important issue in knowledge management since enterprises face keen competition and need to keep the latest knowledge with time in an
   organization. In this paper, we proposed a GP-based knowledge-evolution framework to search for a good integrated classification tree with different evolving time points.
   The proposed approach can learn the evolving knowledge, integrating original and new knowledge, to deal properly with the organizational need for updating the latest
   knowledge as time goes on in a dynamic environment. In addition, we developed the initial population, consisting of four proportions, to accomplish suitable diversity and
   thus raise the search range as well as next learning efficiency in the evolutionary process.
%8 August
%Z Also known as \cite4622798

%A Chan-Sheng Kuo
%A Tzung-Pei Hong
%A Chuen-Lung Chen
%T An Improved Knowledge-Acquisition Strategy Based on Genetic Programming
%J Cybernetics and Systems
%V 39
%N 7
%D 2008
%P 672--685
%I 
%K genetic algorithms, genetic programming
%X Knowledge acquisition can deal with the task of extracting desirable or useful knowledge from data sets for a practical application. In this paper, we have modified our
   previous gp-based learning strategy to search for an appropriate classification tree. The proposed approach consists of three phases: knowledge creation, knowledge
   evolution, and knowledge output. In the creation phase, a set of classification trees are randomly generated to form an initial knowledge population. In the evolution
   phase, the genetic programming technique is used to generate a good classification tree. In the output phase, the final derived classification tree is transferred as a rule
   set, then outputted to the knowledge base to facilitate the inference of new data. One new genetic operator, separation, is designed in this proposed approach to remove
   contradiction, thus producing more accurate classification rules. Experimental results from the diagnosis of breast cancers also show the feasibility of the proposed
   algorithm.
%8 October

%A Huang-Cheng Kuo
%A Kuan-Yu Su
%A Jia-Hao Li
%A Ping-Lin Ong
%T Classification of protein-protein recognition with genetic programming
%B 5th International Conference on Computer Sciences and Convergence Information Technology (ICCIT)
%D 2010
%P 722--726
%I 
%K genetic algorithms, genetic programming, amino acids, physicochemical properties, protein interaction, protein-protein recognition classification, transient protein
   interactions, bioinformatics, pattern classification, proteins
%X Proteins interact with each other with their binding sites. In this paper, we classify types of interactions using physicochemical properties information of the binding
   site. We distinguish between permanent protein interactions and transient protein interactions. Basic data of the amino acids on binding sites are retrieved. A method using
   genetic programming to construct features from the basic data is proposed. The method achieves an average classification accuracy of 80percent.
%8 November  30-  Decemeber  2
%Z Also known as \cite5711148

%A P. Dwight Kuo
%A Wolfgang Banzhaf
%A Andre Leier
%T Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence
%J Biosystems
%V 85
%N 3
%D 2006
%P 177--200
%I 
%K genetic algorithms, genetic programming, Regulatory networks, GRNs, Network motifs, Scale-free, Small-world, Duplication and divergence
%U http://www.sciencedirect.com/science/article/B6T2K-4JVT1VS-1/2/7810c8e58f3e12020b48fe28d9e52097
%X Topological measures of large-scale complex networks are applied to a specific artificial regulatory network model created through a whole genome duplication and divergence
   mechanism. This class of networks share topological features with natural transcriptional regulatory networks. Specifically, these networks display scale-free and
   small-world topology and possess subgraph distributions similar to those of natural networks. Thus, the topologies inherent in natural networks may be in part due to their
   method of creation rather than being exclusively shaped by subsequent evolution under selection. The evolvability of the dynamics of these networks is also examined by
   evolving networks in simulation to obtain three simple types of output dynamics. The networks obtained from this process show a wide variety of topologies and numbers of
   genes indicating that it is relatively easy to evolve these classes of dynamics in this model.

%A Tzu-Wen Kuo
%A Shu-Heng Chen
%T Genetic Programming and International Short-Term Capital Flow
%B Computing in Economics and Finance
%D 2003
%I 
%I Society for Computational Economics
%C University of Washington, Seattle, USA
%K genetic algorithms, genetic programming
%8 11-13  July
%Z 22 aug 2004 http://ideas.repec.org/p/sce/scecf3/74.html CEF 2003

%A Jean-Jacques Kupiec
%T On the lack of specificity of proteins and its consequences for a theory of biological organization
%J Progress in Biophysics and Molecular Biology
%V 102
%N 1
%D 2010
%P 45--52
%I 
%U http://www.sciencedirect.com/science/article/B6TBN-4XPB6TR-2/2/30877a178ddf029281f1d7d248534bc1
%Z Not on GP

%A Kentarou Kurashige
%A Toshio Fukuda
%A Haruo Hoshino
%T Reusing Primitive and Acquired Motion Knowledge for Gait Generation of a Six-legged Robot Using Genetic Programming
%J Journal of Intelligent and Robotic Systems
%V 38
%N 1
%D 2003
%P 121--134
%I 
%K genetic algorithms, genetic programming, primitive motion knowledge, acquired motion knowledge, six-legged robot, gait generation
%X There has been growing interest in motion planning problems for mobile robots. In this field, the main research is to generate a motion for a specific robot and task
   without previously acquired motions. However it is too wasteful not to use hard-earned acquired motions for other tasks. Here, we focus on a mechanism of reusing acquired
   motion knowledge and study a motion planning system able to generate and reuse motion knowledge. In this paper, we adopt a tree-based representation for expressing
   knowledge of motion, and propose a hierarchical knowledge for realizing a reuse mechanism. We construct a motion planning system using hierarchical knowledge as motion
   knowledge and using genetic programming as a learning method. We apply a proposed method for the gait generation task of a six-legged locomotion robot and show its
   availability with computer simulation.
%8 September
%Z Article ID: 5118235

%A Takuya Kuroda
%A Hiroto Iwasawa
%A Eisuke Kita
%T Application of advanced Grammatical Evolution to function prediction problem
%J Advances in Engineering Software
%V 41
%N 12
%D 2010
%P 1287--1294
%I 
%K genetic algorithms, genetic programming, Grammatical Evolution, GE, Backus Naur Form, BNF, Function prediction, Santa Fe trail, Nikkei stock average
%U http://www.sciencedirect.com/science/article/B6V1P-5167DR5-1/2/d992cacdff191a5bc78722add7146d07
%X Grammatical Evolution (GE) is one of the evolutionary algorithms to find functions and programs, which can deal according to a syntax with tree structure by one-dimensional
   chromosome of Genetic Algorithm. An original GE starts from the definition of the syntax by means of Backus Naur Form (BNF). Chromosome in binary number is translated to
   that in decimal number. The BNF syntax selects according to the remainder of the decimal number with respect to the total number of candidate rules. In this study, we will
   introduce three schemes for improving the convergence property of the original GE. In numerical examples, the original GE is compared in function identification problem
   with the Genetic Programming (GP), which is one of the most popular evolutionary algorithm to find unknown functions or programs. Three algorithms are compared in Santa Fe
   trail problem and prediction problem of Nikkei stock average, which finds programs to control artificial ants collecting foods. The results show that the efficiency of
   schemes depends on the problem to be solved and that the schemes 1 and 2 are effective for Santa Fe trail problem and prediction problem of Nikkei stock average,
   respectively.

%A Brett D. Kurtin
%T Solving Coding Problems in Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 39--49
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Ibrahim Kuscu
%A Chris Thornton
%T Design of artificial neural networks using genetic algorithms: review and prospect
%R Cognitive Science Research Paper 319
%D 1994
%I 
%I School of Cognitive and Computing Sciences, University of Sussex
%C Falmer, Brighton, Sussex, UK
%K genetic algorithms, genetic programming
%U ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp319.ps.Z
%X The design of Artificial Neural Networks by Genetic Algorithm is useful in terms of (1) automating and optimising the design and (2) finding biologically plausible models.
   This paper presents a review of the state of the art and research prospects in the area.
%8 30  April

%A Ibrahim Kuscu
%T Evolution of learning rules for supervised tasks I: simple learning problems
%R Cognitive Science Research Paper 394
%D 1995
%I 
%I School of Cognitive and Computing Sciences, University of Sussex
%C Falmer, Brighton, Sussex, UK
%K genetic algorithms, genetic programming
%U ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp394.ps.Z
%X Initial experiments with a genetic-based encoding schema are presented as a potentially powerful tool to discover learning rules by means of evolution. Several simple
   supervised learning tasks are tested. The results indicate the potential of the encoding schema to discover learning rules for more complex and larger learning problems.
%8 10  November

%A Ibrahim Kuscu
%T Evolution of learning rules for supervised tasks II: hard learning problems
%R Cognitive Science Research Paper 395
%D 1995
%I 
%I School of Cognitive and Computing Sciences, University of Sussex
%C Falmer, Brighton, Sussex, UK
%K genetic algorithms, genetic programming
%U ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp395.ps.Z
%X Recent experiments with a genetic-based encoding schema are presented as a potentially powerful tool to discover learning rules by means of evolution. The representation
   used is similar to the one used in Genetic Programming (GP) but it employs only a fixed set of functions to solve a variety of problems. In this paper three Monks' and
   parity problems are tested. The results indicate the usefulness of the encoding schema in discovering learning rules for hard learning problems. The problems and future
   research directions are discussed within the context of GP practices.
%8 10  November

%A Ibrahim Kuscu
%T Incrementally learning the rules for supervised tasks: the Monk's problems
%R Cognitive Science Research Paper 396
%D 1995
%I 
%I School of Cognitive and Computing Sciences, University of Sussex
%C Falmer, Brighton, Sussex, UK
%K genetic algorithms, genetic programming
%U ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp396.ps.Z
%X In previous experiments [4][5] evolution of variable length mathematical expressions containing input variables was found to be useful in finding learning rules for simple
   and hard supervised tasks. However, hard learning problems required special attention in terms of their need for larger size codings of the potential solutions and their
   ability of generalisation over the testing set. This paper describes new experiments aiming to find better solutions to these issues. Rather than evolution a hill climbing
   strategy with an incremental coding of potential solutions is used in discovering learning rules for the three Monks' problems. It is found that with this strategy larger
   solutions can easily be coded for. Although a better performance is achieved in training for the hard learning problems, the ability of the generalisation over the testing
   cases is observed to be poor.
%8 7  Decemeber

%A Ibrahim Kuscu
%T Evolution of Learning Rules for Hard Learning Problems
%B Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming
%E Lawrence J. Fogel and Peter J. Angeline and Thomas Baeck
%D 1996
%I MIT Press Cambridge, MA, USA
%C San Diego
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/184922.html
%8 February  29-  March  3
%Z EP-96 http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383 Details from http://www.cogs.susx.ac.uk/users/ibrahim/academic.html
%@ 0-262-06190-2

%A Ibrahim Kuscu
%T Genetic Programming and Incremental Aproaches to Solve Supervised Learning Problems
%B ICML'96, Evolutionary computing and Machine Learning Workshop
%E T. Fogarty and G. Venturini
%D 1996
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/124438.html
%Z Details from http://www.cogs.susx.ac.uk/users/ibrahim/academic.html

%A Hilan Bensusan
%A Ibrahim Kuscu
%T Constructive Induction using Genetic Programming
%B ICML'96, Evolutionary computing and Machine Learning Workshop
%E T. Fogarty and G. Venturini
%D 1996
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/152060.html
%X A constructive induction model using genetic programming is presented. The model evolves new attributes starting from a random population of possible attributes constructed
   as functions of the original attributes. The model is tested on hard supervised learning problems and its performance is compared with backpropagation and C4.5. The
   performance of the system on learning incomplete 4-bit parity is reported to be better.
%Z Details from http://www.cogs.susx.ac.uk/users/ibrahim/academic.html

%A Ibrahim Kuscu
%T Evolutionary and Incremental Methods to Solve Hard Learning Problems
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 431
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%8 28--31  July
%Z GP-96

%A Ibrahim Kuscu
%T Evolutionary and Incremental Methods to Solve Hard Learning Problems
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 101--106
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670 Hill climbing, supervised learning, 3
   monks and parity problems
%@ 0-18-201031-7

%A Ibrahim Kuscu
%T Generalisation and Artificial Ant Problem
%D 1997
%I 
%K genetic algorithms, genetic programming
%U http://www.cogs.susx.ac.uk/users/ibrahim/stfe.ps broken
%O A very early draft

%A Ibrahim Kuscu
%T Simple Genetic Programming for Supervised Learning Problems
%B Proceedings of Fifth Turkish Syposium on Artificial Intelligence and Neural Networks, TAINN'96
%E E. Alpaydin and U. Cilingiroglu and F. Gurgen and C. Guzelis
%D 1996
%I Bogazici University Press Istanbul
%K genetic algorithms, genetic programming
%U http://books.google.co.uk/books?id=xH1BcgAACAAJ
%8 27-28  June
%Z Details from http://www.cogs.susx.ac.uk/users/ibrahim/academic.html

%A Ibrahim Kuscu
%T Evolving a Generalised Behavior: Artificial Ant Problem Revisited
%B Seventh Annual Conference on Evolutionary Programming
%S LNCS
%E V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben
%V 1447
%D 1998
%P 799--808
%I Springer-Verlag Berlin
%I Natural Selection, Inc.
%C Mission Valley Marriott, San Diego, California, USA
%K genetic algorithms, genetic programming
%8 25-27  March
%Z EP-98. Santa Fe trail
%@ 3-540-64891-7

%A Ibrahim Kuscu
%T A Method of Promoting Generalisation in Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 192
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Ibrahim Kuscu
%T Promoting Generalization of Learned Behaviours in Genetic Programming
%B Fifth International Conference on Parallel Problem Solving from Nature
%S LNCS
%E Agoston E. Eiben and Thomas Back and Marc Schoenauer and Hans-Paul Schwefel
%V 1498
%D 1998
%P 491--500
%I Springer-Verlag Berlin
%C Amsterdam
%K genetic algorithms, genetic programming
%X Recently, growing numbers of research concentrate on robustness of the programs evolved using GP. While some of the researchers report on the brittleness of the solutions
   evolved, some others proposed methods of promoting robustness. It is important that these methods are not ad hoc and specific for a certain experimental setup. In this
   research, brittleness of solutions found for the artificial ant problem is reported, and a new method promoting generalization of the solution is presented.
%8 27-30  September
%Z PPSN-V
%@ 3-540-65078-4

%A Ibrahim Kuscu
%T Generalisation and Domain Specific Functions in Genetic Programming
%B Proceedings of the 2000 Congress on Evolutionary Computation CEC00
%V 2
%D 2000
%P 1393--1400
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C La Jolla Marriott Hotel La Jolla, California, USA
%K genetic algorithms, genetic programming, novel applications ii
%U http://citeseer.ist.psu.edu/502977.html
%X This research presents an evaluation of user defined domain specific functions of genetic programming using relational learning problems, generalisation for this class of
   learning problems and learning bias. After providing a brief theoretical background, two sets of experiments are detailed: experiments and results concerning the Monk-2
   problem and experiments attempting to evolve generalising solutions to parity problems with incomplete data sets. The results suggest that using nonproblem specific
   functions may result in greater generalisation for relational problems.
%8 6-9  July
%Z CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 00TH8512, Library of Congress Number = 00-018644
%@ 0-7803-6375-2

%A Ibrahim Kuscu
%T Evolutionary Generalisation and Genetic Programming
%R Ph.D. Thesis DPhil Thesis
%D 1999
%I 
%I School of Cognitive and Computing Sciences
%C University of Sussex
%K genetic algorithms, genetic programming
%U http://www.mgovernment.org/resurces/myphdthesis.pdf
%X A closer examination of the learning research in Artificial Intelligence reveals that the views of learning as perceived by researchers using genetic based methods seem to
   be different than the views employed by traditional Machine Learning (ML) research. Genetic based learning tends to favour a continuous learning with self organising
   learning systems, which is mainly performance or problem solving oriented. ML research, on the other hand, tend to develop systems with human crafted internal structures,
   which are strong in reaching the goals set by the theoretical work in the area. One of such goals, establishing necessary theoretical grounds, aims at developing learning
   systems which generalise what they learn to new but relevant tasks. The thesis aims at bridging the gap between traditional and genetic based learning research by promoting
   interaction between the two with respect to performance expectations from a learner in the form of generalisation. In searching for this beneficial interaction, the thesis
   explores the tendency in genetic based methods towards a memorisation (i.e., simple look-up table) and compression (i.e., a compact re-representation) oriented learning and
   emphasises the necessity and the requirements for generalisation (i.e., predictive accuracy in responding to unseen cases) oriented learning. A particular emphasis is given
   to a sub-area of genetic based learning research called genetic programming (GP). After identifying the lack of proper consideration of generalisation in GP, several
   experiments involving both supervised learning problems and simulations of learning behaviours are developed in order to explore the ways in which the generalisation
   performance of the solutions produced by GP can be improved. The findings of these GP experiments reflect that borrowing some of the principles from traditional learning
   research provides significant ways of improvement in the approaches to learning in the form of evolutionary generalisation. One of the experiments suggests that
   generalisation of learnt behaviours are possible by using a training regime based on environment sampling. Another set of experiments suggest that generalisation in GP can
   be improved by selection of a set of non-problem-specific functions. Finally, other than improving on the standard applications of GP, a set of experiments presents how GP
   can be used in improving performance of other learners such as back-propagation. Out of many possible ways of a beneficial interaction with the traditional learning
   methods, only a few could be presented in this study. There is, however, an inevitable necessity and rich potential for future improvements in the area, which are also
   presented in this thesis.
%8 13  July
%Z \citekushchu:2002:AIR gives the title as "Evolutionary Generalisation and Genetic Programming"

%A Ibrahim Kushchu
%T An Evaluation of Evolutionary Generalisation in Genetic Programming
%J Artificial Intelligence Review
%V 18
%N 1
%D 2002
%P 3--14
%I 
%K genetic algorithms, genetic programming, learning, robustness
%X Generalisation is one of the most important performance evaluation criteria for artificial learning systems. An increasing amount of research has recently concentrated on
   the robustness or generalisation ability of the programs evolved using Genetic Programming (GP). While some of these researchers report on the brittleness of the solutions
   evolved, some others propose methods of promoting robustness/generalisation. In this paper, a review of research on generalisation in GP and problems with brittleness of
   solutions produced by GP is presented. Also, a brief overview of several new methods promoting robustness/generalisation of the solutions produced by GP are presented.
%8 September
%Z Article ID: 405648 Note author previously spelt name as I Kuscu

%A Ibrahim Kushchu
%T Genetic programming and evolutionary generalization
%J IEEE Transactions on Evolutionary Computation
%R Paper
%V 6
%N 5
%D 2002
%P 431--442
%I 
%K genetic algorithms, genetic programming, generalisation, machine learning, robustness
%X In genetic programming (GP), learning problems can be classified broadly into two types: those using data sets, as in supervised learning, and those using an environment as
   a source of feedback. An increasing amount of research has concentrated on the robustness or generalisation ability of the programs evolved using GP. While some of the
   researchers report on the brittleness of the solutions evolved, others proposed methods of promoting robustness/generalization. It is important that these methods are not
   ad hoc and are applicable to other experimental setups. In this paper, learning concepts from traditional machine learning and a brief review of research on generalisation
   in GP are presented. The paper also identifies problems with brittleness of solutions produced by GP and suggests a method for promoting robustness/generalisation of the
   solutions in simulating learning behaviours using GP.
%8 October
%Z Graduate Sch. of Int. Manage., Int. Univ. of Japan, Niigata. PAC. Santa Fe Ant trail.

%A Andrew Kusiak
%T Evolutionary computation and data mining
%B Intelligent Systems in Design and Manufacturing III
%E Bhaskaran Gopalakrishnan and Angappa Gunasekaran
%V 4192
%D 2000
%P 1--10
%I SPIE
%C Boston, MA, USA
%K genetic algorithms, genetic programming, Evolutionary Algorithm, Evolution Strategy, Artificial Life, Data Mining
%U http://www.icaen.uiowa.edu/~ankusiak/Res-in-Prog/SPIE-1.pdf
%X Future products and processes will be impacted by biology and information technology. In this paper, the product and process design challenges are discussed. Evolutionary
   computation and data mining are two major tools that will cope with these challenges. The basic background of the two tools as well as examples demonstrating their
   applications in design and manufacturing are discussed.
%8 6  November
%Z http://spie.org/x648.html?product_id=403636 http://spiedigitallibrary.org/dbt/dbt.jsp?KEY=PSISDG&Volume=4192&Issue=1&bproc=symp&scode=PE00I

%A Andrew Kusiak
%T Evolutionary Computation in Design and Manufacturing
%J Machine Engineering
%D 2001
%I Technical University Press Zielona Gora, Poland
%I Technical University of Zielona Gora
%K genetic algorithms, genetic programming
%U http://www.iizp.uz.zgora.pl/zlsi/data/mim/kusiak.zip
%X Biology and information technology will have an impact on the design of future products and processes. In this paper, the product and process design challenges are
   discussed. Evolutionary computation is introduced and its potential applications in design and manufacturing are discussed.
%Z Also appears as: "Manufacturing Intergrated Management", Zbigniew Banaszak and Jerzy Jedrzejewski (editors), Politechnika Zielonogorska, Zielona Gora, Poland.
   http://www.iiz.pz.zgora.pl/mim/ Copyright by Wroclaw 2001, Poland Mention of GP especially STROGANOFF and evolving IDEF3 trees. Design periodic table.

%A Andrew Kusiak
%T Innovation: A data-driven approach
%J International Journal of Production Economics
%V 122
%N 1
%D 2009
%P 440--448
%I 
%K genetic algorithms, genetic programming, Innovation science, Data mining, Innovation rules, Innovation framework, Evolutionary computation
%U http://www.sciencedirect.com/science/article/B6VF8-4WKTWVR-2/2/73fdc80f743b54ffb2f1449b44d434cd
%X A newly introduced product or service becomes an innovation after it has been proven in the market. No one likes the fact that market failures of products and services are
   much more common than commercial successes. A data-driven approach to innovation is proposed. It is a natural extension of the system of customer requirements in terms of
   their number and type and the ways of collecting and processing them. The ideas introduced in this paper are applicable to the evaluation of the innovativeness of planned
   introductions of design changes and design of new products and services. In fact, blends of products and services could be the most promising way of bringing innovations to
   the market. The most important toll gates of innovation are the generation of new ideas and their evaluation. People have limited ability to generate and evaluate a large
   number of potential innovation alternatives. The proposed approach is intended to evaluate many alternatives from a market perspective.
%O Transport Logistics and Physical Distribution; Interlocking of Information Systems for International Supply and Demand Chains Management; ICPR19

%A Andrew Kusiak
%A Anoop Verma
%T A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines
%J IEEE Transactions on Sustainable Energy
%V 2
%N 1
%D 2011
%P 87--96
%I 
%K genetic algorithms, genetic programming, blade angle asymmetry, blade angle implausibility, blade pitch faults monitoring, data-mining-based prediction model, genetic
   programming algorithm, wind turbines, data mining, power engineering computing, wind turbines
%X A data-mining-based prediction model is built to monitor the performance of a blade pitch. Two blade pitch faults, blade angle asymmetry, and blade angle implausibility
   were analysed to determine the associations between them and the components/subassemblies of the wind turbine. Five data-mining algorithms have been studied to evaluate the
   quality of the models for prediction of blade faults. The prediction model derived by the genetic programming algorithm resulted in the best accuracy and was selected to
   perform prediction at different time stamps.
%8 January
%Z Also known as \cite5547006

%A Mikael Kuusela
%A Jerry W. Lamsa
%A Eric Malmi
%A Petteri Mehtala
%A Risto Orava
%T Multivariate Techniques for Identifying Diffractive Interactions at the LHC
%D 2009
%I 
%K genetic algorithms, genetic programming, Gene Expression Programming, High Energy Physics - Experiment, High Energy Physics - Phenomenology
%U http://arxiv.org/abs/0909.3039
%X Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data
   at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed
   studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to
   efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive
   (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward
   and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis
   approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines,
   diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the
   self-organizing map algorithm.
%O arXiv
%O Comment: 31 pages, 14 figures, 11 tables
%8 September ~16

%A Tuze Kuyucu
%A Martin A. Trefzer
%A Julian F. Miller
%A Andy M. Tyrrell
%T Task Decomposition and Evolvability in Intrinsic Evolvable Hardware
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P -
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, cartesian genetic programming
%X Many researchers have encountered the problem that the evolution of electronic circuits becomes exponentially more difficult when problems with an increasing number of
   outputs are tackled. Although this is an issue in both intrinsic and extrinsic evolution experiments, overcoming this problem is particularly challenging in the case of
   evolvable hardware, where logic and routing resources are constrained according to the given architecture. Consequently, the success of experiments also depends on how the
   inputs and outputs are interfaced to the evolvable hardware. Various approaches have been made to solve the multiple output problem: partitioning the task with respect to
   the input or output space, incremental evolution of sub-tasks or resource allocation. However, in most cases, the proposed methods can only be applied in the case of
   extrinsic evolution. In this paper, we have accordingly, focused on scaling problem of increasing numbers of outputs when logic circuits are intrinsically evolved. We raise
   a number of questions: how big is the performance drop when increasing the number of outputs? Can the resources of evolvable hardware be structured in a suitable way to
   overcome the complexity imposed by multiple outputs, without including knowledge about the problem domain? Can available resources in hardware still be efficiently used
   when pre-structured? In order to answer these questions, different structural implementations are investigated. We have looked at these issues in solving three problems:
   4-bit parity, 2-bit adder and 2-bit multiplier.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR

%A Vladimir Kvasnieka
%A Jioi Pospichal
%T Simple Implementation of Genetic Programming by Column Tables
%B Soft Computing in Engineering Design and Manufacturing
%E Pravir K. Chawdhry and Rajkumar Roy and Raj K. Pant
%D 1997
%P 48--56
%I Springer-Verlag London Godalming, GU7 3DJ, UK
%K genetic algorithms, genetic programming, directed acyclic graphs, symbolic regression, DAG
%U http://www.springer.com/engineering/mechanical+eng/book/978-3-540-76214-0?cm_mmc=Google-_-Book%20Search-_-Springer-_-0
%X Simple implementation of genetic programming by making use of the column tables is discussed. Implementations of Koza's genetic programming in compiled languages are
   usually not most efficient when crossover is applied. If chromosomes are directed acyclic graphs, more efficient than rooted trees both in memory requirement as well as in
   evaluation time of chromosome, then crossover requires traversing the data structures and their preliminary analysis. Column tables inherently code directed acyclic graphs,
   the implementation of crossover is simple and needs neither traversing nor checking of integrity of resulting data structures and should be therefore more efficient.
   Stochastic transformation operation mutation is also easily defined. Column tables can represent graphs with several output nodes and may be used e.g. for optimization of
   feed-forward neural networks. Simple illustrative examples of symbolic regression based on the column tables are presented.
%8 23-27  June
%Z WSC2 Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing wsc2/ind_paper/p_kvasni.html URL broken 2005
%@ 3-540-76214-0

%A Halina Kwasnicka
%A Ewa Szpunar-Huk
%T Genetic Programming in Data Modelling
%B Genetic Systems Programming: Theory and Experiences
%S Studies in Computational Intelligence
%E Nadia Nedjah and Ajith Abraham and Luiza de Macedo Mourelle
%V 13
%D 2006
%P 105--132
%I Springer
%C Germany
%K genetic algorithms, genetic programming
%O Forthcoming
%Z http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html
%@ 3-540-29849-5

%A Halina Kwasnicka
%A Piotr Wozniak
%T EMOT - Evolutionary Approach to 3D Computer Animation
%B Proceedings of the International Multiconference on Computer Science and Information Technology
%D 2006
%P 111--112
%I 
%C Wisla, Poland
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.ci.pwr.wroc.pl/~kwasnick/download/kwasnickawozniakaaia.pdf
%X Key-framing and Inverse Kinematics are popular animation methods, but new approaches are still developed. We propose a new evolutionary method of animation creation system
   EMOT (Evolutionary MOTion). It allows for automation of motion of animated characters. It uses new evolutionary approach Gene Expression Programming (GEP). Characters are
   controlled by computer programs, an animator has to provide the way of motion evaluation. GEP works with a randomly selected initial population, uses directed but random
   selection. Experiments show that the proposed method can develop robust controllers.
%8 November  6-10
%Z ISSN 1897-7094 C 2006 PIPS Institute of Applied Informatics, Wroclaw University of Technology

%A Roberta Kwok
%T Evolution of Oscillating Patterns in the Game of Life Using Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 244--251
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Yung-Keun Kwon
%A Byung-Ro Moon
%T Personalized Email Marketing with a Genetic Programming Circuit Model
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 1352--1358
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, real world applications, circuit model, data mining, local optimization, personalization, one-to-one marketing, email marketing
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d24.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Yung-Keun Kwon
%A Sung-Soon Choi
%A Byung-Ro Moon
%T Nonlinear parametric regression in genetic programming
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 943--944
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming: Poster, mathematical modelling/curve fitting, system identification
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p943.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Lisa A. Laane
%T Development of Navigational Controllers for Vehicles in Highway Traffic Situations via Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 171--180
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Ewa Lach
%T Learning Intelligent Behaviors of Animated Agents by Means of Genetic Programming
%B Proceedings of the VI International Conference on Artificial Intelligence AI-19
%S Artificial Intelligence Studies
%E Mieczyslaw Klopotek
%V 1(23)
%N 21
%D 2004
%P 159--170
%I 
%C University of Podlasie, Poland
%K genetic algorithms, genetic programming
%U http://www.aistudies.ii.ap.siedlce.pl/article.php?id=16
%X The paper presents the research on use of genetic programming for evolving behaviour of agents situated in virtual environment. The animation of virtual humans became a
   relevant issue in the industry for film, video and games. There is a great demand for the solutions that provide true automation of the extremely time-consuming animation
   process. There are researches on creation of autonomous, aware of environment, animated characters capable of independent planning of their motion. In the proposed approach
   the virtual character is treated as an automation executing some internal program so that to achieve some well defined goal. This program is created using layered learning
   genetic programming technique.
%8 22  September
%Z http://www.sztuczna-inteligencja.ap.siedlce.pl/archiwum/
%@ 83-7051-306-9

%A Ewa Lach
%T Automatic Generation of Animation of Virtual Humans
%B VII International Conference on Artificial Inteligence AI-20'2005
%S Proceedings of Artificial Intelligence Studies
%V 2(25)
%D 2005
%P 109--115
%I 
%K genetic algorithms, genetic programming
%U http://www.aistudies.ii.ap.siedlce.pl/article.php?id=48
%X The paper presents an application of genetic programming (GP) to an evolution of behaviour of agents situated in virtual environment. While synthetic characters are
   currently widely used in games, films, and various other applications, popular techniques such as keyframing and motion capture are still relatively expensive. True
   automation of the animation process of secondary characters might be a solution to this problem. In the paper an approach is presented, in which automatically generated
   decision trees mange behavior of virtual humans. New mutation operator, which removes unused nodes from decision trees is described.

%A Tirthankar Lahiri
%T Gnetic Optimization of Large Join Queries
%B Genetic Algorithms and Genetic Programming at Stanford 1997
%E John R. Koza
%D 1997
%P 128--137
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1997:GAGPs
%@ 0-18-205981-2

%A Tirthankar Lahiri
%T Genetic Optimization Techniques for Large Join Queries
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 535--542
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Timothy Lai
%T Discovery of Understandable Math Formulas Using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 118--127
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/Lai.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A S. Lakshminarayanan
%A H. Fujii
%A B. Grosman
%A E. Dassau
%A D. R. Lewin
%T New product design via analysis of historical databases
%J Computers \& Chemical Engineering
%V 24
%N 2-7
%D 2000
%P 671--676
%I 
%K genetic algorithms, genetic programming, Product design, PLSR, PCR
%U http://www.sciencedirect.com/science/article/B6TFT-448HNR0-2P/2/eecc15ac2e7cad2e662af963aa783893
%X A methodology is presented to define a set of operating conditions to produce a desired product, given a database of historical operating conditions and the product quality
   that they produced. This approach relies on the generation of a reliable model that can be used to predict the quality variables (the Y block) from the decision variables
   (the X block). Genetic programming (GP) is used to automatically generate accurate nonlinear models relating latent vectors for the X and Y blocks. The GP has the
   capability to carry out simultaneous optimisation of model relationship structures and parameters, as well as to identify the most important basis functions. Once an
   adequate model is generated, it is used to predict the required process conditions to meet the new quality target by reverse mapping.

%A Wai Lam
%A Man Leung Wong
%A Kwong Sak Leung
%A Po Shun Ngan
%T Discovering Probabilistic Knowledge from Databases Using Evolutionary Computation and Minimum Description Length Principle
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 786--794
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K evolutionary programming
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Vui Chiap Lam
%T Camera Placement for Optimal Visibility
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 181--190
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A Brian Lam
%A Vic Ciesielski
%T Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 1114--1125
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/3103/31031114.htm
%X In this paper we show how genetic programming can be used to discover useful texture feature extraction algorithms. Grey level histograms of different textures are used as
   inputs to the evolved programs. One dimensional K-means clustering is applied to the outputs and the tightness of the clusters is used as the fitness measure. To test
   generality, textures from the Brodatz library were used in learning phase and the evolved features were used on classification problems based on the Vistex library. Using
   the evolved features gave a test accuracy of 74.8percent while using Haralick features, the most commonly used method in texture classification, gave an accuracy of
   75.5percent on the same problem. Thus, the evolved features are competitive with those derived by human intuition and analysis. Furthermore, when the evolved features are
   combined with the Haralick features the accuracy increases to 83.2percent, indicating that the evolved features are finding texture regularities not used in the Haralick
   approach.
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Brian T. Lam
%A Vic Ciesielski
%T Applying Genetic Programming to Learn Spatial Differences Between Textures Using A Translation Invariant Representation
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2202--2209
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%X an approach to evolving texture feature extraction programs using tree based genetic programming. The programs are evolved from a learning set of 13 textures selected from
   the Brodatz database. In the evolutionary phase, texture images are first "binarised" to 256 grey levels. An encoding of the positions of the black pixels is used as the
   input to the evolved programs. A separate feature extraction program is evolved for each of the 256 grey levels. Fitness is measured by applying the evolved program to all
   of the images in the learning set, using one dimensional clustering on the outputs and then using the separation between the clusters as the fitness value. On two benchmark
   problems using the evolved programs for feature extraction and a nearest neighbour classifier, the evolved features gave test accuracies of 74.6 and 66.2 percent
   respectively for a 13 Brodatz and a 15 Vistex texture problem. This is better than a number of human derived methods on the same problems.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS. whereiswaldo
%@ 0-7803-9363-5

%A Marcelo M. Lamego
%T Applying Genetic Algorithm and Vector Quantization to Neurointerface Training
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 50--57
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Carlos Lamenca-Martinez
%A Julio Cesar Hernandez-Castro
%A Juan M. Estevez-Tapiador
%A Arturo Ribagorda
%T Lamar: A New Pseudorandom Number Generator Evolved by Means of Genetic Programming
%B Parallel Problem Solving from Nature - PPSN IX
%S LNCS
%E Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao
%V 4193
%D 2006
%P 850--859
%I Springer-Verlag Berlin
%C Reykjavik, Iceland
%K genetic algorithms, genetic programming
%U http://ppsn2006.raunvis.hi.is/proceedings/202.pdf
%X Pseudorandom number generation is a key component of many Computer Science algorithms, including mathematical modelling, stochastic processes, Monte Carlo simulations, and
   most cryptographic primitives and protocols. To date, multiple approaches that use Evolutionary Computation (EC) techniques have been proposed for designing useful
   Pseudorandom Number Generators (PRNGs) for certain non-cryptographic applications. However, none of the proposals have been secure nor efficient enough to be of interest
   for the much more demanding crypto world. In this work, we present a general scheme, which uses Genetic Programming (GP), for the automatic design of crypto-quality PRNGs
   by evolving highly nonlinear and extremely efficient functions. A new PRNG named Lamar and obtained using this scheme is proposed, whose C code and preliminary security
   analysis are provided.
%8 9-13  September
%Z PPSN-IX
%@ 3-540-38990-3

%A Steve Lampe
%T An Exercise in Spectrum Allocation: Developing a Frequency Plan for the Conrail Acquisition
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 95--104
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Mark Land
%A Richard K. Belew
%T A Programming Language for Artificial Development
%B Evolutionary Programming IV Proceedings of the Fourth Annual Conference on Evolutionary Programming
%E John Robert McDonnell and Robert G. Reynolds and David B. Fogel
%D 1995
%P 403--413
%I MIT Press
%C San Diego, CA, USA
%K genetic algorithms, cellular automata, autocatalytic, algorithmic chemistry
%8 1-3  March
%Z EP-95
%@ 0-262-13317-2

%A A. Landi
%A P. Piaggi
%A G. Pioggia
%T Backpropagation-Based Non Linear PCA for Biomedical Applications
%B Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on
%D 2009
%P 635--640
%I 
%K allergic bronchial asthma, artificial neural networks, backpropagation-based non linear PCA, biomedical applications, fractional exhaled nitric oxide differences, fuzzy
   logic, human brain, intelligent control, lung function, machine learning methodologies, mathematical models, principal component analysis, supervised learning algorithm,
   unsupervised learning algorithm, medical computing, neural nets, principal component analysis, unsupervised learning
%8 30 2009-  Decemeber  2
%Z Not on GP

%A Yann Landrin-Schweitzer
%A Pierre Collet
%A Evelyne Lutton
%T Interactive GP for Data Retrieval in Medical Databases
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 93--106
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=93
%X We present in this paper the design of ELISE, an interactive GP system for document retrieval tasks in very large medical databases. The components of ELISE have been
   tailored in order to produce a system that is capable of suggesting documents related to the query that may be of interest to the user, thanks to evolved profiling
   information. Tests on the ``Cystic Fibrosis Database'' benchmark [] show that, while suggesting original documents by adaptation of its internal rules to the context of the
   user, ELISE is able to improve its recall rate.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Yann Landrin-Schweitzer
%A Pierre Collet
%A Evelyne Lutton
%T Introducing lateral thinking in search engines
%J Genetic Programming and Evolvable Machines
%V 7
%N 1
%D 2006
%P 9--31
%I 
%K genetic algorithms, genetic programming
%U http://metronum.inria.fr/html/Papers/files/pdf/170_Elise-v9-expedieGPEM.pdf
%X Decomposing a very complex problem into smaller subproblems that are much easier to solve is not a new idea. The "Parisian Approach"[9] applies this principle extensively
   to shatter complexity by cutting down the original problem into many small subproblems that are then globally optimised thanks to an evolutionary algorithm. This paper
   describes how this approach has been used to interactively evolve a user profile to be used by a search engine. User queries are rewritten thanks to the evolved profile,
   resulting in an increased diversity in the retrieved documents that is showing an interesting property: even though precision is lost, retrieved documents relate both to
   the user's query and to his areas of interest in a manner that evokes "lateral thinking". This paper describes ELISE, an Evolutionary Learning Interactive Search Engine
   that interactively evolves rewriting modules and rules (some kind of elaborated user profile) along a Parisian Approach. Results obtained over a public domain benchmark
   (Cystic Fibrosis Database) are presented and discussed.
%8 March
%Z synset, SWISH++ P27 'ELISE show that bloat does not appear'

%A Jacques-Andre Landry
%A Luis {Da Costa}
%A Thomas Bernier
%T Discriminant Feature Selection by Genetic Programming: Towards a domain independent multi-class object detection system
%J Systemics, Cybernetics and Informatics
%V 3
%N 1
%D 2006
%P 76--81
%I International Institute of Informatics and Cybernetics
%K genetic algorithms, genetic programming, Feature selection, pattern recognition
%U http://www.iiisci.org/Journal/sci/Abstract.asp?var=&id=P718270
%X In order to implement a multi-class object detection system, an efficient object representation is needed; in this short paper, we present a feature selection method based
   on the Genetic Programming paradigm. This method allows for the identification of a set of features that best represent the classes in the problem at hand. The idea would
   then be to have a broad set of features to describe any object, and then to use the presented feature selection method to adapt the description to the actual needs of the
   classification problem. Furthermore, the tree like solutions generated by the method can be interpreted and modified for increased generality. A brief review of literature,
   the first implementation of the method and the first results are presented here. The method shows potential to be used as a building block of a detection system, although
   further experimentation is underway in order to fully asses the power of the method.
%Z Ecole de Technologie Superieure, Universite du quebec. http://www.iiisci.org/Journal/sci/Contents.asp?var=&Previous=ISS1042

%A Laura F. Landweber
%A Lila Kari
%T The Evolution of DNA Computing: Nature's Solution to a Computational Problem
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 700--708
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K DNA Computing
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Kevin J. Lang
%T Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis of Koza's
%B Proceedings of the Twelfth International Conference on Machine Learning
%D 1995
%I Morgan Kaufmann San Francisco, CA, USA
%C Tahoe City, California, USA
%K genetic algorithms, genetic programming
%X Problem taken from chapter 9 of GP1. Shows hill climbing more efficient than GP on this problem. Speculates that (for this problem) fragments of high fitness individuals
   have no special value when transplanted into other individuals. See also \citekoza:1995:hcbgs
%8 July

%A W. B. Langdon
%T Quick Intro to simple-gp.c
%R Internal Note IN/95/2
%D 1994
%I 
%I University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/simple/intro-simple-gp.ps
%X Wouldn't it be great if computers could actually write the programs? This is the promise of genetic programming. This document gives an introduction to the program
   simple-gp.c which demonstrates the principles of genetic programming in the C language. This program was designed to show the ideas behind GP, ie to be tinkered with rather
   than to be a definitive implementation. The appendix contains a glossary of evolutionary computing terms
%8 25  April
%Z C Code available from same ftp site.

%A W. B. Langdon
%T Evolving Data Structures Using Genetic Programming
%R Research Note RN/95/1
%D 1995
%I 
%I UCL
%C Gower Street, London, WC1E 6BT, UK
%K genetic algorithms, genetic programming, Automatic Programming, Machine Learning, Artificial Evolution, Data Structures, Object Oriented Programming, Push down Stack,
   First-in first-out (FIFO) Queue, Automatically Defined Functions (ADF), Pareto fitness, Demes
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GPdata_icga-95.ps
%X Genetic programming (GP) is a subclass of genetic algorithms (GAs), in which evolving programs are directly represented in the chromosome as trees. Recently it has been
   shown that programs which explicitly use directly addressable memory can be generated using GP. It is established good software engineering practice to ensure that programs
   use memory via abstract data structures such as stacks, queues and lists. These provide an interface between the program and memory, freeing the program of memory
   management details which are left to the data structures to implement. The main result presented herein is that GP can automatically generate stacks and queues. Typically
   abstract data structures support multiple operations, such as put and get. We show that GP can simultaneously evolve all the operations of a data structure by implementing
   each such operation with its own independent program tree. That is, the chromosome consists of a fixed number of independent program trees. Moreover, crossover only mixes
   genetic material of program trees that implement the same operation. Program trees interact with each other only via shared memory and shared ``Automatically Defined
   Functions'' (ADFs). ADFs, ``pass by reference'' when calling them, Pareto selection, ``good software engineering practice'' and partitioning the genetic population into
   ``demes'' where also investigated whilst evolving the queue in order to improve the GP solutions.
%8 January
%Z Discussed on GP mailing list 6--13 Jan 95, subj:GPdata. Presented at ICGA-95. Reworked into \citeLangdon:1995:GPdata

%A W. B. Langdon
%T Evolving Data Structures Using Genetic Programming
%B Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)
%E Larry J. Eshelman
%D 1995
%P 295--302
%I Morgan Kaufmann San Francisco, CA, USA
%C Pittsburgh, PA, USA
%K genetic algorithms, genetic programming, Automatic Programming, Machine Learning, Artificial Evolution, Data Structures, Object Oriented Programming, Push down Stack,
   First-in first-out (FIFO) Queue, Automatically Defined Functions (ADF), Pareto fitness, Demes
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GPdata_icga-95.ps
%X Genetic programming (GP) is a subclass of genetic algorithms (GAs), in which evolving programs are directly represented in the chromosome as trees. Recently it has been
   shown that programs which explicitly use directly addressable memory can be generated using GP. It is established good software engineering practice to ensure that programs
   use memory via abstract data structures such as stacks, queues and lists. These provide an interface between the program and memory, freeing the program of memory
   management details which are left to the data structures to implement. The main result presented herein is that GP can automatically generate stacks and queues. Typically
   abstract data structures support multiple operations, such as put and get. We show that GP can simultaneously evolve all the operations of a data structure by implementing
   each such operation with its own independent program tree. That is, the chromosome consists of a fixed number of independent program trees. Moreover, crossover only mixes
   genetic material of program trees that implement the same operation. Program trees interact with each other only via shared memory and shared ``Automatically Defined
   Functions'' (ADFs). ADFs, ``pass by reference'' when calling them, Pareto selection, ``good software engineering practice'' and partitioning the genetic population into
   ``demes'' where also investigated whilst evolving the queue in order to improve the GP solutions.
%8 15-19  July
%Z Discussed on GP mailing list 6--13 Jan 95, subj: GPdata. object-oriented, OO class. Mainly as \citeLangdon:1995:GPdataRN but with more details on pareto selection
%@ 1-55860-370-0

%A W. B. Langdon
%T Pareto, Population Partitioning, Price and Genetic Programming
%R Research Note RN/95/29
%D 1995
%I 
%I University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming, Automatic Programming, Machine Learning, Artificial Evolution, Pareto fitness, Demes
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_aaai-pppGP.ps
%X A description of a use of Pareto optimality in genetic programming is given and an analogy with Genetic Algorithm fitness niches is drawn. Techniques to either spread the
   population across many pareto optimal fitness values or to reduce the spread are described. It is speculated that a wide spread may not aid Genetic Programming. It is
   suggested that this might give useful insight into many GPs whose fitness is composed of several sub-objectives. The successful use of demic populations in GP leads to
   speculation that smaller evolutionary steps might aid GP in the long run. An example is given where Price's covariance theorem helped when designing a GP fitness function.
%8 April
%Z Accepted by AAAI Fall 1995 Genetic Programming Symposium but withdrawn due to time pressures multiobjective Pareto front

%A W. B. Langdon
%T Directed Crossover within Genetic Programming
%R Research Note RN/95/71
%D 1995
%I 
%I University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming, Automatic Programming, Machine Learning, Artificial Evolution
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/directed_crossover.ps
%X This paper describes in detail a mechanism used to bias the choice of crossover locations when evolving a list data structure using genetic programming. The data structure
   and it evolution will be described by RN 95/70 \citelangdon:1995:aigpRN. The second section describes current research on biasing the action of reproduction operators
   within the genetic programming field.
%8 September

%A W. B. Langdon
%T Data Structures and Genetic Programming
%R Research Note RN/95/70
%D 1995
%I 
%I University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming, list, evolution, automatic code generation
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.aigp2.ch20.ps
%X In real world applications, software engineers recognise the use of memory must be organised via data structures and that software using the data must be independant of the
   data structures' implementation details. They achieve this by using abstract data structures, such as records, files and buffers. We demonstrate that genetic programming
   can automatically implement simple abstract data structures, considering in detail the task of evolving a list. We show general and reasonably efficient implementations can
   be automatically generated from simple primitives. A model for maintaining evolved code is demonstrated using the list problem. 4 page early abstract at
   http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GPlist_aigp2.ps
%8 September
%Z As \citelangdon:1996:aigp2 Syntax rules restrict for while loops so they are not nested. Run time data used to guide location of crossover points CPU and memory usage
   included in fitness via Pareto tournament selection. Pass by reference and call by reference used inconjunction with ADFs. Scoping rules allow ADFs access to callers data.

%A William B. Langdon
%A Adil Qureshi
%T Genetic Programming -- Computers using ``Natural Selection'' to generate programs
%R Research Note RN/95/76
%D 1995
%I 
%I University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming, Automatic Programming, Machine Learning
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/surveyRN76.ps
%X Computers that ``program themselves''; science fact or fiction? Genetic Programming uses novel optimisation techniques to ``evolve'' simple programs; mimicking the way
   humans construct programs by progressively re-writing them. Trial programs are repeatedly modified in the search for ``better/fitter'' solutions. The underlying basis is
   Genetic Algorithms (GAs). Genetic Algorithms, pioneered by Holland, Goldberg and others, are evolutionary search techniques inspired by natural selection (i.e\ survival of
   the fittest). GAs work with a ``population'' of trial solutions to a problem, frequently encoded as strings, and repeatedly select the ``fitter'' solutions, attempting to
   evolve better ones. The power of GAs is being demonstrated for an increasing range of applications; financial, imaging, VLSI circuit layout, gas pipeline control and
   production scheduling. But one of the most intriguing uses of GAs - driven by Koza - is automatic program generation. Genetic Programming applies GAs to a ``population'' of
   programs - typically encoded as tree-structures. Trial programs are evaluated against a ``fitness function'' and the best solutions selected for modification and
   re-evaluation. This modification-evaluation cycle is repeated until a ``correct'' program is produced. GP has demonstrated its potential by evolving simple programs for
   medical signal filters, classifying news stories, performing optical character recognition, and for target identification. This paper surveys the exciting field of Genetic
   Programming. As a basis it reviews Genetic Algorithms and automatic program generation. Next it introduces Genetic Programming, describing its history and describing the
   technique via a worked example in C. Then using a taxonomy that divides GP researchs into theory/techniques and applications, it surveys recent work from both of these
   perspectives. Extensive bibliographies, glossaries and a resource list are included as appendices.
%8 October

%A William B. Langdon
%T Data Structures and Genetic Programming
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 395--414
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming, list, evolution, automatic code generation
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/412
%X In real world applications, software engineers recognise the use of memory must be organised via data structures and that software using the data must be independant of the
   data structures' implementation details. They achieve this by using abstract data structures, such as records, files and buffers. We demonstrate that genetic programming
   can automatically implement simple abstract data structures, considering in detail the task of evolving a list. We show general and reasonably efficient implementations can
   be automatically generated from simple primitives. A model for maintaining evolved code is demonstrated using the list problem.
%O 20
%Z Syntax rules restrict for while loops so they are not nested. Run time data used to guide location of crossover points CPU and memory usage included in fitness via Pareto
   tournament selection. Pass by reference and call by reference used inconjunction with ADFs. Scoping rules allow ADFs access to caller's argument directly.
%@ 0-262-01158-1

%A William B. Langdon
%T A Bibliography for Genetic Programming
%B Advances in Genetic Programming 2
%R Appendix
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 507--532
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/524
%X 411 references
%O B
%Z See also http://www.cs.bham.ac.uk/~wbl/biblio/README.html
%@ 0-262-01158-1

%A W. B. Langdon
%T Using Data Structures within Genetic Programming
%R Research Note RN/96/1
%D 1996
%I 
%I UCL
%C Gower Street, London, WC1E 6BT, UK
%K genetic algorithms, genetic programming, context free language induction, Reverse Polish Expressions, automatic code generation, Automatic Programming, Machine Learning,
   Artificial Evolution, Data Structures, Object Oriented Programming, Push down Stack, Automatically Defined Functions (ADF), Pareto fitness, Demes
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.gp96.ps
%X In earlier work we showed that GP can automatically generate simple data types (stacks, queues and lists). The results presented herein show, in some cases, provision of
   appropriately structured memory can indeed be advantageous to GP in comparison with directly addressable indexed memory. Three ``classic'' problems are solved. The first
   two require the GP to distinguish between sentences that are in a language and those that are not given positive and negative training examples of the language. The two
   languages are, correctly nested brackets and a Dyck language (correctly nested brackets of different types). The third problem is to evaluate integer Reverse Polish
   (postfix) expressions. Comparisons are made between GP attempting to solve these problems when provided with indexed memory or with stack data structures.
%8 January
%Z Accepted for presentation at GP96. Updated version in proceedings and url, see \citeLangdon:1996:usedata

%A W. B. Langdon
%T Using Data Structures within Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 141--148
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming, context free language induction, context free grammar CFG, matched brackets, Dyck language, Reverse Polish Expressions, automatic
   code generation, Automatic Programming, Machine Learning, Artificial Evolution, Data Structures, Object Oriented Programming, Push down Stack, Automatically Defined
   Functions (ADF), Pareto fitness, Demes
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.gp96.ps
%X Provision of appropriately structured memory is shown, in some cases, to be advantageous to genetic programming (GP) in comparison with directly addressable indexed memory.
   Three ``classic'' problems are solved. The first two require the GP to distinguish between sentences that are in a context free language and those that are not given
   positive and negative training examples of the language. The two languages are, correctly nested brackets and a Dyck language (correctly nested brackets of different
   types). The third problem is to evaluate integer Reverse Polish (postfix) expressions. Comparisons are made between GP attempting to solve these problems when provided with
   indexed memory or with stack data structures.
%8 28--31  July
%Z GP-96. Replaces \citeLangdon:1996:usedataRN

%A W. B. Langdon
%T Genetic Programming and Databases
%R Internal Note IN/96/4
%D 1996
%I 
%I University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming, Database
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/gpdb.ps
%8 11  February
%Z Short survey

%A W. B. Langdon
%T Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming
%R Research Note RN/96/49
%D 1996
%I 
%I University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming, scheduling, maintenance, eletrical power transmission network
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.gpgrid_gp96.ps
%X The National Grid Company Plc is responsible for the maintenance of the high voltage electricity transmission network in England and Wales. It must plan maintenance so as
   to minimize costs taking into account: (1) location and size of demand, (2) generator capacities and availabilities, (3) electricity carrying capacity of the remainder of
   the network, that part not undergoing maintenance. Previous work showed the combination of a Genetic Algorithm using an order or permutation chromosome combined with hand
   coded ``Greedy'' Optimizers can readily produce an optimal schedule for a four node test problem. Following this the same GA has been used to find low cost schedules for
   the South Wales region of the UK high voltage power network. This paper describes the evolution of the best known schedule for the base South Wales problem using Genetic
   Programming starting from the hand coded heuristics used with the GA.
%8 28  June
%Z See GP96 late breaking papers \citelangdon:1996gpgrid and WSC1 see \citelangdon:1996gpgridWSC Previous work in Langdon:1995:4nodeSV

%A W. B. Langdon
%T Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming
%B Late Breaking Papers at the GP-96 Conference
%E John Koza
%D 1996
%P 107--116
%I Stanford Bookstore Stanford, California, 94305-3079 USA
%C Stanford, CA, USA
%K genetic algorithms, genetic programming, scheduling, maintenance, eletrical power transmission network
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.gpgrid_gp96.ps
%X The National Grid Company Plc is responsible for the maintenance of the high voltage electricity transmission network in England and Wales. It must plan maintenance so as
   to minimize costs taking into account: (1) location and size of demand, (2) generator capacities and availabilities, (3) electricity carrying capacity of the remainder of
   the network, that part not undergoing maintenance. Previous work showed the combination of a Genetic Algorithm using an order or permutation chromosome combined with hand
   coded ``Greedy'' Optimizers can readily produce an optimal schedule for a four node test problem. Following this the same GA has been used to find low cost schedules for
   the South Wales region of the UK high voltage power network. This paper describes the evolution of the best known schedule for the base South Wales problem using Genetic
   Programming starting from the hand coded heuristics used with the GA.
%8 28--31  July
%Z GP-96LB

%A W. B. Langdon
%T Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming
%B The 1st Online Workshop on Soft Computing (WSC1)
%D 1996
%I Nagoya University, Japan
%I Research Group on ECOmp of the Society of Fuzzy Theory and Systems (SOFT)
%C http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/
%K genetic algorithms, genetic programming, scheduling, maintenance, eletrical power transmission network
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.gpgrid_gp96.ps
%X The National Grid Company Plc is responsible for the maintenance of the high voltage electricity transmission network in England and Wales. It must plan maintenance so as
   to minimize costs taking into account: (1) location and size of demand, (2) generator capacities and availabilities, (3) electricity carrying capacity of the remainder of
   the network, that part not undergoing maintenance. Previous work showed the combination of a Genetic Algorithm using an order or permutation chromosome combined with hand
   coded ``Greedy'' Optimizers can readily produce an optimal schedule for a four node test problem. Following this the same GA has been used to find low cost schedules for
   the South Wales region of the UK high voltage power network. This paper describes the evolution of the best known schedule for the base South Wales problem using Genetic
   Programming starting from the hand coded heuristics used with the GA.
%8 19--30  August
%Z As \citelangdon:1996gpgridRN email WSC1 organisers wsc@bioele.nuee.nagoya-u.ac.jp

%A W. B. Langdon
%A P. C. Treleaven
%T Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming
%B Artificial Intelligence Techniques in Power Systems
%E Kevin Warwick and Arthur Ekwue and Raj Aggarwal
%D 1997
%P 220--237
%I IEE
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_iee_gpgrid.prn.gz
%X The National Grid Company Plc is responsible for the maintenance of the high voltage electricity transmission network in England and Wales. It must plan maintenance so as
   to minimize costs taking into account: (1) location and size of demand, (2) generator capacities and availabilities, (3) electricity carrying capacity of the remainder of
   the network, that part not undergoing maintenance. Previous work showed the combination of a Genetic Algorithm using an order or permutation chromosome combined with hand
   coded ``Greedy'' Optimizers can readily produce an optimal schedule for a four node test problem. Following this the same GA has been used to find low cost schedules for
   the South Wales region of the UK high voltage power network. This paper describes the evolution of the best known schedule for the base South Wales problem using Genetic
   Programming starting from the hand coded heuristics used with the GA.
%O 10
%Z wbl_iee_gpgrid.prn is MS Postscript and does not display in some versions of ghostview, seems to print ok
%@ 0-85296-897-3

%A W. B. Langdon
%T Evolution of Genetic Programming Populations
%R Research Note RN/96/125
%D 1996
%I 
%I University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming, population variety, diversity, genetic programming, Price's theorem, Fisher's theorem, evolution, automatic code generation
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/WBL.ecj.price.125.ps.gz
%X We investigate in detail what happens as genetic programming (GP) populations evolve. Since we shall use the populations which showed GP can evolve stack data structures as
   examples, we start in Section 1 by briefly describing the stack experiment \citeLangdon:1995:GPdata. In Section 2 we show Price's Covariance and Selection Theorem can be
   applied to Genetic Algorithms (GAs) and GP to predict changes in gene frequencies. We follow the proof of the theorem with experimental justification using the GP runs from
   the stack problem. Section 3 briefly describes Fisher's Fundamental Theorem of Natural Selection and shows in its normal interpretation it does not apply to practical GAs.
   An analysis of the stack populations, in Section 4, explains that the difficulty of the stack problem is due to the presence of ``deceptive'' high scoring partial solutions
   in the population. These cause a negative correlation between necessary primitives and fitness. As Price's Theorem predicts, the frequency of necessary primitives falls,
   eventually leading to their extinction and so to the impossibility of finding solutions like those that are evolved in successful runs. Section 4 investigates the evolution
   of variety in GP populations. Detailed measurements of the evolution of variety in stack populations reveal loss of diversity causing crossover to produce offspring which
   are copies of their parents. Section 5 concludes with measurements that show in the stack population crossover readily produces improvements in performance initially but
   later no improvements at all are made by crossover. Section 6 discusses the importance of these results to GP in general.
%8 September
%Z Abridgment of Chapter 7 of \citelangdon:thesis

%A W. B. Langdon
%T Data Structures and Genetic Programming
%R Ph.D. Thesis
%D 1996
%I 
%I University College, London
%K genetic algorithms, genetic programming
%U http://www.site.uottawa.ca/~fbinard/EA/GP/Articles/LangdonPHDThesis.pdf
%X This thesis investigates the evolution and use of abstract data types within Genetic Programming (GP). In genetic programming the principles of natural evolution (fitness
   based selection and recombination) acts on program code to automatically generate computer programs. The research in this thesis is motivated by the observation from
   software engineering that data abstraction (eg via abstract data types) is essential in programs created by human programmers. We investigate whether abstract data types
   can be similarly beneficial to the automatic production of programs using GP. GP can automatically ``evolve'' programs which solve non-trivial problems but few experiments
   have been reported where the evolved programs explicitly manipulate memory and yet memory is an essential component of most computer programs. So far work on evolving
   programs that explicitly use memory has principally used either problem specific memory models or a simple indexed memory model consisting of a single global shared array.
   Whilst the latter is potentially sufficient to allow any computation to evolve, it is unstructured and allows complex interaction between parts of programs which weaken
   their modularity. In software engineering this is addressed by controlled use of memory using scoping rules and abstract data types, such as stacks, queues and files. This
   thesis makes five main contributions: (1) Proving that abstract data types (stacks, queues and lists) can be evolved using genetic programming. (2) Demonstrating GP can
   evolve general programs which recognise a Dyck context free language, evaluate Reverse Polish expressions and GP with an appropriate memory structure can solve the nested
   brackets problem which had previously been solved using a hybrid GP. (3) In these three cases (Dyck, expression evaluation and nested brackets) an appropriate data
   structure is proved to be beneficial compared to indexed memory. (4) Investigations of real world electrical network maintenance scheduling problems demonstrate that
   Genetic Algorithms can find low cost viable solutions to such problems. (5) A taxonomy of GP is presented, including a critical review of experiments with evolving memory.
   These contributions support our thesis that data abstraction can be beneficial to automatic program generation via artificial evolution.
%8 27  September

%A W. B. Langdon
%A R. Poli
%T Price's Theorem and the MAX Problem
%R Technical Report CSRP-97-4
%D 1997
%I 
%I University of Birmingham, School of Computer Science
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-04.ps.gz
%X We present a detailed analysis of the evolution of GP populations using the problem of finding a program which returns the maximum possible value for a given terminal and
   function set and a depth limit on the program tree (known as the MAX problem). We confirm the basic message of \citeGathercole:1996:aicrtd that crossover together with
   program size restrictions can be responsible for premature convergence to a sub-optimal solution. We show that this can happen even when the population retains a high level
   of variety and show that in many cases evolution from the sub-optimal solution to the solution is possible if sufficient time is allowed. In both cases theoretical models
   are presented and compared with actual runs. Experimental evidence is presented that Price's Covariance and Selection Theorem can be applied to GP populations and the
   practical effect of program size restrictions are noted. Finally we show that covariance between gene frequency and fitness in the first few generations can be used to
   predict the course of GP runs.
%8 January

%A W. B. Langdon
%A R. Poli
%T An Analysis of the MAX Problem in Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 222--230
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.max_gp97.ps
%X We present a detailed analysis of the evolution of GP populations using the problem of finding a program which returns the maximum possible value for a given terminal and
   function set and a depth limit on the program tree (known as the MAX problem). We confirm the basic message of \citeGathercole:1996:aicrtd that crossover together with
   program size restrictions can be responsible for premature convergence to a sub-optimal solution. We show that this can happen even when the population retains a high level
   of variety and show that in many cases evolution from the sub-optimal solution to the solution is possible if sufficient time is allowed. In both cases theoretical models
   are presented and compared with actual runs. Price's Covariance and Selection Theorem is experimentally tested on GP populations. It is shown to hold only in some cases, in
   others program size restrictions cause important deviation from its predictions.
%8 13-16  July
%Z GP-97 Considerable update of \citeLangdon97

%A W. B. Langdon
%A R. Poli
%T Fitness Causes Bloat
%R Technical Report CSRP-97-09
%D 1997
%I 
%I University of Birmingham, School of Computer Science
%C Birmingham, B15 2TT, UK
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-09.ps.gz
%X The problem of evolving an artificial ant to follow the Santa Fe trail is used to demonstrate the well known genetic programming feature of growth in solution length. Known
   variously as ``bloat'', ``redundancy'', ``introns'', ``fluff'', ``Structural Complexity'' with antonyms ``parsimony'', ``Minimum Description Length'' (MDL) and ``Occam's
   razor''. Comparison with runs with and without fitness selection pressure shows the tendency for solutions to grow in size is caused by fitness based selection. We argue
   that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. Since with simple static evaluation search converges
   to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long representations of a given solution
   than short ones of the same solution. Thus with an unbiased random search we expect longer representations to occur more often and so representation length tends to
   increase. I.e. fitness based selection leads to bloat.
%8 24  February

%A W. B. Langdon
%A R. Poli
%T Fitness Causes Bloat
%B Soft Computing in Engineering Design and Manufacturing
%E P. K. Chawdhry and R. Roy and R. K. Pant
%D 1997
%P 13--22
%I Springer-Verlag London Godalming, GU7 3DJ, UK
%K genetic algorithms, genetic programming
%U http://www.springer.com/engineering/mechanical+eng/book/978-3-540-76214-0?cm_mmc=Google-_-Book%20Search-_-Springer-_-0
%X The problem of evolving an artificial ant to follow the Santa Fe trail is used to demonstrate the well known genetic programming feature of growth in solution length. Known
   variously as ``bloat'', ``redundancy'', ``introns'', ``fluff'', ``Structural Complexity'' with antonyms ``parsimony'', ``Minimum Description Length'' (MDL) and ``Occam's
   razor''. Comparison with runs with and without fitness selection pressure shows the tendency for solutions to grow in size is caused by fitness based selection. We argue
   that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. Since with simple static evaluation search converges
   to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long representations of a given solution
   than short ones of the same solution. Thus with an unbiased random search we expect longer representations to occur more often and so representation length tends to
   increase. I.e. fitness based selection leads to bloat.
%8 23-27  June
%Z WSC2 Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing extends \citeLangdon:1997:bloatTR wsc2/ind_paper/p_langd.html URL broken 2005
%@ 3-540-76214-0

%A W. B. Langdon
%T Fitness Causes Bloat in Variable Size Representations
%R Technical Report CSRP-97-14
%D 1997
%I 
%I University of Birmingham, School of Computer Science
%K genetic algorithms, genetic programming, bloat, variable size representation
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-14.ps.gz
%X We argue based upon the numbers of representations of given length, that increase in representation length is inherent in using a fixed evaluation function with a discrete
   but variable length representation. Two examples of this are analysed, including the use of Price's Theorem. Both examples confirm the tendency for solutions to grow in
   size is caused by fitness based selection.
%O Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97
%8 14  May
%Z based upon \citeLangdon:1997:bloatWSC2 but includes data on mutation

%A W. B. Langdon
%A R. Poli
%T Fitness Causes Bloat: Mutation
%B Late Breaking Papers at the GP-97 Conference
%E John Koza
%D 1997
%P 132--140
%I Stanford Bookstore Stanford, California, 94305-3079 USA
%C Stanford, CA, USA
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-16.ps.gz
%X The problem of evolving, using mutation, an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution
   length. Known variously as ``bloat'', ``fluff'' and increasing ``structural complexity'', this is often described in terms of increasing ``redundancy'' in the code caused
   by ``introns''. Comparison between runs with and without fitness selection pressure, backed by Price's Theorem, shows the tendency for solutions to grow in size is caused
   by fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. With simple
   static evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long
   representations of a given solution than short ones. Thus in search (without a length bias) we expect longer representations to occur more often and so representation
   length to tend to increase. I.e. fitness based selection leads to bloat.
%8 13-16  July
%Z GP-97LB Also available as University of Birmingham, School of Computer Science, CSRP-97-16 The email address for the bookstore for mail orders is
   mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-206995-8

%A W. B. Langdon
%T Fitness Causes Bloat: Mutation
%D 1997
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/WBL.gp97lb.poster/WBL.gp97lb.poster.html
%8 July
%Z Poster for \citeLangdon:1997:bloatMUT Includes data on runs with no effective size limit and parents of children worse than themselves

%A Bill Langdon
%A Chris Clack
%T Software -- The Next Generation: Grow Your Own Programs
%R white paper
%D 1997
%P 45--53
%I 
%I UCL, Andersen Consulting
%C University College London, Gower Street, London
%K genetic algorithms, genetic programming
%8 April
%Z Part of 'Emerging Technologies White Papers: Software -- The Next Generation' which reports the 1996 workshop on Emerging technologies held in UCL Computer Science dept.
   for Andersen Consulting's Emerging Technologies Group and others.

%A W. B. Langdon
%T GP97 Conference Report
%J EvoNews
%V 1
%N 5
%D 1997
%P 4--5
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/gp97-report.html
%Z GP-97

%A W. B. Langdon
%T GP97 Conference Report
%J Robotica
%V 16
%N 1
%D 1998
%P 117
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/gp97-report.html
%X The Second International Conference in Genetic Programming (GP-97) \citekoza:gp97, like the first \citekoza:gp96, was held on the beautiful Stanford University campus in
   California under the chairmanship of John Koza. More than 350 people from all over the world gathered together for four days to see presentations, posters, tutorials and
   trade presentations on a range of topics. In addition to GP and related topics in Genetic Algorithms (GAs) and classifier systems, the up and coming fields of evolvable
   hardware (EHW) and biocomputing (also called DNA computing) were also represented.

%A W. B. Langdon
%T Fitness Causes Bloat: Simulated Annealing, Hill Climbing and Populations
%R Technical Report CSRP-97-22
%D 1997
%I 
%I University of Birmingham, School of Computer Science
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/CSRP-97-22.ps.gz
%X In many cases programs length's increase (known as ``bloat'', ``fluff'' and increasing ``structural complexity'') during artificial evolution. We show bloat is not specific
   to genetic programming and suggest it is inherent in search techniques with discrete variable length representations using simple static evaluation functions. We
   investigate the bloating characteristics of three non-population and one population based search techniques using a novel mutation operator. An artificial ant following the
   Santa Fe trail problem is solved by simulated annealing, hill climbing, strict hill climbing and population based search using two variants of the the new subtree based
   mutation operator. As predicted bloat is observed when using unbiased mutation and is absent in simulated annealing and both hill climbers when using the length neutral
   mutation however bloat occurs with both mutations when using a population. We conclude that there are two causes of bloat.
%8 2  September
%Z Published as \citelangdon:1997:pgSAHCP

%A W. B. Langdon
%T The Evolution of Size in Variable Length Representations
%B 1998 IEEE International Conference on Evolutionary Computation
%D 1998
%P 633--638
%I IEEE Press
%I IEEE
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming, structural complexity, introns, Price's Theorem
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/WBL.wcci98_bloat.ps.gz
%X In many cases programs length's increase (known as "bloat", "fluff" and increasing "structural complexity") during artificial evolution. We show bloat is not specific to
   genetic programming and suggest it is inherent in search techniques with discrete variable length representations using simple static evaluation functions. We investigate
   the bloating characteristics of three non-population and one population based search techniques using a novel mutation operator. An artificial ant following the Santa Fe
   trail problem is solved by simulated annealing, hill climbing, strict hill climbing and population based search using two variants of the the new subtree based mutation
   operator. As predicted bloat is observed when using unbiased mutation and is absent in simulated annealing and both hill climbers when using the length neutral mutation
   however bloat occurs with both mutations when using a population. We conclude that there are two causes of bloat 1) search operators with no length bias tend to sample
   bigger trees and 2) competition within populations favours longer programs as they can usually reproduce more accurately.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence based on \citelangdon:1997:bloatSAHCP
%@ 0-7803-4869-9

%A W. B. Langdon
%T Scheduling Planned Maintenance of Electrical Power Transmission Networks Using Genetic Algorithms
%B Artificial Neural Networks and Genetic Algorithms in Power Engineering
%R Technical Report CSRP-97-26
%E Gennady K. Voronovsky and Serguey A. Sergeev
%D 1997
%I OSNOVA
%I University of Birmingham
%C Ukraine
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/CSRP-97-26.ps.gz
%O Translated to Russian, but OSNOVA never published intended book
%Z Emphasis on GA but some GP work mentioned Available as CSRP-97-26

%A W. B. Langdon
%A R. Poli
%T Genetic Programming in Europe
%D 1997
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/evogprep.ps
%O Report of the EvoGP Working Group on Genetic Programming of the European Network of Excellence in Evolutionary Computing
%8 30  November
%Z summary available as \citelangdon:1997:evoGP:summary

%A W. B. Langdon
%A R. Poli
%T EvoGP Report Summary
%J EvoNews
%N 6
%D 1998
%P 6
%I 
%K genetic algorithms, genetic programming
%8 Winter
%Z Summary of \citelangdon:1997:evoGP

%A W. B. Langdon
%A R. Poli
%T Genetic Programming Bloat with Dynamic Fitness
%R Technical Report CSRP-97-29
%D 1997
%I 
%I University of Birmingham, School of Computer Science
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-29.ps.gz
%X In artificial evolution individuals which perform as their parents are usually rewarded identically to their parents. We note that Nature is more dynamic and there may be a
   penalty to pay for doing the same thing as your parents. We report two sets of experiments where static fitness functions are firstly augmented by a penalty for unchanged
   offspring and secondly the static fitness case is replaced by randomly generated dynamic test cases. We conclude genetic programming, when evolving artificial ant control
   programs, is surprisingly little effected by large penalties and program growth is observed in all our experiments.
%8 3  Decemeber

%A W. B. Langdon
%A R. Poli
%T Fitness Causes Bloat: Mutation
%B ET'97 Theory and Application of Evolutionary Computation
%E Chris Clack and Kanta Vekaria and Nadav Zin
%D 1997
%P 59--77
%I 
%C University College London, UK
%K genetic algorithms, genetic programming
%X The problem of evolving, using mutation, an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution
   length. Known variously as "bloat'', "fluff '' and increasing "structural complexity'', this is often described in terms of increasing ``redundancy'' in the code caused by
   "introns''. Comparison between runs with and without fitness selection pressure, backed by Price's Theorem, shows the tendency for solutions to grow in size is caused by
   fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. With simple static
   evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long
   representations of a given solution than short ones. Thus in search (without a length bias) we expect longer representations to occur more often and so representation
   length to tend to increase. I.e. fitness based selection leads to bloat.
%8 15  Decemeber
%Z http://www.cs.ucl.ac.uk/isrg/et97/ As \citeLangdon:1997:bloatMUT but with improved experiments

%A W. B. Langdon
%A R. Poli
%T Fitness Causes Bloat: Mutation
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 37--48
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.euro98_bloatm.ps.gz
%X The problem of evolving, using mutation, an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution
   length. Known variously as "bloat", "fluff" and increasing "structural complexity", this is often described in terms of increasing "redundancy" in the code caused by
   "introns". Comparison between runs with and without fitness selection pressure, backed by Price's Theorem, shows the tendency for solutions to grow in size is caused by
   fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. With simple static
   evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long
   representations of a given solution than short ones. Thus in search (without a length bias) we expect longer representations to occur more often and so representation
   length to tend to increase. I.e. fitness based selection leads to bloat.
%8 14-15  April
%Z EuroGP'98 Based on \citeLangdon:1997:bloatMUT
%@ 3-540-64360-5

%A W. B. Langdon
%A R. Poli
%T Why Ants are Hard
%R Technical Report CSRP-98-4
%D 1998
%I 
%I University of Birmingham, School of Computer Science
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.6500
%X The problem of programming an artificial ant to follow the Santa Fe trail is used as an example program search space. Analysis of shorter solutions shows they have many of
   the characteristics often ascribed to manually coded programs. Enumeration of a small fraction of the total search space and random sampling characterise it as rugged with
   many multiple plateaus split by deep valleys and many local and global optima. This suggests it is difficult for hill climbing algorithms. Analysis of the program search
   space in terms of fixed length schema suggests it is highly deceptive and that for the simplest solutions large building blocks must be assembled before they have above
   average fitness. In some cases we show solutions cannot be assembled using a fixed representation from small building blocks of above average fitness. These suggest the Ant
   problem is difficult for Genetic Algorithms. Random sampling of the program search space suggests on average the density of global optima changes only slowly with program
   size but the density of neutral networks linking points of the same fitness grows approximately linearly with program length. This is part of the cause of bloat. Previously
   reported genetic programming, simulated annealing and hill climbing performance is shown not to be much better than random search on the Ant problem.
%O Presented at GP-98
%8 January
%Z See also \citelangdon:1998:antspace

%A W. B. Langdon
%A R. Poli
%T Why Ants are Hard
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 193--201
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, Santa Fe trail, Random Search (Monte Carlo sampling), Fitness Landscape, Building blocks, Ramped Half-and-half
%U http://www.cs.bham.ac.uk/~wbl/ftp/papers/WBL.antspace_gp98.ps.gz
%X The problem of programming an artificial ant to follow the Santa Fe trail is used as an example program search space. genetic programming, simulated annealing and hill
   climbing performance is shown not to be much better than random search on the Ant problem. Enumeration of a small fraction of the total search space and random sampling
   characterise it as rugged with multiple plateaus split by deep valleys and many local and global optima. This suggests it is difficult for hill climbing algorithms.
   Analysis of the program search space in terms of fixed length schema suggests it is highly deceptive and that for the simplest solutions large building blocks must be
   assembled before they have above average fitness. In some cases we show solutions cannot be assembled using a fixed representation from small building blocks of above
   average fitness. This suggest the Ant problem is difficult for Genetic Algorithms. Random sampling of the program search space suggests on average the density of global
   optima changes only slowly with program size but the density of neutral networks linking points of the same fitness grows approximately linearly with program length. This
   is part of the cause of bloat.
%8 22-25  July
%Z GP-98. Based on \citelangdon:1998:antspaceTR
%@ 1-55860-548-7

%A W. B. Langdon
%A R. Poli
%T Genetic Programming Bloat with Dynamic Fitness
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 96--112
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/335113.html
%X In artificial evolution individuals which perform as their parents are usually rewarded identically to their parents. We note that Nature is more dynamic and there may be a
   penalty to pay for doing the same thing as your parents. We report two sets of experiments where static fitness functions are firstly augmented by a penalty for unchanged
   offspring and secondly the static fitness case is replaced by randomly generated dynamic test cases. We conclude genetic programming, when evolving artificial ant control
   programs, is surprisingly little effected by large penalties and program growth is observed in all our experiments.
%8 14-15  April
%Z EuroGP'98. Based on CSRP-97-29 \citelangdon:1997:dynbloatTR
%@ 3-540-64360-5

%A W. B. Langdon
%T Better Trained Ants
%B Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming
%E Riccardo Poli and W. B. Langdon and Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf
%D 1998
%P 11--13
%I CSRP-98-10, The University of Birmingham, UK School of Computer Science
%C Paris, France
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-10.ps.gz
%X The problem of programming an artificial ant to follow the Santa~Fe trail has been repeatedly used as a benchmark problem. Recently we have shown performance of several
   techniques is not much better than the best performance obtainable using uniform random search. We suggested that this could be because the program fitness landscape is
   difficult for hill climbers and the problem is also difficult for Genetic Algorithms as it contains multiple levels of deception. Here we redefine the problem so the ant is
   obliged to traverse the trail in approximately the correct order. A simple genetic programming system, with no size or depth restriction, is shown to perform approximately
   three times better with the improved training function.
%8 14-15  April
%Z EuroGP'98LB part of \citePoli:1998:egplb Also available as CSRP-98-08 langdon:1998:antlook See also \citelangdon:1998:antlook2TR

%A W. B. Langdon
%A R. Poli
%T Better Trained Ants for Genetic Programming
%R Technical Report CSRP-98-12
%D 1998
%I 
%I University of Birmingham, School of Computer Science
%K genetic algorithms, genetic programming, multi-objective GP
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-12.ps.gz
%X The problem of programming an artificial ant to follow the Santa Fe trail has been repeatedly used as a benchmark problem in GP. Recently we have shown performance of
   several techniques is not much better than the best performance obtainable using uniform random search. We suggested that this could be because the program fitness
   landscape is difficult for hill climbers and the problem is also difficult for Genetic Algorithms as it contains multiple levels of deception. Here we redefine the problem
   so the ant is (1) obliged to traverse the trail in approximately the correct order, (2) to find food quickly. We also investigate (3) including the ant's speed in the
   fitness function, either as a linear addition or as a second objective in a multi-objective fitness function, and (4) GP one point crossover. A simple genetic programming
   system, with no size or depth restriction, is shown to perform approximately three times better with the improved training function. (Extends CSRP-98-08
   \citelangdon:1998:antlook)
%8 April

%A William B. Langdon
%T Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
%S Genetic Programming
%V 1
%D 1998
%I Kluwer
%C Boston
%K genetic algorithms, genetic programming, OOGP, data, memory, stack, list, queue, Pareto multi-objective fitness, analysis, Price's covariance selection theorem
%U http://www.wkap.nl/prod/b/0-7923-8135-1
%Z Computers that 'program themselves' has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving
   programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The
   main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic
   creation of program code including stored data. It is this issue which GENETIC PROGRAMMING AND DATA STRUCTURES addresses. Motivated by the observation from software
   engineering that data abstraction (e.g., via abstract data types) is essential in programs created by human programmers. This book will show that abstract data types can be
   similarly beneficial to the automatic production of programs using GP. GENETIC PROGRAMMING AND DATA STRUCTURES shows how abstract data types (stacks, queues and lists) can
   be evolved using genetic programming, demonstrate GP can evolve general programs which solve the nested brackets problem, recognise a Dyck context free language and
   implement a simple four function calculator. In these cases an appropriate data structure is beneficial compared to simple indexed memory. This book also includes a survey
   of GP, including a critical review of experiments with evolving memory and reports investigations of real world electrical network maintenance scheduling problems that
   demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. GENETIC PROGRAMMING AND DATA STRUCTURES should be of direct interest to computer
   scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, this book will be of interest to
   practitioners working in all of these areas and to those interested in automatic programming. Contents Foreword by John R. Koza. Preface. 1. Introduction. 2. Survey. 3.
   Advanced Genetic Programming Techniques. 4. Evolving a Stack. 5. Evolving a Queue. 6. Evolving a List. 7. Problems Solved Using Data Structures. 8. Evolution of GP
   Populations. 9. Conclusions. Appendices: A. Number of Fitness Evaluations Required. B. Glossary. C. Scheduling Planned Maintenance of the National Grid. D. Implementation.
   Index. Kluwer Accademic Publishers, Order Dept., Box 358, Accord Station, Hingham, MA 02018-0358, USA Tel: +1 781 871-6600 FAX: +1 781 871-6528 Tangent distribution of
   constants. reverse polish notation calculator
%@ 0-7923-8135-1

%A W. B. Langdon
%A R. Poli
%T Boolean Functions Fitness Spaces
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, California, 94305-3079 USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/csrp-98-16
%X We investigate the distribution of performance of the Boolean functions of 3 Boolean inputs (particularly that of the parity functions), the always-on-6 and even-6 parity
   functions. We use enumeration, uniform Monte-Carlo random sampling and sampling random full trees. As expected XOR dramatically changes the fitness distributions. In all
   cases once some minimum size threshold has been exceeded, the distribution of performance is approximately independent of program length. However the distribution of the
   performance of full trees is different from that of asymmetric trees and varies with tree depth. We consider but reject testing the No Free Lunch (NFL) theorems on these
   functions.
%8 22-25  July
%Z GP-98LB, see also \citelangdon:1999:bool, \citelangdon:1999:sptfs Also available as Technical Report CSRP-98-16, University of Birmingham, School of Computer Science, June
   1998.

%A W. B. Langdon
%A R. Poli
%T Why ``Building Blocks'' Don't Work on Parity Problems
%R Technical Report CSRP-98-17
%D 1998
%I 
%I University of Birmingham, School of Computer Science
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-17.ps.gz
%X We investigate the distribution of performance of the parity and always-on Boolean functions given only the appropriate building block. These problems have ``needle in a
   haystack'' fitness landscape and so are unsuitable for genetic programming or other progressive search techniques. Theoretical analysis shows in the limit as program size
   grows the density of solutions is independent of size but falls exponentially with number of inputs.
%8 13  July

%A William B. Langdon
%A Terry Soule
%A Riccardo Poli
%A James A. Foster
%T The Evolution of Size and Shape
%B Advances in Genetic Programming 3
%E Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline
%D 1999
%P 163--190
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming, bloat
%U http://www.cs.bham.ac.uk/~wbl/aigp3/ch08.ps.gz
%X The phenomenon of growth in program size in genetic programming populations has been widely reported. In a variety of experiments and static analysis we test the standard
   protective code explanation and find it to be incomplete. We suggest bloat is primarily due to distribution of fitness in the space of possible programs and because of
   this, in the absence of bias, it is in general inherent in any search technique using a variable length representation. We investigate the fitness landscape produced by
   program tree-based genetic operators when acting upon points in the search space. We show bloat in common operators is primarily due to the exponential shape of the
   underlying search space. Nevertheless we demonstrate new operators with considerably reduced bloating characteristics. We also describe mechanisms whereby bloat arises and
   relate these back to the shape of the search space. Finally we show our simple random walk entropy increasing model is able to predict the shape of evolved programs.
%O 8
%8 June
%Z AiGP3
%@ 0-262-19423-6

%A W. B. Langdon
%A R. Poli
%T Boolean Functions Fitness Spaces
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 1--14
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=1
%X We investigate the distribution of performance of the Boolean functions of 3 Boolean inputs (particularly that of the parity functions), the always-on-6 and even-6 parity
   functions. We use enumeration, uniform Monte-Carlo random sampling and sampling random full trees. As expected XOR dramatically changes the fitness distributions. In all
   cases once some minimum size threshold has been exceeded, the distribution of performance is approximately independent of program size. However the distribution of the
   performance of full trees is different from that of asymmetric trees and varies with tree depth.
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP
%@ 3-540-65899-8

%A W. B. Langdon
%T Scaling of Program Tree Fitness Spaces
%J Evolutionary Computation
%V 7
%N 4
%D 1999
%P 399--428
%I 
%K genetic algorithms, genetic programming, stochastic search, genetic algorithms, tree fitness landscapes, nand gates, Monte Carlo sampling, symbolic regression, Santa Fe
   trail, artificial ant
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.fitnessspaces.ps.gz
%X We investigate the distribution of fitness of programs concentrating upon those represented as parse trees, particularly how such distributions scale with respect to
   changes in size of the programs. By using a combination of enumeration and Monte Carlo sampling on a large number of problems from three very different areas we are lead to
   suggest, in general, once some minimum size threshold has been exceeded, the distribution of performance is approximately independent of program length. We proof this for
   linear programs and for simple side effect free parse trees. We give the density of solutions to the parity problems in program trees composed of XOR building blocks. We
   have so far only conducted limited experiments with programs including side effects and iteration. These suggest a similar result may also hold for this wider class of
   programs.
%8 Winter
%Z Evolutionary Computation (Journal) Special issue on scaling See also \citelangdon:1999:bool, \citelangdon:1998:BBparity Slides at
   http://www.cs.ucl.ac.uk/staff/W.Langdon/fitnessspaces Parity only with building blocks, ie XOR or EQ, is a needle in a haystack. Hence high order parity is impossible using
   only XOR. Presented at Schloss Dagstuhl http://www.dagstuhl.de/files/Reports/00/00071.ps.gz (page 12), and also presented at GECCO'2000. PMID: 10578029

%A W. B. Langdon
%T Size Fair and Homologous Tree Genetic Programming Crossovers
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1092--1097
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, bloat reduction, evolution of shape, subquadratic length growth, linear depth growth, uniform initialisation, binary tree search
   spaces
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-405.ps
%X Size fair and homologous crossover genetic operators for tree based genetic programming are described and tested. Both produce considerably reduced increases in program
   size and no detrimental effect on GP performance. GP search spaces are partitioned by the ridge in the number of program versus their size and depth. A ramped uniform
   random initialisation is described which straddles the ridge. With subtree crossover trees increase about one level per generation leading to sub-quadratic bloat in length.
%8 13-17  July
%Z See also \citelangdon:2000:fairxo and \citelangdon:1999:fairxTR Also known as langdon:1999:SFHTGPC GECCO-99, part of \citebanzhaf:1999:gecco99 A joint meeting of the eighth
   international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A W. B. Langdon
%T Size Fair and Homologous Tree Crossovers
%R Technical Report SEN-R9907
%D 1999
%I 
%I Centrum voor Wiskunde en Informatica
%C CWI, P.O. Box 94079, Kruislaan 413, NL-1090 GB Amsterdam, The Netherlands
%K genetic algorithms, genetic programming, bloat reduction, evolution of shape, sub-quadratic length growth, linear depth growth, uniform initialisation, binary tree search
   spaces
%U ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9907.ps.Z
%X Size fair and homologous crossover genetic operators for tree based genetic programming are described and tested. Both produce considerably reduced increases in program
   size (i.e. less bloat) and no detrimental effect on GP performance. GP search spaces are partitioned by the ridge in the number of program v. their size and depth. While
   search efficiency is little effected by initial conditions, these do strongly influence which half of the search space is searched. However a ramped uniform random
   initialisation is described which straddles the ridge. With subtree crossover trees increase about one level per generation leading to sub-quadratic bloat in program
   length.
%8 11  April
%Z CWI technical report

%T Late-Breaking Papers of EuroGP-99
%E W. B. Langdon and Riccardo Poli and Peter Nordin and Terry Fogarty
%D 1999
%I 
%I EvoGP
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.ps.Z
%X This booklet contains the late-breaking papers of the Second European Workshop on Genetic Programming (EuroGP'99) held in G\"oteborg Sweden 26--27~May~1999. EuroGP'99 was
   one of the EvoNet workshops on evolutionary computing, EvoWorkshops'99. The purpose of the late-breaking papers was to provide attendees with information about research
   that was initiated, enhanced, improved, or completed after the original paper submission deadline in December 1998. To ensure coverage of the most up-to-date research, the
   deadline for submission was set only a month before the workshop. Late-breaking papers were examined for relevance and quality by the organisers of the EuroGP'99, but no
   formal review process took place. The 3 late-breaking papers in this booklet (which was distributed at the workshop) were presented during a poster session held on Thursday
   27 May 1999 during EuroGP'99. Authors individually retain copyright (and all other rights) to their late-breaking papers. This booklet is available as a technical report
   SEN-R9913 from Centrum voor Wiskunde en Informatica, Kruislaan 413, NL-1098 SJ Amsterdam http://www.cwi.nl/static/publications/reports/reports.html
%8 26-27  May
%Z EuroGP'99LB PDF file has formating problems:-( 27 Jul 1999

%A W. B. Langdon
%T Linear Increase in Tree Height Leads to Sub-Quadratic Bloat
%B Foundations of Genetic Programming
%E Thomas Haynes and William B. Langdon and Una-May O'Reilly and Riccardo Poli and Justinian Rosca
%D 1999
%P 55--56
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/langdon
%8 13  July
%Z GECCO'99 WKSHOP, part of \citehaynes:1999:fogp see also \citelangdon:2000:quad

%A W. B. Langdon
%T Size Fair Tree Crossovers
%B Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)
%E Eric Postma and Marc Gyssen
%D 1999
%P 255--256
%I 
%I BNVKI, Dutch and the Belgian AI Association
%C Kasteel Vaeshartelt, Maastricht, Holland
%K genetic algorithms, genetic programming, bloat reduction, evolution of shape, sub-quadratic length growth, linear depth growth, uniform initialisation, binary tree search
   spaces
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/fairxo_bnaic99.ps.gz
%X Size fair crossover genetic operator for tree based genetic programming is described and tested. It produces considerably reduced increases in program size and no
   detrimental effect on GP performance. GP search spaces are partitioned by the ridge in the number of program v. their size and depth. A ramped uniform random initialisation
   is described which straddles the ridge. With subtree crossover trees increase about one level per generation leading to sub-quadratic bloat in length.
%8 3-4  November
%Z Resumission of \citelangdon:1999:fairxo http://www.cs.unimaas.nl/~bnvki/

%A W. B. Langdon
%A J. P. Nordin
%T Seeding GP Populations
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 304--315
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming, Pareto multi-objective fitness
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=304
%X We show GP populations can evolve from ``perfect'' programs which match the training material under the influence of a Pareto multi-objective fitness and program size
   selection scheme to generalise. The technique is demonstrated upon programmatic image compression, two machine learning benchmark problems (Pima Diabetes and Wisconsin
   Breast Cancer) and a consumer profiling task (Benelearn99).
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A William B. Langdon
%T Size Fair and Homologous Tree Genetic Programming Crossovers
%J Genetic Programming and Evolvable Machines
%V 1
%N 1/2
%D 2000
%P 95--119
%I 
%K genetic algorithms, genetic programming, bloat reduction, evolution of shape, subquadratic length growth, linear depth growth, uniform initialisation, binary tree search
   spaces
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_fairxo.ps.gz
%X Size fair and homologous crossover genetic operators for tree based genetic programming are described and tested. Both produce considerably reduced increases in program
   size (ie less bloat) and no detrimental effect on GP performance. GP search spaces are partitioned by the ridge in the number of program versus their size and depth. While
   search efficiency is little effected by initial conditions, these do strongly influence which half of the search space is searched. However a ramped uniform random
   initialisation is described which straddles the ridge. With subtree crossover trees increase about one level per generation leading to sub-quadratic bloat in program
   length.
%8 April
%Z Improved version of \citelangdon:1999:fairxTR See also \citelangdon:1999:fairxo, langdon:1999:fairxo2p Article ID: 253706

%A W. B. Langdon
%T GECCO'99 Student Workshop
%J Newsletter BVNKI
%V 16
%N 5
%D 1999
%P 144--143
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.unimaas.nl/~bnvki/archive/1999/16.5.pdf
%8 October
%Z http://www.cs.unimaas.nl/~bnvki/nieuwsbr.htm See also \citelangdon:2000:geccostudent

%A W. B. Langdon
%T GECCO'99 Student Workshop
%J Robotica
%V 18
%N 1
%D 2000
%P 87
%I 
%K genetic algorithms, genetic programming
%8 January
%Z See also \citelangdon:1999:geccostudent

%A William B. Langdon
%T Genetic Programming and Evolvable Machines: Books and other Resources
%J Genetic Programming and Evolvable Machines
%V 1
%N 1/2
%D 2000
%P 165--169
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_gppubs.ps.gz
%X Presents summaries of published GP/EM literature, Internet based sources of GP/EM information and our plans for future reviews of GP/EM resources (books, world wide web
   (www) sites, products and programs).
%8 April
%Z Article ID: 253708 See also \citelangdon:2005:gpembooks

%A W. B. Langdon
%T Genetic Programming Approach to Benelearn 99: I
%B The Benelearn 1999 Competition
%E Peter van der Putten and Maarten van Someren
%D 1999
%P 3.5
%I 
%C Sociaal-Wetenschappelijke Informatica, Universiteit van Amsterdam
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.benelearn1.99.ps.gz
%X We briefly describe our first genetic programming technique used to automatically evolve profiles of potential insurance customers the task is part of the Benelearn'99
   competition. The information about customers consists of 86 variables and includes product usage data and socio-demographic data derived from zip codes. The data was
   supplied by the Dutch data mining company Sentient Machine Research, and is based on real world business data. Profiles which correctly identified more than 40 percent of
   customers were automatically evolved using genetic programming.
%8 2  November
%Z http://www.swi.psy.uva.nl/benelearn99/comppage.html Note WBL.benelearn2.99.pdf contains version II update (5 pages)

%A W. B. Langdon
%A W. Banzhaf
%T Genetic Programming Bloat without Semantics
%B Parallel Problem Solving from Nature - PPSN VI 6th International Conference
%S LNCS
%E Marc Schoenauer and Kalyanmoy Deb and G\"unter Rudolph and Xin Yao and Evelyne Lutton and Juan Julian Merelo and Hans-Paul Schwefel
%V 1917
%D 2000
%P 201--210
%I Springer Verlag
%C Paris, France
%K genetic algorithms, genetic programming, evolution of shape, subquadratic length growth, linear depth growth, binary tree search spaces
%U http://www.cs.mun.ca/~banzhaf/papers/ppsn00.pdf
%X To investigate the fundamental causes of bloat, six artificial random binary tree search spaces are presented. Fitness is given by program syntax (the genetic programming
   genotype). GP populations are evolved on both random problems and problems with ``building blocks''. These are compared to problems with explicit ineffective code (introns,
   junk code, inviable code). Our results suggest the entropy random walk explanation of bloat remains viable. The hard building block problem might be used in further
   studies, e.g. of standard subtree crossover.
%8 16-20  September
%Z C++ code at ftp://cs.ucl.ac.uk/wblangdon/gp-code/ http://www-syntim.inria.fr/fractales/PPSN2000/program.html#108 PPSN'2000
   http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-41056-2

%A W. B. Langdon
%T Quadratic Bloat in Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 451--458
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, bloat, introns, ineffective code, evolution of shape, subquadratic length growth, linear depth growth, binary tree search spaces
%U http://citeseer.ist.psu.edu/316810.html
%X In earlier work we predicted program size would grow in the limit at a quadratic rate and up to fifty generations we measured bloat O(generations**(1.2-1.5)). On two simple
   benchmarks we test the prediction of bloat O(generations**2.0) up to generation 600. In continuous problems the limit of quadratic growth is reached but convergence in the
   discrete case limits growth in size. Measurements indicate subtree crossover ceases to be disruptive with large programs (1,000,000) and the population effectively
   converges (even though variety is near unity). Depending upon implementation, we predict run time O(number of generations**(2.0-3.0)) and memory O(number of
   generations**(1.0-2.0)).
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A William B. Langdon
%A Bernard F. Buxton
%T Evolving Receiver Operating Characteristics for Data Fusion
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 87--96
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Data Fusion, Data Mining, Knowledge Discovery, Receiver Operating Characteristics, ROC, Combining Classifiers
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=87
%X It has been suggested that the ``Maximum Realisable Receiver Operating Characteristics'' for a combination of classifiers is the convex hull of their individual ROCs [Scott
   et al., 1998]. As expected in at least some cases better ROCs can be produced. We show genetic programming (GP) can automatically produce a combination of classifiers whose
   ROC is better than the convex hull of the supplied classifier's ROCs.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A William B. Langdon
%A Peter Nordin
%T Evolving Hand-Eye Coordination for a Humanoid Robot with Machine Code Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 313--324
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Humanoid Robotics, Genetic Reasoning, Brain Building, Robotic Arm, Robots, Inverse Kinematics, Stereo Vision, Discipulus
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=313
%X We evolve, using AIMGP machine code genetic programming, Discipulus, an approximation of the inverse kinematics of a real robotics arm with many degrees of freedom. Elvis
   is a bipedal robot with human-like geometry and motion capabilities --- a humanoid, primarily controlled by evolutionary adaptive methods. The GP system produces a useful
   inverse kinematic mapping, from target 3-D points (via pairs of stereo video images) to a vector of arm controller actuator set points.
%8 18-20  April
%Z See movie at http://www.cs.ucl.ac.uk/staff/W.Langdon/elvis EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A W. B. Langdon
%T Long Random Linear Programs Do Not Generalize
%J Genetic Programming and Evolvable Machines
%V 2
%N 2
%D 2001
%P 95--100
%I 
%K genetic algorithms, genetic programming, generality, random search
%X The chance of solving a problem by random search of linear random programs tends to a limit as their size increases. When all outputs are equally used this limit is no more
   than 2**(-|test set|). Where |test set| is the size of the total test set. This is a generalisation of a previous result, \citelangdon:1999:sptfs. Secondly, we show the
   chance of finding a long linear general solution by random search is exponentially small.
%8 June
%Z No Generalise See also RN/01/14 http://www.cs.ucl.ac.uk/staff/W.Langdon/maxproduct/ Article ID: 335707

%A W. B. Langdon
%A B. F. Buxton
%T Genetic Programming for Combining Classifiers
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 66--73
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, data fusion, data mining, knowledge discovery, Receiver Operating Characteristics, ensemble of classifiers, size fair crossover
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf
%X Genetic programming (GP) can automatically fuse given classifiers to produce a combined classifier whose Receiver Operating Characteristics (ROC) are better than
   scott:1998:BMVC ``Maximum Realisable Receiver Operating Characteristics'' (MRROC). I.e. better than their convex hull. This is demonstrated on artificial, medical and
   satellite image processing bench marks.
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%T Dynamics of Evolutionary Algorithms
%E W. B. Langdon
%D 2001
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2001dea.html
%O www
%8 July
%Z Notes of workshop held at GECCO-2001

%A W. B. Langdon
%A B. F. Buxton
%T Genetic Programming for Improved Receiver Operating Characteristics
%B Second International Conference on Multiple Classifier System
%S LNCS
%E Josef Kittler and Fabio Roli
%V 2096
%D 2001
%P 68--77
%I Springer Verlag
%C Cambridge
%K genetic algorithms, genetic programming, data fusion, data mining, knowledge discovery, Receiver Operating Characteristics, ensemble of classifiers, size fair crossover,
   cost-sensitive, cost trade off, non-uniform penalty
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2096&spage=68
%X Genetic programming (GP) can automatically fuse given classifiers to produce a combined classifier whose Receiver Operating Characteristics (ROC) are better than [Scott et
   al. 1998]'s \citescott:1998:BMVC ``Maximum Realisable Receiver Operating Characteristics'' (MRROC). I.e. better than their convex hull. This is demonstrated on artificial,
   medical and satellite image processing bench marks.
%8 2-4  July
%Z http://www.diee.unica.it/mcs/ Technique in \citelangdon:2001:gROC used to combine different classifiers on trained on different data.
%@ 3-540-42284-6

%A W. B. Langdon
%T Evolutionary Data Fusion
%R Technical Report RN/01/19
%D 2001
%I 
%I University College, London
%C UK
%K genetic algorithms, genetic programming, ROC
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/roc
%8 3  April
%Z Distributed at 25 April 2001 Faraday meeting http://www.npl.co.uk/intersect/

%A W. B. Langdon
%A S. J. Barrett
%A B. F. Buxton
%T Genetic Programming for Combining Neural Networks for Drug Discovery
%B Soft Computing and Industry Recent Applications
%E Rajkumar Roy and Mario K\"oppen and Seppo Ovaska and Takeshi Furuhashi and Frank Hoffmann
%D 2001
%P 597--608
%I Springer-Verlag
%K genetic algorithms, genetic programming, data fusion, data mining, knowledge discovery, Receiver Operating Characteristics, ensemble of classifiers, size fair crossover
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_wsc6.pdf
%X We have previously shown on a range of benchmarks \citelangdon:2001:gROC Genetic programming (GP) can automatically fuse given classifers of diverse types to produce a
   combined classifer whose Receiver Operating Characteristics (ROC) are better than \citescott:1998:BMVC's "Maximum Realisable Receiver Operating Characteristics" (MRROC).
   I.e. better than their convex hull. Here our technique is used in a blind trial where artifcial neural networks ANN. are trained by Clementine on P450 pharmaceutical data.
   Using just the networks GP automatically evolves a composite classifer.
%O Published 2002
%8 10--24  September
%Z Out of print? http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394
%@ 1-85233-539-4

%A W. B. Langdon
%T Prediction
%R Technical Report
%D 2001
%I 
%I Commercial
%K genetic algorithms, genetic programming

%A W. B. Langdon
%A Riccardo Poli
%T Foundations of Genetic Programming
%D 2002
%I Springer-Verlag
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/FOGP/
%@ 3-540-42451-2

%A William B. Langdon
%T Structure of the Genetic Programming Search Space
%B Theory of Evolutionary Algorithms
%R Report 330
%E Hans-Georg Beyer and Ken De Jong and Colin Reeves and Ingo Wegener
%D 2002
%P 12
%I 
%I Dagstuhl
%C Germany
%K genetic algorithms, genetic programming
%U http://www.dagstuhl.de/files/Reports/02/02031.ps.gz
%X It is known that the fitness distribution of programs tends to a limit as they get bigger. We use Markov chain convergence theorems to give both upper and lower bounds on
   program size needed for convergence. Results are presented for four types of computer models. The bounds on the length of random programs needed to converge depends upon
   the size of memory N. Bounds are exponential in N, N log N and smaller, depending on the model.
%8 13-18  January
%Z Seminar No. 02031. See also \citelangdon:2002:crlp

%A W. B. Langdon
%T Characteristics of the Genetic Programming Search Space
%B Software Evolution and Evolutionary Computation Symposium Abstracts
%R Technical Report 364
%E C. L. Nehaniv and M. Loomes and P. Marrow and P. Wernick
%D 2002
%P 10
%I 
%I University of Hertfordshire, Computer Science
%C University of Hertfordshire
%K genetic algorithms, genetic programming
%U http://homepages.feis.herts.ac.uk/~nehaniv/EN/seec.html
%8 2  February

%A William B. Langdon
%A S. J. Barrett
%A B. F. Buxton
%T Combining Decision Trees and Neural Networks for Drug Discovery
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 60--70
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming, drug design, Receiver Operating Characteristics (ROC), ensemble of classifiers, data fusion, artificial neural networks,
   clementine, decision trees C4.5, high through put screening (HTS)
%U http://link.springer.de/link/service/series/0558/bibs/2278/22780060.htm
%X Genetic programming (GP) offers a generic method of automatically fusing together classifiers using their receiver operating characteristics (ROC) to yield superior
   ensembles. We combine decision trees (C4.5) and artificial neural networks (ANN) on a difficult pharmaceutical data mining (KDD) drug discovery application. Specifically
   predicting inhibition of a P450 enzyme. Training data came from high throughput screening (HTS) runs. The evolved model may be used to predict behaviour of virtual (i.e.
   yet to be manufactured) chemicals. Measures to reduce over fitting are also described.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%T GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, DNA computing, adaptive behavior, agents, ant colony optimization, artificial life, evolution strategies, evolutionary programming,
   evolutionary robotics, evolutionary routing, evolutionary scheduling evolvable hardware, learning classifier systems, methodology, molecular computing, pedagogy,
   philosophy, real world applications, search-based software engineering
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-00.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A W. B. Langdon
%T Convergence Rates for the Distribution of Program Outputs
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 812--819
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, Fitness Landscapes, Markov analysis, Mutation convergence time, Total Variation Distance, Markov Minorization, Random walk
   eigenvalues, Average computer
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%X Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain convergence theorems give general upper bounds on the linear program sizes
   needed for convergence. Tight bounds (exponential in N, N log N, and smaller) are given for five computer models (any, average, cyclic, bit flip and Boolean). Mutation
   randomizes a genetic algorithm population in 0.25 (l+1)(log(l)+4) generations. Results for a genetic programming (GP) like model are confirmed by experiment.
%8 9-13  July
%Z GECCO-2002 A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) Part
   of \citelangdon:2002:GECCO
%@ 1-55860-878-8

%A William B. Langdon
%T Was Occam Wrong? Blunting Occam's Razor
%J BNVKI newsletter
%V 19
%N 3
%D 2002
%P 56--57
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.unimaas.nl/~bnvki/archive/2002/19.3.PDF
%8 June
%Z http://www.cs.unimaas.nl/~bnvki/Nieuwsbr.htm

%A W. B. Langdon
%T Random Search is Parsimonious
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 308--315
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_gecco2002lb.ps.gz
%X We model in detail the distribution of Boolean functions implemented by random non-recursive programs, similar to linear genetic programming. Most functions are constants,
   the remainder are mostly simple. Bounds on how long programs need to be before the distribution of their functionality is close to its limiting distribution are provided in
   general and for average computers.
%8 9-13  July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp Superseded by \citelangdon:2002:foga

%A W. B. Langdon
%T How many Good Programs are there? How Long are they?
%B Foundations of Genetic Algorithms VII
%E Kenneth A. De Jong and Riccardo Poli and Jonathan E. Rowe
%D 2002
%P 183--202
%I Morgan Kaufmann San Francisco, CA, USA
%C Torremolinos, Spain
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_foga2002.ps.gz
%X We model the distribution of functions implemented by non-recursive programs, similar to linear genetic programming (GP). Most functions are constants, the remainder are
   mostly parsimonious. The effect of ad-hoc rules on GP are described and new heuristics are proposed. Bounds on how long programs need to be before the distribution of their
   functionality is close to its limiting distribution are provided in general and for average computers. Results for average computers and a model like genetic programming
   are experimentally tested.
%O Published 2003
%8 4-6  September
%Z Workshop in September 2002, published 2003
%@ 0-12-208155-2

%A W. B. Langdon
%T Size of Random Programs to ensure Uniformity
%B Proceedings of the Fourteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02)
%E Hendrik Blockeel and Marc Denecker
%D 2002
%P 459--460
%I 
%I BNVKI, Dutch and the Belgian AI Association
%C Leuven, Belgium
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_bnaic2002.ps.gz
%X Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain convergence theorems give general upper bounds on the linear program sizes
   needed for convergence. Tight bounds (exponential in N, N log(N) and smaller) are given in \citelangdon:2002:crlp for the outputs of five computer models (any, average,
   cyclic, bit flip and Boolean). Mutation randomises a genetic algorithm population in 0.25(l+1)(log(l)+4) generations. While \citelangdon:2002:foga considers convergence of
   functions. We restate the results 0.5N(log(m)+4) and O(N)-O(N^3/2) for a genetic programming (GP) like model.
%8 21-22  October
%Z 2 page summary of \citelangdon:2002:crlp Katholieke Universiteit Leuven and Universite Libre de Bruxelles in collaboration with PharmaDM and under the auspices of
   BNVKI/AIABN (the Belgian-Dutch Association for Artificial Intelligence), SIKS (School for Information and Knowledge Systems), and SNN (the Foundation for Neural Networks).

%A W. B. Langdon
%A B. F. Buxton
%A S. J. Barrett
%T Combining Machine Learning techniques to Predict Compounds' Cytochrome P450 High Throughput Screening Inhibition
%D 2002
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_kdmdd2002.pdf
%O Knowledge Discovery meets Drug Discovery
%O Poster
%8 23  October
%Z http://www.kdnet.org/workshop_overview1_bioinfoLeuven02.htm

%A W. B. Langdon
%T Application of genetic programming in drug lead discovery
%D 2002
%I 
%C Seville, Spain
%K genetic algorithms, genetic programming
%U http://www.lsi.us.es/iberamia2002/docs/plenarysession.pdf
%O 8th Iberoamerican Conference on Artificial Intelligence
%O Invited conference speaker
%8 12  November
%Z http://www.lsi.us.es/iberamia2002/principal_eng.html

%A William B. Langdon
%T A Hybrid Genetic Programming Neural Network Classifier for Use in Drug Discovery
%B Soft Computing Systems - Design, Management and Applications
%S Frontiers in Artificial Intelligence and Applications Vol. 87
%E Ajith Abraham and Javier Ruiz-del-Solar and Mario K\"oppen
%D 2002
%P 6
%I IOS Press Amsterdam, Berlin, Oxford, Tokyo, Washington D.C.
%I Department of Computer Science -- University College London -- UK
%C Universidad de Chile, Chile
%K genetic algorithms, genetic programming
%U http://www.cec.uchile.cl/~his02/index_files/abs_drug.pdf
%X We have shown genetic programming (GP) can automatically fuse given classifiers of diverse types to produce a hybrid classifier. Combinations of neural networks, decision
   trees and Bayes classifier shave been formed. On a range of benchmarks the evolved multiple classifier system is better than all of its components. Indeed its Receiver
   Operating Characteristics (ROC) are better than [Scott et al., 1998]s "Maximum Realisable Receiver Operating Characteristics" MRROC (convex hull) An important component in
   the drug discovery is testing potential drugs for activity with P450 cell membrane molecules. Our technique has been used in a blind trial where artificial neural networks
   are trained by Clementine on P450 pharmaceutical data. Using just the trained networks, GP automatically evolves a composite classifier. Recent experiments with boosting
   the networks will be compared with genetic programming.
%O Abstracts of HIS02 Plenary Presentations
%O Invited conference speaker
%8 1-4  Decemeber
%Z http://www.cec.uchile.cl/~his02/index.html old key langdon:2002:his ISBN? = 4 274 90558 6 C3055
%@ 1-58603-297-6

%A William B. Langdon
%T Evolutionary Computation II Session at BNAIC 2002
%J Newsletter BNVKI
%V 19
%N 6
%D 2002
%P 145--146
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.unimaas.nl/~bnvki/archive/2002/19.6.pdf
%X The session consisted of three presentations: Towards a Relation Between Learning Agents and Evolutionary Dynamics, Karl Tuyls. Improving Opponent Intelligence by Machine
   Learning, Pieter Spronck. Genetic Programming Produces Strategies for Agents in a Dynamic Environment, Walter A. Kosters \citekeller:2002:bnaic
%8 Decemeber
%Z http://www.cs.unimaas.nl/~bnvki/nieuwsbr.htm

%A W. B. Langdon
%A S. J. Barrett
%A B. F. Buxton
%T Comparison of AdaBoost and Genetic Programming for combining Neural Networks for Drug Discovery
%B Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM
%S LNCS
%E G\"unther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and
   Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf
%V 2611
%D 2003
%P 87--98
%I Springer-Verlag Berlin
%I EvoNet
%C University of Essex, UK
%K genetic algorithms, genetic programming, adaboost, drug design, Receiver Operating Characteristics (ROC), ensemble of classifiers, data fusion, artificial neural networks,
   clementine, high through put screening (HTS)
%U http://link.springer.de/link/service/series/0558/bibs/2278/22780060.htm
%X Genetic programming (GP) based data fusion and AdaBoost can both improve in vitro prediction of Cytochrome P450 activity by combining artificial neural networks (ANN).
   Pharmaceutical drug design data provided by high throughput screening (HTS) is used to train many base ANN classifiers. In data mining (KDD) we must avoid over fitting. The
   ensembles do extrapolate from the training data to other unseen molecules. I.e. they predict inhibition of a P450 enzyme by compounds unlike the chemicals used to train
   them. Thus the models might provide in silico screens of virtual chemicals as well as physical ones from GlaxoSmithKline (GSK)'s cheminformatics database. The receiver
   operating characteristics (ROC) of boosted and evolved ensemble are given.
%8 14-16  April
%Z EvoWorkshops2003

%A W. B. Langdon
%T The distribution of Reversible Functions is Normal
%B Genetic Programming Theory and Practice
%E Rick L. Riolo and Bill Worzel
%D 2003
%P 173--188
%I Kluwer
%K genetic algorithms, genetic programming, fitness landscape, evolutionary computation, quantum computing, CCNOT, Toffoli, low power consumption
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_reversible.ps.gz
%X The distribution of reversible programs tends to a limit as their size increases. For problems with a Hamming distance fitness function the limiting distribution is
   binomial with an exponentially small chance (but non~zero) chance of perfect solution. Sufficiently good reversible circuits are more common. Expected RMS error is also
   calculated. Random unitary matrices may suggest possible extension to quantum computing. Using the genetic programming (GP) benchmark, the six multiplexor, circuits of
   Toffoli gates are shown to give a fitness landscape amenable to evolutionary search. Minimal CCNOT solutions to the six multiplexer are found but larger circuits are more
   evolvable.
%O 11
%Z Extension of work presented at Dagstuhl See page 11 of \citebeyer_et_al:DSP:2006:498 Part of \citeRioloWorzel:2003
%@ 1-4020-7581-2

%A W. B. Langdon
%T Convergence of Program Fitness Landscapes
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1702--1714
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, fitness landscapes, route map, expected runtime, reversible computer, random computer, amorphous computing, point mutation,
   Neighborhood, hill climbing, modulo computing, bit flip computer, general proof, boolean function, linear genetic programming, average computer
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_gecco2003.ps.gz
%X Point mutation has no effect on almost all linear programs. In two genetic programming (GP) computers (cyclic and bit flip) we calculate the fitness evaluations needed
   using steepest ascent and first ascent hill climbers and evolutionary search. We describe how the average fitness landscape scales with program length and give general
   bounds.
%8 12-16  July
%Z GECCO-2003 A joint meeting of the twelvth international conference on genetic algorithms (ICGA-99) and the eigth annual genetic programming conference (GP-2003)
%@ 3-540-40603-4

%A W. B. Langdon
%T Predicting Cancer
%J UCL Science
%V 17
%D 2003
%P 2
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ucl_science/predictingcancer.html
%X Computers are being used to evolve predictors based on DNA gene chips. Recently we have evolved programs that use mRNA extracted from cancerous cells to try to forecast
   patient outcomes.
%8 September

%A W. B. Langdon
%A B. F. Buxton
%T The Application of Genetic Programming for Drug Discovery in the Pharmaceutical Industry. EPSRC RAIS Secondment with GlaxoSmithKline
%D 2003
%I 
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/langdon03application.html
%X Individual Grant Review (IGR) on EPSRC Research Assistant Industrial Secondment (RAIS) grant GR/S03546/01
%O EPSRC project closing report
%8 24  November

%A W. B. Langdon
%A S. J. Barrett
%A B. F. Buxton
%T Predicting Biochemical Interactions -- Human P450 2D6 Enzyme Inhibition
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 807--814
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming, QSAR, drug, P450, Pareto multi-objective fitness
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2003.ps.gz
%X In silico screening of chemical libraries or virtual chemicals may reduce drug discovery and medicine optimisation lead times and increase the probability of success by
   directing search through chemical space. About a dozen intelligent pharmaceutical QSAR modelling techniques were used to predict IC50 concentration (three classes) of drug
   interaction with a cell wall enzyme (P450 CYC2D6). Genetic programming gave comprehensible cheminformatics models which generalised best. This was shown by a blind test on
   GlaxoWelcome molecules of machine learning knowledge nuggets mined from SmithKline Beecham compounds. Performance on similar chemicals (interpolation) and diverse chemicals
   (extrapolation) suggest generalisation is more difficult than avoiding over fitting. Two GP approaches, classification via regression using a multi-objective fitness
   measure and a direct winner takes all (WTA) or one versus all (OVA) classification, are described. Predictive rules were compressed by separate follow up GP runs seeded
   with the best program.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A W. B. Langdon
%A S. J. Barrett
%T Genetic Programming in Data Mining for Drug Discovery
%B Evolutionary Computing in Data Mining
%S Studies in Fuzziness and Soft Computing
%E Ashish Ghosh and Lakhmi C. Jain
%V 163
%D 2004
%P 211--235
%I Springer
%K genetic algorithms, genetic programming, drug discovery, ROC fitness, ADMET
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980376-0,00.html
%X Genetic programming (GP) is used to extract from rat oral bioavailability (OB) measurements simple, interpretable and predictive QSAR models which both generalise to rats
   and to marketed drugs in humans. Receiver Operating Characteristics (ROC) curves for the binary classifier produced by machine learning show no statistical difference
   between rats (albeit without known clearance differences) and man. Thus evolutionary computing offers the prospect of in silico ADME screening e.g. for virtual chemicals,
   for pharmaceutical drug discovery.
%O 10
%Z wbl_bioavail postscript and PDF page numbering and figures NOT identical to published book
%@ 3-540-22370-3

%A W. B. Langdon
%A B. F. Buxton
%T Genetic Programming for Mining DNA Chip data from Cancer Patients
%J Genetic Programming and Evolvable Machines
%V 5
%N 3
%D 2004
%P 251--257
%I 
%K genetic algorithms, genetic programming, attribute selection, leave one out cross fold validation, data mining, machine learning, classification, evolutionary algorithms,
   DNA chip gene expression, predicting cancer
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_dnachip.ps.gz
%X In machine learning terms DNA (gene) chip data is unusual in having thousands of attributes (the gene expression values) but few (<100) records (the patients). A GP based
   method for both feature selection and generating simple models based on a few genes is demonstrated on cancer data.
%8 September
%Z Article ID: 5272972

%A W. B. Langdon
%T Global Distributed Evolution of L-Systems Fractals
%B Genetic Programming, Proceedings of EuroGP'2004
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 349--358
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming, www, Lindenmayer system, D0L, mozilla browser, javascript, perl, cgi, turtle graphics, no bloat, evolving agents, interactive
   evolution
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=349
%X Internet based parallel genetic programming (GP) creates fractal patterns like Koch's snow flake. Pfeiffer, http://www.cs.ucl.ac.uk/staff/W.Langdon/pfeiffer.html, by
   analogy with seed/embryo development, uses Lindenmayer grammars and LOGO style turtle graphics written in Javascript and Perl. 298 novel pictures were produced. Images are
   placed in animated snow globes (computerised snowstorms) by www web browsers anywhere on the planet. We discuss artificial life (Alife) evolving autonomous agents and
   virtual creatures in higher dimensions from a free format representation in the context of neutral networks, gene duplication and the evolution of higher order genetic
   operators.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoWorkshops'2003 and EvoCOP'2004 See also \citelangdon:2004:EuroGPA Current active system
   http://www.cs.ucl.ac.uk/staff/W.Langdon/pfeiffer.html
%@ 3-540-21346-5

%A W. B. Langdon
%A W. Banzhaf
%T Repeated Sequences in Linear GP Genomes
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, Evolutionary computation, artificial evolution, frequent patterns, repeated sequences, hierarchical building blocks, repetitive
   elements, microsatellites, unequal crossover, duplication tandemly repeated genes, growth of genomes, repeats finder, SSR tracts
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP048.pdf
%X Biological chromosomes are replete with repetitive sequences, microsatellites, SSR tracts, ALU, etc. in their DNA base sequences. We discover hierarchical repeating
   sequences (building blocks?) are evolved by genetic programming in linear Mackey Glass chaotic time series prediction programs.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp See \citelangdon:2005:CS for extended version.

%A W. B. Langdon
%T Lindenmayer Systems Fractals Evolved by Pfeiffer 10~September -- 9~November 2003
%R Research Note RN/04/13
%D 2004
%I 
%I University College, London
%K genetic algorithms, genetic programming, www, Lindenmayer system, D0L, mozilla browser, javascript, perl, cgi, turtle graphics, evolving agents, interactive evolution
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/rn0413.ps.gz
%X Pfeiffer http://www.cs.ucl.ac.uk/staff/W.Langdon/pfeiffer.html is an interactive JavaScript based system for evolving fractal patterns similar to Koch's snow flake. All
   patterns evolved during a two month trial period during the Autumn of 2003 are given. This research note should be viewed as an appendix to: \citelangdon:2004:EuroGP and
   \citelangdon:2005:metas. %\citelangdon:2004:full
%8 19  July

%A William B. Langdon
%A Wolfgang Banzhaf
%T Repeated Sequences in Linear Genetic Programming Genomes
%J Complex Systems
%V 15
%N 4
%D 2005
%P 285--306
%I 
%K genetic algorithms, genetic programming, Evolutionary computation, artificial evolution, frequent patterns, repeated sequences, hierarchical building blocks, repetitive
   elements, microsatellites, unequal crossover, duplication tandemly repeated genes, growth of genomes, repeats finder, SSR tracts, GPengine, Discipulus
%U http://www.complex-systems.com/pdf/15-4-2.pdf
%X Biological chromosomes are replete with repetitive sequences, microsatellites, SSR tracts, ALU, etc. in their DNA base sequences. We started looking for similar phenomena
   in evolutionary computation. First studies find copious repeated sequences, which can be hierarchically decomposed into shorter sequences, in programs evolved using both
   homologous and two point crossover but not with headless chicken crossover or other mutations. In bloated programs the small number of effective or expressed instructions
   appear in both repeated and non-repeated code. Hinting that building-blocks or code reuse may evolve in unplanned ways. Mackey-Glass chaotic time series prediction and
   eukaryotic protein localisation (both previously used as artificial intelligence machine learning benchmarks) demonstrate evolution of Shannon information (entropy) and
   lead to models capable of lossy Kolmogorov compression. Our findings with diverse benchmarks and GP systems suggest this emergent phenomenon may be widespread in genetic
   systems.
%Z Extended version of \citelangdon:2004:geccolb

%A W. B. Langdon
%A Riccardo Poli
%T Evolutionary Solo Pong Players
%R Technical Report CSM-423
%D 2005
%I 
%I Department of Computer Science, University of Essex
%C Colchester, UK
%K genetic algorithms, genetic programming, XPS, games, AI, PSO
%U http://www.cs.essex.ac.uk/technical-reports/2005/csm423.pdf
%X An Internet Java Applet http://www.cs.essex.ac.uk/staff/poli/SoloPong/ allows users anywhere to play the Solo Pong game. We compare people's performance to a hand coded
   ``Optimal'' player and programs automatically produced by artificial intelligence. The AI techniques are: genetic programming, including a hybrid of GP and a human designed
   algorithm, and a particle swarm optimiser. The AI approaches are not fine tuned. GP and PSO find good players. Evolutionary computation (EC) is able to beat both human
   designed code and human players.
%8 17  March
%Z Replaced by langdon:2005:CECa

%A William B. Langdon
%A Wolfgang Banzhaf
%T Repeated Patterns in Tree Genetic Programming
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 190--202
%I Springer Berlin
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming, frequent subtrees, frequent subgraphs
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=190
%X We extend our analysis of repetitive patterns found in genetic programming genomes \citelangdon:2005:CS to tree based GP. As in linear GP, repetitive patterns are present
   in large numbers. Size fair crossover limits bloat in automatic programming, preventing the evolution of recurring motifs. We examine these complex properties in detail:
   e.g. using depth v. size Catalan binary tree shape plots, subgraph and subtree matching, information entropy, syntactic and semantic fitness correlations and diffuse
   introns. We relate this emergent phenomenon to considerations about building blocks in GP and how GP works.
%8 30  March  - 1  April
%Z Also known as eurogp:LangdonB05 Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A W. B. Langdon
%T Pfeiffer -- A Distributed Open-ended Evolutionary System
%B AISB'05: Proceedings of the Joint Symposium on Socially Inspired Computing (METAS 2005)
%E Bruce Edmonds and Nigel Gilbert and Steven Gustafson and David Hales and Natalio Krasnogor
%D 2005
%P 7--13
%I 
%I AISB
%C University of Hertfordshire, Hatfield, UK
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_metas2005.pdf
%X Pfeiffer contains a population of fractals which has been evolving continuously for more than three years. The animations are developed from embryos using a Lindenmayer
   grammar (L-System). These open generative representations potentially allow gene duplication and the evolution of higher order genetic operators and might be a step towards
   the emergence of social intelligence in swarms of artificial life (alife) agents. The fitness function is simply do the snowflake patterns appeal to the users: interactive
   evolution (IEC). To this end, images are placed in animated snow globes (computerised snowstorms) by world wide web (www) browsers (Netscape, Mozilla, Internet Explorer,
   Firefox, etc.) anywhere on the planet. More than 600 people have used http://www.cs.ucl.ac.uk/staff/W.Langdon/pfeiffer.html.
%O SSAISB 2005 Convention
%8 12-15  April
%Z METAS is a one-day symposium (12 April) run by Natalio Krasnogor and Steven Gustafson, in the joint-symposium ``Socially Inspired Computing'', in the AISB 2005 Convention
   ``Social Intelligence and Interaction in Animals, Robots and Agents''. http://aisb2005.feis.herts.ac.uk/

%A W. B. Langdon
%T The Distribution of Amorphous Computer Outputs
%B The Grand Challenge in Non-Classical Computation: International Workshop
%E Susan Stepney and Stephen Emmott
%D 2005
%I 
%I University of York and Microsoft Research
%C York, UK
%K genetic algorithms, genetic programming
%U http://eprints.ucl.ac.uk/archive/00000483/
%X Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov minorization gives upper bounds ((15.3 + 2.30 m)/log(I)) on the length of program
   run on random or average computing devices. I is the size of the instruction set and m size of output register. Almost all programs are constants. Convergence is
   exponential with 90% of programs of length 1.6 n 2**N yielding constants (n=size input register and size of memory=N). This is supported by experiment.
%8 18-19  April
%Z http://www.cs.york.ac.uk/nature/workshop/

%A W. B. Langdon
%A S. Gustafson
%T Genetic Programming and Evolvable Machines: Five years of Reviews
%J Genetic Programming and Evolvable Machines
%V 6
%N 2
%D 2005
%P 221--228
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gppubs5.ps.gz
%X Summary of the resource reviews GP+EM has published and compares this with our promises in volume 1. Also updates the statistics published in \citelangdon:2000:gpembooks
%8 June

%A W. B. Langdon
%A Riccardo Poli
%A Owen Holland
%A Thiemo Krink
%T Understanding Particle Swarm Optimisation by Evolving Problem Landscapes
%B Proceedings SIS 2005 IEEE Swarm Intelligence
%E Luca Maria Gambardella and Payman Arabshahi and Alcherio Martinoli
%D 2005
%P 30--37
%I IEEE
%C Pasadena, California, USA
%K genetic algorithms, genetic programming, XPS
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2005_SIS.ps.gz
%X Genetic programming (GP) is used to create fitness landscapes which highlight strengths and weaknesses of different types of PSO and to contrast population-based swarm
   approaches with non stochastic gradient followers (i.e. hill climbers). These automatically generated benchmark problems yield insights into the operation of PSOs,
   illustrate benefits and drawbacks of different population sizes and constriction (friction) coefficients, and reveal new swarm phenomena such as deception and the
   exploration/exploitation tradeoff. The method could be applied to any type of optimizer.
%8 8-10  June
%@ 0-7803-8917-4

%A William B. Langdon
%A Riccardo Poli
%T Evolutionary Solo Pong Players
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2621--2628
%I IEEE Press
%C Edinburgh, UK
%K genetic algorithms, genetic programming, PSO
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/pong_cec2005.ps.gz
%X An Internet Java Applet http://www.cs.essex.ac.uk/staff/poli/SoloPong/ allows users anywhere to play the Solo Pong game. We compare people's performance to a hand coded
   'Optimal' player and programs automatically produced by computational intelligence. The computational intelligence techniques are: genetic programming, including a hybrid
   of GP and a human designed algorithm, and a particle swarm optimiser. The computational intelligence approaches are not fine tuned. GP and PSO find good players.
   Evolutionary computation (EC) is able to beat both human designed code and human players.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-9363-5

%A William B. Langdon
%A Riccardo Poli
%T Evolving Problems to Learn about Particle Swarm and other Optimisers
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 1
%D 2005
%P 81--88
%I IEEE Press
%C Edinburgh, UK
%K genetic algorithms, genetic programming, PSO
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2005.ps.gz
%X We use evolutionary computation (EC) to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics. In particular we analyse
   Particle Swarm Optimization (PSO) and Differential Evolution (DE). Both evolutionary algorithms are contrasted with a robust deterministic gradient based searcher (based on
   Newton-Raphson). The fitness landscapes made by genetic programming (GP) are used to illustrate difficulties in GAs and PSOs thereby explaining how they work and allowing
   us to devise better extended particle swarm systems (XPS).
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the EPS, and the IEE. Shorter version available as \citelangdon:2005:CECb2p.
%@ 0-7803-9363-5

%A W. B. Langdon
%T Evolving Benchmarks
%B Proceedings of the Seventeenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC 2005)
%E Katja Verbeeck and Karl Tuyls and Ann Nowe and Bernard Manderick and Bart Kuijpers
%D 2005
%P 365--366
%I Royal Flemish Academy of Belgium for Science and Arts, KVAB
%I BNVKI, Dutch and the Belgian AI Association
%C Koninklijke Vlaamse Academie van Belgie voor Wetenschappen en Kunsten, Brussel, Belgium
%K genetic algorithms, genetic programming, algorithm analysis, particle swarm optimisation, differential evolution
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_bnaic2005.ps.gz
%X Genetic programming (GP) is used to evolve global optimisation test problems. These automatically generated performance metrics are used to show strengths and weaknesses of
   Particle Swarm Optimization (PSO) and Differential Evolution (DE). Knowledge gained will help when choosing maximisers (and their tuning parameters) and in research into
   new search tools (which might include hyperheuristics).
%8 17-18  October
%Z 2 page summary of \citelangdon:2005:CECb http://como.vub.ac.be/bnaic2005/ Described in BNVKI newsletter 22(6) p135.

%A W. B. Langdon
%A Riccardo Poli
%A Christopher R. Stephens
%T Kernel methods for PSOs
%R Technical Report CSM-443
%D 2005
%I 
%I Computer Science, University of Essex
%C UK
%K genetic algorithms, genetic programming, Particle Swarm Optimisation, Hill Climbing
%U http://www.cs.essex.ac.uk/technical-reports/2005/csm-443.pdf
%X We report initial experiments on evolving kernel functions which describe the average behaviour of a swarm of particles as if it was responding as a single point moving on
   a landscape transformed by the kernel. Such kernels may help explain swarm and population approaches leading to extended population systems, XPS. The swarm of particles are
   from a simple particle swarm optimiser solving one dimensional multi-modal 3 peaks and Rastrigin problems. The standard Java genetic programming implementation TinyGP is
   used.
%8 Decemeber

%A W. B. Langdon
%A R. Poli
%T On Turing complete T7 and MISC F-4 program fitness landscapes
%R Technical Report CSM-445
%D 2005
%I 
%I Computer Science, University of Essex
%C UK
%K genetic algorithms, genetic programming
%U http://www.cs.essex.ac.uk/technical-reports/2005/csm-445.pdf
%X We use the minimal instruction set F-4 computer to define a minimal Turing complete T7 computer suitable for genetic programming (GP) and amenable to theoretical analysis.
   Experimental runs and mathematical analysis of the T7, show the fraction of halting programs is drops to zero as bigger programs are run.
%8 Decemeber
%Z Much extended than \citelangdon:2006:eurogp also known as \citelangdon_et_al:DSP:2006:595

%A W. B. Langdon
%A R. Poli
%T The Halting Probability in von~Neumann Architectures
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 225--237
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming, Turing complete
%U http://link.springer.de/link/service/series/0558/papers/3905/39050225.pdf
%X Theoretical models of Turing complete linear genetic programming (GP) programs suggest the fraction of halting programs is vanishingly small. Convergence results proved for
   an idealised machine, are tested on a small T7 computer with (finite) memory, conditional branches and jumps. Simulations confirm Turing complete fitness landscapes of this
   type hold at most a vanishingly small fraction of usable solutions.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006 See also \citelangdon:2005:CSM445
%@ 3-540-33143-3

%A William B. Langdon
%A Riccardo Poli
%T Finding Social Landscapes for PSOs via Kernels
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 6118--6125
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming, PSO, XPS
%U http://ieeexplore.ieee.org/iel5/11108/35623/01688507.pdf?tp=&arnumber=1688507&isnumber=35623
%X Particle swarm optimiser and genetic algorithm populations are macro-organisms, which perceive their environment as if filtered via a kernel. The kernel assimilates each
   individual's sensory abilities so that the collective moves using a greedy hill-climbing strategy. This model is fitted to data collected in real PSO and GA runs by using
   genetic programming to evolve the kernel. In nature animals tend to live within groups. The social interactions effectively transform the fitness selection landscape seen
   by an isolated individual. In some cases a group behaves (or even can be said to think) like a single organism. Kernels provide a lens which coarse-grains or averages
   individual senses and so may help explain joint actions and social responses. The original multi-modal problem is smoothed by convolving it with a problem specific filter
   designed by GP. Because populations see the transformed social fitness landscape, they can pass over local optima. GP can give a good fit between the predicted behaviour of
   the macroscopic organism and the actual runs.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A W. B. Langdon
%A R. Poli
%T Evolving Problems to Learn about Particle Swarm Optimisers and other Search Algorithms
%R Technical Report CSM-455
%D 2006
%I 
%I Computer Science, University of Essex
%C UK
%K genetic algorithms, genetic programming, PSO, XPS, DE, CMA
%U http://www.cs.essex.ac.uk/technical-reports/2006/csm-455.pdf
%X We use evolutionary computation (EC) to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics. In particular we analyse
   Particle Swarm Optimization (PSO), Differential Evolution (DE) and Covariance Matrix Adaptation--Evolution Strategy (CMA-ES). Each evolutionary algorithms is contrasted
   with the others and with a robust non-stochastic gradient follower (i.e. a hill climber) based on Newton-Raphson. The evolved benchmark problems yield insights into the
   operation of PSOs, illustrate benefits and drawbacks of different population sizes, velocity limits and constriction (friction) coefficients. The fitness landscapes made by
   genetic programming (GP) reveal new swarm phenomena, such as deception, thereby explaining how they work and allowing us to devise better extended particle swarm systems.
   The method could be applied to any type of optimiser.
%8 June
%Z To be published as \citelangdon:2006:TEC

%A William B. Langdon
%A Riccardo Poli
%T On Turing complete T7 and MISC F--4 program fitness landscapes
%B Theory of Evolutionary Algorithms
%S Dagstuhl Seminar Proceedings
%E Dirk V. Arnold and Thomas Jansen and Michael D. Vose and Jonathan E. Rowe
%N 06061
%D 2006
%I Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany
%C Dagstuhl, Germany
%K genetic algorithms, genetic programming
%U http://drops.dagstuhl.de/opus/volltexte/2006/595
%X We use the minimal instruction set F-4 computer to define a minimal Turing complete T7 computer suitable for genetic programming (GP) and amenable to theoretical analysis.
   Experimental runs and mathematical analysis of the T7, show the fraction of halting programs is drops to zero as bigger programs are run.
%O $<$http://drops.dagstuhl.de/opus/volltexte/2006/595$>$ [date of citation: 2006-01-01]
%8 5-10  February
%Z also available as \citelangdon:2005:CSM445

%A W. B. Langdon
%T The Halting Probability in von Neumann Architectures
%R Technical Report CSM-456
%D 2006
%I 
%I Computer Science, University of Essex
%C UK
%K genetic algorithms, genetic programming
%U http://www.cs.essex.ac.uk/technical-reports/2006/csm456.pdf
%X Theoretical models of Turing complete linear genetic programming (GP) programs suggest the fraction of halting programs is vanishingly small. Convergence results proved for
   an idealised machine, are tested on a small T7 computer with (finite) memory, conditional branches and jumps. Simulations confirm Turing complete fitness landscapes of this
   type hold at most a vanishingly small fraction of usable solutions.
%8 July
%Z 2 page summary of \citelangdon:2006:eurogp

%A W. B. Langdon
%T Predicting Ten Thousand Bits from Ten Thousand Inputs
%R Technical Report CSM-457
%D 2006
%I 
%I Department of Computer Science, University of Essex
%C Colchester, UK
%K genetic algorithms, genetic programming
%U http://www.cs.essex.ac.uk/technical-reports/2006/csm457.pdf
%X Submachine code genetic programming and multiple classification GP runs are used to winow the predictive inputs and then evolve an entry to the WCCI-2007 binary time series
   prediction task held at the Congress on Evolutionary Computation (CEC-2006) held in Vancouver.
%8 10  August

%A W. B. Langdon
%T Mapping Non-conventional Extensions of Genetic Programming
%B Unconventional Computing 2006
%S LNCS
%E Cristian S. Calude and Michael J. Dinneen and Gheorghe Paun and Grzegorz Rozenberg and Susan Stepney
%V 4135
%D 2006
%P 166--180
%I Springer-Verlag Berlin Heidelberg
%C York
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/4135/41350166.htm
%X Conventional genetic programming research excludes memory and iteration. We have begun an extensive analysis of the space through which GP or other unconventional AI
   approaches search and extend it to consider explicit program stop instructions (T8) and any time models (T7). We report halting probability, run time and functionality
   (including entropy of binary functions) of both halting and anytime programs. Turing complete program fitness landscapes, even with halt, scale poorly.
%8 4-8  September
%Z http://www.cs.york.ac.uk/nature/uc06/ see also \citelangdon:2007:NC
%@ 3-540-38593-2

%A W. B. Langdon
%A Riccardo Poli
%T Evolving Problems to Learn about Particle Swarm Optimisers and other Search Algorithms
%J IEEE Transactions on Evolutionary Computation
%V 11
%N 5
%D 2007
%P 561--578
%I 
%K genetic algorithms, genetic programming, PSO, DE, CMA-ES, local search, XPS, Differential evolution (DE), fitness landscapes, hill-climbers, particle swarms
%U http://ieeexplore.ieee.org/iel5/4235/26785/101109TEVC2006886448.pdf?arnumber=101109TEVC2006886448&isnumber=26785
%X We use evolutionary computation (EC) to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics. In particular we analyse
   Particle Swarm Optimization (PSO), Differential Evolution (DE) and Covariance Matrix Adaptation-Evolution Strategy (CMA-ES). Each evolutionary algorithms is contrasted with
   the others and with a robust non-stochastic gradient follower (i.e. a hill climber) based on Newton-Raphson. The evolved benchmark problems yield insights into the
   operation of PSOs, illustrate benefits and drawbacks of different population sizes, velocity limits and constriction (friction) coefficients. The fitness landscapes made by
   genetic programming (GP) reveal new swarm phenomena, such as deception, thereby explaining how they work and allowing us to devise better extended particle swarm systems.
   The method could be applied to any type of optimiser.
%8 October
%Z See also \citelangdon:2006:CSM455

%A William B. Langdon
%A Riccardo Poli
%T Mapping Non-conventional Extensions of Genetic Programming
%J Natural Computing
%V 7
%D 2008
%P 21--43
%I 
%K genetic algorithms, genetic programming, entropy, information theory
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2007_NC.pdf
%X Conventional genetic programming research excludes memory and iteration. We have begun an extensive analysis of the space through which GP or other unconventional AI
   approaches search and extend it to consider explicit program stop instructions (T8), including Markov analysis and any time models (T7). We report halting probability, run
   time and functionality (including entropy of binary functions) of both halting and anytime programs. Irreversible Turing complete program fitness landscapes, even with
   halt, scale poorly however loops lock-in variation allowing more interesting functions.
%O Invited contribution to special issue on Unconventional computing
%8 March
%Z Halting problem solved. Update of \citelangdon:2006:UC

%A W. B. Langdon
%T Web Usage of the GP Bibliography
%J SIGEvolution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation
%V 1
%N 4
%D 2006
%P 16--21
%I 
%K genetic algorithms, genetic programming, webbot
%U http://www.sigevolution.org/2006/04/issue.pdf
%X A recent upgrade to the Genetic Programming bibliography enables monitoring of its www usage. Down loads are dominated by automated software robotic agents (web bots),
   particularly Yahoo and other search engine spiders. These are very variable and cast doubt that some world wide web hits statistics relate to people. Some 62percent of GP
   papers are on line. PDF is twice as common as postscript. On average papers in the cache can be down loaded in two seconds.
%8 Decemeber

%A W. B. Langdon
%A W. Banzhaf
%T Repeated Patterns in Genetic Programming
%J Natural Computing
%V 7
%N 4
%D 2008
%P 589--613
%I 
%K genetic algorithms, genetic programming, SINE, ALU, Frequent subgraphs, frequent subtrees, Mackey-Glass, Poly-10, nuclear protein localisation, tinyGP, GPquick, evolution
   of program shape, sensitivity analysis
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2005_NC.pdf
%X Evolved genetic programming trees contain many repeated code fragments. Size fair crossover limits bloat in automatic programming, preventing the evolution of recurring
   motifs. We examine these complex properties in detail using depth v. size Catalan binary tree shape plots, subgraph and subtree matching, information entropy, sensitivity
   analysis, syntactic and semantic fitness correlations. Programs evolve in a self-similar fashion, akin to fractal random trees, with diffuse introns. Data mining frequent
   patterns reveals that as software is progressively improved a large proportion of it is exactly repeated subtrees as well as exactly repeated subgraphs. We relate this
   emergent phenomenon to building blocks in GP and suggest GP works by jumbling subtrees which already have high fitness on the whole problem to give incremental improvements
   and create complete solutions with multiple identical components of different importance.
%8 Decemeber
%Z Published online: 26 May 2007

%A W. B. Langdon
%T A SIMD interpreter for Genetic Programming on GPU Graphics Cards
%R Technical Report CSM-470
%D 2007
%I 
%I Department of Computer Science, University of Essex
%C Colchester, UK
%K genetic algorithms, genetic programming, GPU, parallel computing architecture
%U http://cswww.essex.ac.uk/technical-reports/2007/csm_470.pdf
%X Mackey-Glass chaotic time series prediction and non-nuclear protein classification show the feasibility of evaluating genetic programming populations on SPMD parallel
   computing consumer gaming graphics processing units. The C++ framework with a regular disk less Linux KDE desktop equipped with a single leading nVidia GeForce 8800 GTX
   graphics processing unit card is demonstrated evolving programs at Giga GP operation per second (895 million GPops). The RapidMind general processing on GPU (GPGPU)
   framework supports evaluating an entire population of a quarter of a million individual programs on a non-trivial problem in 4 seconds. An efficient reverse polish notation
   (RPN) tree based GP is given.
%8 3  July
%Z Memorial University. Replaced by \citelangdon:2008:eurogp

%A William B. Langdon
%A Wolfgang Banzhaf
%T A SIMD Interpreter for Genetic Programming on GPU Graphics Cards
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 73--85
%I Springer
%C Naples
%K genetic algorithms, genetic programming, GPU, parallel computing architecture
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_eurogp.pdf
%X Mackey-Glass chaotic time series prediction and nuclear protein classification show the feasibility of evaluating genetic programming populations directly on parallel
   consumer gaming graphics processing units. Using a Linux KDE computer equipped with an nVidia GeForce 8800 GTX graphics processing unit card the C++ SPMD interpretter
   evolves programs at giga GP operation per second (895 million GPops). We use the RapidMind general processing on GPU (GPGPU) framework to evaluate an entire population of a
   quarter of a million individual programs on a non-trivial problem in 4 seconds. An efficient reverse polish notation (RPN) tree based GP is given.
%8 26-28  March
%Z also known as \citeconf/eurogp/LangdonB08 Memorial University Animation http://www.cs.ucl.ac.uk/staff/W.Langdon/pi2_movie.html Code
   http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/ Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A W. B. Langdon
%A R. Poli
%A W. Banzhaf
%T An Eigen Analysis of the GP Community
%J Genetic Programming and Evolvable Machines
%V 9
%N 3
%D 2008
%P 171--182
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp_coauthors.pdf
%X The coauthorship and coeditorship relations as recorded in the genetic programming bibliography provide a quantitative view of the GP community. Eigen analysis shows the
   major eigenvalues and eigenvectors are responsible for 70per cent of the data. Top eigen authors are given.
%8 September

%A W. B. Langdon
%T Genetic Programming for Drug Discovery
%R Technical Report CES-481
%D 2008
%I 
%I Computing and Electronic Systems, University of Essex
%C Wivenhoe Park, Colchester CO4 3SQ, UK
%K genetic algorithms, genetic programming, Bioinformatics, GSK
%U http://www.essex.ac.uk/dces/research/publications/technicalreports/2007/ces-481.pdf
%X Genetic programming \citekoza:book \citebanzhaf:1997:book \citelangdon:fogp is an established artificial intelligence technique which is used in many areas. Essentially it
   applies the older genetic algorithm technique \citeholland:book \citegoldberg:book to automatically generate programs. (See Figure 1.) The next section will introduce
   genetic programming, including a very short over view of the many areas where GP has been applied, before Section 4 concentrates on uses of genetic programming in the
   Pharmaceutical industry. We finish with a worked example (Section 5) which describes an evaluation of machine learning for predicting potential drugs' activity with an
   pharmaceutically important enzyme (Human cytochrome P450 2D6) made by GlaxoSmithKline chemists \citelangdon:2001:wsc6, \citelangdon:2002:EuroGP, \citelangdon:2002:kdmdd,
   \citelangdon:2002:iberamia, \citehis02Plenary:Langdon, \citelangdon:2003:CEC. Appendix A gives pointers to further information.
%8 26  February

%A W. B. Langdon
%T A Fast High Quality Pseudo Random Number Generator for Graphics Processing Units
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 459--465
%I IEEE
%C Hong Kong
%K GPU, Park-Miller, PRNG
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_CIGPU.ps
%X Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The Park-Miller PRNG is programmed using G80's native
   Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via
   http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/random-numbers/gpu_park-miller.tar.gz
%8 1-6  June
%Z Replaces \citelangdon:2007:CES-477 CEC 2008 See also \citelangdon:2009:CIGPU for CUDA implmentation
   http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2009_CIGPU.pdf

%A W. B. Langdon
%T Evolving GeneChip Correlation Predictors on Parallel Graphics Hardware
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 4152--4157
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, GPU, bioinformatics, microarray, performance
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_CIGPU2.ps.gz
%X A GPU is used to datamine five million correlations between probes within Affymetrix HG-U133A probesets across 6685 human tissue samples from NCBI's GEO database. These
   concordances are used as machine learning training data for genetic programming running on a Linux PC with a RapidMind OpenGL GLSL backend. GPGPU is used to identify
   technological factors influencing High Density Oligonuclotide Arrays (HDONA) performance. GP suggests mismatch (PM/MM) and Adenosine/Guanine ratio influence microarray
   quality. Initial results hint that Watson-Crick probe self hybridisation or folding is not important. Under GPGPGPU an nVidia GeForce 8800 GTX interprets 300 million GP
   primitives/second (300 MGPops, approx 8 GFLOPS).
%8 1-6  June
%Z RapidMind OpenGL Shading Language (GLSL) back end WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A W. B. Langdon
%A A. P. Harrison
%T GP on SPMD parallel Graphics Hardware for mega Bioinformatics Data Mining
%J Soft Computing
%V 12
%N 12
%D 2008
%P 1169--1183
%I 
%K genetic algorithms, genetic programming, breast cancer, decorin, C17orf81, S-adenosylhomocysteine hydrolase, fibulin 1, Lance Miller's Uppsala GEO GSE3494 tumour biopsy,
   Affymetrix HG-U133A, HG-U133B, data mining, consumer graphics hardware, GPU, Graphics Processing Unit, SIMD, parallel computing, genetic programming, soft computing,
   evolutionary algorithm, RapidMind Ubuntu GCC C++
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_SC.ps.gz
%X We demonstrate a SIMD C++ genetic programming system on a single 128 node parallel nVidia GeForce 8800 GTX GPU under RapidMind's GPGPU Linux software by predicting ten
   year+ outcome of breast cancer from a dataset containing a million inputs. NCBI GEO GSE3494 contains hundreds of Affymetrix HG-U133A and HG-U133B GeneChip biopsies.
   Multiple GP runs each with a population of 5 million programs winnow useful variables from the chaff at more than 500 million GPops per second. Sources available via FTP.
%O Special Issue on Distributed Bioinspired Algorithms
%8 October
%Z http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/gpu_gp_2.tar.gz

%A W. B. Langdon
%T A field guide to genetic programming
%D 2008
%I 
%K genetic algorithms, genetic programming
%U http://kathrin.dagstuhl.de/files/Materials/08/08051/08051.LangdonWilliam.Paper.ps
%X I would like to thank Dagstuhl and the organisers of Seminar 08051 'Theory of Evolutionary Algorithm' for hosting all three authors of 'A field guide to genetic
   programming' \citepoli08:fieldguide. Great progress was made during our week at the castle. I hope you will be please to learn that the book is now finished. A Field Guide
   to Genetic Programming is an introductary level text. Therefore the authors wanted it to be as widely available as possible. In practise, this means, as affordable as
   possible. Also, at the start of the twenty first century, this means it must be accessible via the world wide web. To both ends, we decided to make the whole book available
   as a freely down loadable PDF. The PDF makes wide use of hyperlinks, both internally and externally, via the genetic programming bibliography, to the literature cited.
   However anticipating some would still prefer to hold the text in their hands but be unwilling to print 250 pages, we opted to use an inexpensive print on demand service
   offered almost at cost by lulu. The book sold out at its launch at EuroGP and down loads past 2000 copies in less than two weeks
%O Thank you to Seminar 08051
%8 March

%A W. B. Langdon
%T A Field Guide to Genetic Programing
%J Wyvern
%D 2008
%P 8
%I 
%K genetic algorithms, genetic programming
%U http://www.essex.ac.uk/wyvern/2008-04/Wyvern%20April%2008%207126.pdf
%X A Field Guide to Genetic Programing is aimed at those new to genetic programming
%8 April
%Z Newsletter University of Essex, review of \citepoli08:fieldguide

%A W. B. Langdon
%A A. P. Harrison
%T Evolving Regular Expressions for GeneChip Probe Performance Prediction
%R Technical Report CES-483
%D 2008
%I 
%I Computing and Electronic Systems
%C University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
%K genetic algorithms, genetic programming, Bioinformatics, Affymetrix GeneChip, strongly typed genetic programming, STGP, grammar, regular expression, egrep, gawk
%U http://www.essex.ac.uk/dces/research/publications/technicalreports/2008/CES-483.pdf
%X Commercial GeneChips provide highly redundant but noisy data. Rapid identification and subsequent rejection of bad data effectively increases the quality of the remaining
   data at little cost whilst serving as a basis for better understanding the bio-physics of short surface mounted DNA sequences. Affymetrix High Density Oligonuclotide Arrays
   (HDONA) simultaneously measure expression of thousands of genes using millions of probes. Regular expressions can be evolved from a Backus-Naur form (BNF) context-free
   grammar using tree based strongly typed genetic programming written in gawk. Fitness is given by egrep. The quality of individual HG-U133A probes is indicated by its
   correlation across 6685 human tissue samples from NCBI's GEO database with other measurements for the same gene. Low concordance indicates a poor probe. The evolved data
   mined motif is better at predicting poor DNA sequences than an existing human generated RE, suggesting runs of Cytosine and Guanine and mixtures should all be avoided.
   Section 4.6 gives more RE GP gawk implementation details. Code is available at ftp://cs.ucl.ac.uk/genetic/gp-code/RE_gp.tar
%8 27  April

%A W. B. Langdon
%A A. P. Harrison
%T Evolving Regular Expressions for GeneChip Probe Performance Prediction
%B Parallel Problem Solving from Nature - PPSN X
%S LNCS
%E Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume
%V 5199
%D 2008
%P 1061--1070
%I Springer
%C Dortmund
%K genetic algorithms, genetic programming, Bioinformatics, Affymetrix GeneChip, strongly typed genetic programming, grammars, regular expressions
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_ppsn_2008.ps.gz
%X Affymetrix High Density Oligonuclotide Arrays (HDONA) simultaneously measure expression of thousands of genes using millions of probes. We use correlations between
   measurements for the same gene across 6685 human tissue samples from NCBI's GEO database to indicated the quality of individual HG-U133A probes. Low concordance indicates a
   poor probe. Regular expressions can be data mined by a Backus-Naur form (BNF) context-free grammar using strongly typed genetic programming written in gawk and using egrep.
   The automatically produced motif is better at predicting poor DNA sequences than an existing human generated RE, suggesting runs of Cytosine and Guanine and mixtures should
   all be avoided. Code is available ftp://cs.ucl.ac.uk/genetic/gp-code/re_gp.tar
%8 13-17  September
%Z Implementation details in \citelangdon:2008:CES-483 Updated by \citelangdon:2009:AMB PPSN X
%@ 3-540-87699-5

%A W. B. Langdon
%T Scaling of Program Functionality
%J Genetic Programming and Evolvable Machines
%V 10
%N 1
%D 2009
%P 5--36
%I 
%K genetic algorithms, genetic programming, search landscapes, evolutionary computation
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_scale_prog_func.pdf
%X The distribution of fitness values (landscapes) of programs tends to a limit as the programs get bigger. We use Markov chain convergence theorems to give general upper
   bounds on the length of programs needed for convergence. How big programs need to be to approach the limit depends on the type of the computer they run on. We give bounds
   (exponential in $N$, $N\log N$ and smaller) for five computer models: any, average or amorphous or random, cyclic, bit flip and 4 functions (AND, NAND, OR and NOR).
   Programs can be treated as lookup tables which map between their inputs and their outputs. Using this we prove similar convergence results for the distribution of functions
   implemented by linear computer programs. We show most functions are constants and the remainder are mostly parsimonious. The effect of ad-hoc rules on genetic programming
   (GP) are described and new heuristics are proposed. We give bounds on how long programs need to be before the distribution of their functionality is close to its limiting
   distribution, both in general and for average computers. The computational importance of destroying information is discussed with respect to reversible and quantum
   computers. Mutation randomizes a genetic algorithm population in \frac14(l+1)(\log(l)+4) generations. Results for average computers and a model like genetic programming are
   confirmed experimentally.
%8 March

%A W. B. Langdon
%T Is this the Future of Academic Publishing?
%J SIGEvolution
%V 3
%N 1
%D 2008
%P 16
%I 
%U http://www.sigevolution.org/issues/pdf/SIGEVOlution200801.pdf
%8 Spring
%Z first 3 month progress report on \citepoli08:fieldguide

%A W. B. Langdon
%A A. P. Harrison
%T Evolving DNA motifs to Predict GeneChip Probe Performance
%J Algorithms in Molecular Biology
%V 4
%N 6
%D 2009
%I 
%K genetic algorithms, genetic programming, Bioinformatics, Affymetrix GeneChip, strongly typed genetic programming, grammars, regular expressions
%U http://www.almob.org/content/4/1/6
%X Background: Affymetrix High Density Oligonuclotide Arrays (HDONA) simultaneously measure expression of thousands of genes using millions of probes. We use correlations
   between measurements for the same gene across 6685 human tissue samples from NCBI's GEO database to indicated the quality of individual HG-U133A probes. Low correlation
   indicates a poor probe. Results: Regular expressions can be automatically created from a Backus-Naur form (BNF) context-free grammar using strongly typed genetic
   programming. Conclusions: The automatically produced motif is better at predicting poor DNA sequences than an existing human generated RE, suggesting runs of Cytosine and
   Guanine and mixtures should all be avoided.
%8 19  March
%Z Based on \citelangdon:2008:PPSN. PMID: 19298675 PMCID: PMC2679018

%A William B. Langdon
%A J. Rowsell
%A A. P. Harrison
%T Creating Regular Expressions as mRNA Motifs with GP to Predict Human Exon Splitting
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1789--1790
%I ACM New York, NY, USA
%I SIGEVO
%C Montreal
%K genetic algorithms, genetic programming, Poster, Gene expression and regulation, alternative splicing, Microarray analysis, Integration of genetic programming into
   bioinformatics, Biological interpretation of computer generated motifs, Bioinformatics, Affymetrix GeneChip, strongly typed genetic programming, grammar, regular
   expression, Alternative splicing of Homosapiens exons, HDONA
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2009_gecco.pdf
%X RNAnet \citeCES-486 http://bioinformatics.essex.ac.uk/users/wlangdon/rnanet/ allows the user to calculate correlations of gene expression, both between genes and between
   components within genes. We investigate all of Ensembl http://www.ensembl.org and find all the Homo Sapiens exons for which there are sufficient robust Affymetrix HG-U133
   Plus 2 GeneChip probes. Calculating correlation between mRNA probe measurements for the same exon shows many exons whose components are consistently up regulated and down
   regulated. However we identify other Ensembl exons where sub-regions within them are self consistent but these transcript blocks are not well correlated with other blocks
   in the same exon. We suggest many current Ensembl exon definitions are incomplete. Secondly, having identified exon with substructure we use machine learning to try and
   identify patterns in the DNA sequence lying between blocks of high correlation which might yield biological or technological explanations. A Backus-Naur form (BNF)
   context-free grammar constrains strongly typed genetic programming (STGP) to evolve biological motifs in the form of regular expressions (RE) (e.g. TCTTT) which classify
   gene exons with potential alternative mRNA expression from those without. We show biological patterns can be data mined by a GP written in gawk and using egrep from NCBI's
   GEO http://www.ncbi.nlm.nih.gov/geo/ database. The automatically produced DNA motifs suggest that alternative polyadenylation is not responsible. (Full version in TR-09-02
   \citelangdon:2009:TR-09-02.) Blocky exons can be found in http://bioinformatics.essex.ac.uk/users/wlangdon/tr-09-02.tar.gz
%8 8-12  July
%Z t03p220. Longer version in \citelangdon:2009:TR-09-02. GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the
   fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092. Also known as \citeDBLP:conf/gecco/LangdonRH09

%A W. B. Langdon
%A J. Rowsell
%A A. P. Harrison
%T Creating Regular Expressions as mRNA Motifs with GP to Predict Human Exon Splitting
%R Technical Report TR-09-02
%D 2009
%I 
%I Department of Computer Science, Crest Centre, King's College, London
%C Strand, London, WC2R 2LS, UK
%K genetic algorithms, genetic programming, Gene expression and regulation, alternative splicing, Microarray analysis, Integration of genetic programming into bioinformatics,
   Biological interpretation of computer generated motifs, Bioinformatics, Affymetrix GeneChip, strongly typed genetic programming, grammar, regular expression, Alternative
   splicing of Homosapiens exons, HDONA
%U http://www.dcs.kcl.ac.uk/technical-reports/papers/TR-09-02.pdf
%X Low correlation between mRNA concentrations measured at different locations for the same exon show many current Ensembl exon definitions are incomplete. Automatically
   created patterns (e.g. TCTTT) identify potential new alternative transcripts. Strongly typed grammar based genetic programming (GP) is used to evolve regular expressions
   (RE) to classify gene exons with potential alternative mRNA expression from those without. http://bioinformatics.essex.ac.uk/users/wlangdon/rnanet RNAnet gives us
   correlations between Affymetrix HG-U133 Plus 2 GeneChip probe measurements for the same exon across 2757 Homo Sapiens tissue samples from NCBI's GEO database. We identify
   many non-atomic Ensembl exons. I.e. exons with substructure. Biological patterns can be data mined by a Backus-Naur form (BNF) context-free grammar using a strongly typed
   GP written in gawk and using egrep. The automatically produced DNA motifs suggest that alternative polyadenylation is not responsible. The training data is available on the
   http://bioinformatics.essex.ac.uk/users/wlangdon/tr-09-02.tar.gz internet.
%8 19  March
%Z Long version of \citelangdon:2009:gecco

%A W. B. Langdon
%A Mark Harman
%A Yue Jia
%T Multi Objective Higher Order Mutation Testing with GP
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1945
%I ACM New York, NY, USA
%I SIGEVO
%C Montreal
%K genetic algorithms, genetic programming, Poster, strongly typed genetic programming, grammar, Pareto optimality, mutation testing, higher order mutation, Indirect encoding,
   Software engineering, triangle, schedule, tcas
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2009_gecco2.pdf
%X Mutation testing is a powerful software engineering technique for fault finding. It works by injecting known faults (mutations) into software and seeing if the test suite
   finds them. It remains very expensive and the few valuable traditional mutants that resemble real faults are mixed in with many others that denote unrealistic faults. The
   expense and lack of realism inhibit industrial uptake of mutation testing. Genetic programming searches the space of complex faults to find realistic higher order mutants.
   Despite the much larger search space, we have found mutants composed of multiple changes to the C source code that challenge the tester and which cannot be represented in
   the first order space.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   t14pp387, replaced by \citelangdon:2009:TAICPART (10 pages) ACM Order Number 910092. Also known as \citeDBLP:conf/gecco/LangdonHJ09

%A William B. Langdon
%A Mark Harman
%A Yue Jia
%T Multi Objective Mutation Testing with Genetic Programming
%B TAIC-PART
%E Leonardo Bottaci and Gregory Kapfhammer and Neil Walkinshaw
%D 2009
%P 21--29
%I IEEE
%C Windsor, UK
%K genetic algorithms, genetic programming, strongly typed genetic programming, grammar, Pareto optimality, mutation testing, higher order mutation, Indirect encoding,
   Software engineering, SBSE, triangle, schedule, tcas
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2009_TAICPART.ps.gz
%X In academic empirical studies, mutation testing has been demonstrated to be a powerful technique for fault finding. However, it remains very expensive and the few valuable
   traditional mutants that resemble real faults are mixed in with many others that denote unrealistic faults. These twin problems of expense and realism have been a
   significant barrier to industrial uptake of mutation testing. Genetic programming is used to search the space of complex faults (higher order mutants). The space is much
   larger than the traditional first order mutation space of simple faults. However, the use of a search based approach makes this scalable, seeking only those mutants that
   challenge the tester, while the consideration of complex faults addresses the problem of fault realism; it is known that 90percent of real faults are complex (i.e. higher
   order). We show that we are able to find examples that pose challenges to testing in the higher order space that cannot be represented in the first order space.
%8 4-6  September
%Z replaces \citelangdon:2009:gecco2

%A W. B. Langdon
%T A CUDA SIMT Interpreter for Genetic Programming
%R Technical Report TR-09-05
%D 2009
%I 
%I Department of Computer Science, King's College London
%C Strand, WC2R 2LS, UK
%K genetic algorithms, genetic programming, GPU, GPGPU, Tesla, sub-machine code GP, CUDA
%U http://www.gpgpgpu.com/gecco2009/5.pdf
%X A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the whole GP population of quarter of a million RPN expressions on graphics
   cards and nVidia Tesla T10P. Using sub-machine code GP a sustain peak performance of 212 billion GP operations per second (3300 speed up) and an average of 4.5 peta GP ops
   per day is reported for a single card on a Boolean induction benchmark never attempted before, let alone solved.
%O Revised
%8 18  June
%Z Revised entry to GPUs for Genetic and Evolutionary Computation \hrefhttp://www.gpgpgpu.com/gecco2009/ Competition, GECCO 2009, 8 July. Code
   http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/gp32cuda.tar.gz Slides http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2009/slides/langdon_SIMT_cigpu.html

%A W. B. Langdon
%T Large Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units
%B Parallel and Distributed Computational Intelligence
%S Studies in Computational Intelligence
%E Francisco Fernandez de Vega and Erick Cantu-Paz
%V 269
%D 2010
%P 113--141
%I Springer
%K genetic algorithms, genetic programming, GPU
%U http://www.springer.com/engineering/book/978-3-642-10674-3
%X A suitable single instruction multiple data GP interpreter can achieve high (Giga GPop/second) performance on a SIMD GPU graphics card by simultaneously running multiple
   diverse members of the genetic programming population. SPMD dataflow parallelisation is achieved because the single interpreter treats the different GP programs as data. On
   a single 128 node parallel nVidia GeForce 8800 GTX GPU, the interpreter can out run a compiled approach, where data parallelisation comes only by running a single program
   at a time across multiple inputs. The RapidMind GPGPU Linux C++ system has been demonstrated by predicting ten year+ outcome of breast cancer from a dataset containing a
   million inputs. NCBI GEO GSE3494 contains hundreds of Affymetrix \mboxHG-U133A and HG-U133B GeneChip biopsies. Multiple GP runs each with a population of five million
   programs winnow useful variables from the chaff at more than 500 million GPops per second. Sources available via
   \hrefhttp://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/gpu_gp_2.tar.gz FTP.
%O 5
%8 January
%Z part of \citeFernandezdeVega:pdci

%A W. B. Langdon
%A Olivia {Sanchez Graillet}
%A A. P. Harrison
%T Automated DNA Motif Discovery
%D 2010
%I 
%K genetic algorithms, genetic programming, pseudogene, short and microRNAs, non-coding transcripts, systems biology, machine learning, Bioinformatics, motif, regular
   expression, strongly typed genetic programming, context-free grammar
%U http://arxiv.org/abs/1002.0065v1
%X Ensembl's human non-coding and protein coding genes are used to automatically find DNA pattern motifs. The Backus-Naur form (BNF) grammar for regular expressions (RE) is
   used by genetic programming to ensure the generated strings are legal. The evolved motif suggests the presence of Thymine followed by one or more Adenines etc. early in
   transcripts indicate a non-protein coding gene.
%O arXiv
%8 30  January

%A W. B. Langdon
%T A Many Threaded CUDA Interpreter for Genetic Programming
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 146--158
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming, GPU, GPGPU, Tesla, sub-machine code GP, Reverse Polish Notation
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2010_eurogp.ps.gz
%X A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the whole GP population of 0.25 million reverse polish notation (RPN)
   expressions on GeForce 295 GTX graphics cards and nVidia Tesla. Using sub-machine code tree GP a sustain peak performance of 665 billion GP operations per second (10,000
   speed up) and an average of 22 peta GP ops per day is reported for a single GPU card on a Boolean induction benchmark never attempted before, let alone solved.
%8 7-9  April
%Z C code available via http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/gp32cuda.tar.gz Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with
   EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A W. B. Langdon
%A M. Harman
%T Evolving gzip matches Kernel from an nVidia CUDA Template
%R Technical Report TR-10-02
%D 2010
%I 
%I Department of Computer Science, King's College London
%C London, WC2R 2LS, UK
%K genetic algorithms, genetic programming, GIP, automatic coding, GPU, gpgpu, Genetic Interface Programming, SIR, gawk, strongly typed genetic programming, BNF grammar
%U http://www.dcs.kcl.ac.uk/technical-reports/papers/TR-10-02.pdf
%X Rather than attempting to evolve a complete program from scratch we demonstrate genetic interface programming by automatically generating a parallel CUDA kernel with
   identical functionality to existing highly optimised ancient sequential C code. Generic GPGPU nVidia kernel C++ code is converted into a BNF grammar. Strongly typed genetic
   programming uses the BNF to generate compilable and executable graphics card kernels. Their fitness is given by running the population on a GPU with randomised subsets of
   training data itself given by running the original code's test suite.
%8 5  February

%A W. B. Langdon
%A S. M. Gustafson
%T Genetic Programming and Evolvable Machines: ten years of reviews
%J Genetic Programming and Evolvable Machines
%V 11
%N 3/4
%D 2010
%P 321--338
%I 
%K genetic algorithms, genetic programming, EHW
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gppubs10.pdf
%X The journal, and in particular the resource reviews have been running for ten years. There are a number of activities being planned to celebrate. However it is a good time
   to revisit our original and updated goals again \citelangdon:2000:gpembooks \citelangdon:2005:gpembooks, compare them with what the journal has achieved and make new plans.
   Section 2 onwards gives up to date statistics on the genetic programming and evolvable hardware literature and electronic resources.
%O Tenth Anniversary Issue: Progress in Genetic Programming and Evolvable Machines
%8 September
%Z Open Access

%A W. B. Langdon
%A M. Harman
%T Evolving a CUDA Kernel from an nVidia Template
%B 2010 IEEE World Congress on Computational Intelligence
%E Pilar Sobrevilla
%D 2010
%P 2376--2383
%I IEEE
%I IEEE Computational Intelligence Society
%C Barcelona
%K genetic algorithms, genetic programming, GPU, grammar
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2010_cigpu.ps.gz
%X Rather than attempting to evolve a complete program from scratch we demonstrate genetic interface programming by automatically generating a parallel CUDA kernel with
   identical functionality to existing highly optimised ancient sequential C code. Generic GPGPU nVidia kernel C++ code is converted into a BNF grammar. Strongly typed genetic
   programming uses the BNF to generate compilable and executable graphics card kernels. Their fitness is given by running the population on a GPU with randomised subsets of
   training data itself given by running the original code's test suite.
%8 18-23  July
%Z BNF grammar and training examples http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/langdon_2010_cigpu.tar.gz WCCI 2010. CEC 2010. Also known as \cite5585922

%A William B. Langdon
%A Mark Harman
%A Yue Jia
%T Efficient multi-objective higher order mutation testing with genetic programming
%J Journal of Systems and Software
%V 83
%N 12
%D 2010
%P 2416--2430
%I 
%K genetic algorithms, genetic programming, Pareto optimality, mutation testing, higher order mutation, SBSE, Monte Carlo, NSGA-II, strongly typed GP, grammar based GP,
   non-determinism, triangle, schedule, tcas, gzip
%U http://www.sciencedirect.com/science/article/B6V0N-50J9GRW-3/2/b1769fb8aaf4ea90164109756b3e2dc6
%X It is said ninety percent of faults that survive manufacturer's testing procedures are complex. That is, the corresponding bug fix contains multiple changes. Higher order
   mutation testing is used to study defect interactions and their impact on software testing for fault finding. We adopt a multi-objective Pareto optimal approach using Monte
   Carlo sampling, genetic algorithms and genetic programming to search for higher order mutants which are both hard-to-kill and realistic. The space of complex faults (higher
   order mutants) is much larger than that of traditional first order mutations which correspond to simple faults, nevertheless search based approaches make this scalable. The
   problems of non-determinism and efficiency are overcome. Easy to detect faults may become harder to detect when they interact and impossible to detect single faults may be
   brought to light when code contains two such faults. We use strong typing and BNF grammars in search based mutation testing to find examples of both in ancient heavily
   optimised every day C code.
%O TAIC PART 2009 - Testing: Academic \& Industrial Conference - Practice And Research Techniques
%8 Decemeber
%Z Extended version of \citelangdon:2009:TAICPART PII:S0164-1212(10)00188-3

%A W. B. Langdon
%A M. Harman
%T Evolving a CUDA Kernel from an nVidia Template
%R Technical Report TR-10-07
%D 2010
%I 
%I Department of Computer Science, Crest Centre, King's College, London
%C Strand, London, WC2R 2LS, UK
%K genetic algorithms, genetic programming, gipsy, strongly typed genetic programming, grammar, sbse, software engineering
%U http://www.dcs.kcl.ac.uk/technical-reports/papers/TR-10-07.pdf
%X We automatically generate an nVidia parallel CUDA graphics card kernel with identical functionality to existing highly optimised ancient sequential C code. Essentially
   generic GPGPU C++ code supplied by the hardware manufacturer is converted into a BNF grammar. Strongly typed genetic programming uses the BNF to generate compilable and
   runnable graphics card kernels, which terminate. Their fitness is given by running the population on a GPU against a test suite used to test the original sequential code.
%O Invited presentation to the Natural Computing Applications Forum, http://www.ncaf.org.uk, 12-13 July 2010, Surrey University
%8 6  July

%A William B. Langdon
%A Robert I. McKay
%A Lee Spector
%T Genetic Programming
%B Handbook of Metaheuristics
%S International Series in Operations Research \& Management Science
%E Michel Gendreau and Jean-Yves Potvin
%V 146
%D 2010
%P 185--225
%I Springer
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2010_hbmh.pdf
%X Welcome to genetic programming where the forces of nature are used to automatically evolve computer programs. We give a flavour of where GP has been successfully applied
   (its far too wide an area to cover everything) and interesting current and future research but start with a tutorial of how to get started and finish with common pitfalls
   to avoid.
%O 7

%A W. B. Langdon
%T 2-bit Flip Mutation Elementary Fitness Landscapes
%R Research Note RN/10/04
%D 2010
%I 
%I Department of Computer Science, University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming, search, optimisation, graph theory, Laplacian, Hamming cube
%U http://drops.dagstuhl.de/opus/volltexte/2010/2814/pdf/10361.LangdonWilliam.Paper.2814.pdf
%X Genetic Programming parity with only XOR is not elementary. GP parity can be represented as the sum of 1+0.5k elementary landscapes. Statistics, including fitness distance
   correlation (FDC), of Parity's fitness landscape are calculated. Using Walsh analysis the eigen values and eigenvectors of the Laplacian of the two bit flip fitness
   landscape are given. Tests support lambda/node degree as a measure of the ruggedness of elementary landscapes for predicting problem difficulty. An elementary needle in a
   haystack (NIH) landscape is given.
%O Presented at Dagstuhl Seminar 10361, Theory of Evolutionary Algorithms, 8 September 2010
%8 15  September
%Z Cf \citelangdon:2011:foga Slides http://www.dagstuhl.de/Materials/Files/10/10361/10361.LangdonWilliam.Slides.pdf Also available as part of: booktitle = Theory of
   Evolutionary Algorithms, year = 2010, editor = Anne Auger and Jonathan L. Shapiro and L. Darrell Whitley and Carsten Witt, number = 10361, series = Dagstuhl Seminar
   Proceedings, ISSN = 1862-4405, publisher = Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany, address = Dagstuhl, Germany, URL =
   http://drops.dagstuhl.de/opus/volltexte/2010/2814, Also known as \citelangdon:DSP:2010:2814

%A W. B. Langdon
%T Elementary Bit String Mutation Landscapes
%B Foundations of Genetic Algorithms
%E Hans-Georg Beyer and W. B. Langdon
%D 2011
%P 25--41
%I ACM
%I SigEvo
%C Schwarzenberg, Austria
%K genetic algorithms, genetic programming, search, optimisation, graph theory, Laplacian, Hamming cube, Walsh transform, fitness distance correlation, elementary fitness
   autocorrelation, F.2.m, G.2.2, G.1.6, G.3, I.2.8
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2011_foga.ps.gz
%X Genetic Programming parity with only XOR is not elementary. GP parity can be represented as the sum of k/2+1 elementary landscapes. Statistics, including fitness distance
   correlation (FDC), of Parity's fitness landscape are calculated. Using Walsh analysis the eigen values and eigenvectors of the Laplacian of the two bit, three bit, n-bit
   and mutation only Genetic Algorithm fitness landscapes are given. Indeed all elementary bit string landscapes are related to the discrete Fourier functions. However most
   are rough (lambda/d approx 1). Also in many cases fitness autocorrelation falls rapidly with distance. GA runs support eigenvalue/graph degree (lambda/d) as a measure of
   the ruggedness of elementary landscapes for predicting problem difficulty. The elementary needle in a haystack (NIH) landscape is described.
%8 5-9  January
%Z ACM order number 910114

%A W. B. Langdon
%T Graphics Processing Units and Genetic Programming: An overview
%J Soft Computing
%V 15
%D 2011
%P 1657--1669
%I 
%K genetic algorithms, genetic programming, CUDA, nVidia, GPGPU, Survey, SIMD parallel interpreter, Reverse Polish Notation, nvcc compiler, GPU, CIGPU, CELL, PlayStation 3,
   Affymetrix GeneChip, breast cancer, decorin, S-adenosylhomocysteine hydrolase, gzip
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_gpu.pdf
%X A top end graphics card (GPU) plus a suitable SIMD interpreter, can deliver a several hundred fold speed up, yet cost less than the computer holding it. We give highlights
   of AI and computational intelligence applications in the new field of general purpose computing on graphics hardware (GPGPU). In particular we survey genetic programming
   (GP) use with GPU. We give several applications from Bioinformatics and show how the fastest GP is based on an interpreter rather than compilation. Finally using GP to
   generate GPU CUDA kernel C++ code is sketched.
%8 August

%A W. B. Langdon
%T Minimising Testing in Genetic Programming
%R Technical Report RN/11/10
%D 2011
%I 
%I Computer Science, University College London
%C Gower Street, London WC1E 6BT, UK
%K genetic algorithms, genetic programming, search, heuristic methods, artificial intelligence, software engineering, theory, over fitting, evolutionary learning, deceptive
   fitness landscapes, population convergence, correlations, GPU, GPGPU, 11-Mux, 20-mux, 37-multiplexor, bloat
%U http://www-typo3.cs.ucl.ac.uk/fileadmin/UCL-CS/images/Research_Student_Information/RN_11_10.pdf
%X The cost of optimisation can be reduced by evaluating candidate designs on only a fraction of all possible use cases. We show how genetic programming (GP) can avoid
   overfitting and evolve general solutions from fitness test suites as small as just one dynamic training case. Search effort can be greatly reduced.
%8 11  April
%Z Technical report version of \citelangdon:2011:gecco

%A William B. Langdon
%T Generalisation in Genetic Programming
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 205
%I ACM
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming: Poster, AI, Problem Solving, Control Methods, and Search, Heuristic methods, Theory, 11-Mux, GPGPU, GPU, bloat, over fitting
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2011_gecco.ps.gz
%X The cost of optimisation can be reduced by evaluating the value of candidate designs on only a fraction of all possible fitness cases. We show how genetic programming (GP)
   can avoid overfitting and evolve general solutions from test suites as small as just one dynamic training case, thereby greatly reducing search effort.
%8 12-16  July
%Z Fuller version in \citelangdon:2011:geccoRN Also known as \cite2001972 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A J{\"o}rg Langeheine
%A Martin Trefzer
%A Daniel Br{\"u}derle
%A Karlheinz Meier
%A Johannes Schemmel
%T On the Evolution of Analog Electronic Circuits Using Building Blocks on a CMOS FPTA
%B Genetic and Evolutionary Computation -- GECCO-2004, Part I
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3102
%D 2004
%P 1316--1327
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, EHW
%U http://link.springer.de/link/service/series/0558/bibs/3102/31021316.htm
%X We summarise two experiments using building blocks to find analog electronic circuits on a CMOS Field Programmable Transistor Array (FPTA). The FPTA features 256
   programmable transistors whose channel geometry and routing can be configured to form a large variety of transistor level analog circuits. The transistor cells are either
   of type PMOS or NMOS and are arranged in a checkerboard pattern. Two case studies focus on improving artificial evolution by using a building block library of four digital
   gates consisting of a NOR, a NAND, a buffer and an inverter. The methodology is applied to the design of the more complex logic gates XOR and XNOR as well as to the
   evolution of circuits discriminating between square waves of different frequencies.
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22344-4

%A A. E. Langham
%A P. W. Grant
%T A Multilevel k-way Partitioning Algorithm for Finite Element Meshes using Competing Ant Colonies
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1602--1608
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, real world applications, ant systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-715.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Guido Lanza
%A William Mydlowec
%A John R. Koza
%T Automatic creation of a genetic network for the lac operon from observed data by means of genetic programming
%B First International Conference on Systems Biology (ICSB)
%D 2000
%I 
%I Japan Society for Bioinformatics
%C Tokyo
%K genetic algorithms, genetic programming, Biology, genetic networks, reverse engineering, lac operon
%U http://www.genetic-programming.com/jkpdf/icsb2000geneticnetwork.pdf
%X This paper demonstrates that it is possible to use genetic programming to automatically create (reverse engineer) a computer program representing the logic underlying the
   genetic network for the expression level of the lac operon (composed of the Z, Y, and A genes) as measured by its mRNA. Genetic programming starts with observed time-domain
   expression levels of two genes (REPRESSOR or CAP) and the concentrations of two substances (GLUCOSE or LACTOSE) and automatically creates both a topological arrangement of
   conditional and comparative functions as well as all necessary numerical parameters of a genetic network whose behavior matches observed time-domain data.
%8 14-16  November
%Z ICSB-2000 8 Feb 2001 ghostview and our printers barf at icsb2000gn.ps population size 10,000

%A Pier Luca Lanzi
%T An Analysis of the Memory Mechanism of XCSM
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 643--651
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, classifiers
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Pier Luca Lanzi
%A Marco Colombetti
%T An Extension to the XCS Classifier System for Stochastic Environments
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 353--360
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-894.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Pier Luca Lanzi
%T Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 337--344
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-893.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Pier Luca Lanzi
%A Alessandro Perrucci
%T Extending the Representation of Classifier Conditions Part II: From Messy Coding to S-Expressions
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 345--352
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, classifier systems
%X XCS, Lisp S-expressions, XCSL, 6-multiplexor, woods
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Pier Luca Lanzi
%T XCS with stack-based genetic programming
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 1186--1191
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%U http://webspace.elet.polimi.it/lanzi/papers//lanzi2003cecstack.pdf
%X We present an extension of the learning classifier system XCS in which classifier conditions are represented by RPN expressions and stack-based Genetic Programming is used
   to recombine and mutate classifiers. In contrast with other extensions of XCS involving tree-based Genetic Programming, the representation we apply here produces conditions
   that are linear programs, interpreted by a virtual stack machine (similar to a pushdown automaton), and recombined through standard genetic operators. We test the version
   of XCS extended with stack-based conditions on a set of problems of different complexity.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Pier Luca Lanzi
%T An Analysis of Generalization in XCS with Symbolic Conditions
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 2149--2156
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X We analyse generalisation in the eXtended Classifier System (XCS) with symbolic conditions, based on genetic programming, briefly XCSGP. We start from the results presented
   in the literature, which showed that XCSGP could not reach optimality in Boolean problems when classifier conditions involved logical disjunctions.We apply a new
   implementation of XCSGP to the learning of Boolean functions and show that our version can actually reach optimality even when disjunctions are allowed in classifier
   conditions. We analyse the evolved generalisations and explain why logical disjunctions can make the learning more difficult in XCS models and why our version performs
   better than the earlier one. Then, we show that in problems that allow many generalizations, so that or clauses are less "convenient", XCSGP tends to develop solutions that
   do not exploit logical disjunctions as much as one might expect. However, when the problems allow few generalizations, so that or clauses become an interesting way to
   introduce simple generalizations, XCSGP exploit them so as to evolve more compact solutions.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A J. L. J. Laredo
%A A. E. Eiben
%A M. {van Steen}
%A J. J. Merelo
%T EvAg: a scalable peer-to-peer evolutionary algorithm
%J Genetic Programming and Evolvable Machines
%V 11
%N 2
%D 2010
%P 227--246
%I 
%K genetic algorithms, Peer-to-peer computing Evolutionary algorithms, Scalability analysis Diversity
%X This paper studies the scalability of an Evolutionary Algorithm (EA) whose population is structured by means of a gossiping protocol and where the evolutionary operators
   act exclusively within the local neighborhoods. This makes the algorithm inherently suited for parallel execution in a peer-to-peer fashion which, in turn, offers great
   advantages when dealing with computationally expensive problems because distributed execution implies massive scalability. In this paper we show another advantage of this
   algorithm: We experimentally demonstrate that it scales up better than traditional alternatives even when executed in a sequential fashion. In particular, we analyze the
   behavior of several EAs on well known deceptive trap functions with varying sizes and levels of deceptiveness. The results show that the new EA requires smaller optimal
   population sizes and fewer fitness evaluations to reach solutions. The relative advantage of the new EA is more outstanding as problem hardness and size increase. In some
   cases the new algorithm reduces the computational efforts of the traditional EAs by several orders of magnitude.
%8 June

%A Juan Luis Jim\'enez Laredo
%A Daniel {Lombra\~na Gonz\'alez}
%A Francisco {Fern\'andez de Vega}
%A Maribel Garc\'ia Arenas
%A Juan Juli\'an {Merelo Guerv\'os}
%T A Peer-to-Peer Approach to Genetic Programming
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 108--117
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X This paper proposes a fine-grained parallelization of the Genetic Programming paradigm (GP) using the Evolvable Agent model evagbp The algorithm is decentralised in order
   to take full-advantage of a massively parallel Peer-to-Peer infrastructure. In this context, GP is particularly demanding due to its high requirements of computational
   power. To assess the viability of the approach, the evag model has been empirically analysed in a simulated Peer-to-Peer environment where experiments were conducted on two
   well-known GP problems. Results show that the spatially structured nature of the algorithm is able to yield a good quality in the solutions. Additionally, parallelisation
   improves times to solution by several orders of magnitude.
%8 27-29  April
%Z Newscast P2P protocol from EU DREAM project. Agents are lightweight computing threads. Possibly many threads per node. Emigration within distributed GP population
   integrated with stochastic establishment of directly links in P2P application layer protocol. Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011
   EvoBIO2011 and EvoApplications2011

%A Fiacc Larkin
%A Conor Ryan
%T Good News: Using News Feeds with Genetic Programming to Predict Stock Prices
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 49--60
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Patrick LaRoche
%A A. Nur Zincir-Heywood
%T 802.11 Network Intrusion Detection using Genetic Programming
%B Genetic and Evolutionary Computation Conference (GECCO2005) workshop program
%E Franz Rothlauf and Misty Blowers and J\"urgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J\"orn Grahl and Gregory Hornby and Edwin D. de Jong and
   Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor\`a and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and
   Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton
   and Alden H. Wright
%D 2005
%P 170--171
%I ACM Press
%C Washington, D.C., USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0170.pdf
%X Genetic Programming (GP) based Intrusion Detection Systems (IDS) use connection state network data during their training phase. These connection states are recorded as a
   set of features that the GP uses to train and test solutions which allow for the efficient and accurate detection of given attack patterns. However, when applied to a
   802.11 network that is faced with attacks specific to the 802.11 protocol, the GP's detection rate reduces dramatically. We discuss what causes this effect, and what can be
   done to improve GP's performance on 802.11 networks.
%8 25-29  June
%Z Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006

%A Patrick LaRoche
%A A. Nur Zincir-Heywood
%T 802.11 De-authentication Attack Detection using Genetic Programming
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 1--12
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050001.pdf
%X This paper presents a genetic programming approach to detect deauthentication attacks on wireless networks based on the 802.11 protocol. To do so we focus on developing an
   appropriate fitness function and feature set. Results show that the intrusion system developed not only performs incredibly well - 100 percent detection rate and 0.5
   percent false positive rate - but also developed a solution that is general enough to detect similar attacks, such as disassociation attacks, that were not present in the
   training data.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Patrick LaRoche
%A A. Nur Zincir-Heywood
%T Genetic Programming Based WiFi Data Link Layer Attack Detection
%B 4th Annual Communication Networks and Services Research Conference (CNSR'06)
%D 2006
%P 285--292
%I IEEE Computer Society Los Alamitos, CA, USA
%K genetic algorithms, genetic programming
%X a genetic programming based detection system for Data Link layer attacks on a WiFi network. We explore the use of two different fitness functions in order to achieve both a
   high detection rate and a low false positive rate. Results show that the detection system developed can achieve a detection rate above 90per cent and a false positive rate
   below 1per cent
%@ 0-7695-2578-4

%A Patrick LaRoche
%A Nur Zincir-Heywood
%A Malcolm I. Heywood
%T Evolving TCP/IP packets: A case study of port scans
%B IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009
%D 2009
%P 1--8
%I 
%K genetic algorithms, genetic programming, IDS, TCP/IP packets, fuzzing system, port scans, vulnerability analysis, cryptographic protocols, fuzzy systems, security of data,
   transport protocols
%X In this work, we investigate the ability of genetic programming techniques to evolve valid network packets, including all relevant header values, towards a specific goal.
   We see this as a first step in building a fuzzing system that can learn to adapt for vulnerability analysis. By developing a system that learns the packets that are
   required to be transmitted towards targets, using feedback from an external network source, we make a step towards having a system that can intelligently explore the
   capabilities of a given security system. In order to validate our system's capabilities we evolve a variety of port scan patterns while running the packets through an IDS,
   with the goal to minimizes the alarms raised during the scanning process. Results show that the system not only successfully evolves valid TCP packets, but also remains
   stealthy in its activity.
%8 July
%Z Also known as \cite5356541

%A Patrick LaRoche
%A Nur Zincir-Heywood
%A Malcolm Heywood
%T Using Code Bloat to Obfuscate Evolved Network Traffic
%B EvoCOMNET
%S LNCS
%E Cecilia Di Chio and Anthony Brabazon and Gianni A. Di Caro and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and
   Michael O'Neill and Ernesto Tarantino and Neil Urquhart
%V 6025
%D 2010
%P 101--110
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X In this work, we investigate the ability of genetic programming techniques to evolve valid network patterns, while avoiding detectability by obfuscating the intent of the
   traffic. In order to validate our system's capabilities, we choose to evolve a port scan attack while running the packets through an Intrusion Detection System (IDS). In
   turn, the evolutionary process uses feedback such that it minimizes the alarms raised while port scanning across a network range. Results build off of previous work allow
   us to further analyze and understand what the role of introns, code bloat, play in the systems ability to reduce the detectability of it malicious behaviour.
%8 7-9  April
%Z EvoCOMNET'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Patrick LaRoche
%A A. Nur Zincir-Heywood
%A Malcolm I. Heywood
%T Protocol Discovery and Analysis via Live Interaction
%B Applications of Evolutionary Computing, EvoApplications2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC
%S LNCS
%E Cecilia Di Chio and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. Fernandez de Vega and Gianni A. Di Caro and Rolf Drechsler and Aniko Ekart and Anna I
   Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero
   and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis
%V 7248
%D 2012
%P 11--20
%I Springer Verlag
%C Malaga, Spain
%K genetic algorithms, genetic programming
%X In this work, we explore the use of evolutionary computing toward protocol analysis. The ability to discover, analyse, and experiment with unknown protocols is paramount
   within the realm of network security; our approach to this crucial analysis is to interact with a network service, discovering sequences of commands that do not result in
   error messages. In so doing, our work investigates the real-life responses of a service, allowing for exploration and analysis of the protocol in question. Our system
   initiates sequences of commands randomly, interacts with and learns from the responses, and modifies its next set of sequences accordingly. Such an exploration results in a
   set of command sequences that reflect correct uses of the service in testing. These discovered sequences can then be used to identify the service, unforeseen uses of the
   service, and, most importantly, potential weaknesses.
%8 11-13  April
%Z FTP SMTP Part of \citeDiChio:2012:EvoApps EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012

%A Jan Larres
%A Mengjie Zhang
%A Will N Browne
%T Using unrestricted loops in genetic programming for image classification
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Loops are an important part of classic programming techniques, but are rarely used in genetic programming. This paper presents a method of using unrestricted, i.e. nesting,
   loops to evolve programs for image classification tasks. Contrary to many other classification methods where pre-extracted features are typically used, we perform
   calculations on image regions determined by the loops. Since the loops can be nested, these regions may depend on previously computed regions, thereby allowing a simple
   version of conditional evaluation. The proposed GP approach with unrestricted loops is examined and compared with the canonical GP method without loops and the GP approach
   with restricted loops on one synthesised character recognition problem and two texture classification problems. The results suggest that unrestricted loops can have an
   advantage over the other two methods in certain situations for image classification.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586305

%A Christian W. G. Lasarczyk
%A Peter Dittrich
%A Wolfgang Banzhaf
%T Dynamic Subset Selection Based on a Fitness Case Topology
%J Evolutionary Computation
%V 12
%N 2
%D 2004
%P 223--242
%I 
%K genetic algorithms, genetic programming, search space, topology, diversity
%U http://ls11-www.cs.uni-dortmund.de/people/lasar/publication/LasarDittBanz_TBS_2004/LasarDittBanz_TBS_2004.pdf
%X A large training set of fitness cases can critically slow down genetic programming, if no appropriate subset selection method is applied. Such a method allows an individual
   to be evaluated on a smaller subset of fitness cases. we suggest a subset selection method that takes the problem structure into account, while being problem independent at
   the same time. In order to achieve this, information about the problem structure is acquired during evolutionary search by creating a topology (relationship) on the set of
   fitness cases. The topology is induced by individuals of the evolving population. This is done by increasing the strength of the relation between two fitness cases, if an
   individual of the population is able to solve both of them. Our new topology based subset selection method chooses a subset, such that fitness cases in this subset are as
   distantly related as is possible with respect to the induced topology. We compare topology based selection of fitness cases with dynamic subset selection and stochastic
   subset sampling on four different problems. On average, runs with topology based selection show faster progress than the others.
%8 Summer
%Z preprint at http://ls11-www.cs.uni-dortmund.de/people/lasar/publication/LasarDittBanz_TBS_2004/LasarDittBanz_TBS_2004.pdf

%A Christian Lasarczyk
%A Wolfgang Banzhaf
%T An Algorithmic Chemistry for Genetic Programming
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 1--12
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=1
%X Genetic Programming has been slow at realizing other programming paradigms than conventional, deterministic, sequential von- Neumann type algorithms. In this contribution
   we discuss a new method of execution of programs introduced recently: Algorithmic Chemistries. Therein, register machine instructions are executed in a non-deterministic
   order, following a probability distribution. Program behaviour is thus highly dependent on frequency of instructions and connectivity between registers. Here we demonstrate
   the performance of GP on evolving solutions to a parity problem in a system of this type.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Christian W. G. Lasarczyk
%A Wolfgang Banzhaf
%T Total synthesis of algorithmic chemistries
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1635--1640
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, algorithmic chemistries, total synthesis
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1635.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Nicolas Lassabe
%A Stephane Sanchez
%A Herve Luga
%A Yves Duthen
%T Genetically programmed strategies for chess endgame
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 831--838
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, chess, evolving strategies
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p831.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Jean-Louis Lassez
%A Ryan Rossi
%A Stephen Sheel
%A Srinivas Mukkamala
%T Signature Based Intrusion Detection Using Latent Semantic Analysis
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 1068--1074
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, automated classification algorithms, feature selection, latent semantic analysis, linear genetic programming, real-time intrusion
   detection systems, signature based intrusion detection, singular value decomposition, support vector decision function, digital signatures, singular value decomposition,
   support vector machines
%X We address the problem of selecting and extracting key features by using singular value decomposition and latent semantic analysis. As a consequence, we are able to
   discover latent information which allows us to design signatures for forensics and in a dual approach for real-time intrusion detection systems. The validity of this method
   is shown by using several automated classification algorithms (Maxim, SYM, LGP). Using the original data set we classify 99.86percent of the calls correctly. After feature
   extraction we classify 99.68percent of the calls correctly, while with feature selection we classify 99.78percent of the calls correctly, justifying the use of these
   techniques in forensics. The signatures obtained after feature selection and extraction using LSA allow us to class 95.69percent of the calls correctly with features that
   can be computed in real time. We use Support Vector Decision Function and Linear Genetic Programming for feature selection on a real data set generated on a live
   performance network that consists of probe and denial of service attacks. We find that the results reinforce our feature selection method.
%8 1-6  June
%Z Also known as \cite4633931 WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Dorota Latek
%A Andrzej Kolinski
%T Contact prediction in protein modeling: Scoring, folding and refinement of coarse-grained models
%J BMC Structural Biology
%V 8
%N 36
%D 2008
%I 
%K genetic algorithms, genetic programming
%X Several different methods for contact prediction succeeded within the Sixth Critical Assessment of Techniques for Protein Structure Prediction (CASP6). The most relevant
   were non-local contact predictions for targets from the most difficult categories: fold recognition-analogy and new fold. Such contacts could provide valuable structural
   information in case a template structure cannot be found in the PDB. Results We described comprehensive tests of the effectiveness of contact data in various aspects of de
   novo modeling with CABS, an algorithm which was used successfully in CASP6 by the Kolinski-Bujnicki group. We used the predicted contacts in a simple scoring function for
   the post-simulation ranking of protein models and as a soft bias in the folding simulations and in the fold-refinement procedure. The latter approach turned out to be the
   most successful. The CABS force field used in the Replica Exchange Monte Carlo simulations cooperated with the true contacts and discriminated the false ones, which
   resulted in an improvement of the majority of Kolinski-Bujnicki's protein models. In the modeling we tested different sets of predicted contact data submitted to the CASP6
   server. According to our results, the best performing were the contacts with the accuracy balanced with the coverage, obtained either from the best two predictors only or
   by a consensus from as many predictors as possible. Conclusion Our tests have shown that theoretically predicted contacts can be very beneficial for protein structure
   prediction. Depending on the protein modeling method, a contact data set applied should be prepared with differently balanced coverage and accuracy of predicted contacts.
   Namely, high coverage of contact data is important for the model ranking and high accuracy for the folding simulations.
%8 August  11
%Z PMID:

%A William H. Latham
%A Miki Shaw
%A Stephen Todd
%A Frederic F. Leymarie
%A Benjamin R. Jefferys
%A Lawrence A. Kelley
%T Using DNA to Generate 3D Organic Art Forms
%B Proceedings of EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Applications of Evolutionary Computing, EvoWorkshops
%S Lecture Notes in Computer Science
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Rolf Drechsler and Anik\'o Ek\'art and Anna Esparcia-Alc\'azar and Muddassar Farooq and
   Andreas Fink and Jon McCormack and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Sima Uyar and Shengxiang Yang
%V 4974
%D 2008
%P 433--442
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%X A novel biological software approach to define and evolve 3D computer art forms is described based on a re-implementation of the FormGrow system produced by Latham and Todd
   at IBM in the early 1990s. This original work is extended by using DNA sequences as the input to generate complex organic-like forms. The translation of the DNA data to 3D
   graphic form is performed by two contrasting processes, one intuitive and one informed by the biochemistry. The former involves the development of novel, but simple,
   look-up tables to generate a code list of functions such as the twisting, bending, stacking, and scaling and their associated parametric values such as angle and scale. The
   latter involves an analysis of the biochemical properties of the proteins encoded by genes in DNA, which are used to control the parameters of a fixed FormGrow structure.
   The resulting 3D data sets are then rendered using conventional techniques to create visually appealing art forms. The system maps DNA data into an alternative
   multi-dimensional space with strong graphic visual features such as intricate branching structures and complex folding. The potential use in scientific visualisation is
   illustrated by two examples. Forms representing the sickle cell anaemia mutation demonstrate how a point mutation can have a dramatic effect. An animation illustrating the
   divergent evolution of two proteins with a common ancestor provides a compelling view of an evolutionary process lost in millions of years of natural history.
%8 26-28  March

%A James I. Lathrop
%T Compression Depth and Genetic Programs
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 370--379
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Genetic Algorithms
%8 13-16  July
%Z Complexity of cellular automata, organisational complexity of populations of finite state machines that play prisoner's dilemma. computational depth p371 "Computational
   depth... its non-computability renders it useless for actual complexity measurements". Compression depth cf gzip p372 GA "produces complexity as the population ages".
   Bennett "slow-growth law => "complexity is produced slowly and thus cannot be created without commensurate history of computation". p375 IPD FSA GA without fitness
   selection "no significant increase in compression depth". With normal GA fitness selection "average compression depth" (ie .gz size) "of the ten most fit players generally
   increases as more generations (computation time) is provided." --p376 "even if fitness does not" (Is this just FSM bloat? WBL) GP-97

%A Wai Shing Lau
%A Gang Li
%A Kin-Hong Lee
%A Kwong-Sak Leung
%A Sin Man Cheang
%T Multi-logic-Unit Processor: A Combinational Logic Circuit Evaluation Engine for Genetic Parallel Programming
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 167--177
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=167
%X Genetic Parallel Programming (GPP) is a novel Genetic Programming paradigm. GPP Logic Circuit Synthesiser (GPPLCS), is a combinational logic circuit learning system based
   on GPP. The GPPLCS comprises a Multi-Logic-Unit Processor (MLP) which is a hardware processor built on a Field Programmable Gate Array (FPGA). The MLP is designed to speed
   up the evaluation of genetic parallel programs that represent combinational logic circuits. Four combinational logic circuit problems are presented to show the performance
   of the hardware-assisted GPPLCS. Experimental results show that the hardware MLP speeds up evolutions over 10 times. For difficult problems such as the 6-bit priority
   selector and the 6-bit comparator, the speedup ratio can be up to 22.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Wai Shing Lau
%A Kin Hong Lee
%A Kwong Sak Leung
%T A hybridized genetic parallel programming based logic circuit synthesizer
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 839--846
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, a hybridised genetic parallel programming logic circuit synthesiser, design aids, field programmable gate array, flowMap, genetic
   parallel programming, look up table, performance and experimentation, technology mapping
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p839.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A D. Laucelli
%A O. Giustolisi
%A V. Babovic
%A M. Keijzer
%T Ensemble modeling approach for rainfall/groundwater balancing
%J Journal of Hydroinformatics
%V 9
%N 2
%D 2007
%P 95--106
%I IWA Publishing
%K genetic algorithms, genetic programming, ensemble modelling, groundwater, hydrology
%U http://www.iwaponline.com/jh/009/0095/0090095.pdf
%X This paper introduces an application of machine learning, on real data. It deals with Ensemble Modelling, a simple averaging method for obtaining more reliable
   approximations using symbolic regression. Considerations on the contribution of bias and variance to the total error, and ensemble methods to reduce errors due to variance,
   have been tackled together with a specific application of ensemble modeling to hydrological forecasts. This work provides empirical evidence that genetic programming can
   greatly benefit from this approach in forecasting and simulating physical phenomena. Further considerations have been taken into account, such as the influence of Genetic
   Programming parameter settings on the model's performance.
%Z Piana di Brindisi

%A Kurt F. Lauckner
%A Zenia C. Bahorski
%T The Computer Continuum
%D 2009
%I Pearson Custom Publishing
%K genetic algorithms, genetic programming
%O 12.6 Evolutionary Systems
%8 10  September
%Z Text book. The Genetic Algorithm - Genetic Programming - THE CUTTING EDGE: The Computer as an Inventor http://www.mypearsonstore.com/bookstore/product.asp?isbn=0558345166

%A K. Lavangnananda
%T A Genetic Programming Approach to Inductive Learning
%B 2004 International Conference on Computational Intelligence for Modelling, Control and Automation - Cimca'2004
%E Masoud Mohammadian
%D 2004
%P 279--290
%I 
%C Gold Coast, Australia
%K genetic algorithms, genetic programming, data mining, evolutionary computation, inductive learning
%U http://dummy/Lavangnananda_2004_CIMCA.pdf
%X There have been many applications of artificial intelligence data mining recently. One of its many benefits includes the ability to cluster or generate patterns from large
   amount of data when conventional statistical methods are proven ineffective. One such techniques in data mining is inductive learning. There have been applications of
   evolutionary computation in inductive learning where genetic algorithms have been employed in chromosomes representation. This paper describes an attempt to use genetic
   programming in inductive learning. A program known as Genetic Programming for Inductive Learning (GPIL) is described. It uses genetic programming and rectifies the short
   comings of chromosomes representation in genetic algorithms. The program has been tested on a benchmark data set. It achieved better performance with higher accuracy than
   previous works on the same data set. The paper also discusses relevant aspects in using genetic programming in inductive learning and suggests directions for future work.
%8 12-14  July
%Z http://www.ise.canberra.edu.au/ An early version of \citeLavangnananda:2006:ieeeMWALS

%A K. Lavangnananda
%T Self-adjusting Associative Rules Generator for Classification : An Evolutionary Computation Approach
%B 2006 IEEE Mountain Workshop on Adaptive and Learning Systems
%D 2006
%P 237--242
%I IEEE
%C Logan, UT, USA
%K genetic algorithms, genetic programming
%U http://dummy/Lavangnananda_2006_ieeeMWALS.pdf
%X The problem of generating efficient association rules can seen as search problem since many different sets of rules are possible from a given set of instances. As the
   application of evolutionary computation in searching is well studied, it is possible to use evolutionary computation in mining for efficient association rules. In this
   paper, a program known as self-adjusting associative rules generator (SARG) is described. SARG is a data mining program which can generate associative rules for
   classification. It is an improvement of the data mining program called genetic programming for inductive learning (GPIL). Both use evolutionary computation in inductive
   learning. The shortcoming of GPIL lies in the operations crossover and selection. These two operations were inflexible and not able to adjust themselves in order to select
   suitable methods for the task at hand. SARG introduces new method of crossover known as MaxToMin crossover together with a self-adjusting reproduction. It has been tested
   on several benchmark data sets available in the public domain. Comparison between GPIL and SARG revealed that SARG achieved better performance and was able to classify
   these data sets with higher accuracy. The paper also discusses relevant aspects of SARG and suggests directions for future work
%8 24-26  July
%Z Title should have been: "Self-adjusting Association Rules Generator for Classification : An Evolutionary Computation Approach" INSPEC Accession Number: 9131818 Sch. of Inf.
   Technol., King Mongkut's Inst. of Technol., Bangkok;
%@ 1-4244-0166-6

%A S. Lavington
%A N. Dewhurst
%A E. Wilkins
%A A. Freitas
%T Interfacing knowledge discovery algorithms to large database management systems
%J Information and Software Technology
%V 41
%N 9
%D 1999
%P 605--617
%I 
%K genetic algorithms, genetic programming, data mining, KDD primitives, decision trees, client-server
%U http://www.sciencedirect.com/science/article/B6V0B-3WN7DYN-8/1/cdabdda09c085c6a4536aa5e116366ee
%X The efficient mining of large, commercially credible, databases requires a solution to at least two problems: (a) better integration between existing Knowledge Discovery
   algorithms and popular DBMS; (b) ability to exploit opportunities for computational speedup such as data parallelism. Both problems need to be addressed in a generic
   manner, since the stated requirements of end-users cover a range of data mining paradigms, DBMS, and (parallel) platforms. In this paper we present a family of generic,
   set-based, primitive operations for Knowledge Discovery in Databases (KDD). We show how a number of well-known KDD classification metrics, drawn from paradigms such as
   Bayesian classifiers, Rule-Induction/Decision Tree algorithms, Instance-Based Learning methods, and Genetic Programming, can all be computed via our generic primitives. We
   then show how these primitives may be mapped into SQL and, where appropriate, optimised for good performance in respect of practical factors such as client-server
   communication overheads. We demonstrate how our primitives can support C4.5, a widely-used rule induction system. Performance evaluation figures are presented for
   commercially available parallel platforms, such as the IBM SP/2.
%O special issue on data mining
%8 25  June

%A Kin Lun Law
%T Generating hard satisfiability problems using genetic programming
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 171--174
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%8 13  July
%Z GECCO-99LB

%A Ken Law
%T Traffic Rules Discovery using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 105--114
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Steve Lawrence
%T Free online availability substantially increases a paper's impact
%J Nature
%V 411
%N 6837
%D 2001
%P 521
%I 
%K jrnl, citeseer, www, world wide web, papers, publications, online, citations, cited, impact factor, c2001, c200x, c20xx
%U http://citeseer.ist.psu.edu/online-nature01/
%X ...analyzed 119,924 conference articles in computer science and related disciplines, obtained from DBLP (dblp.uni-trier.de). In computer science, conference articles are
   typically formal publications and are often more prestigious than journal articles, with acceptance rates at some conferences below 10\%. Citation counts and online
   availability were estimated using ResearchIndex....
%8 31  May
%Z Free online availability of scientific literature offers substantial benefits to science and society. To maximize impact, minimize redundancy and speed scientific progress,
   authors and publishers should aim to make research easy to access.

%A Ming-Yi Lay
%T Application of genetic programming in analyzing multiple steady states of dynamical systems
%B Proceedings of the 1994 IEEE World Congress on Computational Intelligence
%V 1
%D 1994
%P 333--336b
%I IEEE Press
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, Hopf bifurcation points, dynamical systems, genetic programming paradigm, multiple steady states, bifurcation, linear programming,
   search problems
%X Multiple steady states are very interesting phenomena in dynamical systems. However, it is hard to analyse these kinds of phenomena directly by using traditional numerical
   methods. It is shown that the genetic programming paradigm could be used to directly analyze the existence of multiple steady states in dynamical systems and it could even
   possibly be applied in analysing other kinds of behaviour in dynamical systems, e.g., the Hopf bifurcation points
%8 27-29  June
%Z Uses GP to search for steady states in a reaction vessel. The equations for the behaviour of the chemicals is known but not how to solve them. GP is able to find to high
   accuracy (7 figure) the steady states. States are divined by two floating point variables. Each represented within the prog by an effectively independent tree, ie they
   don't exchange via crossover.

%A Finbar Leahy
%T Social Programming: Investigations in Grammatical Swarm
%R M.S. Thesis Master of Science in Software Engineering
%D 2005
%I 
%I University of Limerick
%C University of Limerick, Ireland
%K genetic algorithms, genetic programming, grammatical evolution, grammatical swarm

%A Hugh Leather
%A Edwin Bonilla
%A Michael O'Boyle
%T Automatic Feature Generation for Machine Learning Based Optimizing Compilation
%B 2009. International Symposium on Code Generation and Optimization
%D 2009
%P 81--91
%I 
%K genetic algorithms, genetic programming, SBSE, Pentium 6, automatic feature generation, compilation, compiler writer, feature generation technique, grammar, loop unrolling,
   machine learning, predictive modeling, grammars, learning (artificial intelligence), program compilers
%X Recent work has shown that machine learning can automate and in some cases outperform hand crafted compiler optimizations. Central to such an approach is that machine
   learning techniques typically rely upon summaries or features of the program. The quality of these features is critical to the accuracy of the resulting machine learned
   algorithm; no machine learning method will work well with poorly chosen features. However, due to the size and complexity of programs, theoretically there are an infinite
   number of potential features to choose from. The compiler writer now has to expend effort in choosing the best features from this space. This paper develops a novel
   mechanism to automatically find those features which most improve the quality of the machine learned heuristic. The feature space is described by a grammar and is then
   searched with genetic programming and predictive modeling. We apply this technique to loop unrolling in GCC 4.3.1 and evaluate our approach on a Pentium 6. On a benchmark
   suite of 57 programs, GCC's hard-coded heuristic achieves only 3percent of the maximum performance available, while a state of the art machine learning approach with
   hand-coded features obtains 59percent. Our feature generation technique is able to achieve 76percent of the maximum available speedup, outperforming existing approaches.
%8 March
%Z Also known as \cite4907653

%A Ricky D. Ledwith
%A Julian F. Miller
%T Introducing Flexibility in Digital Circuit Evolution: Exploiting Undefined Values in Binary Truth Tables
%B Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010
%S Lecture Notes in Computer Science
%E Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller
%V 6274
%D 2010
%P 25--36
%I Springer
%C York
%K genetic algorithms, genetic programming, Evolutionary Algorithms, Cartesian Genetic Programming (CGP), Evolvable Hardware, Don't Care Logic
%X Evolutionary algorithms can be used to evolve novel digital circuit solutions. This paper proposes the use of flexible target truth tables, allowing evolution more freedom
   where values are undefined. This concept is applied to three test circuits with different distributions of don't care values. Two strategies are introduced for using the
   undefined output values within the evolutionary algorithm. The use of flexible desired truth tables is shown to significantly improve the success of the algorithm in
   evolving circuits to perform this function. In addition, we show that this flexibility allows evolution to develop more hardware efficient solutions than using a
   fully-defined truth table.
%8 September  6-8

%A Dong-Gyu Lee
%A Han-Gon Kim
%A Won-Pil Baek
%A Soon Heung Chang
%T Critical heat flux prediction using genetic programming for water flow in vertical round tubes
%J International Communications in Heat and Mass Transfer
%V 24
%N 7
%D 1997
%P 919--929
%I 
%K genetic algorithms, genetic programming
%U http://www.sciencedirect.com/science/article/B6V3J-3SN6JCK-3/2/6b7c99878889f319da36b00d1c087dfd
%X The genetic programming method is used to develop critical heat flux (CHF) correlations for upward water flow in vertical round tubes under low pressure and low flow
   conditions. The genetic programming, as a symbolic regression tool, finds both the functional form and fitting coefficients of a correlation without any initial
   assumptions. Inlet and local condition type correlations are developed based on 414 and 314 CHF data from KAIST CHF data bank, respectively. The inlet condition type
   correlation shows the rms error of 15.2% and the local condition type one shows the rms errors of 32.7% and 13.2% by the direct substitution method and the heat balance
   method, respectively. Prediction errors are smaller than or comparable to those for other existing correlations.
%8 November

%A Dong-Wook Lee
%A Chang-Bong Ban
%A Kwee-Bo Sim
%A Ho-Sik Seok
%A Kwang-Ju Lee
%A Byoung-Tak Zhang
%T Behavior evolution of autonomous mobile robot using genetic programming based on evolvable hardware
%B IEEE International Conference on Systems, Man, and Cybernetics
%V 5
%D 2000
%P 3835--3840
%I 
%C Nashville, TN, USA
%K genetic algorithms, genetic programming, EHW
%X This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be a useful control method
   for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent the tree structured chromosome in hardware, and it is difficult to
   use the crossover operator in hardware. Therefore, genetic programming is not as popular as genetic algorithms in the evolvable hardware community in spite of its possible
   strengths. We propose a chromosome representation method and a hardware implementation method that can be helpful for this situation. Our method uses a context switchable
   identical block structure to implement a genetic tree in evolvable hardware. We compose an evolutionary strategy for evolvable hardware by combining the proposed method
   with other research results. The proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness
%8 8-11  October
%Z IEEE site broken 13 Oct 2004

%A Geum Yong Lee
%T Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series
%B Advances in Genetic Programming 3
%E Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline
%D 1999
%P 401--423
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1197
%X I explore several extensions to genetic programming for applications involving the forecasting of real world chaotic time series. We first used Genetic Symbolic Regression
   (GSR),which is the standard genetic programming technique applied to the forecasting problem in the same way that it is often applied to symbolic regression problems [ Koza
   1992, 1994]. We observed that the performance of GSR depends on the characteristics of the time series, and in particular that it worked better for deterministic time
   series than it did for stochastic or volatile time series. Taking a hint from this observation, an assumption was made in this study that the dynamics of a time series
   comprise a deterministic and a stochastic part. By subtracting the model built by GSR for the deterministic part from the original time series, the stochastic part would be
   obtained as a residual time series. This study noted the possibility that GSR could be used recursively to model the residual time series of rather stochastic dynamics,
   which may still comprise another deterministic and stochastic part. An algorithm called GRR (Genetic Recursive Regression) has been developed to apply GSR recursively to
   the sequence of residual time series of stochastic dynamics, giving birth to a sequence of sub-models for deterministic dynamics extractable at each recursive application.
   At each recursive application and after some termination conditions are met, the submodels become the basis functions for a series-expansion type representation of a model.
   The numerical coefficients of the model are calculated by the least square method with respect to the predetermined region of the time series data set. When the region
   includes the latest data set, the model reflects the most recent changes in the dynamics of a time series, thus increasing the forecasting performance. This chapter shows
   how GRR has been successfully applied to many real world chaotic time series. The results are compared with those from other GSR-like methods and various soft-computing
   technologies such as neural networks. The results show that GRR saves much computational effort while achieving enhanced forecasting performance for several selected
   problems.
%O 17
%8 June
%Z AiGP3
%@ 0-262-19423-6

%A Ho Cheong Lee
%A Ming Xi Tang
%T Generating stylistically consistent product form designs using interactive evolutionary parametric shape grammars
%B 7th International Conference on Computer-Aided Industrial Design and Conceptual Design, CAIDCD '06
%D 2006
%P 1--6
%I IEEE
%C Hangzhou
%K genetic algorithms, genetic programming
%X Interactive grammar based design systems (IGBDS) are capable of generating large numbers of alternative designs. Prior to the application of IGBDS, a set of rules should be
   defined based on the theoretical theories and practical experiences in designing the objects, or by analysing the existing sets of objects. However, due to the time
   constraints in developing theoretical theories, gaining the practical skills of experts is of qualitative in nature and stylistically inconsistent designs to be analysed,
   it is difficult to systematically derive a set of shape grammar rules in generating designs which fulfils specific requirements. To address this problem, this paper
   presents an IGBDS enhanced by evolutionary computing to evolve a set of grammar rules for product form design. In this research, the forms of a product are analysed to
   derive shape features in the form of shape grammar (SG) rules. The rules are then encoded as the code scripts of a genetic algorithm (GA) representation in order to
   generate new shape grammar rules. The parameters in GA representation are modified by genetic programming (GP) which is regulated by control planning strategies named
   GP-GA-SG. The GP-GA-SG control planning strategy first defines ways on how the control variables in GP should modify the variables in GA representation. The GP-GA-SG
   control planning strategy then defines ways on how the control variables in GA should modify the variables in SG representation. The SG rules modified by the GA variables
   define a new combination of shape features for alternative designs. In this way, traditional shape grammar is extended to an interactive context in which generative and
   evolutionary computing methods are combined. Both product component design as well as product configuration are supported in this framework. In this paper, we describe how
   this framework is formulated and discuss its potentials in supporting product design, with initial examples showing how the system is intended to work
%8 17-19  November
%Z INSPEC Accession Number: 9487202 Design Technol. Res. Centre, Hong Kong Polytech. Univ.;
%@ 1-4244-0684-6

%A Ho Cheong Lee
%A Ming Xi Tang
%T Evolving product form designs using parametric shape grammars integrated with genetic programming
%J Artificial Intelligence for Engineering Design, Analysis and Manufacturing
%V 23
%N 2
%D 2009
%P 131--158
%I Cambridge University Press
%K genetic algorithms, genetic programming, Configuration Designs, Evolutionary Shape Grammars, Interactive Grammar-Based Design Systems, Product Designs
%X The two critical issues related to product design exploration are addressed: the balance between stylistic consistency and innovation, and the control of design process
   under a great diversity of requirements. To address these two issues, the view of understanding product design exploration is first sought. In this view, the exploration of
   designs is not only categorized as a problem-solving activity but also as a problem-finding activity. A computational framework is developed based on this view, and it
   encompasses the belief that these two activities go hand in hand to accomplish the design tasks in an interactive design environment. The framework adopts an integration
   approach of two key computational techniques, shape grammars and evolutionary computing, for addressing the above two critical issues. For the issues of stylistic
   consistency, this paper focuses on the computational techniques in balancing the conflicts of stylistic consistency and innovation with shape grammars. For the issues of
   controlling design process, the practical concerns of monitoring the design process through various activities starting from the preparation works to the implementation of
   shape grammars have been emphasized in the development of this framework. To evaluate the effectiveness of the framework, the experiments have been set up to reflect the
   practical situations with which the designers have to deal. The system generates a number of models from scratch with numerical analysis that can be evaluated effectively
   by the designers. This reduces the designers' time and allows the designers to concentrate their efforts on performing higher level of design activities such as evaluation
   of designs and making design decisions.
%Z Camera example. ACIS

%A K. H. Lee
%A Y. S. Yeun
%A W. S. Ruy
%A Y. S. Yang
%T Polynomial Genetic Programming for Response Surface Modeling
%B 4th International Workshop on Frontiers in Evolutionary Algorithms
%E Manuel Grana Romay and Richard Duro
%D 2002
%I 
%C North Carolina, USA
%K genetic algorithms, genetic programming
%8 8-14  March
%Z FEA2002 In conjunction with Sixth Joint Conference on Information Sciences
%@ 0-9707890-1-7

%A Kyung Ho Lee
%A Yun Seog Yeun
%A Young Soon Yang
%A Jang Hyun Lee
%A June Oh
%T Data Analysis and Utilization Method Based on Genetic Programming in Ship Design
%B Computational Science and Its Applications - ICCSA 2006, Part II
%S Lecture Notes in Computer Science
%E Marina L. Gavrilova and Osvaldo Gervasi and Vipin Kumar and Chih Jeng Kenneth Tan and David Taniar and Antonio Lagan\`a and Youngsong Mun and Hyunseung Choo
%V 3981
%D 2006
%P 1199--1209
%I Springer
%C Glasgow, UK
%K genetic algorithms, genetic programming
%X Although Korean shipyards have accumulated a great amount of data, they do not have appropriate tools to use the data in practical works. Engineering data contains the
   experiences and know-how of experts. Data mining technique is useful to extract knowledge or information from the accumulated existing data. This paper presents a machine
   learning method based on genetic programming (GP), which can be one of the components for the realization of data mining. The paper deals with linear models of GP for
   regression or approximation problems when the given learning samples are not sufficient.
%8 May  8-11
%@ 3-540-34072-6

%A Kyung Ho Lee
%A June Oh
%A Jong Hoon Park
%T Development of Data Miner for the Ship Design Based on Polynomial Genetic Programming
%B Australian Conference on Artificial Intelligence
%S Lecture Notes in Computer Science
%E Abdul Sattar and Byeong Ho Kang
%V 4304
%D 2006
%P 981--985
%I Springer
%C Hobart, Australia
%K genetic algorithms, genetic programming, PGP, regularisation
%X Engineering data contains the experiences and know-how of experts. Data mining technique is useful to extract knowledge or information from the accumulated existing data.
   This paper deals with generating optimal polynomials using genetic programming (GP) as the module of Data Miner. The Data Miner for the ship design based on polynomial
   genetic programming is presented.
%8 Decemeber  4-8
%@ 3-540-49787-0

%A Kwang-Ju Lee
%A Byoung-Tak Zhang
%T Learning robot behaviors by evolving genetic programs
%B 26th Annual Conference of the IEEE Industrial Electronics Society, IECON
%V 4
%D 2000
%P 2867--2872
%I IEEE
%C Nagoya
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/454905.html
%X A method for evolving behavior-based robot controllers using genetic programming is presented. Due to their hierarchical nature, genetic programs are useful representing
   high-level knowledge for robot controllers. One drawback is the difficulty of incorporating sensory inputs. To overcome the gap between symbolic representation and direct
   sensor values, the elements of the function set in genetic programming is implemented as a single-layer perceptron. Each perceptron is composed of sensory input nodes and a
   decision output node. The robot learns proper behavior rules based on local, limited sensory information without using an internal map. First, it learns how to discriminate
   the target using single-layer perceptrons. Then, the learned perceptrons are applied to the function nodes of the genetic program tree which represents a robot controller.
   Experiments have been performed using Khepera robots. The presented method successfully evolved high-level genetic programs that control the robot to find the light source
   from sensory inputs
%O The Pennsylvania State University CiteSeer Archives
%8 22-28  October

%A Jack Y. B. Lee
%A P. C. Wong
%T The effect of function noise on GP efficiency
%B Progress in Evolutionary Computation
%S Lecture Notes in Artificial Intelligence
%E Xin Yao
%V 956
%D 1995
%P 1--16
%I Springer-Verlag
%C Heidelberg, Germany
%K genetic algorithms, genetic programming
%X Genetic Programming (GP) has been applied to many problems and there are indications [1,2,3] that GP is potentially useful in evolving algorithms for problem solving. This
   paper investigates one problem with algorithmic evolution using GP - Function Noise. We show that the performance of GP could be severely degraded even in the presence of
   minor noise in the GP functions. We investigated two counter noise schemes, Multi-Sampling Function and Multi-Testcases. We show that the Multi-Sampling Function scheme can
   reduce the effect of noise in a predictable way while the Multi-Test cases scheme evolves radically different program structures to avoid the effect of noise. Essentially,
   the two schemes lead the GP to evolve into different approaches to solving the same problem.
%Z Artificial ant on Santa Fe Trail with noisy IfFoodAhead GP does poorly even with small amounts of with noise. Sometimes population abandons use of IfFoodAhead entirely
   (what else could it do?)

%A G. Y. Lee
%T Explicit Models for Chaotic and Noisy Time Series Through the Genetic Recursive Regression
%D 1995
%I 
%K genetic algorithms, genetic programming
%O unpublished
%Z Draft submitted to Advances in Genetic Programming 2 Peter J. Angeline and K. E. Kinnear, Jr. (Eds.) MIT Press, 1996.

%A Sang M. Lee
%A Arben A. Asllani
%T Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming
%J Omega
%V 32
%N 2
%D 2004
%P 145--153
%I 
%K genetic algorithms, genetic programming, Dual criteria scheduling, Sequence dependent setup times, 0-1 mathematical programming
%U http://www.sciencedirect.com/science/article/B6VC4-4B42761-1/2/94e937150cd10b51245fedaa40f1d3cc
%X Flexibility, speed, and efficiency are major challenges for operations managers in today's knowledge-intensive organisations. Such requirements are converted into three
   production scheduling criteria: (a) minimise the impact of setup times in flexible production lines when moving from one product to another, (b) minimize number of tardy
   jobs, and (c) minimize overall production time, or makespan, for a given set of products or services. There is a wide range of solution methodologies for such NP-hard
   scheduling problems. While mathematical programming models provide optimal solutions, they become too complex to model for large scheduling problems. Simultaneously,
   heuristic approaches are simpler and very often independent of the problem size, but provide "good" rather than optimal solutions. This paper proposes and compares two
   alternative solutions: 0-1 mixed integer linear programming and genetic programming. It also provides guidelines that can be used by practitioners in the process of
   selecting the appropriate scheduling methodology.
%8 April  2004

%A Wei-Po Lee
%A John Hallam
%A Henrik Hautop Lund
%T A Hybrid GP/GA Approach for Co-evolving Controllers and Robot Bodies to Achieve Fitness-Specified Tasks
%B Proceedings of the 1996 IEEE International Conference on Evolutionary Computation
%D 1996
%I IEEE Press
%I IEEE Neural Network Council
%C Nagoya, Japan
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/lee96hybrid.html
%X Evolutionary approaches have been advocated to automate robot design. Some research work has shown the success of evolving controllers for the robots by genetic approaches.
   As we can observe, however, not only the controller but also the robot body itself can affect the behaviour of the robot in a robot system. We develop a hybrid GP/GA
   approach to evolve both controllers and robot bodies to achieve behavior-specified tasks. In order to assess the performance of the developed approach, it is used to evolve
   a simulated agent, with its own controller and body, to do obstacle avoidance in the simulated environment. Experimental results show the promise of this work. In addition,
   the importance of co-evolving controllers and robot bodies is analysed and discussed
%8 20-22  May
%Z ICEC-96 Evolves controller for (simulated?) mobile robot
%@ 0-7803-2902-3

%A Wei-Po Lee
%A John Hallam
%A Henrik Hautop Lund
%T Applying Genetic Programming to Evolve Behavior Primitives and Arbitrators for Mobile Robots
%B Proceedings of IEEE 4th International Conference on Evolutionary Computation
%V 1
%D 1997
%I IEEE Press
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/lee97applying.html
%X The behaviour-based approach has been successfully applied to designing robot control systems. This paper presents our work, based on evolutionary algorithms, to program
   behavior-based robots automatically. Instead of hand-coding all the behaviour controllers or evolving an entire control system for an overall task, we suggest our approach
   at the intermediate level: it includes evolving behaviour primitives and behaviour arbitrators for a mobile robot to achieve the specified tasks. To examine the developed
   approach, we evolve a control system for a moderately complicated box-pushing task as an example. We first evolved the controllers in a simulation and then transferred them
   to the Khepera miniature robot. Experimental results show the promise of our approach, and the evolved controllers are transferred to the real robot without loss of
   performance
%O to appear
%Z Khepera. Division of Informatics, Research Paper #832, Edinburgh University

%A Wei-Po Lee
%A John Hallam
%A Henrik Hautop Lund
%T Learning Complex Robot Behaviours by Evolutionary Approaches
%B 6th European Workshop on Learning Robots, EWLR-6
%E Andreas Birk and John Demiris
%D 1997
%P 42--51
%I 
%C Hotel Metropole, Brighton, UK
%K genetic algorithms, genetic programming
%U http://130.203.133.150/showciting;jsessionid=326C58F9018712DAB6892709F2110874?cid=1315116&sort=recent
%8 1-2  August
%Z Task of getting Khepera to push a box to a light source broken up by hand into 4 subtasks. Fitness function etc devised for each task and GP used to evolve code to solve it
   in simulation. Evolved codes put together and run on real robot. Published as \citelee:1997:lcrbeaLNAI

%A Wei-Po Lee
%A John Hallam
%A Henrik Hautop Lund
%T Learning Complex Robot Behaviours by Evolutionary Computing with Task Decomposition
%B Learning Robots, 6th European Workshop, EWLR-6, Proceedings
%S LNAI
%E Andreas Birk and John Demiris
%V 1545
%D 1997
%P 155--172
%I Springer Verlag
%C Hotel Metropole, Brighton, UK
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/bibs/1545/15450155.htm
%X Building robots can be a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to
   cope the difficulties in designing robots is to adopt learning methods. Evolution-based approaches are a special kind of machine learning method and during the last few
   years some researchers have shown the advantages of using this kind of approach to automate the design of robots. However, the tasks achieved so far are fairly simple. In
   this work, we analyse the difficulties of applying evolutionary approaches to learn complex behaviours for mobile robots. And, instead of evolving the controller as a
   whole, we propose to take the control architecture of a behavior-based system and to learn the separate behaviours and the arbitration by the use of an evolutionary
   approach. By using the technique of task decomposition, the job of defining fitness functions becomes more straightforward and the tasks become easier to achieve. To assess
   the performance of the developed approach, we have evolved a control system to achieve an application task of box-pushing as an example. Experimental results show the
   promise and efficiency of the presented approach.
%8 1-2  August
%Z Published version may be different from that in proceedings \citelee:1997:lcrbea
%@ 3-540-65480-1

%A Wei-Po Lee
%T Evolving Robots: from Simple Behaviours to Complete Systems
%R Ph.D. Thesis
%D 1997
%I 
%I Department of Artificial Intelligence. University of Edinburgh
%K genetic algorithms, genetic programming
%U http://books.google.co.uk/books/about/Evolving_robots.html?id=uIkKcgAACAAJ&redir_esc=y
%Z OCLC Number: 607678835

%A Wei-Po Lee
%T Evolving complex robot behaviors
%J Information Sciences
%V 121
%N 1-2
%D 1999
%P 1--25
%I 
%K genetic algorithms, genetic programming, Evolutionary computing, Computational intelligence, Robot learning, Automatic robot programming
%U http://www.sciencedirect.com/science/article/B6V0C-3Y3XPFF-1/2/ddd56f7f1c35319bee24c38eb8db5652
%X Building robots is a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to such
   difficulties in designing robots is to adopt learning methods. The evolution-based approach is a special method of machine learning and it has been advocated to automate
   the design of robots. Yet, the tasks achieved so far are fairly simple. In this work, we first analyze the difficulties of applying evolutionary approaches to synthesize
   robot controllers for complicated tasks, and then suggest an approach to resolve them. Instead of directly evolving a monolithic control system, we propose to decompose the
   overall task to fit in the behavior-based control architecture, and then to evolve the separate behavior modules and arbitrators using an evolutionary approach.
   Consequently, the job of defining fitness functions becomes more straightforward and the tasks easier to achieve. To assess the performance of the developed approach, we
   evolve a control system to achieve an application task of box-pushing as an example. Experimental results show the promise and efficiency of the presented approach.
%Z Khepera Information Sciences http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt

%A Wei-Po Lee
%A John Hallam
%T Evolving reliable and robust controllers for real robots by genetic programming
%J Soft Computing -- A Fusion of Foundations, Methodologies and Applications
%V 3
%N 2
%D 1999
%P 63--75
%I 
%K genetic algorithms, genetic programming
%X Using Genetic Programming (GP)-based approaches to evolve robot controllers has the advantage of operating variable-size genotype. This is an important feature for evolving
   robot control systems as it allows complete freedom for the control architecture in respect to the task complexity which is difficult to predict. However, GP-based work in
   evolving controllers has been questioned in the verification of the performance on real robots, the generalisation of defining primitives, and the computational cost
   needed. In this paper, we present our GP framework in which a special representation of the robot controller is designed; this representation can capture well the
   characteristic of a behaviour controller so that our system can efficiently evolve desired robot behaviours by a relatively low computational cost. This system has been
   successfully used to evolve reliable and robust controllers working on a real robot, for a variety of tasks.
%8 September

%A Wei-Po Lee
%A Kung-Cheng Yang
%T Applying Intelligent Computing Techniques to Modeling Biological Networks from Expression Data
%J Genomics, Proteomics \& Bioinformatics
%V 6
%N 2
%D 2008
%P 111--120
%I 
%K genetic algorithms, genetic programming, reverse engineering, system modeling, recurrent neural network, expression data
%U http://www.sciencedirect.com/science/article/B82XM-4TSTVT9-6/2/3622f6428cf373014593567706357973
%X Constructing biological networks is one of the most important issues in systems biology. However, constructing a network from data manually takes a considerable large
   amount of time, therefore an automated procedure is advocated. To automate the procedure of network construction, in this work we use two intelligent computing techniques,
   genetic programming and neural computation, to infer two kinds of network models that use continuous variables. To verify the presented approaches, experiments have been
   conducted and the preliminary results show that both approaches can be used to infer networks successfully.

%A Wo-Chiang Lee
%T Genetic Programming Decision Tree for Bankruptcy Prediction
%B Proceedings of the 2006 Joint Conference on Information Sciences, JCIS 2006
%D 2006
%I Atlantis Press
%C Kaohsiung, Taiwan, ROC
%K genetic algorithms, genetic programming
%U http://www.atlantis-press.com/php/download_paper?id=8
%X In this paper, we apply the CART ,C5.0 , GP decision tree classifiers and compares with logic model and ANN model for Taiwan listed electronic companies bankruptcy
   prediction. Results reveal that the GP decision tree can outperform all the classifiers either in overall percentage of correct or k-fold cross validation test in out
   sample. That is to say, GP decision tree model have the highest accuracy and lowest expected misclassification costs. It can provide an efficient alternative to
   discriminates financial distress problems in Taiwan.
%8 October  8-11
%Z CIEF-76
%@ 90-78677-01-5

%A Chang-Yong Lee
%A Yoonseon Song
%T Evolutionary Programming using the Levy Probability Distribution
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 886--893
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/ES-203.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Joo-Young Lee
%A Sung-Bae Cho
%T Incorporating Human Preference into Content-based Image Retrieval Using Interactive Genetic Algorithm
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1788
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-749.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Miler Lee
%T Finding Solutions to the Knight's Tour Problem using Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 252--260
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A G. Y. Lee
%T Time Series Perturbation by Genetic Programming
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 403--409
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, Time Series, Perturbation Theory
%X We present a new algorithm that combines perturbation theory and genetic programming for modelling and forecasting real-world chaotic time series. Both perturbation theory
   and time series modeling have to build symbolic models for very complex system dynamics. Perturbation theory does not work without a well-defined system equation.
   Difficulties in modelling time series lie in the fact that we cannot have or assume any system equation. The new algorithm shows how genetic programming can be combined
   with perturbation theory for time series modelling. Detailed discussions on successful applications to chaotic time series from practically important fields of science and
   engineering are given. Computational resources were negligible as compared with earlier similar regression studies based on genetic programming. A desktop PC provides
   sufficient computing power to make the new algorithm very useful for real-world chaotic time series. Especially, it worked very well for deterministic or stationary time
   series, while stochastic or nonstationary time series needed extended effort, as it should be
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =
%@ 0-7803-6658-1

%A Peter Lee
%T Evolving Presentations of Genetic Information: Motivation, Methods, and Analysis
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 119--128
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2002/Lee.pdf
%8 June
%Z part of \citekoza:2002:gagp Positional information on genes => do shorter schema survive? Matlab 6.1.0.450 SUN Blade 2000

%A Seung-Kyu Lee
%A Byung Ro Moon
%T Finding attractive rules in stock markets using a modular genetic programming
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1933--1934
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Poster
%X We propose a new modular genetic programming for finding attractive and statistically sound technical rules. We restrict the problem space using well-known technical rules
   to discover attractive technical rules. Experimental results show that our modular genetic programming can successfully find unknown attractive technical rules for Korean
   stock market.
%8 8-12  July
%Z GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic programming conference (GP-2009).
   ACM Order Number 910092.

%A Seung-Kyu Lee
%A Byung-Ro Moon
%T A new modular genetic programming for finding attractive technical patterns in stock markets
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 1219--1226
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, modular genetic programming, Real world applications
%X We propose a new modular genetic programming for finding attractive and statistically sound technical patterns for stock trading. We restrict the problem space to
   combinations of modules for more effective space search. We carefully prepared the set of modules based on existing studies of technical indicators and our own experience.
   Our modular genetic programming successfully found unknown attractive technical patterns for the Korean stock market. A trading simulation with the generated patterns by a
   commercial tool showed significantly higher accumulative returns than the KOSPI index.
%8 7-11  July
%Z Also known as \cite1830704 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Tae-Mun Lee
%A Hyunje Oh
%A Youn-Kyoo Choung
%A Sanghoun Oh
%A Moongu Jeon
%A Joon Ha Kim
%A Sook Hyun Nam
%A Sangho Lee
%T Prediction of membrane fouling in the pilot-scale microfiltration system using genetic programming
%J Desalination
%V 247
%N 1-3
%D 2009
%P 285--294
%I 
%K genetic algorithms, genetic programming, Membrane fouling, Prediction
%U http://www.sciencedirect.com/science/article/B6TFX-4X502WT-11/2/27587f58d0280f3d90f2898992cdab65
%X In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) or genetic algorithm (GA) have been increasingly used to model membrane
   fouling and performance. In the present study, we select genetic programming (GP) for modeling and prediction of the membrane fouling rate in a pilot-scale drinking water
   production system. The model used input parameters for operating conditions (flow rate and filtration time) and feed water quality (turbidity, temperature, algae pH). GP
   was applied to discover the mathematical function for the pattern of the membrane fouling rate. The GP model allows predicting satisfactorily the filtration performances of
   the pilot plant obtained for different water quality and changing operating conditions. A valuable benefit of GP modeling was that the models did not require underlying
   descriptions of the physical processes. GP has displayed the potential to evaluate membrane performance as a feed-forward simulator toward an 'intelligent' membrane system.
%8 October
%Z Presented at the First IWA Asia Pacific Young Water Professionals Conference, Gwangju, South Korea, December 8-10, 2008

%A Yi-Shian Lee
%A Lee-Ing Tong
%T Forecasting energy consumption using a grey model improved by incorporating genetic programming
%J Energy Conversion and Management
%V 52
%N 1
%D 2011
%P 147--152
%I 
%K genetic algorithms, genetic programming, Energy consumption, Grey forecasting model
%U http://www.sciencedirect.com/science/article/B6V2P-50JPRY8-1/2/2a8da744ea8e078b297748c80fb2890c
%X Energy consumption is an important economic index, which reflects the industrial development of a city or a country. Forecasting energy consumption by conventional
   statistical methods usually requires the making of assumptions such as the normal distribution of energy consumption data or on a large sample size. However, the data
   collected on energy consumption are often very few or non-normal. Since a grey forecasting model, based on grey theory, can be constructed for at least four data points or
   ambiguity data, it can be adopted to forecast energy consumption. In some cases, however, a grey forecasting model may yield large forecasting errors. To minimise such
   errors, this study develops an improved grey forecasting model, which combines residual modification with genetic programming sign estimation. Finally, a real case of
   Chinese energy consumption is considered to demonstrate the effectiveness of the proposed forecasting model.

%A Yi-Shian Lee
%A Lee-Ing Tong
%T Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming
%J Knowledge-Based Systems
%V 24
%N 1
%D 2011
%P 66--72
%I 
%K genetic algorithms, genetic programming, ARIMA, Hybrid model, Forecasting, Artificial neural network
%U http://www.sciencedirect.com/science/article/B6V0P-50JHBSY-1/2/1501a7c1121cbfcf9683f1a0d781806b
%X The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time
   series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network
   (ANN) can be used to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult
   and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic
   programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed
   forecasting model.

%A Yao Leehter
%A Lin Chin-Chin
%T Genetic Programming Based Multichannel Identification of Nonlinear Systems by Volterra Filters
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Simon M. Lucas and Gary Fogel and Graham Kendall and Ralf Salomon and Byoung-Tak Zhang and Carlos A. Coello Coello and Thomas Philip Runarsson
%D 2006
%P 2864--2871
%I IEEE Press
%C Vancouver, BC, Canada
%K genetic algorithms, genetic programming
%U http://ieeexplore.ieee.org/servlet/opac?punumber=11108
%X Genetic Programming (GP) is used to search the optimal structure of Volterra filter in this paper. The Volterra filter with high order and large memories contains great
   amount of cross product terms. In stead of applying GP to search all cross products, GP is used to search a smaller set of primary signals which evolve to the whole set of
   cross products. With GP's optimisation capability, the important primary signals and the associated cross products of input signals attributing most to the outputs will be
   chosen while the primary signals and their associated cross products of input signals which are trivial to the outputs will be excluded from the possible candidate primary
   signals.
%8 16-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Vasco Leemans
%T Modelling local order book dynamics in financial markets
%R Ph.D. Thesis PhD
%D 2008
%I 
%I Judge Business School, Faculty of Business and Management, Cambridge University
%Z http://www.cfr.statslab.cam.ac.uk/people/alumni.html#2008

%A Martin Lefley
%A Martin J. Shepperd
%T Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 2477--2487
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, Search Based Software Engineering
%X various techniques including genetic programming, with public data sets, to attempt to model and hence estimate software project effort. The main research question is
   whether genetic programs can offer `better' solution search using public domain metrics rather than company specific ones. Unlike most previous research, a realistic
   approach is taken, whereby predictions are made on the basis of the data available at a given date. Experiments are reported, designed to assess the accuracy of estimates
   made using data within and beyond a specific company. This research also offers insights into genetic programming's performance, relative to alternative methods, as a
   problem solver in this domain. The results do not find a clear winner but, for this data, GP performs consistently well, but is harder to configure and produces more
   complex models. The evidence here agrees with other researchers that companies would do well to base estimates on in house data rather than incorporating public data sets.
   The complexity of the GP must be weighed against the small increases in accuracy to decide whether to use it as part of any effort prediction estimation.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Raluca Lefticaru
%A Florentin Ipate
%A Cristina Tudose
%T Automated Model Design Using Genetic Algorithms and Model Checking
%B Fourth Balkan Conference in Informatics, BCI '09
%D 2009
%P 79--84
%I 
%K genetic algorithms, automated model design, computer program evolution, finite state based models, metaheuristic search algorithms, model checking, software engineering
   activities, software testing, complete computer programs, formal specification, formal verification, search problems, software performance evaluation
%X In recent years there has been a growing interest in applying metaheuristic search algorithms in model-checking. On the other hand, model checking has been used far less in
   other software engineering activities, such as model design and software testing. In this paper we propose an automated model design strategy, by integrating genetic
   algorithms (used for model generation) with model checking (used to evaluate the fitness, which takes into account the satisfied/unsatisfied specifications). Genetic
   programming is the process of evolving computer programs, by using a fitness value determined by the program's ability to perform a given computational task. This
   evaluation is based on the output produced by the program for a set of training input samples. The consequence is that the evolved program can function well for the sample
   set used for training, but there is no guarantee that the program will behave properly for every possible input. Instead of training samples, in this paper we use a model
   checker, which verifies if the generated model satisfies the specifications. This approach is empirically evaluated for the generation of finite state-based models.
   Furthermore, the previous fitness function proposed in the literature, that takes into account only the number of unsatisfied specifications, presents plateaux and so does
   not offer a good guidance for the search. This paper proposes and evaluates the performance of a number of new fitness functions, which, by taking also into account the
   counterexamples provided by the model checker, improve the success rate of the genetic algorithm.
%8 September
%Z Not GP. temporal logic formula:-( LTL, SMV specification, NuSMV traffic lights 14 integer genes, search space 279936, multiple possible solutions. JGAP pop=20, 20 gens, 1pt
   crossover or UXO. PWR safety injection, category injection, one solution in whole search space 221184 Vehicle, one solution in search space 2**40 (10^12) Also known as
   \cite5359333

%A Chris Leger
%T Automated Synthesis and Optimisation of Robot Configurations: An Evolutionary Approach
%R Ph.D. Thesis
%D 1999
%I 
%I The Robotics Institute, Carnegie Mellon University
%C Pittsbugh, PA 15213, USA
%K genetic algorithms, genetic programming, Darwin2K
%U http://www.ri.cmu.edu/pub_files/pub2/leger_patrick__chris__1999_1/leger_patrick__chris__1999_1.pdf
%X Robot configuration design is hampered by the lack of established, well-known design rules, and designers cannot easily grasp the space of possible designs and the impact
   of all design variables on a robot's performance. Realistically, a human can only design and evaluate several candidate configurations, though there may be thousands of
   competitive designs that should be investigated. In contrast, an automated approach to configuration synthesis can create tens of thousands of designs and measure the
   performance of each one without relying on previous experience or design rules. This thesis creates Darwin2K, an extensible, automated system for robot configuration
   synthesis. This research focuses on the development of synthesis capabilities required for many robot design problems: a flexible and effective synthesis algorithm, useful
   simulation capabilities, appropriate representation of robots and their properties, and the ability to accomodate application-specific synthesis needs. Darwin2K can
   synthesize and optimize kinematics, dynamics, structural geometry, actuator selection, and task and control parameters for a wide range of robots. Darwin2K uses an
   evolutionary algorithm to synthesize robots, and uses two new multi-objective selection procedures that are applicable to other evolutionary design domains. The
   evolutionary algorithm can effectively optimize multiple performance objectives while satisfying multiple performance constraints, and can generate a range of solutions
   representing different trade-offs between objectives. Darwin2K uses a novel representation for robot configurations called the parameterized module configuration graph,
   enabling efficient and extensible synthesis of mobile robots, of single, multiple and bifurcating manipulators, and of robots with either modular or monolithic
   construction. Task-specific simulation is used to provide the synthesis algorithm with performance measurements for each robot. Darwin2K can automatically derive dynamic
   equations for each robot it simulates, enabling dynamic simulation to be used during synthesis for the first time. Darwin2K also includes a variety of simulation
   components, including Jacobian and PID controllers, algorithms for estimating link deflection and for detecting collisions; modules for robot links, joints (including
   actuator models), tools, and bases (fixed and mobile); and metrics such as task coverage, task completion time, end effector error, actuator saturation, and link
   deflection. A significant component of the system is its extensible object-oriented software architecture, which allows new simulation capabilities and robot modules to be
   added without impacting the synthesis algorithm. The architecture also encourages re-use of existing toolkit components, allowing task-specific simulators to be quickly
   constructed. Darwin2K's synthesis algorithm, simulation capabilities, and extensible architecture combine to allow synthesis of robots for a wide range of tasks. Results
   are presented for nearly 150 synthesis experiments for six different applications, including synthesis of a free-flying 22-DOF robot with multiple manipulators and a
   walking machine for zero-gravity truss walking. The synthesis system and results represent a significant advance in the state-of-the-art in automated synthesis for
   robotics.
%O CMU-RI-TR-99-43
%8 9  Decemeber

%A Claire {Le Goues}
%A Stephanie Forrest
%A Westley Weimer
%T The case for software evolution
%B Proceedings of the FSE/SDP workshop on Future of software engineering research, FoSER'10
%E Gruia-Catalin Roman and Kevin J. Sullivan
%D 2010
%P 205--210
%I ACM New York, NY, USA
%I ACM SIGSOFT
%C Santa Fe, New Mexico, USA
%K genetic algorithms, genetic programming, sbse, evolutionary computation, program repair, software engineering
%U http://www.cs.virginia.edu/~weimer/p/p205-legoues.pdf
%X Many software systems exceed our human ability to comprehend and manage, and they continue to contain unacceptable errors. This is an unintended consequence of Moore's Law,
   which has led to increases in system size, complexity, and interconnectedness. Yet, software is still primarily created, modified, and maintained by humans. The
   interactions among heterogeneous programs, machines and human operators has reached a level of complexity rivalling that of some biological ecosystems. By viewing software
   as an evolving complex system, researchers could incorporate biologically inspired mechanisms and employ the quantitative analysis methods of evolutionary biology. This
   approach could improve our understanding and analysis of software; it could lead to robust methods for automatically writing, debugging and improving code; and it could
   improve predictions about functional and structural transitions as scale increases. In the short term, an evolutionary perspective challenges several research assumptions,
   enabling advances in error detection, correction, and prevention.
%8 November  7-11

%A Claire {Le Goues}
%A ThanhVu Nguyen
%A Stephanie Forrest
%A Westley Weimer
%T GenProg: A Generic Method for Automatic Software Repair
%J IEEE Transactions on Software Engineering
%V 38
%N 1
%D 2012
%P 54--72
%I 
%K genetic algorithms, genetic programming, sbse, Automatic programming, corrections, testing and debugging
%U http://www.cs.virginia.edu/~weimer/p/weimer-tse2012-genprog.pdf
%X This paper describes GenProg, an automated method for repairing defects in off-the-shelf, legacy programs without formal specifications, program annotations, or special
   coding practices. GenProg uses an extended form of genetic programming to evolve a program variant that retains required functionality but is not susceptible to a given
   defect, using existing test suites to encode both the defect and required functionality. Structural differencing algorithms and delta debugging reduce the difference
   between this variant and the original program to a minimal repair. We describe the algorithm and report experimental results of its success on 16 programs totalling 1.25 M
   lines of C code and 120K lines of module code, spanning eight classes of defects, in 357 seconds, on average. We analyse the generated repairs qualitatively and
   quantitatively to demonstrate that the process efficiently produces evolved programs that repair the defect, are not fragile input memorisations, and do not lead to serious
   degradation in functionality.
%8 January -  February

%A Claire {Le Goues}
%A Michael Dewey-Vogt
%A Stephanie Forrest
%A Westley Weimer
%T A Systematic Study of Automated Program Repair: Fixing 55 out of 105 bugs for \$8 Each
%B International Conference on Software Engineering (ICSE)
%E Martin Glinz
%D 2012
%I 
%C Zurich
%K genetic algorithms, genetic programming, automated program repair, cloud computing
%U http://dijkstra.cs.virginia.edu/genprog/
%X There are more bugs in real-world programs than human programmers can realistically address. This paper evaluates two research questions: What fraction of bugs can be
   repaired automatically? and How much does it cost to repair a bug automatically? In previous work, we presented GenProg, which uses genetic programming to repair defects in
   off-the-shelf C programs. To answer these questions, we: (1) propose novel algorithmic improvements to GenProg that allow it to scale to large programs and find repairs
   68percent more often, (2) exploit GenProg's inherent parallelism using cloud computing resources to provide grounded, human competitive cost measurements, and (3) generate
   a large, indicative benchmark set to use for systematic evaluations. We evaluate GenProg on 105 defects from 8 open-source programs totalling 5.1 million lines of code and
   involving 10,193 test cases. GenProg automatically repairs 55 of those 105 defects. To our knowledge, this evaluation is the largest available of its kind, and is often two
   orders of magnitude larger than previous work in terms of code or test suite size or defect count. Public cloud computing prices allow our 105 runs to be reproduced for 403
   USA dollars; a successful repair completes in 96 minutes and costs $7.32, on average.
%8 June  2-9
%Z GenProg >> ClearView, AutoFix-E, AFix. Bug bounties, Tarsnap. Chrome is ordered list of AST edit operations. Delete, insert, swap, uniform crossover. 10 GP runs (population
   40, <= ten generations, <12 hours) in parallel on Amazon EC2 (c1.medium, 1.7Gbyte RAM) cloud. Less than 10percent of children fail to compile. fbc, gmp, gzip, libtiff,
   lighttpd, php, Python, wireshark approx 5.1 million lines of C code. Human difficulty of bugfix != GP difficulty? http://www.ifi.uzh.ch/icse2012/

%A Pierrick Legrand
%A Claire Bourgeois-Republique
%A Vincent Pean
%A Esther Harboun-Cohen
%A Jacques Levy-Vehel
%A Bruno Frachet
%A Evelyne Lutton
%A Pierre Collet
%T Interactive evolution for cochlear implants fitting
%J Genetic Programming and Evolvable Machines
%V 8
%N 4
%D 2007
%P 319--354
%I 
%K Interactive evolution, ES, Cochlear implants fitting, Signal processing, Classification, HEVEA project
%X Cochlear implants (CI) are devices that become more and more sophisticated and adapted to the need of patients, but at the same time they become more and more difficult to
   parameterise. After a deaf patient has been surgically implanted, a specialised medical practitioner has to spend hours during months to precisely fit the implant to the
   patient. This process is a complex one implying two intertwined tasks: the practitioner has to tune the parameters of the device (optimisation) while the patient's brain
   needs to adapt to the new data he receives (learning). This paper presents a study that intends to make the implant more adaptable to environment (auditive ecology) and to
   simplify the process of fitting. Real experiments on volunteer implanted patients are presented, that show the efficiency of interactive evolution for this purpose.
%O special issue on medical applications of Genetic and Evolutionary Computation
%8 Decemeber
%Z EASEA, GALib, PDA, IEA

%A Guillermo Leguizamon
%T Arthur K. Kordon: Applying computational intelligence: how to create value, Springer, 2009, Hardcover, 459 pages, ISBN: 978-3-540-69910-1
%J Genetic Programming and Evolvable Machines
%V 12
%N 1
%D 2011
%P 85--86
%I 
%K genetic algorithms, genetic programming
%O Book Review
%8 March
%Z Review of \citeKordon:book

%A Joel Lehman
%A Kenneth O. Stanley
%T Efficiently evolving programs through the search for novelty
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 837--844
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, premature convergence, program bloat
%X A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to
   genetic programming a method that originated in neuroevolution (i.e. the evolution of artificial neural networks) that circumvents the problem of deceptive local optima.
   The main idea is to search only for behavioural novelty instead of for higher fitness values. Although such novelty search abandons following the gradient of the fitness
   function, if such gradients are deceptive they may actually occlude paths through the search space towards the objective. Because there are only so many ways to behave, the
   search for behavioral novelty is often computationally feasible and differs significantly from random search. Counter intuitively, in both a deceptive maze navigation task
   and the artificial ant benchmark task, genetic programming with novelty search, which ignores the objective, outperforms traditional genetic programming that directly
   searches for optimal behaviour. Additionally, novelty search evolves smaller program trees in every variation of the test domains. Novelty search thus appears less
   susceptible to bloat, another significant problem in genetic programming. The conclusion is that novelty search is a viable new tool for efficiently solving some deceptive
   problems in genetic programming.
%8 7-11  July
%Z Maze, artificial ant (Santa Fe, Los Altos). LilGP. p838 'novelty search ... ignores the objective'. p839 'novelty needs to be measured'. k-nearest (k=25) phenotype
   clustering (cf fitness sharing, hall-of-fame coevolution archive, tabu) Mouret. Fitness = Euclidean distance between time sequences of food collection (fixed length
   vectors?) Pop=1000, 1000 generations, binary tournaments. Santa Fe Ant Wrong number of time steps. Performance similar to GP on Ant, much better on hardest maze. Also known
   as \cite1830638 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic programming
   conference (GP-2010)

%A Joel Lehman
%A Kenneth O. Stanley
%T Novelty Search and the Problem with Objectives
%B Genetic Programming Theory and Practice IX
%S Genetic and Evolutionary Computation
%E Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore
%D 2011
%P 37--56
%I Springer
%C Ann Arbor, USA
%K genetic algorithms, genetic programming, Novelty search, objective-based search, non-objective search, deception, evolutionary computation
%X By synthesising a growing body of work in search processes that are not driven by explicit objectives, this paper advances the hypothesis that there is a fundamental
   problem with the dominant paradigm of objective-based search in evolutionary computation and genetic programming: Most ambitious objectives do not illuminate a path to
   themselves. That is, the gradient of improvement induced by ambitious objectives tends to lead not to the objective itself but instead to dead end local optima. Indirectly
   supporting this hypothesis, great discoveries often are not the result of objective-driven search. For example, the major inspiration for both evolutionary computation and
   genetic programming, natural evolution, innovates through an open-ended process that lacks a final objective. Similarly, large-scale cultural evolutionary processes, such
   as the evolution of technology, mathematics, and art, lack a unified fixed goal. In addition, direct evidence for this hypothesis is presented from a recently-introduced
   search algorithm called novelty search. Though ignorant of the ultimate objective of search, in many instances novelty search has counter-intuitively outperformed searching
   directly for the objective, including a wide variety of randomly-generated problems introduced in an experiment in this chapter. Thus a new understanding is beginning to
   emerge that suggests that searching for a fixed objective, which is the reigning paradigm in evolutionary computation and even machine learning as a whole, may ultimately
   limit what can be achieved. Yet the liberating implication of this hypothesis argued in this paper is that by embracing search processes that are not driven by explicit
   objectives, the breadth and depth of what is reachable through evolutionary methods such as genetic programming may be greatly expanded.
%O 3
%8 12-14  May
%Z part of \citeRiolo:2011:GPTP

%A Joel Lehman
%A Kenneth O. Stanley
%T Abandoning Objectives: Evolution through the Search for Novelty Alone
%J Evolutionary Computation
%V 19
%N 2
%D 2011
%P 189--223
%I 
%K genetic algorithms, genetic programming
%X In evolutionary computation, the fitness function normally measures progress towards an objective in the search space, effectively acting as an objective function. Through
   deception, such objective functions may actually prevent the objective from being reached. While methods exist to mitigate deception, they leave the underlying pathology
   untreated: Objective functions themselves may actively misdirect search towards dead ends. This paper proposes an approach to circumventing deception that also yields a new
   perspective on open-ended evolution: Instead of either explicitly seeking an objective or modelling natural evolution to capture open-endedness, the idea is to simply
   search for behavioural novelty. Even in an objective-based problem, such novelty search ignores the objective. Because many points in the search space collapse to a single
   behavior, the search for novelty is often feasible. Furthermore, because there are only so many simple behaviours, the search for novelty leads to increasing complexity. By
   decoupling open-ended search from artificial life worlds, the search for novelty is applicable to real world problems. Counterintuitively, in the maze navigation and biped
   walking tasks in this paper, novelty search significantly outperforms objective-based search, suggesting the strange conclusion that some problems are best solved by
   methods that ignore the objective. The main lesson is the inherent limitation of the objective-based paradigm and the unexploited opportunity to guide search through other
   means.
%8 Summer

%A Wang Lei
%A Jiao Licheng
%T The immune evolutionary programming and its convergence
%B Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference
%E Scott Brave and Annie S. Wu
%D 1999
%P 175--183
%I 
%C Orlando, Florida, USA
%K immune evolutionary programming, antibody, TSP
%8 13  July
%Z GECCO-99LB

%A Andr{\'e} Leier
%A Wolfgang Banzhaf
%T Evolving Hogg's Quantum Algorithm Using Linear-Tree GP
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2723
%D 2003
%P 390--400
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, DNA, Molecular, and Quantum Computing
%X Intermediate measurements in quantum circuits compare to conditional branchings in programming languages. Due to this, quantum circuits have a natural linear-tree
   structure. In this paper a Genetic Programming system based on linear-tree genome structures developed for the purpose of automatic quantum circuit design is introduced. It
   was applied to instances of the 1-SAT problem, resulting in evidently and "visibly" scalable quantum algorithms, which correspond to Hogg's quantum algorithm.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40602-6

%A Andre Leier
%A Wolfgang Banzhaf
%T Exploring the search space of quantum programs
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%V 1
%D 2003
%P 170--177
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%U http://www.cs.mun.ca/~leier/publications/cec03.pdf
%X This work is a first study of search spaces and fitness landscapes in the context of quantum program evolution. Considering small instances of the Deutsch-Josza problem as
   a staring point for explorations of quantum program search spaces, we analyse the structure of mutation landscapes using autocorrelation characteristics and information
   measures. Our motivation is to obtain insights into the relationship between landscape characteristic and quantum circuit evolution with the aim to improve the efficiency
   of evolutionary search.
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE. H,Rx,Ry,NOT,CNOT,Ph quantum gates. autocorrelation, information content, partial information
   content,
%@ 0-7803-7804-0

%A Andr{\'e} Leier
%T Evolution of Quantum Algorithms using Genetic Programming
%R Ph.D. Thesis
%D 2004
%I 
%I Dortmund University
%C Germany
%K genetic algorithms, genetic programming, Quantum Computing, Quantum Circuits, Evolutionary Circuit Design
%U http://hdl.handle.net/2003/2745
%X Quantenalgorithmen sind hochgradig unintuitiv und einsetzbare Quantenrechner sind (noch) nicht verf\"ugbar. Dies erschwert den manuellen Entwurf von Quantenalgorithmen und
   motiviert die Suche nach Techniken zum computerunterst\"utzten bzw. automatischen Entwurf. Simulationen von Quantenschaltkreisen (QS) auf konventionellen Rechnern sind aber
   leider sehr rechenintensiv. Aufgrund der (in der Anzahl der Qubits) exponentiell anwachsenden Kosten ist nur eine Simulation kleiner Quantensysteme (mit wenig Qubits)
   akzeptabel. Zudem sind die Suchr\"aume quasi beliebig gro\ss, worin wohl auch begr\"undet liegt, warum der evolution\"are Ansatz bislang nicht zu einem Durchbruch in der
   Entwicklung neuer Quantenalgorithmen f\"uhrte. Zum gegenw\"artigen Zeitpunkt muss man sich daher mit der Evolution bekannter (black-box) Quantenalgorithmen begn\"ugen. Die
   vorliegende Arbeit pr\"asentiert empirische Ergebnisse zur Evolution von QS mit Hilfe des Genetischen Programmierens. F\"ur die Experimente wurde ein effizienter
   Quantensimulator entwickelt, der in einem umgebenden GP-System zum Einsatz kommt. Dabei wurden zun\"achst linear-tree (erlaubt Zwischenmessungen), sp\"ater auch rein
   lineare Genom-Strukturen f\"ur die Programmrepr\"asentation verwendet. Die Evolvierbarkeit von QS wird an Hand von Experimenten f\"ur kleine Probleminstanzen des 1-SAT
   Problems und des Deutsch-Jozsa Problems gezeigt. Die Experimente best\"atigen, dass die Evolution von QS nur f\"ur gen\"ugend kleine Probleminstanzen praktisch machbar ist.
   Vor diesem Hintergrund ist gerade die Skalierbarkeit von QS besonders wichtig. Es wird gezeigt, dass skalierbare QS bis zu einem gewissen Grad evolviert werden konnen.
   Dabei wird ein allgemeiner Schaltkreis von den evolvierten Losungen f\"ur sehr kleine Probleminstanzen abgeleitet. Die Methode der 'Vorevolution', so belegen weitere
   Experimente, ist f\"ur die Evolution skalierbarer QS wirksam einsetzbar. Bei dieser Methode werden der Startpopulation einer Probleminstanz bereits evolvierte Losungen
   einer kleineren Probleminstanz 'eingeimpft'. Ferner werden Fitnesslandschaften untersucht und ein Vergleich von Selektionsstrategien angestellt, mit dem Ziel, durch diese
   Erkenntnisse zu einer Effizienzsteigerung der evolution\"aren Suche zu gelangen. Dabei ist ein beachtenswertes Resultat, dass die Verwendung eines Crossover Operators der
   Evolution von QS eher schadet, als ihr n\"utzt.
%O Fachbereich 4; Universit\"at Dortmund
%8 July ~21

%A Andr\'{e} Leier
%A P. Dwight Kuo
%A Wolfgang Banzhaf
%A Kevin Burrage
%T Evolving noisy oscillatory dynamics in genetic regulatory networks
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 290--299
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050290.pdf
%X We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our
   representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications.
   The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results
   show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006
%@ 3-540-33143-3

%A Michal Lemczyk
%A Malcolm Heywood
%T Pareto-coevolutionary genetic programming classifier
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 945--946
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming: Poster, coevolution, evolutionary computation, parameter learning, subset selection, supervised learning
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p945.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Michal Lemczyk
%A Malcolm I. Heywood
%T Training Binary GP Classifiers Efficiently: a Pareto-coevolutionary Approach
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 229--240
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In
   particular, the coevolutionary aspect of the IPCA algorithm is used to simultaneously evolve a subset of the training data that provides distinctions between candidate
   classifiers. Empirical results indicate that such a scheme significantly reduces the computational overhead of fitness evaluation on large binary classification data sets.
   Moreover, unlike the performance of GP classifiers trained using alternative subset selection algorithms, the proposed Pareto-coevolutionary approach is able to match or
   better the classification performance of GP trained over all training exemplars. Finally, problem decomposition appears as a natural consequence of assuming a Pareto model
   for coevolution. In order to make use of this property a voting scheme is used to integrate the results of all classifiers from the Pareto front, post training.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Alexandre Lemieux
%A Christian Gagn\'e
%A Marc Parizeau
%T Genetical Engineering of Handwriting Representations
%B Eighth International Workshop on Frontiers in Handwriting Recognition 2002 (IWFHR 2002)
%D 2002
%P 145--150
%I 
%C Niagara-on-the-Lake, Ontario, Canada
%K genetic algorithms, genetic programming, handwriting recognition, feature extraction, fuzzy set theory, handwritten character recognition, pattern classification, Unipen
   database, character frame decomposition, feature sets, floating regions, fuzzy operators, fuzzy-regional representation, handwriting representations, handwritten character
   recognition, region base representation,
%U http://citeseer.ist.psu.edu/509026.html
%X This paper presents experiments with genetically engineered feature sets for recognition of on-line handwritten characters. These representations stem from a nondescript
   decomposition of the character frame into a set of rectangular regions, possibly overlapping, each represented by a vector of 7 fuzzy variables. Efficient new feature sets
   are automatically discovered using genetic programming techniques. Recognition experiments conducted on isolated digits of the Unipen database yield improvements of more
   than 3percent over a previously manually designed representation where region positions and sizes were fixed.
%8 August  6-8

%A Tom Lenaerts
%A Bernard Manderick
%T Building a Genetic Programming Framework: The Added-Value of Design Patterns
%B Proceedings of the First European Workshop on Genetic Programming
%S LNCS
%E Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty
%V 1391
%D 1998
%P 196--208
%I Springer-Verlag Berlin
%C Paris
%K genetic algorithms, genetic programming
%X A large body of public domain software exists which addresses standard implementations of the Genetic Programming paradigm. Nevertheless researchers are frequently
   confronted with the lack of flexibility and reusability of the tools when for instance one wants to alter the genotype representation or the overall behavior of the
   evolutionary process. This paper addresses the construction of a object-oriented Genetic Programming framework using on design patterns to increase its flexibility and
   reusability.
%8 14-15  April
%Z EuroGP'98
%@ 3-540-64360-5

%A Jack Lenahan
%T The Synthesis of Evolutionary Algorithms and Quantum Computing
%J SIGEVOlution
%V 1
%N 3
%D 2006
%P 36--39
%I 
%K genetic algorithms, genetic programming
%U http://www.sigevolution.org/2006/03/issue.pdf
%X The purpose of this letter is to describe the beneficial relationship between evolutionary and quantum computational models. Evolutionary computation has proved successful
   in achieving human competitive results [1] in varied disciplines including the evolution of quantum algorithms. Similarly, applying quantum computing models to evolutionary
   computation has also been shown to exceed the capabilities of traditional evolutionary algorithms in selected cases. The emerging fusion of evolutionary computation with
   quantum computing models is simply elegant.
%8 September
%Z Staff Scientist, Imagine-One Corporation

%A Terje Lensberg
%T A Genetic Programming Experiment on Investment Behavior under Knightian Uncertainty
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 231--239
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Lensberg_1997_GPeibuku.pdf
%8 13-16  July
%Z See \citeLensberg:1999:JEDC GP-97

%A Terje Lensberg
%T Investment behavior under Knightian uncertainty - An evolutionary approach
%J Journal of Economic Dynamics and Control
%V 23
%N 9-10
%D 1999
%P 1587--1604
%I 
%K genetic algorithms, genetic programming, Knightian uncertainty, Bayesian rationality
%U http://www.sciencedirect.com/science/article/B6V85-3Y9RKX5-G/2/6c6369b7934fdea4d1937c49a35ada38
%X The 'as if ' view of economic rationality defends the profit maximisation hypothesis by pointing out that only those firms who act as if they maximize profits can survive
   in the long run. Recently, the problem of arriving at a logically consistent definition of rational behavior in games has shown that one must sometimes study explicitly the
   evolutionary processes that form the basis of this view. The purpose of this paper is to investigate the usefulness of genetic programming as a tool for generating
   hypotheses about rational behavior in situations where explicit maximization is not well defined. We use an investment decision problem with Knightian uncertainty as a
   borderline test case, and show that when the artificial agents receive the same information about the unknown probability distributions, they develop behavior rules as if
   they were expected utility maximisers with Bayesian learning rules.
%Z JEL classification codes: B41; C63; D83

%A Terje Lensberg
%A Aasmund Eilifsen
%A Thomas E. McKee
%T Bankruptcy theory development and classification via genetic programming
%J European Journal of Operational Research
%V 169
%N 2
%D 2006
%P 677--697
%I 
%K genetic algorithms, genetic programming, Going concern, Bankruptcy, Fraud risk
%U http://www.sciencedirect.com/science/article/B6VCT-4D5P6FY-8/2/b08574948226f93f16a6013ffef1cd19
%X Bankruptcy is a highly significant worldwide problem with high social costs. Traditional bankruptcy risk models have been criticised for falling short with respect to
   bankruptcy theory building due to either modelling assumptions or model complexity. Genetic programming minimises the amount of a priori structure that is associated with
   traditional functional forms and statistical selection procedures, but still produces easily understandable and implementable models. Genetic programming was used to
   analyse 28 potential bankruptcy variables found to be significant in multiple prior research studies, including 10 fraud risk factors. Data was taken from a sample of 422
   bankrupt and non-bankrupt Norwegian companies for the period 1993-1998. Six variables were determined to be significant. A genetic programming model was developed for the
   six variables from an expanded sample of 1136 bankrupt and non-bankrupt Norwegian companies. The model was 81% accurate on a validation sample, slightly better than prior
   genetic programming research on US public companies, and statistically significantly better than the 77% accuracy of a traditional logit model developed using the same
   variables and data. The most significant variable in the final model was the prior auditor opinion, thus validating the information value of the auditor's report. The model
   provides insight into the complex interaction of bankruptcy related factors, especially the effect of company size. The results suggest that accounting information,
   including the auditor's evaluation of it, is more important for larger than smaller firms. It also suggests that for small firms the most important information is liquidity
   and non-accounting information. The genetic programming model relationships developed in this study also support prior bankruptcy research, including the finding that
   company size decreases bankruptcy risk when profits are positive. It also confirms that very high profit levels are associated with increased bankruptcy risk even for large
   companies an association that may be reflecting the potential for management to be "Cooking the Books".
%8 1  March

%A Terje Lensberg
%A Klaus Reiner Schenk-Hoppe
%T On the Evolution of Investment Strategies and the Kelly Rule A Darwinian Approach
%J Review of Finance
%V 11
%N 1
%D 2007
%P 25--50
%I 
%K genetic algorithms, genetic programming, Evolutionary finance, portfolio choice
%U http://www.nccr-finrisk.unizh.ch/media/pdf/RoF07_Vol11_pages25_50.pdf
%X This paper complements theoretical studies on the Kelly rule in evolutionary finance by studying a Darwinian model of selection and reproduction in which the diversity of
   investment strategies is maintained through genetic programming.We find that investment strategies which optimise long-term performance can emerge in markets populated by
   unsophisticated investors. Regardless whether the market is complete or incomplete and whether states are i.i.d. or Markov, the Kelly rule is obtained as the asymptotic
   outcome. With price-dependent rather than just state-dependent investment strategies, the market portfolio plays an important role as a protection against severe losses in
   volatile markets
%Z http://www.revfin.org/

%A Brian Lent
%T Evolution of Trade Strategies using Genetic Algorithms and Genetic Programming
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 87--98
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 Decemeber
%Z This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic Programming" (Computer
   Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html

%A Kian Fai Leong
%T Genetically Solving a Rubik's Cube
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 58--67
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Patricio Lerena
%A Michele Courant
%T Complexity in Mate Choice
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1446
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-034.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jim Lesko
%T Building a Better Wumpuus Hunter: Evaluating Memory in the World of the Wumpus Using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 115--121
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Malcolm Lett
%A Mengjie Zhang
%T New Fitness Functions in Genetic Programming for Object Detection
%R Technical Report CS-TR-04-12
%D 2004
%I 
%I Computer Science, Victoria University of Wellington
%C New Zealand
%K genetic algorithms, genetic programming
%U http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-04-12.abs.html
%X Object detection is an important field of research in computer vision which genetic programming has been applied to recently. This paper describes two new fitness functions
   in genetic programming for object detection. Both fitness functions are based on recall and precision of genetic programs. The first is a tolerance based fitness function
   and the second is a weighted fitness function. The merits and effectiveness of the two fitness function are discussed. The two fitness functions are examined and compared
   on three object detection problems of increasing difficulty. The results suggest that both fitness functions perform very well on the relatively easy problem, the weighted
   fitness function outperforms the tolerance based fitness function on the relatively difficult problems.

%A Malcolm Lett
%A Mengjie Zhang
%T New Fitness Functions in Genetic Programming for Object Detection
%B Proceeding of Image and Vision Computing International Conference
%E David Pairman and Heather North and Stephen McNeill
%D 2004
%P 441--446
%I Lincoln, Landcare Research
%C Akaroa, New Zealand
%K genetic algorithms, genetic programming, object detection, object localisation, fitness function
%X Object detection is an important field of research in computer vision which genetic programming has been applied to recently. This paper describes two new fitness functions
   in genetic programming for object detection. Both fitness functions are based on recall and precision of genetic programs. The first is a tolerance based fitness function
   and the second is a weighted fitness function. The merits and effectiveness of the two fitness function are discussed. The two fitness functions are examined and compared
   on three object detection problems of increasing dificulty. The results suggest that both fitness functions perform very well on the relatively easy problem, the weighted
   fitness function outperforms the tolerance based fitness function on the relatively dificult problems.
%8 November
%Z Fri, 02 Jun 2006 17:03:20 +0800 IVCNZ

%A Kwong Sak Leung
%A Kin Hong Lee
%A Sin Man Cheang
%T Genetic Parallel Programming - Evolving Linear Machine Codes on a Multiple-ALU Processor
%B Proceedings of International Conference on Artificial Intelligence in Engineering and Technology - ICAIET 2002
%E Sazali Yaacob and R. Nagarajan and Ali Chekima
%D 2002
%P 207--213
%I Universiti Malaysia Sabah
%I School of Engineering and Information Technology, Universiti Malaysia Sabah
%K genetic algorithms, genetic programming
%X Genetic Programming (GP) is a robust method in Evolutionary Computation. There are two main streams in GP, namely, Tree-based GP (TGP) and Linear GP (LGP). TGP evolves
   programs represented in tree structure. LGP evolves sequential programs directly. LGP suffers from inflexibility while TGP suffers from inefficiency. This paper proposes a
   novel framework of an integrated system called Genetic Parallel Programming (GPP) for evolving optimal parallel programs by LGP. The core of the GPP consists of a Multi-ALU
   Processor (MAP) and an Evolution Engine (EE). The MAP uses Multiple Instruction streams Multiple Data streams (MIMD) architecture. The EE uses a two-phase evolutionary
   approach and a new GP operation to swap sub-instructions in a parallel program. Three experiments (i.e. Cubic function, Sextic function and Artificial Ant - Santa Fe Trail)
   are given as examples to show that GPP could discover novel parallel programs that fully use the processor's parallelism. The GPP opens up an entire new opportunity for
   solving problems with appropriate parallel architecture and learning optimal programs/algorithms automatically.
%8 June
%@ 983-2188-92-X

%A Kwong Sak Leung
%A Kin Hong Lee
%A Sin Man Cheang
%T Evolving Parallel Machine Programs for a Multi-ALU Processor
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 1703--1708
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%X a novel Genetic Parallel Programming (GPP) paradigm for evolving optimal parallel programs running on a multi-ALU processor by Linear Genetic Programming. GPP uses a
   two-phase evolution approach. It evolves completely correct solution programs in the first phase. Then it optimizes execution speeds of solution programs in the second
   phase. Besides, GPP also employs a new genetic operation that swaps sub-instructions of a solution program. Three experiments (Sextic, Fibonacci and Factorial) are given as
   examples to show that GPP could discover novel parallel programs that fully use the processor's parallelism.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Kwong Sak Leung
%A Kin Hong Lee
%A Sin Man Cheang
%T Parallel Programs are More Evolvable than Sequential Programs
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 107--118
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=107
%X This paper presents a novel phenomenon of the Genetic Parallel Programming (GPP) paradigm - the GPP accelerating phenomenon. GPP is a novel Linear Genetic Programming
   representation for evolving parallel programs running on a Multi-ALU Processor (MAP). We carried out a series of experiments on GPP with different number of ALUs. We
   observed that parallel programs are more evolvable than sequential programs. For example, in the Fibonacci sequence regression experiment, evolving a 1-ALU sequential
   program requires 51 times on average of the computational effort of an 8-ALU parallel program. This paper presents three benchmark problems to show that the GPP can
   accelerate evolution of parallel programs. Due to the accelerating evolution phenomenon of GPP over sequential program evolution, we could increase the normal GP's
   evolution efficiency by evolving a parallel program by GPP and if there is a need, the evolved parallel program can be translated into a sequential program so that it can
   run on conventional hardware.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A James R. Levenick
%T Swappers: Introns promote flexibility, diversity and invention
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 361--368
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.willamette.edu/~levenick/swappers.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Felipe Hoppe Levin
%A Carlos A. Heuser
%T Using Genetic Programming to Evaluate the Impact of Social Network Analysis in Author Name Disambiguation
%B Proceedings of the 4th Alberto Mendelzon International Workshop on Foundations of Data Management, Buenos Aires, Argentina, May 17-20, 2010
%S CEUR Workshop Proceedings
%E Alberto H. F. Laender and Laks V. S. Lakshmanan
%V 619
%D 2010
%I CEUR-WS.org
%K genetic algorithms, genetic programming
%U http://ceur-ws.org/Vol-619/paper2.pdf

%A John Levine
%A David Humphreys
%T Learning Action Strategies for Planning Domains using Genetic Programming
%B Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM
%S LNCS
%E G\"unther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and
   Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf
%V 2611
%D 2003
%P 684--695
%I Springer-Verlag Berlin
%I EvoNet
%C University of Essex, England, UK
%K genetic algorithms, genetic programming, evolutionary computation, applications
%U http://citeseer.ist.psu.edu/569259.html
%X There are many different approaches to solving planning problems, one of which is the use of domain specific control knowledge to help guide a domain independent search
   algorithm. This paper presents L2Plan which represents this control knowledge as an ordered set of control rules, called a policy, and learns using genetic programming. The
   genetic program's crossover and mutation operators are augmented by a simple local search. L2Plan was tested on both the blocks world and briefcase domains. In both
   domains, L2Plan was able to produce policies that solved all the test problems and which outperformed the hand-coded policies written by the authors.
%8 14-16  April
%Z EvoWorkshops2003

%A Jeremy R. Levitt
%T The Genetic Algorithm applied to Gate Sizing
%B Genetic Algorithms and Genetic Programming at Stanford 1995
%E John R. Koza
%D 1995
%P 191--198
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 11  Decemeber
%Z part of \citekoza:1995:gagp
%@ 0-18-195720-5

%A T. L. Lew
%A A. B. Spencer
%A F. Scarpa
%A K. Worden
%A A. Rutherford
%A F. Hemez
%T Identification of response surface models using genetic programming
%J Mechanical Systems and Signal Processing
%V 20
%N 8
%D 2006
%P 1819--1831
%I 
%K genetic algorithms, genetic programming, Surrogate/replacement model, Response surface models, Symbolic regression
%X There is a move in modern research in Structural Dynamics towards analysing the inherent uncertainty in a given problem. This may be quantifying or fusing uncertainty
   models, or can be propagation of uncertainty through a system or calculation. If the system of interest is represented by, e.g. a large Finite Element (FE) model the large
   number of computations involved can rule out many approaches due to the expense of carrying out many runs. One way of circumnavigating this problem is to replace the true
   system by an approximate surrogate/replacement model, which is fast-running compared to the original. In traditional approaches using response surfaces a simple
   least-squares multinomial model is often adopted. The objective of this paper is to extend the class of possible models considerably by carrying out a general symbolic
   regression using a Genetic Programming approach. The approach is demonstrated on both univariate and multivariate problems with both computational and experimental data.
%8 November

%A Daniel R. Lewin
%A Sivan Lachman-Shalem
%A Benyamin Grosman
%T The role of process system engineering (PSE) in integrated circuit (IC) manufacturing
%J Control Engineering Practice
%V 15
%N 7
%D 2006
%P 793--802
%I 
%K genetic algorithms, genetic programming, Integrated circuit manufacturing, Process systems engineering, Model-based control, Process monitoring, Yield enhancement
%X The manufacture of integrated circuits is driven by a demand for faster calculation capabilities and lower costs, which will require the development of a new generation of
   manufacturing tools to increase yield productivity, spearheaded by improved measurement devices and advanced process control. The objectives of this paper are to review of
   the challenges in applying two areas of expertise in process systems engineering (PSE), namely process monitoring and control, and to motivate more academics working in PSE
   to get actively involved. PSE solutions appropriate for these challenges involve harnessing multivariate statistics, automated modelling approaches like genetic
   programming, and multivariable model-based control. The paper is illustrated with several example applications, all tested in fabrication facilities in Israel.
%O Special Issue on Award Winning Applications, 2005 IFAC World Congress
%8 July

%A Matthew Lewis
%T Aesthetic Video Filter Evolution in an Interactive Real-time Framework
%B Applications of Evolutionary Computing, EvoWorkshops2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC
%S LNCS
%E Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori
   and Franz Rothlauf and George D. Smith and Giovanni Squillero
%V 3005
%D 2004
%P 409--418
%I Springer Verlag Berlin
%C Coimbra, Portugal
%K genetic algorithms, genetic programming, evolutionary computation
%U http://accad.osu.edu/research/interactive_performance_htmls/video.pdf
%X A data-flow network-based interactive evolutionary design framework is presented which will provide a testbed for the development and exploration of a diverse range of
   visual artistic design spaces. The domain of real-time layered video filters is focused on as the primary example. The system supports both real-time video streams and
   prerecorded video. Issues of stylistic signature, GA vs. GP-based approaches, rapid tuning of fitness distributions, and desirable traits of generic evolutionary design
   systems are discussed.
%8 5-7  April
%Z EvoWorkshops2004
%@ 3-540-21378-3

%A M. Anthony Lewis
%A Andrew H. Fagg
%A Alan Solidum
%T Genetic Programming Approach to the Construction of a Neural Network Control of a Walking Robot
%B Proceedings of the 1992 IEEE InternationalConference on Robotics and Automation
%D 1992
%P 2618--2623
%I Electronica Bks
%I IEEE
%C Nice, France
%K genetic algorithms
%8 May
%Z NOT a Koza style GP but a conventional 65 bit binary string using Genesis. Evolves ANN controller for real 6 legged robot, 2 stages first uses human to score 2 neuron
   controller as ocsillator, when 50% of pop can do this nest stage evolves 6 such oscilators together to control robot. All runs produced tripod gait eventually, intermediate
   states wave gait and marked tendancy to walk backwards.

%A Tim Lewis
%A Neil Fanning
%A Gary Clemo
%T Enhancing IEEE802.11 DCF using Genetic Programming
%B IEEE 63rd Vehicular Technology Conference, VTC 2006-Spring
%V 3
%D 2006
%P 1261--1265
%I IEEE
%C Melbourne, Australia
%K genetic algorithms, genetic programming
%X This paper introduces a method of designing optimised MAC layer algorithms using genetic programming. By evolving entire algorithmic behaviour rather than optimising a set
   of values to tune a parameterised design, a much wider space of behaviour can be explored automatically. This technique is illustrated using the variation of contention
   window size that is part of the distributed coordination function of 802.11. When applied to the example of a variable sized network under saturated load this approach
   produces expressions that comfortably outperform the standard 802.11b behaviour. Also, despite being automatically generated, these solutions achieve the throughput
   performance of the best enhancements to this aspect of the protocol
%8 7-10  May
%Z Res. Lab., Toshiba Telecommun., Bristol
%@ 0-7803-9392-9

%A Tim Lewis
%A Russell J. Haines
%T Formal verification to enhance evolution of protocols
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1889--1890
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, Poster
%X This paper describes a combined evolutionary system whereby formal correctness properties are used to augment a standard functional fitness score. This system was applied
   to the problem of evolving the receive side of an alternating bit protocol, represented by a Petrinet. The fitness function combined a test for freedom from deadlock in
   addition to a functional scoring system. The efficiency gain produced nets of equal functional fitness requiring approximately one third of the number of evaluations
   required when functional tests were used alone. This result has wider applicability in any genetic programming evolution where formal correctness tests of the algorithms
   can be carried out.
%8 8-12  July
%Z Toshiba Telecommunications Laboratory, Bristol, United Kingdom GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and
   the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.

%A Tony E. Lewis
%A George D. Magoulas
%T TREAD: A new genetic programming representation aimed at research of long term complexity growth
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1339--1340
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, artificial intelligence, representations: Poster, TREAD
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1339.pdf
%X Several forms of computer program (or representation) have been proposed for Genetic Programming (GP) systems to evolve, such as linear, tree based or graph based.
   Typically, GP representations are highly effective during the initial search phases of evolution but stagnate before deep levels of complexity are acquired. A new
   representation, TREAD, is proposed to combine aspects of flow of execution and flow of data systems. The distinguishing features of TREAD are designed for researching
   improvements to the long term acquisition of novel features in GP (at the expense of the speed of the initial search if necessary). TREAD is validated on a symbolic
   regression problem and is found to be capable of successfully developing solutions through artificial evolution.
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389353 PADO \citeteller:1995:PADO. Data flow, flow of execution. PDGP.

%A Tony E. Lewis
%A George D. Magoulas
%T Strategies to minimise the total run time of cyclic graph based genetic programming with GPUs
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1379--1386
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming, cyclic cartesian genetic programming, GPU, deme, parallel interpretter
%X In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (GP) can be accelerated on one machine using currently available
   mid-range Graphics Processing Units (GPUs). Cyclic graphs pose different problems for evaluation than do trees and we describe how our CUDA based, "population parallel"
   evaluator tackles these problems. Previous similar work has focused on the evaluation alone. Unfortunately large reductions in the evaluation time do not necessarily
   translate to similar reductions in the total run time because the time spent on other tasks becomes more significant. We show that this problem can be tackled by having the
   GPU execute in parallel with the Central Processing Unit (CPU) and with memory transfers. We also demonstrate that it is possible to use a second graphics card to further
   improve the acceleration of one machine. These additional techniques are able to reduce the total run time of the GPU system by up to 2.83 times. The combined architecture
   completes a full cyclic GP run 434.61 times faster than the single-core CPU equivalent. This involves evaluating at an average rate of 3.85 billion GP operations per second
   over the course of the whole run.
%8 8-12  July
%Z twin GPU and dual CPU, superclocked GeForce 8800 GT, CUDA 2.0 GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and
   the fourteenth annual genetic programming conference (GP-2009). ACM Order Number 910092.

%A Tony E. Lewis
%A George D. Magoulas
%T Tweaking a tower of blocks leads to a TMBL: Pursuing long term fitness growth in program evolution
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%P 4465--4472
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming, TMBL, evolutionary computation, genetic programming, long term fitness growth, program evolution, tower of blocks, tweaking
   mutation behaviour learning, behavioural sciences computing, biology computing
%X If a population of programs evolved not for a few hundred generations but for a few hundred thousand or more, could it generate more interesting behaviours and tackle more
   complex problems? We begin to investigate this question by introducing Tweaking Mutation Behaviour Learning (TMBL), a form of evolutionary computation designed to meet this
   challenge. Whereas Genetic Programming (GP) typically involves creating a large pool of initial solutions and then shuffling them (with crossover and mutation) over
   relatively few generations, TMBL focuses on the cumulative acquisition of small adaptive mutations over many generations. In particular, we aim to reduce limits on long
   term fitness growth by encouraging tweaks: changes which affect behaviour without ruining the existing functionality. We use this notion to construct a standard
   representation for TMBL. We then experimentally compare TMBL against linear GP and tree-based GP and find that TMBL shows strong signs of being more conducive to the long
   term growth of fitness.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586375

%A Tony E. Lewis
%A George D. Magoulas
%T Identifying Similarities in TMBL Programs with Alignment to Quicken Their Compilation for GPUs
%B GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)
%E Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis
%D 2011
%P 447--454
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin
%K genetic algorithms, genetic programming, Artificial Intelligence, Automatic Programming, program synthesis, Performance, Tweaking Mutation Behaviour Learning (TMBL),
   Alignment, Graphics Card, Graphics Processing Unit, GPU, CUDA
%X The most impressive accelerations of Genetic Programming (GP) using the Graphics Processing Unit (GPU) have been achieved by dynamically compiling new GPU code for each
   batch of individuals to be evaluated. This approach suffers an overhead in compilation time. We aim to reduce this penalty by pre-processing the individuals to identify and
   draw out their similarities, hence reducing duplication in compilation work. We use this approach with Tweaking Mutation Behaviour Learning (TMBL), a form focused on long
   term fitness growth. For individuals of 300 instructions, the technique is found to reduce compilation time 4.817 times whilst only reducing evaluation speed by
   3.656percent.
%8 12-16  July
%Z wk307c-lewis.pdf Computational Intelligence on Consumer Games and Graphics Hardware Also known as \cite2002032 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Tony E. Lewis
%A George D. Magoulas
%T TMBL Kernels for CUDA GPUs Compile Faster Using PTX
%B GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU)
%E Simon Harding and W. B. Langdon and Man Leung Wong and Garnett Wilson and Tony Lewis
%D 2011
%P 455--462
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, GPU, Artificial Intelligence, Automatic Programming, program synthesis, Performance, Tweaking Mutation Behaviour Learning (TMBL),
   Parallel Thread EXecution (PTX), Graphics Card, Graphics Processing Unit, GPU, CUDA
%X Many of the most effective attempts to harness the power of the Graphics Processing Unit (GPU) to accelerate Genetic Programming (GP) have dynamically compiled code for
   individuals as they are to be evaluated. This approach executes very quickly on the GPU but is slow to compile, hence only vast data-sets fully reap its rewards. To reduce
   compilation time, we generate and compile code in the lower-level language PTX. We investigate this in the context of implementing Tweaking Mutation Behaviour Learning
   (TMBL) on the GPU. We find that for programs of 300 instructions, using PTX reduces the compile time 5.861 times and even increases the evaluation speed by 23.029percent
%8 12-16  July
%Z wk308d-lewis.pdf Computational Intelligence on Consumer Games and Graphics Hardware Also known as \cite2002033 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Bing Li
%A Shingo Mabu
%A Kotaro Hirasawa
%T Automatic program generation with genetic network programming using subroutines
%B Proceedings of SICE Annual Conference 2010
%D 2010
%P 3089--3094
%I 
%I SICE
%C Taipei, Taiwan
%K genetic algorithms, genetic programming, automatic program generation, evolutionary algorithm, genetic network programming, genotype phenotype mapping technology, graph
   based structure, subroutine program, automatic programming, performance evaluation, subroutines
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5602565
%X Genetic Network Programming with Automatic Program Generation (GNP-APG) is an evolutionary algorithm to generate programs. Genotype-phenotype mapping technology is
   introduced in this algorithm to create legal programs. With the help of graph-based structures of Genetic Network Programming (GNP), GNP-APG can efficiently generate robust
   programs to cope with problems. In this paper, the extended algorithm of GNP-APG is proposed which can create a hierarchy program, in other words, a program which contains
   a main function and subroutines. The proposed method works like Automatic Defined Functions (ADFs) in Genetic Programming (GP). By using subroutines, a complex program can
   be decomposed to several simple programs which are obtained more easily. Moreover, these subroutines might be called many times, which results in reducing the size of the
   program significantly. In simulations, different tile-worlds between the training phase and testing phase are used for performance evaluations and the results shows that
   GNP-APG with subroutines (GNP-APGsr) could have better performances than GNP-APG.
%8 18-21  August
%Z tile world. Grad. Sch. of Inf., Production & Syst., Waseda Univ., Fukuoka, Japan. Also known as \cite5602565

%A Bing Li
%A Shingo Mabu
%A Kotaro Hirasawa
%T Tile-world - A case study of Genetic Network Programming with automatic program generation
%B IEEE International Conference on Systems Man and Cybernetics (SMC 2010)
%D 2010
%P 2708--2715
%I 
%K genetic algorithms, genetic programming, Tile-world, automatic program generation, data mining, elevator control system, evolutionary algorithm, genetic network
   programming, genotype-phenotype mapping process, graph-based structure, performance evaluation, automatic programming, data mining, lifts
%X Genetic Network Programming (GNP) is a novel evolutionary algorithm. It has graph-based structures which is extended from Genetic Algorithm (GA) and Genetic Programming
   (GP). Up to now, GNP has been applied to many research fields such as data mining and elevator control systems. On the other hand, automatic program generation is a way to
   obtain a program without explicitly programming it, and Genetic Programming is the traditional paradigm in this field. Drawn from the inspiration of GP, GNP for Automatic
   Program Generation (GNP-APG) has been proposed. In this paper, GNP-APG is applied to the Tile-world, which is a famous test bed with dynamic and uncertain characteristics.
   GNP-APG uses a kind of genotype-phenotype mapping process to create program. The procedure of the program generation based on evolution is demonstrated in this paper. In
   simulations, different tile-worlds between the training phase and the testing phase are used for performance evaluations and the results shows that GNP-APG could have
   better performances than the conventional GNP methods.
%8 10-13  October
%Z Grad. Sch. of Inf., Production & Syst., Waseda Univ., Fukuoka, Japan. Also known as \cite5641793

%A Bing Li
%A Xianneng Li
%A Shingo Mabu
%A Kotaro Hirasawa
%T Variable Size Genetic Network Programming with Binomial Distribution
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 972--979
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, genetic network programming, Algorithms:
%X This paper proposes a different type of Genetic Network Programming (GNP) -- Variable Size Genetic Network Programming (GNPvs) with Binomial Distribution. In contrast to
   the individuals with fixed size in Standard GNP, GNPvs will change the size of the individuals and obtain the optimal size of them during evolution. The proposed method
   defines a new type of crossover to implement the new feature of GNP. The new crossover will select the number of nodes to move from each parent GNP to another parent GNP.
   The probability of selecting the number of nodes to move satisfies the binomial probability distribution. The proposed method can keep the effectiveness of crossover and
   improve the performance of GNP. In order to verify the performance of the proposed method, a well-known benchmark problem - - Tile-world is used in the simulations. The
   simulation results show the effectiveness of the proposed method.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Boyang Li
%A Han Yu
%A Zhiqi Shen
%A Chunyan Miao
%T Evolutionary organizational search
%B 8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009)
%E Carles Sierra and Cristiano Castelfranchi and Keith S. Decker and Jaime Sim\~ao Sichman
%V 2
%D 2009
%P 1329--1330
%I IFAAMAS
%C Budapest, Hungary
%K genetic algorithms, genetic programming, Poster, Experimental; Systems, Biologically-Inspired Approaches, Organizational Planning, Multi-Agent Systems
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.4762; http://www.ifaamas.org/Proceedings/aamas09/pdf/02_Extended_Abstract/D_SP_0876.pdf
%X In this paper, we proposed Evolutionary Organizational Search (EOS), an optimization method for the organizational control of multi-agent systems (MASs) based on genetic
   programming (GP). EOS adds to the existing armory a metaheuristic extension, which is capable of efficient search and less vulnerable to stalling at local optima than
   greedy methods due to its stochastic nature. EOS employs a flexible genotype which can be applied to a wide range of tree-shaped organizational forms. EOS also considers
   special constraints of MASs. A novel mutation operator, the redistribution operator, was proposed. Experiments optimizing an information retrieval system illustrated the
   adaptation of solutions generated by EOS to environmental changes.
%O Extended Abstract
%8 May  10-15
%Z Poster: http://www3.ntu.edu.sg/home/BYLI/paper/aamas_poster.pdf

%A Cuimin Li
%A Tomoyuki Hiroyasu
%A Mitsunori Miki
%T Stress-based crossover operator for structure topology optimization using small population size and variable length chromosome
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1341--1342
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, stress-based crossover, structural topology optimisation, Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1341.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389354

%A Gang Li
%A Kwong Sak Leung
%A Kin Hong Lee
%T IMGP - A Novel Instruction Matrix based Genetic Programming
%B Proceedings of the 5th International Conference on Recent Advances in Soft Computing
%E Ahmad Lotfi
%D 2004
%P 403--409
%I Nottingham Trent University
%C Nottingham, United Kingdom
%K genetic algorithms, genetic programming
%8 Decemeber  16-18
%Z http://www.rasc2004.info
%@ 1-84233-110-8

%A Gang Li
%A Kin-Hong Lee
%A Kwong-Sak Leung
%T Evolve Schema Directly Using Instruction Matrix Based Genetic Programming
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 271--280
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=271
%X This paper proposes a new architecture for tree-based genetic programming to evolve schemata directly. It uses fixed length hs-expressions to represent program trees in any
   shape, keeps schemata information in an instruction matrix, and extracts individuals from it. In order to manipulate the instruction matrix and the hs-expression, new
   genetic operators and a new fitness evaluation function are developed. The experimental results verify that its results are much better than those of the canonical genetic
   programming on the problems tested in this paper.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005
%@ 3-540-25436-6

%A Gang Li
%A Kin Hong Lee
%A Kwong-Sak Leung
%T Using Instruction Matrix Based Genetic Programming to Evolve Programs
%B Proceedings of the Second International Symposium on Computation and Intelligence, ISICA 2007
%S Lecture Notes in Computer Science
%E Lishan Kang and Yong Liu and Sanyou Y. Zeng
%V 4683
%D 2007
%P 631--640
%I Springer
%C Wuhan, China
%K genetic algorithms, genetic programming, Instruction Matrix based Genetic Programming
%X In Genetic Programming (GP), evolving tree nodes separately would be an ideal approach to reduce the huge solution space of GP. We use Instruction Matrix based Genetic
   Programming (IMGP) to evolve tree nodes separately while taking into account their interdependencies in the form of subtrees. IMGP uses an Instruction Matrix (IM) to
   maintain the statistical data of tree nodes and subtrees. IMGP extracts program trees from IM, and updates IM with the information of the extracted program trees. The
   experiments have verified that the results of IMGP are better than those the related GP algorithms in terms of the qualities of the solutions and the number of program
   evaluations.
%8 September  21-23
%Z 'no explicit population' cites \citeshan:2004:gmpe \citeShan:2003:Pewel assumes full binary tree (hs-expression) so every part of tree has a known grid position. (Limits
   three height) 'like context preserving crossover' \citeDhaeseleer:1994:cpcGP IMGP matrix shuffle. 6-parity also with sin,cos,exp,rlog. Max. Symbolic regression like PIPE?
   Fitness of primitive by grid position (rather than by neighbours in sub-tree).

%A Gang Li
%A Jin Feng Wang
%A Kin Hong Lee
%A Kwong-Sak Leung
%T Instruction-Matrix-Based Genetic Programming
%J IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
%V 38
%N 4
%D 2008
%P 1036--1049
%I 
%K genetic algorithms, genetic programming, benchmark classification problems, condition matrix, instruction-matrix-based genetic programming, multiclass classification
   problems, program trees, rule learning, tree nodes, feature extraction, learning (artificial intelligence), matrix algebra, pattern classification, trees (mathematics),
   Algorithms, Artificial Intelligence, Computer Simulation, Feedback, Models, Genetic, Models, Theoretical, Pattern Recognition, Automated, Programming, Linear, Systems
   Theory
%X In genetic programming (GP), evolving tree nodes separately would reduce the huge solution space. However, tree nodes are highly interdependent with respect to their
   fitness. In this paper, we propose a new GP framework, namely, instruction-matrix (IM)-based GP (IMGP), to handle their interactions. IMGP maintains an IM to evolve tree
   nodes and subtrees separately. IMGP extracts program trees from an IM and updates the IM with the information of the extracted program trees. As the IM actually keeps most
   of the information of the schemata of GP and evolves the schemata directly, IMGP is effective and efficient. Our experimental results on benchmark problems have verified
   that IMGP is not only better than those of canonical GP in terms of the qualities of the solutions and the number of program evaluations, but they are also better than some
   of the related GP algorithms. IMGP can also be used to evolve programs for classification problems. The classifiers obtained have higher classification accuracies than four
   other GP classification algorithms on four benchmark classification problems. The testing errors are also comparable to or better than those obtained with well-known
   classifiers. Furthermore, an extended version, called condition matrix for rule learning, has been used successfully to handle multiclass classification problems.
%8 August
%Z Also known as \cite4510842

%A Geng Li
%A Xiao-Jun Zeng
%T Bottom-Up Tree Evaluation in Tree-Based Genetic Programming
%B Advances in Swarm Intelligence, First International Conference, ICSI 2010, Beijing, China, June 12-15, 2010, Proceedings, Part I
%S Lecture Notes in Computer Science
%E Ying Tan and Yuhui Shi and Kay Chen Tan
%V 6145
%D 2010
%P 513--522
%I Springer
%K genetic algorithms, genetic programming
%U http://dx.doi.org/10.1007/978-3-642-13495-1

%A Geng Li
%A Xiao-Jun Zeng
%T Genetic programming with a norm-referenced fitness function
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1323--1330
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming
%X Not presented
%8 12-16  July
%Z Also known as \cite2001755 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Hongyan Li
%A Shan Jiang
%A Xinhua Bao
%T Application of Genetic Programming to Identifying Water-Level and Storage-Capacity Curve of the Xingxingshao Reservoir
%B Asia-Pacific Power and Energy Engineering Conference, APPEEC 2009
%D 2009
%P 1--3
%I 
%K genetic algorithms, genetic programming, Xingxingshao reservoir, reservoir flood routing programming, storage-capacity curve fitting, water-level curve, curve fitting,
   geophysics computing, level measurement, reservoirs
%X Water-level and storage-capacity curve (WSC) fitting is the foundation and key link of reservoir flood routing programming. Also, its precision directly determines the
   accuracy of flood routing. In this paper, based on the measured hydrological data, the correlations of water-level and storage-capacity are identified using genetic
   programming (GP), and the equations of water-level and storage-capacity curve are established. Then, the research results are applied to the feasibility study to enhance
   the flood limit level of Xingxingshao reservoir. And the results indicate that, compared to the measured data, the water-level and storage-capacity curve identified by GP
   has a more satisfied accuracy, which provides a fundamental guarantee for the accurate flood routing.
%8 March
%Z Also known as \cite4918182

%A Huang Li
%A Lixin Ding
%T Research on Two Stage Evolutionary Modeling Based on Gene Expression Programming
%B International Conference on Computational Intelligence and Software Engineering, CiSE 2009
%D 2009
%I 
%K genetic algorithms, genetic programming, gene expression programming, convergence analysis, expression tree encoding, linear chromosomes, ordinary differential equations
   model, two stage evolutionary modelling, convergence, differential equations, linear programming, trees (mathematics)
%X Gene expression programming is presented here for two stage evolutionary modelling. It uses character linear chromosomes composed of genes which encode expression trees.
   This feature which is different from existing algorithms allows the algorithm to perform with high efficiency when dealing with the same problem. And then, the analysis of
   convergence of this algorithm is mentioned. Finally, a numeric experiment is given to verify the efficiency of this algorithm. The results show that multiple highly precise
   ordinary differential equations (ODEs) model can be found out, and its predict values surprisingly coincide with the exact solutions.
%8 Decemeber
%Z Also known as \cite5365685

%A Jin Li
%A Edward P. K. Tsang
%T Investment Decision Making Using FGP: A Case Study
%B Proceedings of the Congress on Evolutionary Computation
%E Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala
%V 2
%D 1999
%P 1253--1259
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE
%C Mayflower Hotel, Washington D.C., USA
%K genetic algorithms, genetic programming, forecasting
%U http://citeseer.ist.psu.edu/237547.html
%X Financial investment decision making is extremely difficult due to the complexity of the domain. Many factors could influence the change of share prices. FGP (Financial
   Genetic Programming) is a genetic programming based forecasting system, which is designed to help users evaluate impact of factors and explore their interactions in
   relation to future prices. Users channel into FGP factors that they believe are relevant to the prediction. Examples of such factors may include fundamental factors such as
   "price-earning ratio", "inflation rate" or/and technical factors such as "5-days moving average", "63-days trading range breakout", etc. FGP uses the power of genetic
   programming to generate decision trees through combination of technical rules with self-adjusted thresholds. In earlier papers, we have reported how FGP used well-known
   technical analysis rules to make investment decisions. This paper tests the versatility of FGP by testing it on shorter-term investment decisions. To evaluat...
%8 6-9  July
%Z CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143
%@ 0-7803-5537-7 (Microfiche)

%A Jin Li
%T FGP: A Genetic Programming Tool for Financial Prediction
%B GECCO-99 Student Workshop
%E Una-May O'Reilly
%D 1999
%P 374
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, stock market, prediction
%U http://privatewww.essex.ac.uk/~jli/GPTool.htm
%8 13  July
%Z GECCO-99WKS Part of wu:1999:GECCOWKS

%A Jin Li
%A Edward P. K. Tsang
%T Reducing Failures in Investment Recommendations using Genetic Programming
%B Computing in Economics and Finance
%D 2000
%I 
%C Universitat Pompeu Fabra, Barcelona, Spain
%K genetic algorithms, genetic programming
%U http://ideas.repec.org/p/sce/scecf0/332.html
%X FGP (Financial Genetic Programming) is a genetic programming based system that specialises in financial forecasting. In the past, we have reported that FGP-1 (the first
   version of FGP) is capable of producing accurate predictions in a variety of data sets. It can accurately predict whether a required rate of return can be achieved within a
   user-specified period. This paper reports further development of FGP, which is motivated by realistic needs as described below: a recommendation "not to invest" is often
   less interesting than a recommendation "to invest". The former leads to no action. If it is wrong, the user loses an investment opportunity, which may not be serious if
   other investment opportunities are available. On the other hand, a recommendation to invest leads to commitment of funds. If it is wrong, the user fails to achieve the
   target rate of return. Our objective is to reduce the rate of failure when FGP recommends to invest. In this paper, we present a method of tuning the rate of failure by FGP
   to reflect the user's preference. This is achieved by introducing a novel constraint-directed fitness function to FGP. The new system, FGP-2, was extensively tested on
   historical Dow Jones Industrial Average (DJIA) Index. Trained with data from a seven-and-a-half-years period, decision trees generated by FGP-2 were tested on data from a
   three-and-a-half-years out-of-sample period. Results confirmed that one can tune the rate of failure by adjusting a constraint parameter in FGP-2. Lower failure rate can be
   achieved at the cost of missing opportunities, but without affecting the overall accuracy of the system. The decision trees generated were further analysed over three
   sub-periods with down trend, side-way trend and up trend, respectively. Consistent results were achieved. This shows the robustness of FGP-2. We believe there is scope to
   generalise the constrained fitness function method to other applications.
%8 6-8  July
%Z http://enginy.upf.es/SCE/index2.html

%A Jin Li
%T FGP: A genetic programming based tool for financial forecasting
%R Ph.D. Thesis
%D 2000
%I 
%I department of computer science, university of Essex
%C UK
%K genetic algorithms, genetic programming
%X Computers-aided financial forecasting has been made possible following continuous increase in machine power at reduced price, increasingly easy access to financial
   information, and advances in artificial intelligence (AI) techniques. In this thesis, we present a genetic programming based machine-learning tool called FGP (Financial
   Genetic Programming). We apply FGP to financial forecasting. Two versions of FGP, namely, FGP-1 and FGP-2, have been designed and implemented to address two research goals
   that we set. FGP-1 is intended to improve prediction accuracy over the predictions given. FGP-2 is aimed at improving prediction precision. Predictions are available to
   users from different sources. We investigate whether FGP-1 has the capability of improving on them by combining them together. Based on the experiments presented in this
   thesis, we conclude that FGP-1 is capable of improving the given predictions in terms of prediction accuracy. This partly attributes the capability of FGP-1 of finding
   positive interactions between the predictions given. However, caution should be excised for choosing its parameters in such applications. Improving prediction precision is
   highly demanded in financial forecasting. Our investigation is based on a set of specific prediction problems: to predict whether a required rate of return can be achieved
   within a user-specified period. In order to improve prediction precision, without affecting the overall prediction accuracy much, we invent a novel constrained fitness
   function, which is used by FGP-2. The effectiveness of FGP-2 is demonstrated and analysed in a variety of prediction tasks and data sets. The constrained fitness function
   provides the user with a handle to improve prediction precision at the price of missing opportunities. This thesis reports the utility of FGP applications to financial
   forecasting to a certain extent. As a tool, FGP aims to help users make the best use of information available to them. It may assist the user to make more reliable
   decisions that would otherwise not be achieved without it.
%8 6  October

%A Jin Li
%A Xin Yao
%A Colin Frayn
%A Habib G. Khosroshahi
%A Somak Raychaudhury
%T An Evolutionary Approach to Modeling Radial Brightness Distributions in Elliptical Galaxies
%B Parallel Problem Solving from Nature - PPSN VIII
%S LNCS
%E Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\vno Ata Kab\'an and Hans-Paul
   Schwefel
%V 3242
%D 2004
%P 591--601
%I Springer-Verlag Berlin
%C Birmingham, UK
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=591
%X A reasonably good description of the luminosity profiles of galaxies is needed as it serves as a guide towards understanding the process of galaxy formation and evolution.
   To obtain a radial brightness profile model of a galaxy, the way varies both in terms of the exact mathematical form of the function used and in terms of the algorithm used
   for parameters fitting for the function given. Traditionally, one builds such a model by means of fitting parameters for a functional form assumed beforehand. As a result,
   such a model depends crucially on the assumed functional form. In this paper we propose an approach that enables one to build profile models from data directly without
   assuming a functional form in advance by using evolutionary computation. This evolutionary approach consists of two major steps that serve two goals. The first step applies
   the technique of genetic programming with the aim of finding a promising functional form, whereas the second step takes advantage of the power of evolutionary programming
   with the aim of fitting parameters for functional forms found at the first step. The proposed evolutionary approach has been applied to modelling 18 elliptical galaxies
   profiles and its preliminary results are reported.
%8 18-22  September
%Z PPSN-VIII
%@ 3-540-23092-0

%A Jin Li
%A Xiaoli Li
%A Xin Yao
%T Cost-Sensitive Classification with Genetic Programming
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2114--2121
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~xin/papers/LiLiYaoCEC05.pdf
%X Cost-sensitive classification is an attractive topic in data mining. Although genetic programming (GP) technique has been applied to general classification, to our
   knowledge, it has not been exploited to address cost-sensitive classification in the literature, where the costs of misclassification errors are non-uniform. To investigate
   the applicability of GP to cost-sensitive classification, this paper first reviews the existing methods of cost-sensitive classification in data mining. We then apply GP to
   address cost-sensitive classification by means of two methods through: a) manipulating training data, and b) modifying the learning algorithm. In particular, a constrained
   genetic programming (CGP), a GP based cost-sensitive classifier, has been introduced in this study. CGP is capable of building decision trees to minimise not only the
   expected number of errors, but also the expected misclassification costs through a novel constraint fitness function. CGP has been tested on the heart disease dataset and
   the German credit dataset from the UCI repository. Its efficacy with respect to cost has been demonstrated by comparisons with noncost-sensitive learning methods and
   cost-sensitive learning methods in terms of the costs.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Jin Li
%A Sope Taiwo
%T Enhancing Financial Decision Making Using Multi-Objective Financial Genetic Programming
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 7935--7942
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/MOFGP-JinSope.pdf
%X a multi-objective genetic programming based financial forecasting system, MOFGP. MOFGP is built upon our previous decision-making tool, FGP (Financial Genetic Programming)
   [1]-[5]. By taking advantage of the techniques of multi-objective evolutionary algorithms (MOEAs), MOFGP enhances FGP in a number of ways. Firstly, MOFGP is faster in
   obtaining the same quantity of diverse forecasting models optimised with respect to multiple conflicting objectives. This is attributed to the inherent property of MOEAs,
   i.e., a set of Pareto front solutions can be obtained in a single execution of its algorithm. Secondly, MOFGP is friendlier and simpler from the user's perspective. It is
   friendlier because it eliminates a number of user-supplied parameters previously required by FGP. Consequently, it becomes simpler as the user no longer needs to have a
   priori domain knowledge required for the proper use of those parameters. Finally, compared with FGP, which exploits a canonical single-objective approach to tackle a
   multi-criterion financial forecasting problem, MOFGP demonstrates the above advantages without seriously sacrificing its forecasting performance, although it suffers from
   an inadequate generalisation capability over the test data in this study. Given its strengths and weaknesses, MOFGP could be employed as a useful starting investigative
   tool for financial decision making.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Jin Li
%A Zhu Shi
%A Xiaoli Li
%T Genetic Programming with Wavelet-Based Indicators for Financial Forecasting
%J Transactions of the Institute of Measurement and Control
%V 28
%N 3
%D 2006
%P 285--297
%I 
%K genetic algorithms, genetic programming, wavelet analysis, financial forecasting
%U http://tim.sagepub.com/content/vol28/issue3/
%X Wavelet analysis, as a promising technique, has been used to approach numerous problems in science and engineering. Recent years have witnessed its novel application in
   economic and finance. This paper is to investigate whether features (or indicators) extracted using the wavelet analysis technique could improve financial forecasting by
   means of Financial Genetic Programming (FGP), a genetic programming based forecasting tool (i.e., Li, 2001). More specifically, to predict whether Down Jones Industrial
   Average (DJIA) Index will rise by 2.2per cent or more within the next 21 trading days, we first extract some indicators based on wavelet coefficients of the DJIA time
   series using a discrete wavelet transform; we then feed FGP with those wavelet-based indicators to generate decision trees and make predictions. By comparison with the
   prediction performance of our previous study (i.e., Li and Tsang, 2000), it is suggested that wavelet analysis be capable of bringing in promising indicators, and improving
   the forecasting performance of FGP.
%8 August

%A Ju Hui Li
%A Meng Hiot Lim
%A Qi Cao
%T Evolvable Fuzzy Hardware for Real-time Embedded Control in Packet Switching
%B Evolvable Machines: Theory \& Practice
%S Studies in Fuzziness and Soft Computing
%E Nadia Nedjah and Luiza de Macedo Mourelle
%V 161
%D 2004
%P 205--227
%I Springer
%C Berlin
%K genetic algorithms, evolvable hardware
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html
%O 9
%Z Springer says published in 2005 but available Nov 2004
%@ 3-540-22905-1

%A L. Li
%A G. Q. Huang
%A Stephen T. Newman
%T Interweaving genetic programming and genetic algorithm for structural and parametric optimization in adaptive platform product customization
%J Robotics and Computer-Integrated Manufacturing
%V 23
%N 6
%D 2007
%P 650--658
%I 
%K genetic algorithms, genetic programming, Product platform, Platform product customisation, GBOM, Evolutionary algorithm
%X An adaptive product platform offers high customisability for generating feasible product variants for customer requirements. Customisation takes place not only to product
   platform structure but also to its relevant parameters. Structural and parametric optimisation processes are interwoven with each other to achieve the total optimality.
   This paper presents an evolutionary method dealing with interwoven structural and parametric optimisation of adaptive platform product customisation. The method combines
   genetic programming and genetic algorithm for handling structural and parametric optimization, respectively. Efficient genetic representation and operation schemes are
   carefully adapted. While designing these schemes, features specific to structural and parameter customisation are considered for the simplification of platform product
   management. The experimental results show that the performance of the proposed algorithm outperforms that of the tandem evolutionary algorithm in which a genetic algorithm
   for parametric optimisation is totally nested in a genetic programming for structural optimisation.
%O 16th International Conference on Flexible Automation and Intelligent Manufacturing
%8 Decemeber

%A Li Li
%A Wei Jiang
%A Xia Li
%A Kathy L. Moser
%A Zheng Guo
%A Lei Du
%A Qiuju Wang
%A Eric J. Topol
%A Qing Wang
%A Shaoqi Rao
%T A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset
%J Genomics
%V 85
%N 1
%D 2005
%P 16--23
%I 
%K genetic algorithms, genetic programming, Feature gene selection, Support vector machine, DNA Microarray
%X Development of a robust and efficient approach for extracting useful information from microarray data continues to be a significant and challenging task. Microarray data
   are characterised by a high dimension, high signal-to-noise ratio, and high correlations between genes, but with a relatively small sample size. Current methods for
   dimensional reduction can further be improved for the scenario of the presence of a single (or a few) high influential gene(s) in which its effect in the feature subset
   would prohibit inclusion of other important genes. We have formalised a robust gene selection approach based on a hybrid between genetic algorithm and support vector
   machine. The major goal of this hybridisation was to exploit fully their respective merits (e.g., robustness to the size of solution space and capability of handling a very
   large dimension of feature genes) for identification of key feature genes (or molecular signatures) for a complex biological phenotype. We have applied the approach to the
   microarray data of diffuse large B cell lymphoma to demonstrate its behaviours and properties for mining the high-dimension data of genome-wide gene expression profiles.
   The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99percent) for prediction of independent microarray samples in comparisons with
   marginal filters and a hybrid between genetic algorithm and K nearest neighbours.
%8 January
%Z department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China college of Biological Science and Technology, Tongji University, Shanghai
   200092, People's Republic of China department of Computer Science, Harbin Institute of Technology, Harbin 150080, People's Republic of China department of Medicine,
   Institute of Human Genetics, University of Minnesota, Minneapolis?St. Paul, MN 55455, USA department of Otorhinolaryngology/Head and Neck Surgery, Institute of
   Otolaryngology, Chinese PLA General Hospital, Beijing 100853, People's Republic of China department of Cardiovascular Medicine and Department of Molecular Cardiology, The
   Cleveland Clinic Foundation, Cleveland, OH 44195, USA http://www.elsevier.com/wps/find/journaldescription.cws_home/622838/description#description

%A Kangshun Li
%A Weifeng Pan
%A Wensheng Zhang
%A Zhangxin Chen
%T Automatic Modeling of a Novel Gene Expression Programming Based on Statistical Analysis and Critical Velocity
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 169--173
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, gene expression programming
%X The basic principle of GEP is briefly introduced. And considering the defects of classic GEP such as lack of variety, the problem of convergence and blind searching without
   learning mechanism, a novel GEP based on statistical analysis and stagnancy velocity is proposed (called AMACGEP). It mainly has the following characteristics: First,
   improve the initial population by statistic analysis of repeated bodies. Second, introduce the concept of stagnancy velocity to adjust the searching space, evolution
   velocity, the diversity of individuals and the accuracy of prediction. Third, introduce dynamic mutation operator to improve the diversity of individuals and the velocity
   of convergence. Compared with other methods like traditional methods, methods of neural network, classic GEP and other improved GEPs in automatic modelling of complex
   function, the simulation results show that the AMACGEP set up by this paper is better.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Kangshun Li
%A Jiusheng Liang
%A Wensheng Zhang
%A Feng Wang
%T A New Method of Evolving Digital Circuit Based on Gene Expression Programming
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, gene expression programming
%X Evolutionary Hardware (EHW) is a new focus in recent research work. The new method of design hardware is combined evolution algorithm with programmable logic device.
   Optimization digital circuit is a main domain of EHW. The algebra way and Karnaugh map way are the traditionary methods, but they will meet trouble with the large scale
   ones to get optimisation structure of circuit. This paper proposes a new method (GEP) to optimise the complex digital circuit and designs a new function fitness. The
   experiments demonstrate the GEP is not only fast convergence but also optimisation large circuit. It conquers the slow convergence even not convergence of the traditionary
   method. The GEP algorithm is simpler and more efficient than the traditional ones.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Liangmin Li
%A Liangsheng Qu
%T Fault Detection Based on Genetic Programming and Support Vector Machines
%J Journal of Xi'an Jiaotong University
%V 38
%N 3
%D 2004
%I 
%K genetic algorithms, genetic programming, fault detection, support vector machines, SVM, rolling bearing
%U http://unit.xjtu.edu.cn/unit/xb/zrb/04/0403/xbe0405.html
%X A new classification model based on genetic programming and support vector machine for machine fault diagnosis was proposed.In this model,genetic programming constructs and
   selects features from original feature set.The selected features form input feature set of support vector machines.Then multi-class support vector machine is applied to
   detect abnormal cases from normal ones.Experiments of rolling bearings fault detection are carried out to test the performance of this model.Practical results show that the
   compound features generated by genetic programming possess better recognition ability than the initial time domain features do.The classification ability of multi-class
   support vector machine is improved after feature extraction and selection.
%8 March
%Z http://unit.xjtu.edu.cn/unit/xb/zrb/ School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049,China

%A Ming Li
%A Paul M. B. Vitanyi
%T Inductive Reasoning and Kolmogorov Complexity
%J Journal of Computer and System Sciences
%V 44
%N 2
%D 1992
%P 343--384
%I Academic Press
%8 April
%Z April's issue was devoted to proceedings of the fourth annual conference on Structure in Complexity Theory, IEEE Computer Society, held in University or Oregon, 19-22 June
   1989.

%A Ming Li
%T Computational Machine Learning in Theory and Praxis
%R NeuroCOLT technical report series NC-TR-95-052
%D 1995
%I 
%I Royal Holloway and Bedford New College, University of London
%C Surrey, UK
%K ML
%U http://www.neurocolt.com/abs/1995/../../tech_reps/1995/nc-tr-95-052.ps.gz
%X In the last few decades a computational approach to machine learning has emerged based on paradigms from recursion theory and the theory of computation. Such ideas include
   learning in the limit, learning by enumeration, and probably approximately correct (pac) learning. These models usually are not suitable in practical situations. In
   contrast, statistics based inference methods have enjoyed a long and distinguished career. Currently, Bayesian reasoning in various forms, minimum message length (MML) and
   minimum description length (MDL), are widely applied approaches. They are the tools to use with particular machine learning praxis such as simulated annealing, genetic
   algorithms, genetic programming, artificial neural networks, and the like. These statistical inference methods select the hypothesis which minimizes the sum of the length
   of the description of the hypothesis (also called `model') and the length of the description of the data relative to the hypothesis. It app...
%O The Pennsylvania State University CiteSeer Archives
%8 September
%Z not a CP paper

%A Piji Li
%A Jun Ma
%T Learning to rank for web image retrieval based on genetic programming
%B 2nd IEEE International Conference on Broadband Network Multimedia Technology, IC-BNMT '09
%D 2009
%P 137--142
%I 
%K genetic algorithms, genetic programming, WIRank, Web image retrieval, graph theory, image-based feature, information retrieval system, link structure analysis, ranking,
   temporal information, text information, Internet, graph theory, image retrieval, text analysis
%X Ranking is a crucial task in information retrieval systems. This paper proposes a novel ranking model named WIRank, which employs a layered genetic programming architecture
   to automatically generate an effective ranking function, by combining various types of evidences in Web image retrieval, including text information, image-based features
   and link structure analysis. This paper also introduces a new significant feature to represent images: Temporal information, which is rarely used in the current information
   retrieval systems and applications. The experimental results show that the proposed algorithms are capable of learning effective ranking functions for Web image retrieval.
   Significant improvement in relevancy obtained, in comparison to some other well-known ranking techniques, in terms of MAP, NDCG@n and D@n.
%8 October
%Z Also known as \cite5348465

%A Qingyong Li
%A Hong Hu
%A Zhongzhi Shi
%T Semantic feature extraction using genetic programming in image retrieval
%B Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
%V 1
%D 2004
%P 648--651
%I 
%K genetic algorithms, genetic programming, content-based retrieval, feature extraction, image retrieval, image texture, visual perception Tamura texture model, content based
   image retrieval, human visual perception, linguistic expression, semantic feature extraction, texture extraction, visual feature extraction
%X One of the big hurdles facing current content-based image retrieval (CBIR) is the semantic gap between the low-level visual features and the high-level semantic features.
   We proposed an approach to describe and extract the global texture semantic features. According to the Tamura texture model, we use the linguistic variable to describe the
   texture semantics, so it becomes possible to depict the image in linguistic expression such as coarse, fine. We use genetic programming to simulate the human visual
   perception and extract the semantic features value. Our experiments show that the semantic features have good accordance with the human perception, and also have good
   retrieval performance. In some extent, our approach bridges the semantic gap in CBIR.
%8 August
%Z also known as \cite1334248

%A Qu Li
%A Min Yao
%A Weihong Wang
%A Xiaohong Cheng
%T Dynamic Split-Point Selection Method for Decision Tree Evolved by Gene Expression Programming
%B 2009 IEEE Congress on Evolutionary Computation
%E Andy Tyrrell
%D 2009
%P 736--740
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Trondheim, Norway
%K genetic algorithms, genetic programming, gene expression programming, C4.5, classification accuracy, decision tree, dynamic split-point selection method, evolutionary
   computation theory, heuristic method, optimal split points, tree splitting, data handling, decision trees
%X Gene Expression Programming(GEP) is a kind of heuristic method based on evolutionary computation theory. GEP has been used to evolve parsimonious decision tree with high
   accuracy comparable to C4.5. However, the basic GEPDT do not distinguish different attributes, whose boundaries are usually quite different. The basic GEPDT often fails to
   find optimal split points for some branches and thus handicapped the learning tasks. In this paper, we proposed a simple but effective Split-point Selection Method for GEP
   evolved decision tree to improve the performance of tree splitting and classification accuracy. Results show that our method can find better generalized ability rules and
   it is especially suitable for difficult problems with many attributes in different boundaries.
%8 18-21  May
%Z CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as \cite4983018

%A Maolin Li
%A Lin Liang
%A Sunan Wang
%A Xiaohu Li
%T Feature generation in fault diagnosis based on immune programming
%B 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)
%D 2009
%P 183--187
%I 
%K genetic algorithms, genetic programming, affinity function, antibody representation, clonal selection optimal algorithm, fault diagnosis, feature generation, immune
   programming, polynomial expressions, premature convergence, symptom feature discovery, tree-like structure, fault diagnosis, pattern recognition, polynomials
%X In the symptom feature discovery, genetic programming has the shortage of premature convergence. So a new feature generation method based on immune programming is put
   forward. The new features are constructed by polynomial expressions of the original features. And then, with the immune operators such as antibody representation and
   mutation of tree-like structure, affinity function defined by classification performance of every individual, the clonal selection optimal algorithm is adopted to search
   the best feature that has excellent classification performance. The experiments of sound signal for gasoline engine show that, due to the diversity of antibodies is
   maintained by clonal selection principle, the best compound feature founded by immune programming has better classification ability than feature optimism by genetic
   programming.
%8 15-18  Decemeber
%Z Sch. of Mech. Eng. & the Eng. Workshop, Xi'an Jiaotong Univ., Xi'an, China. Also known as \cite5423210

%A Miao Li
%A Jian Zhang
%A Ze-lin Hu
%A Yuan Yuan
%A Lu-jiu Li
%T An Algorithm of Fertilization Model Fitting Based On Mixed Intelligent Computation
%B International Conference on Advanced Computer Control, ICACC '09
%D 2009
%P 425--429
%I 
%K genetic algorithms, genetic programming, adaptive algorithm, fertilisation model fitting, kalium, mixed intelligent computation, nitrogen, phosphorus, pluralistic
   fertilisation model construction, CAD, fertilisers, nitrogen, phosphorus
%X During the process of pluralistic fertilisation model construction, the unreasonable ratio of nitrogen, phosphorus and kalium easily results in the deviation of fertilising
   model. This paper has proposed an adaptive algorithm of fertilisation model fitting based mixed intelligent computing of GP/GA, and solved the issue of structure and
   parameters optimisation of adaptive fertilisation model. This algorithm has carried out the research of applying control factors to adjust the parameters of fitting
   function, the appropriate ratio of nitrogen, phosphorus and kalium is regarded as control factors of heuristic search to adjust models, on the basis of history test data
   dynamical models are generated, and the optimisation and correction of models based appropriate ratio of nutrients are achieved.
%8 22-24  January
%Z Also known as \cite4777379

%A Shaobo Li
%A Jianjun Hu
%T Evolving Vibration Absorbers Based on Genetic Programming and Bond Graphs
%B 2006 International Conference on Computational Intelligence and Security
%V 1
%D 2006
%P 202--207
%I IEEE
%C Guangzhou
%K genetic algorithms, genetic programming
%X Conceptual innovation in mechanical engineering design has been extremely challenging compared to the wide applications of automated design systems in digital circuits.
   This paper presents an automated methodology for open-ended synthesis of mechanical vibration absorbers based on genetic programming and bond graphs. It is shown that our
   automated design system can automatically evolve passive vibration absorbers that are close to or better than the standard passive vibration absorbers invented in 1912. A
   variety of other vibration absorbers with competitive performance are also evolved automatically using a desktop PC in less than 10 hours
%8 November
%Z CAD/CIMS Inst., Guizhou Univ., Guiyang
%@ 1-4244-0605-6

%A Shaobo Li
%A Guanci Yang
%A Qingsheng Xie
%T Automatic Design Method of Dynamic Systems Based on Hungarian Algorithm and Genetic Programming
%B 4th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM '08
%D 2008
%P 1--4
%I 
%K genetic algorithms, genetic programming, Hungarian algorithm, automatic design method, bond graphs, dynamic systems, fitness definition, graph theory, telecommunication
   network topology
%X This paper summarizes the present research status of automated design method for dynamic systems, investigates efficient method of fitness definition for automated design
   method of dynamic systems based on bond graphs and genetic programming. The automated design method based on Hungarian algorithm and genetic programming (HAGP) is proposed,
   and the statistic results of domain independent - an eigenvalues -placement design problem, which is tested for some sample target sets of eigenvalues, strongly shows the
   search capability of HAGP is good enough to make feasible automated design and obtain high-quality, well evolutionary solutions with less computational efforts, rapid speed
   in convergence compared to other state-of art algorithms.
%8 October
%Z Also known as \cite4678460

%A Shaobo Li
%A Jianning Qiang
%A Juanjuan Wang
%A Gao Jie
%T Synthesis of Analog Filter Based on Genetic Program and Network Topology Transformation
%B International Symposium on Intelligent Information Technology Application Workshops, IITAW '08
%D 2008
%P 1033--1036
%I 
%K genetic algorithms, genetic programming, analog filter design synthesis, electro-circuit optimization, genetic program, network topology transformation, tree structure,
   analogue circuits, circuit optimisation, filters, network topology
%X This paper briefly introduces GA and GP and succeeds in designing analog filter with the aid of GP, expressing electro-circuit by woking use of tree structure, then
   proceeding genetic operation. In the process of evolution, network transformation is involved in so that parameter could advance select optimization. Finally, this paper
   sets forth a concrete example of the design in which the procedures and the outcome are listed clearly and the designing is simulated by Dyloma 7.0 dynamic simulating
   software.
%8 Decemeber
%Z Also known as \cite4732114

%A Shaobo Li
%A Jianjun Hu
%T Genetic Programming and Creative Design of Mechatronic Systems
%D 2009
%I China Machine Press
%K genetic algorithms, genetic programming, bond graphs, evolutionary design
%U http://product.dangdang.com/product.aspx?product_id=20470877
%X The research in this book spans several fields, including large-scale scientific computation, simulation of mechatronic and control systems, computational intelligence and
   genetic programming technology, automated design, and parameter optimization. By employing genetic programming and simulation of dynamic systems in a bond graph expression,
   this book provides a systematic exposition of automated design of hybrid mechatronic systems that include mechanical, electronic and control systems. The research results
   in this book include a systematic method for creative design of modern mechatronic products. Based on GP, the hybrid topology algorithm, mixing bond graphs with control
   block diagrams and circuit diagrams, has many advantages, such as searching structures in an open-ended way and simultaneously searching for the optimal parameters of the
   components of the structure. This hybrid searching method breaks through the restrictions of the classical parametric optimization of designs based on GA. And it also
   implements automated design of complicated systems. This hybrid search algorithm can evolve some complicated mechatronic products with designs that are superior to those
   done by human designers, in areas such as circuits, controllers, vibration absorbers, etc. The design theory, methods, and prototype systems for evolutionary computation
   based on GP can also potentially improve design practice for other types of engineering systems. The important advances described in this book give it profound academic
   value and application significance. The content of this book is easy to understand. It can be used as a guide for creative design theory and practice. It also will be a
   practical toolbox for students in mechanical engineering, computer science and related majors. Of course, it will also be a good choice as teaching reference book for
   postgraduate students or doctoral students. I enthusiastically recommend it for study by persons interested in studying the automated design of mechatronic systems. Erik D.
   Goodman Professor and Design Coordinator, Electrical and Computer Engineering Professor, Mechanical Engineering Michigan State University Vice President for Technology, Red
   Cedar Technology, Inc. Founding Chair, ACM Special Interest Group on Genetic and Evolutionary Computation

%A Shaobo Li
%A Weijie Pan
%A Guanci Yang
%A Linna Chen
%T Optimization of 3G Wireless Network Using Genetic Programming
%B Second International Symposium on Computational Intelligence and Design, ISCID '09
%V 2
%D 2009
%P 131--134
%I 
%C Changsha, China
%K genetic algorithms, genetic programming, 3G wireless network, automated optimization design, base station configuration plans, evolutional topological operators, network
   design, 3G mobile communication
%X We proposed a genetic programming (GP) based method for automated optimization design of base station configuration plans of 3G wireless networks. This method aims to
   address the disadvantages of the current methods on wireless network optimization and to satisfy new requirements for network design. Evolutional topological operators and
   terminal set are designed for GP the Prim is used to guide the evolutionary design. The result of topological graph shows that the algorithm can balance topology and
   parameter search, and it works well for 3G network optimization.
%8 Decemeber
%Z Also known as \cite5368796

%A Shengen Li
%A Xiaofei Niu
%A Peiqi Li
%A Lin Wang
%T Generating New Features Using Genetic Programming to Detect Link Spam
%B 2011 International Conference on Intelligent Computation Technology and Automation (ICICTA)
%V 1
%D 2011
%P 135--138
%I 
%K genetic algorithms, genetic programming, GP classifier, SVM, WEBSPAM-UK2006, classification method, link spam detection, link-based feature generation, search engine,
   search result quality, Internet, feature extraction, information retrieval, pattern classification, search engines, support vector machines
%X Link spam techniques can enable some pages to achieve higher-than-deserved rankings in the results of a search engine. They negatively affect the quality of search results.
   Classification methods can detect link spam. For classification problem, features play an important role. This paper proposes to derive new features using genetic
   programming from existing link-based features and use the new features as the inputs to SVM and GP classifiers for the identification of link spam. Experiments on
   WEBSPAM-UK2006 show that the classification results of the classifiers that use 10 newly generated features are much better than those of the classifiers that use original
   41 link-based features and equivalent to those of the classifiers that use 138 transformed link-based features. The newly generated features can improve the link spam
   classification performance.
%8 March
%Z Also known as \cite5750574

%A Shuguang Li
%A Jianping Yuan
%A Jianjun Luo
%A Weihua Ma
%T Satellite attitude control through evolving a neural network
%B 2010 International Conference on Mechatronics and Automation (ICMA)
%D 2010
%P 553--559
%I 
%C Xi'an, China
%K genetic algorithms, genetic programming, direct graph encoding method, evolutionary learning, genetic operator, neural network, pure topological recurrent network
   controller, satellite attitude control, sinusoidal function, training method, artificial satellites, attitude control, directed graphs, encoding, neurocontrollers,
   recurrent neural nets
%X We propose a pure topological recurrent network controller for satellite attitude control, which has random binary connections in hidden layer, and all hidden neurons are
   activated by sinusoidal functions. A direct graph encoding method and four genetic operators are implemented for using genetic programming to train this controller.
   Moreover, a simulated small satellite which equipped with three reaction wheels was developed, then this simulator was employed to test the controller and training method
   for a given simple attitude adjusting mission. The experimental results reveal that this controller has the simplicity, usability and potentials for satellite attitude
   control through evolutionary learning.
%8 4-7  August
%Z Also known as \cite5588493

%A Shuguang Li
%A Jianping Yuan
%A Xiaokui Yue
%A Jianjun Luo
%T The binary-weights neural network for robot control
%B 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2010
%D 2010
%P 765--770
%I 
%K genetic algorithms, genetic programming, binary-weight neural network, direct graph encoding, evolutionary learning, genetic operator, navigation, nonlinear problems,
   obstacle avoidance, random binary connection, robot control, simulated mobile robot, sinusoidal functions, topological recurrent artificial neural network controller,
   collision avoidance, encoding, learning (artificial intelligence), mobile robots, neurocontrollers, random functions, recurrent neural nets
%X We propose a pure topological recurrent networks controller, which has random binary connections in hidden layer, and all hidden neurons are activated by sinusoidal
   functions. A direct graph encoding method and four genetic operators are implemented for using genetic programming to train this controller. Firstly, its feasibility and
   efficiency were validated by a pair of function approximation experiments, the results show that through evolutionary learning, this novel RNN controller can handle
   nonlinear problems as well as common RNN even without adjustable weights. Moreover, a simulated mobile robot was equipped with this controller, and the robot was navigated
   around obstacles toward a goal in physical simulation environments; during tests, this robot exhibited four successful behaviours just by topological evolving on the simple
   controller. This experiment reveals that this controller has the simplicity, usability and potential for robot control, it then raises the hope for further works in
   exploring network motifs from high level controllers.
%8 26-29  September
%Z is this GP? Northwestern Polytech. Univ., Xi'an, China. Also known as \cite5626893

%A Taiyong Li
%A Changjie Tang
%A Jiang Wu
%A Xuzhong Wei
%A Chuan Li
%A Shucheng Dai
%A Jun Zhu
%T GEP-NFM: Nested Function Mining Based on Gene Expression Programming
%B Fourth International Conference on Natural Computation, ICNC '08.
%V 6
%D 2008
%P 283--287
%I 
%K genetic algorithms, genetic programming, gene expression programming, data mining, function discovery, knowledge discovery, machine learning, nested function mining, data
   mining, learning (artificial intelligence)
%X Mining the interesting functions from the large scale data sets is an important task in KDD. Traditional gene expression programming (GEP) is a useful tool to discover
   functions. However, it cannot mine very complex functions. To resolve this problem, a novel method of function mining is proposed in this paper. The main contributions of
   this paper include: (1) analysing the limitations of function mining based on traditional GEP, (2) proposing a nested function mining method based on GEP (GEP-NFM), and (3)
   experimental results suggest that the performance of GEP-NFM is better than that of the existing GEP-ADF. Averagely, compared with traditional GEP-ADF, the successful rate
   of GEP-NFM increases 20percent and the number of evolving generations decrease 25percent.
%8 October
%Z Also known as \cite4667846

%A Taiyong Li
%A Changjie Tang
%A Ting He
%A Jiang Wu
%A Wenbing Qin
%T Gene Expression Programming without Reduplicate Individuals
%B Fifth International Conference on Natural Computation, 2009. ICNC '09
%E Haiying Wang and Kay Soon Low and Kexin Wei and Junqing Sun
%D 2009
%P 249--253
%I IEEE Computer Society
%C Tianjian, China
%K genetic algorithms, genetic programming, gene expression programming
%8 14-16  August

%A Taiyong Li
%A Tiangang Dong
%A Jiang Wu
%A Ting He
%T Function mining based on gene Expression Programming and Particle Swarm Optimization
%B 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009
%D 2009
%P 99--103
%I 
%K genetic algorithms, genetic programming, gene expression programming, GEP, PSO, evolutionary process, function mining, particle swarm optimisation, random numerical
   constants algorithm, data mining, particle swarm optimisation
%X Gene expression programming (GEP) is a powerful tool widely used in function mining. However, it is difficult for GEP to generate appropriate numeric constants for function
   mining. In this paper, a novel approach of creating numeric constants, GEPPSO, was proposed, which embedded particle swarm optimization (PSO) into GEP. In the approach, the
   evolutionary process was divided into 2 phases: in the first phase, GEP focused on optimising the structure of function expression, and in the second one, PSO focused on
   optimising the constant parameters. The experimental results on function mining problems show that the performance of GEPPSO is better than that of the existing GEP random
   numerical constants algorithm (GEP-RNC).
%8 August
%Z Also known as \cite5234621

%A Te-Sheng Li
%A Cheng-Lung Huang
%A Zong-Yuan Wu
%T Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System
%J Journal of Intelligent Manufacturing
%V 17
%N 3
%D 2006
%P 355--361
%I 
%K genetic algorithms, genetic programming, Data mining, Feature selection, Yield prediction, Semiconductor manufacturing
%X he complexity of semiconductor manufacturing is increasing due to the smaller feature sizes, greater number of layers, and existing process reentry characteristics. As a
   result, it is difficult to manage and clarify responsibility for low yields in specific products. This paper presents a comprehensive data mining method for predicting and
   classifying the product yields in semiconductor manufacturing processes. A genetic programming (GP) approach, capable of constructing a yield prediction system and
   performing automatic discovery of the significant factors that might cause low yield, is presented. Comparison with the results then is performed using a decision tree
   induction algorithm. Moreover, this research illustrates the robustness and effectiveness of this method using a well-known DRAM fab's real data set, with discussion of the
   results.
%8 June

%A Wen-Xiu Li
%A Lan-Fang Dai
%A Xiao-Bing Hou
%A Wen Lei
%T Fuzzy genetic programming method for analysis of ground movements due to underground mining
%J International Journal of Rock Mechanics and Mining Sciences
%V 44
%N 6
%D 2007
%P 954--961
%I 
%K genetic algorithms, genetic programming, Fuzzy measures, Underground mining, Ground surface movement
%X The prediction of ground surface movements is an important problem in rock and soil mechanics in the excavation activities especially the coal and metal mining. Based on
   results of the statistical analysis of a large amount of measured data in underground excavation engineering, the fuzzy genetic programming method (FGPM) of ground surface
   movements is given by using the theory of fuzzy probability measures and genetic programming (GP). And genetic programming approach is proposed to determine the parameter
   of ground surface movements due to underground mining of coal in this paper. Genetic programming is trained by used practical mining induced surface movement data. The
   agreement of the theoretical results with the field measurements shows that the FGPM is satisfactory and the formulae obtained are valid and thus can be effectively used
   for predicting the ground surface movements due to underground mining, especially the mining of coal and metal.
%8 September
%Z College of Machinery and Civil Engineering, Hebei University, Baoding 071002, PR China Research Institute of Geotechnical Engineering, Hebei University, Baoding 071002, PR
   China

%A Y. Li
%A K. F. Man
%A K. S. Tang
%T Multiobjective Genetic Algorithm for Rolling-Horizon Production Planning
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1789
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-708.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Yamin Li
%A Jinru Ma
%A Qiuxia Zhao
%T Two Improvements in Genetic Programming for Image Classification
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 2492--2497
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X A new classification algorithm for multi-image classification in genetic programming (GP) is introduced, which is the centred dynamic class boundary determination with
   quick-decreasing power value of arithmetic progression. In the classifier learning process using GP for multi-image classification, different sets of power values are
   tested to achieve a more suitable range of margin values for the improvement of the accuracy of the classifiers. In the second development, the program size is introduced
   into the fitness function to control the size of program growth during the evolutionary learning process. The approach is examined on a Chinese character image data set and
   a grass leaves data set, both of which have four or more classes. The experimental results show that while dealing with complicated problems of multi-image classification,
   the new approach can be used for more accurate classification and work better than the previous algorithms of either static or dynamic class boundary determination. With
   the fitness function, the size of the programs in the population can be controlled effectively and shortened considerably during evolution. Thus, the readability of the
   programs could be seemingly improved.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Ya-Min Li
%A Jin-Ru Ma
%A Li-Juan Cui
%A Qiu-Xia Zhao
%T On the control of the growth of program sizes in the course of classifier evolution in image classification in genetic programming
%B International Conference on Machine Learning and Cybernetics
%V 2
%D 2008
%P 976--980
%I 
%K genetic algorithms, genetic programming, classifier evolution, image classification, program size growth control, image classification
%X The main indicators of the performance of an image classifier include classification accuracy, classification efficiency and evolution efficiency, whereas the sizes of the
   programs involved in genetic programming stand as one of the major factors that influence the performance of the classifier. Some effective means are introduced in this
   paper for the control of program sizes, which is done through scientific construction of fitness functions of the classifier in image classification in genetic programming.
   Tests showed that, the growth of program sizes were effectively put under control during the course of evolution, which, in turn, greatly improved the general performance
   of the image classifier in genetic programming.
%8 July
%Z Also known as \cite4620546

%A Yugang Li
%A Fangyu Han
%A Shiqing Zheng
%A Shuguang Xiang
%A Xinshun Tan
%T An automatic approach to design water utilization network
%B Process Systems Engineering 2003, 8th International Symposium on Process Systems Engineering
%S Computer Aided Chemical Engineering
%E Bingzhen Chen and Arthur W. Westerberg
%V 15, Part 2
%D 2003
%P 922--927
%I Elsevier
%K genetic algorithms, genetic programming, Water network, Wastewater reuse
%U http://www.sciencedirect.com/science/article/B8G5G-4P40D5S-1C/2/0893b2e39b14ef5b2d394d10c926f0b0
%X This paper presents an automatic approach for the design of water network(WUN). In this work, water network design is formulated as an optimisation problem of network
   configuration and design variables, where both operating and investment cost are optimised simultaneously. Genetic Programming(GP) was used to solve the optimization
   problem. The encode mode to represent WUN configuration has been studied. Several general unit operations have been selected, such as column, splitter, mix, regenerate,
   etc., which constitute the GP function set. The primary advantage of this approach is the automatic search for potential promising alternatives without any pre-defined
   superstructures.
%O Process Systems Engineering 2003, 8th International Symposium on Process Systems Engineering, China. Edited by: Bingzhen Chen and Arthur W. Westerberg, ISBN: 9780444514042

%A Xiang Li
%A Vic Ciesielski
%T Using Loops in Genetic Programming for a Two Class Binary Image Classification Problem
%B AI 2004: Advances in Artificial Intelligence: Proceedings of the 17th Australian Joint Conference on Artificial Intelligence
%S Lecture Notes in Computer Science
%E Geoffrey I. Webb and Xinghuo Yu
%V 3339
%D 2004
%P 898--909
%I Springer
%C Cairns, Australia
%K genetic algorithms, genetic programming, image classification, classification problem
%U http://www.springerlink.com/index/6MDEKV7A1821E0UY
%X Loops are rarely used in genetic programming (GP), because they lead to massive computation due to the increase in the size of the search space. We have investigated the
   use of loops with restricted semantics for a problem in which there are natural repetitive elements, that of distinguishing two classes of images. Using our formulation,
   programs with loops were successfully evolved and performed much better than programs without loops. Our results suggest that loops can successfully used in genetic
   programming in situations where domain knowledge is available to provide some restrictions on loop semantics.
%8 Decemeber  4-6
%@ 3-540-24059-4

%A Vic Ciesielski
%A Xiang Li
%T Analysis of genetic programming runs
%B Proceedings of The Second Asian-Pacific Workshop on Genetic Programming
%E R I Mckay and Sung-Bae Cho
%D 2004
%I 
%C Cairns, Australia
%K genetic algorithms, genetic programming, analysis of runs
%U http://goanna.cs.rmit.edu.au/~xiali/pub/ai04.vc.pdf
%X We have analysed runs of 12 different genetic programming problems. Some of the problems are the `toy' problems used in generic programming research and some are
   significant real world applications. We have generated log files of the runs and looked for recurring and unusual patterns and whether there are any differences between the
   toy problems and the real world problems. The major finding is that some programs are being evaluated many times. In the real-world problems 30-78per cent of the time was
   spent on reevaluating programs that had already been evaluated. For problems where the evaluation function is expensive significant savings are possible if evaluated
   programs are cached. A surprising finding was that, for two of the real world problems, a very large number of the evaluations were of 1-node programs.
%8 6-7  Decemeber
%Z http://www.itee.adfa.edu.au/~rim/ASPGP/programme.html

%A Xiang Li
%A Vic Ciesielski
%T An Analysis of Explicit Loops in Genetic Programming
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 3
%D 2005
%P 2522--2529
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming, iteration, forloops, modified ant, ADL, STGP
%X we analyse the reasons why evolving programs with a restricted form of loops is superior to evolving programs without loops for two problems which have underlying
   repetitive characteristics - a visit every- square problem and a modified Santa Fe ant problem. We show that in the case of loops there is a larger number of solutions with
   smaller tree sizes. We show that the computational patterns captured in the bodies of the loops are reflective of repeating patterns in the domain. We show that the
   increased computational cost of evaluating an individual can be controlled by domain knowledge.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Xiang Li
%T Utilising Restricted For-Loops in Genetic Programming
%R Ph.D. Thesis
%D 2007
%I 
%I Department of Computer Science, RMIT
%C Australia
%K genetic algorithms, genetic programming
%U http://goanna.cs.rmit.edu.au/~vc/papers/li-phd.pdf
%X Genetic programming is an approach that uses the power of evolution to allow computers to evolve programs with little human involvement. It has demonstrated its usefulness
   in solving many experimental problems as well as many real world problems. However, it suffers from weaknesses in using repetitions effectively. While loops are natural
   components of most programming languages and appear in every reasonably-sized application, they are rarely used in genetic programming. Extending the power of genetic
   programming by encouraging more use of loops will bridge the gap between the current state-of-the-art in programs evolved with genetic programming and those written by
   humans, and improve this automatic programming method. The goal of the work is to investigate a number of restricted looping constructs in which infinite loops are not
   possible and to determine whether any significant benefits can be obtained with these restricted loops. Possible benefits include: Solving problems which cannot be solved
   without loops, evolving smaller sized solutions which can be more easily understood by human programmers and solving existing problems quicker by using fewer evaluations.
   In this thesis, a number of explicit restricted loop formats were formulated and tested on the Santa Fe ant problem, a modified ant problem, a sorting problem, a
   visit-every-square problem and a difficult object classification problem. A maximum number of iterations based on domain knowledge was used to avoid the infinite iteration
   problem. The experimental results showed that these explicit loops can be successfully used in genetic programming. The evolutionary process can decide when, where and how
   to use them. Runs with these loops tended to generate smaller sized solutions in fewer evaluations. Solutions with loops were found to some problems that could not be
   solved without loops. From these experimental problems, the modified ant problem and the visit-every-square problem were selected to analyse differences between using and
   not using loops with respect to the search spaces, the patterns captured by genetic programming and the sensitivity to changes in the maximum number of iterations on CPU
   time. The analysis of the search spaces found that there were more fitter programs within a limited tree depth for programs with loops. To solve the same problem without
   loops required a larger tree depth and this exponentially increases the number of possible programs and may decrease the chance of finding a good solution. The analysis of
   the patterns captured found that runs with loops captured repetitive patterns of the problem domain and repeated them to improve the fitness. The analysis of the effect of
   different values of maximum number of iterations showed that CPU time per evaluation increased as the maximum number of iterations increased. However, solutions were found
   in fewer evaluations. There was a large range of values for maximum number of iterations for which the overall CPU time was lower. Good choices for maximum number of
   iterations could be found from domain knowledge. Overall, the results and analysis have established that there are significant benefits in using loops in genetic
   programming. Restricted loops can avoid the difficulties of evolving consistent programs and the infinite iterations problem. Researchers and practioners of genetic
   programming should not be afraid of loops.
%8 28  February

%A Xianneng Li
%A Shingo Mabu
%A Huiyu Zhou
%A Kaoru Shimada
%A Kotaro Hirasawa
%T Genetic Network Programming with Estimation of Distribution Algorithms and its application to association rule mining for traffic prediction
%B ICCAS-SICE, 2009
%D 2009
%P 3457--3462
%I 
%K genetic algorithms, genetic programming, genetic network programming, association rule mining, association rules, directed graph structures, estimation of distribution
   algorithms, evolutionary optimisation algorithm, evolutionary paradigm, probabilistic distribution, traffic prediction, data mining, directed graphs, probability
%X In this paper, a novel evolutionary paradigm combining Genetic Network Programming (GNP) and Estimation of Distribution Algorithms (EDAs) is proposed and used to find
   important association rules in time-related applications, especially in traffic prediction. GNP is one of the evolutionary optimisation algorithms, which uses
   directed-graph structures. EDAs is a novel algorithm, where the new population of individuals is produced from a probabilistic distribution estimated from the selected
   individuals from the previous generation. This model replaces random crossover and mutation to generate offspring. Instead of generating the candidate association rules
   using conventional GNP, the proposed method can obtain a large number of important association rules more effectively. The purpose of this paper is to compare the proposed
   method with conventional GNP in traffic prediction systems in terms of the number of rules obtained.
%8 August
%Z Also known as \cite5334374

%A Xianneng Li
%A Shingo Mabu
%A Huiyu Zhou
%A Kaoru Shimada
%A Kotaro Hirasawa
%T Genetic Network Programming with Estimation of Distribution Algorithms for class association rule mining in traffic prediction
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming, Genetic Network Programming
%X As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), a new approach named Genetic Network Programming (GNP) has been proposed in the evolutionary
   computation field. GNP uses multiple reusable nodes to construct directed-graph structures to represent its solutions. Recently, many research has clarified that GNP can
   work well in data mining area. In this paper, a novel evolutionary paradigm named GNP with Estimation of Distribution Algorithms (GNP-EDAs) is proposed and used to solve
   traffic prediction problems using class association rule mining. In GNP-EDAs, a probabilistic model is constructed by estimating the probability distribution from the
   selected elite individuals of the previous generation to replace the conventional genetic operators, such as crossover and mutation. The probabilistic model is capable of
   enhancing the evolution to achieve the ultimate objective. In this paper, two methods are proposed based on extracting the probabilistic information on the node connections
   and node transitions of GNP-EDAs to construct the probabilistic model. A comparative study of the proposed paradigm and the conventional GNP is made to solve the traffic
   prediction problems using class association rule mining. The simulation results showed that GNP-EDAs can extract the class association rules more effectively, when the
   number of the candidate class association rules increases. And the classification accuracy of the proposed method shows good results in traffic prediction systems.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586456

%A Xianneng Li
%A Shingo Mabu
%A Kotaro Hirasawa
%T Towards the maintenance of population diversity: A hybrid genetic network programming
%J Transaction of the Japanese Society for Evolutionary Computation
%V 1
%N 1
%D 2010
%P 89--101
%I 
%K genetic algorithms, genetic programming, genetic network programming, probabilistic model building evolutionary algorithm, PMBEA, estimation of distribution algorithm, EDA,
   GNP, probabilistic model building genetic network programming, PMBGNP, diversity maintenance
%U http://www.jpnsec.org/online_journal/1_1/1_89.pdf
%X Some researchers have investigated that the diversity loss will significantly decrease the performance of Probabilistic Model Building Genetic Algorithm (PMBGA), especially
   under large search space, leading to the premature convergence and local optimum. However, few work has been done on the diversity maintenance in the Probabilistic Model
   Building Evolutionary Algorithms (PMBEAs) with more complex chromosome structures, such as tree structure based Probabilistic Model Building Genetic Programming (PMBGP) and
   graph structure based Probabilistic Model Building Genetic Network Programming (PMBGNP). For the PMBEAs with more complex chromosome structures, the required sample size is
   usually much larger than that of binary structure based PMBGA. Therefore, these algorithms usually become much more sensitive to the population diversity. In order to
   obtain enough population diversity, the large population size is needed, which is not the best way. the maintenance of the population diversity is studied in PMBGNP, which
   is a kind of PMBEA, but has its unique characteristics because of its directed graph structure. This paper proposed a hybrid PMBGNP algorithm to maintain the population
   diversity to avoid the premature convergence and local optimum, and presented a theoretical analysis of the diversity loss in PMBGA, PMBGP and PMBGNP. Two techniques have
   been proposed for the diversity maintenance when the population size is set at not large values, which are multiple probability vectors and genetic operators. The proposed
   algorithm is applied and evaluated in a kind of autonomous robot, Khepera robot. The simulation study demonstrates that the proposed hybrid PMBGNP is often able to achieve
   a better performance than the conventional algorithms.
%8 12

%A Xianneng Li
%A Bing Li
%A Shingo Mabu
%A Kotaro Hirasawa
%T A Novel Estimation of Distribution Algorithm Using Graph-based Chromosome Representation and Reinforcement Learning
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 37--44
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, EDA, estimation of distribution algorithms, probabilistic model building genetic network programming
%X This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from
   the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to
   develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed
   algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with
   the conventional algorithms.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Xianneng Li
%A Shingo Mabu
%A Kotaro Hirasawa
%T Use of infeasible individuals in probabilistic model building genetic network programming
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 601--608
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, genetic network programming, Estimation of distribution algorithms
%X Classical EDAs generally use truncation selection to estimate the distribution of the feasible (good) individuals while ignoring the infeasible (bad) ones. However, various
   research in EAs reported that the infeasible individuals may affect and help the problem solving. This paper proposed a new method to use the infeasible individuals by
   studying the sub-structures rather than the entire individual structures to solve Reinforcement Learning (RL) problems, which generally factorise their entire solutions to
   the sequences of state-action pairs. This work was studied in a recent graph-based EDA named Probabilistic Model Building Genetic Network Programming (PMBGNP) which can
   solve RL problems successfully. The effectiveness of this work is verified in a RL problem, i.e., robot control, comparing with some other related work.
%8 12-16  July
%Z Also known as \cite2001659 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Xiaodong Li
%A Wenjian Luo
%A Xin Yao
%T Theoretical foundations of evolutionary computation
%J Genetic Programming and Evolvable Machines
%V 9
%N 2
%D 2008
%P 107--108
%I 
%K genetic algorithms
%O Special Issue on Theoretical foundations of evolutionary computation
%8 June
%Z Editorial SEAL-2006

%A Xin Li
%A Chi Zhou
%A Peter C. Nelson
%A Thomas M. Tirpak
%T Investigation of Constant Creation Techniques in the Context of Gene Expression Programming
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, GEP
%U http://www.cs.uic.edu/~xli1/papers/GEPConstantCreation(GECCO04_LBP).pdf
%X Gene Expression Programming (GEP) is a new technique of Genetic Programming (GP) that implements a linear genotype representation. It uses fixed-length chromosomes to
   represent expression trees of different shapes and sizes, which results in unconstrained search of the genome space while still ensuring validity of the programs output.
   However, GEP has some difficulty in discovering suitable function structures because the genetic operators are more disruptive than traditional tree-based GP. One possible
   remedy is to specifically assist the algorithm in discovering useful numeric constants. In this paper, the effectiveness of several constant creation techniques for GEP has
   been investigated through two symbolic regression benchmark problems. Our experimental results show that constant creation methods applied to the whole population for
   selected generations perform better than methods that are applied only to the best individuals. The proposed tune-up process for the entire population can significantly
   improve the average fitness of the best solutions.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Xin Li
%A Chi Zhou
%A Weimin Xiao
%A Peter C. Nelson
%T Prefix Gene Expression Programming
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2005)
%E Franz Rothlauf
%D 2005
%I 
%C Washington, D.C., USA
%K genetic algorithms, genetic programming, gene expression programming
%U http://www.cs.uic.edu/~xli1/papers/PGEP_GECCOLateBreaking05_XLi.pdf
%X Gene Expression Programming (GEP) is a powerful evolutionary method derived from Genetic Programming (GP) for model learning and knowledge discovery. However, when dealing
   with complex problems, its genotype under Karva notation does not allow hierarchical composition of the solution, which impairs the efficiency of the algorithm. We propose
   a new representation scheme based on prefix notation that overcomes the original GEP's drawbacks. The resulted algorithm is called Prefix GEP (P-GEP). The major advantages
   with P-GEP include the natural hierarchy in forming the solutions and more protective genetic operations for substructure components. An artificial symbolic regression
   problem and a set of benchmark classification problems from UCI machine learning repository have been tested to demonstrate the applicability of P-GEP. The results show
   that P-GEP follows a faster fitness convergence curve and the rules generated from P-GEP consistently achieve better average classification accuracy compared with GEP
%8 25-29  June
%Z Distributed on CD-ROM at GECCO-2005

%A Xin Li
%A Chi Zhou
%A Weimin Xiao
%A Peter C. Nelson
%T Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures
%B The Fourth International Conference on Machine Learning and Applications (ICMLA'05)
%D 2005
%P 337--342
%I IEEE press
%C Los Angeles, California
%K genetic algorithms, genetic programming, Prefix Gene Expression Programming
%U http://www.cs.uic.edu/~xli1/papers/Substructures(ICMLA05)_XLi.pdf
%X Linear genotype representation and modularity have continuously received extensive attention from the Genetic Programming (GP) community. The advantages of a linear
   genotype include a convenient and efficient implementation scheme. However, most existing techniques using a linear genotype follow the imperative programming language
   paradigm and a direct hierarchical composition for the functionality of the solution is under achieved. Our work is based on Prefix Gene Expression Programming (P-GEP), a
   new GP method featured by a prefix notation based linear genotype representation. Since P-GEP uses a functional language paradigm, its framework results in natural self
   emergence of substructures as functional components during the evolution. We propose to preserve and use potentially useful emergent substructures via a dynamic
   substructure library, empowering the algorithm to focus the search on a higher level of the solution structure. Preliminary experiments on the benchmark regression problems
   have shown the effectiveness of this approach.
%8 Decemeber  15-17
%Z cited by \citeSpector:2011:GECCO http://www.cs.csubak.edu/~icmla/icmla05/CFP_Program.html

%A Xin Li
%T Self-Emergence of Structures in Gene Expression Programming
%B Proceedings, The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference
%E Manuela M. Veloso and Subbarao Kambhampati
%D 2005
%P 1650--1651
%I AAAI Press AAAI Press / The MIT Press
%C Pittsburgh, Pennsylvania, USA
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.cs.uic.edu/~xli1/papers/AAAI053LiX.pdf
%X This thesis work aims at improving the problem solving ability of the Gene Expression Programming (GEP) algorithm to fulfill complex data mining tasks by preserving
   evolutionary process. The main contributions include the investigation of the constant creation techniques for promoting good functional structures emergent in the
   evolution, analysis of the limitation with the current implementation scheme of GEP,
%O The Tenth AAAI/SIGART Doctoral Consortium
%8 July  9-13
%@ 1-57735-236-X

%A Xin Li
%A Chi Zhou
%A Weimin Xiao
%A Peter C. Nelson
%T Introducing Emergent Loose Modules into the Learning Process of a Linear Genetic Programming System
%B 5th International Conference on Machine Learning and Applications, ICMLA '06
%D 2006
%P 219--224
%I IEEE
%C Orlando, USA
%K genetic algorithms, genetic programming, gene expression programming
%X Modularity and building blocks have drawn attention from the genetic programming (GP) community for a long time. The results are usually twofold: a hierarchical evolution
   with adequate building block reuse can accelerate the learning process, but rigidly defined and excessively employed modules may also counteract the expected advantages by
   confining the reachable search space. In this work, we introduce the concept of emergent loose modules based on a new linear GP system, prefix gene expression programming
   (P-GEP), in an attempt to balance between the stochastic exploration and the hierarchical construction for the optimal solutions. Emergent loose modules are dynamically
   produced by the evolution, and are reusable as sub-functions in later generations. The proposed technique is fully illustrated with a simple symbolic regression problem.
   The initial experimental results suggest it is a flexible approach in identifying the evolved regularity and the emergent loose modules are critical in composing the best
   solutions
%8 Decemeber
%Z fixed sized linear genome Dept. of Comput. Sci., Illinois Univ., Chicago, IL
%@ 0-7695-2735-3

%A Yang Li
%A Changjun Hu
%A Ming Chen
%A Jingyuan Hu
%T Investigating Aesthetic Features to Model Human Preference in Evolutionary Art
%B Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012
%S LNCS
%E Penousal Machado and Juan Romero and Adrian Carballal
%V 7247
%D 2012
%P 152--163
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K Aesthetic learning, evolutionary art, interactive evolutionary computation, computational aesthetics
%X In this paper we investigate aesthetic features in learning aesthetic judgements in an evolutionary art system. We evolve genetic art with our evolutionary art system,
   BioEAS, by using genetic programming and an aesthetic learning model. The model is built by learning both phenotype and genotype features, which we extracted from internal
   evolutionary images and external real world paintings, which could lead to more interesting paths. By learning aesthetic judgment and applying the knowledge to evolve
   aesthetical images, the model helps user to automate the process of evolutionary process. Several independent experimental results show that our system is efficient to
   reduce user fatigue in evolving art.
%8 11-13  April
%Z Part of \citeMachado:2012:EvoMusArt EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012

%A Houjun Liang
%A Wenjian Luo
%A Xufa Wang
%T A three-step decomposition method for the evolutionary design of sequential logic circuits
%J Genetic Programming and Evolvable Machines
%V 10
%N 3
%D 2009
%P 231--262
%I 
%K evolvable hardware,Adaptive system, Evolutionary computation, Sequential circuit Decomposition, EHW
%X Evolvable hardware (EHW) refers to an automatic circuit design approach, which employs evolutionary algorithms (EAs) to generate the configurations of the programmable
   devices. The scalability is one of the main obstacles preventing EHW from being applied to real-world applications. Several techniques have been proposed to overcome the
   scalability problem. One of them is to decompose the whole circuit into several small evolvable sub-circuits. However, current techniques for scalability are mainly used to
   evolve combinational logic circuits. In this paper, in order to decompose a sequential logic circuit, the state decomposition, output decomposition and input decomposition
   are united as a threestep decomposition method (3SD). A novel extrinsic EHW system, namely 3SD-ES, which combines the 3SD method with the (l, k) ES (evolution strategy), is
   proposed, and is used for the evolutionary designing of larger sequential logic circuits. The proposed extrinsic EHW system is tested extensively on sequential logic
   circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library. The results demonstrate that 3SD-ES has much better performance in terms of
   scalability. It enables the evolutionary designing of larger sequential circuits than have ever been evolved before.
%8 September

%A Wen-Yau Liang
%A Chun-Che Huang
%T A hybrid approach to constrained evolutionary computing: Case of product synthesis
%J Omega
%V 36
%N 6
%D 2008
%P 1072--1085
%I 
%K genetic algorithms, Evolutionary computing, Rough set, Product synthesis
%X Evolutionary computing (EC) is comprised of techniques involving evolutionary programming, evolution strategies, genetic algorithms (GA), and genetic programming. It has
   been widely used to solve optimisation problems for large scale and complex systems. However, when insufficient knowledge is incorporated, EC is less efficient in terms of
   searching for an optimal solution. In addition, the GA employed in previous literature is modelled to solve one problem exactly. The GA needs to be redesigned, at a cost,
   for it to be applied to another problem. Due to these two reasons, this paper develops a generic GA incorporating knowledge extracted from the rough set theory. The
   advantages of the proposed solution approach include: (i) solving problems that can be decomposed into functional requirements, and (ii) improving the performance of the GA
   by reducing the domain range of initial population and constraining crossover using the rough set theory. The solution approach is exemplified by solving the problem of
   product synthesis, where there is a conflict between performance and cost. Manufacturing or assembling a product of high performance and quality at a low cost is critical
   for a company to maximise its advantages. Based on our experimental results, this approach has shown great promise and has reduced costs when the GA is in processing.
%O A Special Issue Dedicated to the 2008 Beijing Olympic Games
%8 Decemeber

%A Benjamin Penyang Liao
%T Goal-Directed Portfolio Insurance Strategies
%R Ph.D. Thesis
%D 2006
%I 
%I Department of Information Management, National Central University
%C ROC
%K genetic algorithms, genetic programming, forest genetic programming, GDPI, implicit piecewise linear GDPI strategy, piecewise nonlinear GDPI strategy, piecewise linear GDPI
   strategy, goal-directed strategy, Portfolio insurance strategy
%U http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/getfile?URN=87443004&filename=87443004.pdf
%X Traditional portfolio insurance (PI) strategy such as constant proportion portfolio insurance (CPPI) only considers the floor constraint but not the goal aspect. There
   seems to be two contradictory risk-attitudes according to different studies: low wealth risk aversion and high wealth risk aversion. Although low wealth risk aversion can
   be explained by the CPPI strategy, high wealth risk aversion can not be explained by CPPI. We argue that these contradictions can be explained from two perspectives: the
   portfolio insurance perspective and the goal-directed perspective. This study proposes a goal-directed (GD) strategy to express an investor's goal-directed trading
   behaviour and combines this floor-less GD strategy with the goal-less CPPI strategy to form a piecewise linear goal-directed CPPI (GDCPPI) strategy. The piecewise linear
   GDCPPI strategy shows that there is a wealth position M at the intersection of the GD strategy and CPPI strategy. This M position guides investors to apply CPPI strategy or
   GD strategy depending on whether the current wealth is less than or greater than M respectively. In addition, we extend the piecewise linear GDCPPI strategy to a piecewise
   nonlinear GDCPPI strategy. Moreover, we extend the piecewise GDCPPI strategy to the piecewise GDTIPP strategy by applying the time invariant portfolio protection (TIPP)
   idea, which allows variable floor and goal comparing to the constant floor and goal for piecewise GDCPPI strategy. Therefore, piecewise GDCPPI strategy and piecewise GDTIPP
   strategy are two special cases of piecewise goal-directed portfolio insurance (GDPI) strategies. When building the piecewise nonlinear GDPI strategies, it is difficult to
   preassign an explicit $M$ value when the structures of nonlinear PI strategies and nonlinear GD strategies are uncertain. To solve this problem, we then apply the minimum
   function to build the piecewise nonlinear GDPI strategies, which these strategies still apply the $M$ concept but operate it in an implicit way. Also, the piecewise linear
   GDPI strategies can attain the same effect by applying the minimum function to form implicit piecewise linear GDPI strategies. This study performs some experiments to
   justify our propositions for piecewise GDPI strategies: there are nonlinear GDPI strategies that can outperform the linear GDPI strategies and there are some data-driven
   techniques that can find better linear GDPI strategies than the solutions found by Brownian technique. The GA and forest genetic programming (GP) are two data-drive
   techniques applied in this study. This study applies genetic algorithm (GA) technique to find better piecewise linear GDPI strategy parameters than those under Brownian
   motion assumption. This study adapts traditional GP to a forest GP in order to generate piecewise nonlinear GDPI strategies. The statistical tests show that the GP strategy
   outperforms the GA strategy which in turn outperforms the Brownian strategy. These statistical tests therefore justify our propositions.
%8 June

%A Pawel Lichocki
%A Krzysztof Krawiec
%A Wojciech Jaskowski
%T Evolving Teams of Cooperating Agents for Real-Time Strategy Game
%B EvoGAMES
%S Lecture Notes in Computer Science
%E Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Aniko Ekart and Anna Esparcia-Alcazar and Muddassar Farooq and Andreas Fink and Penousal
   Machado
%V 5484
%D 2009
%P 333--342
%I Springer
%K genetic algorithms, genetic programming, real-time strategy games, artificial intelligence
%X We apply gene expression programing to evolve a player for a real-time strategy (RTS) video game. The paper describes the game, evolutionary encoding of strategies and the
   technical implementation of experimental framework. In the experimental part, we compare two setups that differ with respect to the used approach of task decomposition. One
   of the setups turns out to be able to evolve an effective strategy, while the other leads to more sophisticated yet inferior solutions. We discuss both the quantitative
   results and the behavioural patterns observed in the evolved strategies.

%A Pawel Lichocki
%A Steffen Wischmann
%A Laurent Keller
%A Dario Floreano
%T Evolving team compositions by agent swapping
%J IEEE Transactions on Evolutionary Computation
%I 
%K genetic algorithms, genetic programming, Multiagent systems, cooperation, crossover, evolutionary computation, team composition, team optimisation
%X Optimising collective behaviour in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within
   a team. Over the last two decades, evolutionary methods have been shown to be a promising approach for the design of agents and their compositions into teams. The choice of
   a crossover operator that facilitates the evolution of optimal team composition is recognised to be crucial, but so far it has never been thoroughly quantified. Here we
   highlight the limitations of two different crossover operators that exchange entire agents between teams: restricted agent swapping that exchanges only corresponding agents
   between teams and free agent swapping that allows an arbitrary exchange of agents. Our results show that restricted agent swapping suffers from premature convergence,
   whereas free agent swapping entails insufficient convergence. Consequently, in both cases the exploration and exploitation aspects of the evolutionary algorithm are not
   well balanced resulting in the evolution of suboptimal team compositions. To overcome this problem we propose to combine the two methods. Our approach first applies free
   agent swapping to explore the search space and then restricted agent swapping to exploit it. This mixed approach turns out to be a much more efficient strategy for the
   evolution of team compositions compared to either strategy alone. Our results suggest that such a mixed agent swapping algorithm should always be preferred whenever the
   optimal composition of individuals in a multiagent system is unknown.
%O Accepted for future publication
%Z also known as \cite6171841

%A Peter Lichodzijewski
%A Nur Zincir-Heywood
%A Malcolm Heywood
%T Cascaded GP Models for Data Mining
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 2258--2264
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming
%U http://flame.cs.dal.ca/~piotr/01331178.pdf
%X The Cascade Architecture for incremental learning is demonstrated within the context of Genetic Programming. Such a scheme provides the basis for building steadily more
   complex models until a desired degree of accuracy is reached. The architecture is demonstrated for several data mining datasets. Efficient training on standard computing
   platforms is retained through the use of the RSS-DSS algorithm for stochastically sampling datasets in proportion to exemplar 'difficulty' and 'age'. Finally, the ensuing
   empirical study provides the basis for recommending the utility of sum square cost functions in the datasets considered.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Peter Lichodzijewski
%A Malcolm I. Heywood
%A A. Nur Zincir-Heywood
%T CasGP: Building Cascaded Hierarchical Models Using Niching
%B Proceedings of the 2005 IEEE Congress on Evolutionary Computation
%E David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali
   Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L.
   Gwenn Volkert and Dan Ashlock and Marc Schoenauer
%V 2
%D 2005
%P 1180--1187
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%C Edinburgh, UK
%K genetic algorithms, genetic programming, RSS, DSS, C4.5, boosting, naive Bayes
%U http://flame.cs.dal.ca/~piotr/01554824.pdf
%X A Cascaded model is introduced for mining large datasets using Genetic Programming without recourse to specialist hardware. Such an algorithm satisfies the seeming
   conflicting requirements of scalability and accuracy on large datasets by incrementally building GP classifiers through the use of a hierarchical Dynamic Subset Selection
   algorithm. Models are built incrementally with each layer of the cascade receiving as input the original feature vector, plus the output from the previous layer(s). In
   order to encourage each layer to explicitly solve new aspects of the problem a combination of Sum Square Error and Niching is used. Thus, previous layers of the model are
   considered a niche, and the cost function is a shared error metric.
%8 2-5  September
%Z CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.
%@ 0-7803-9363-5

%A Peter Lichodzijewski
%A Malcolm I. Heywood
%T GP Classifier Problem Decomposition Using First-Price and Second-Price Auctions
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 137--147
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X This work details an auction-based model for problem decomposition in Genetic Programming classification. The approach builds on the population-based methodology of Genetic
   Programming to evolve individuals that bid high for patterns that they can correctly classify. The model returns a set of individuals that decompose the problem by way of
   this bidding process and is directly applicable to multi-class domains. An investigation of two auction types emphasises the effect of auction design on the properties of
   the resulting solution. The work demonstrates that auctions are an effective mechanism for problem decomposition in classification problems and that Genetic Programming is
   an effective means of evolving the underlying bidding behaviour.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Peter Lichodzijewski
%A Malcolm I. Heywood
%T Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 1
%D 2007
%P 464--471
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Coevolution, problem decomposition, subset selection, supervised learning, training efficiency
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p464.pdf
%X A bid-based approach for coevolving Genetic Programming classifiers is presented. The approach Co-evolves a population of learners that decompose the instance space by way
   of their aggregate bidding behaviour. To reduce computation overhead, a small, relevant, subset of training exemplars is (competitively) coevolved alongside the learners.
   The approach solves multi-class problems using a single population and is evaluated on three large datasets. It is found to be competitive, especially compared to
   classifier systems, while significantly reducing the computation overhead associated with training.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Peter Lichodzijewski
%A Malcolm I. Heywood
%T Managing team-based problem solving with symbiotic bid-based genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 363--370
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, active learning, classification, coevolution, efficiency, problem decomposition, supervised learning, teaming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p363.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389162

%A Peter Lichodzijewski
%A Malcolm I. Heywood
%T Coevolutionary bid-based genetic programming for problem decomposition in classification
%J Genetic Programming and Evolvable Machines
%V 9
%N 4
%D 2008
%P 331--365
%I 
%K genetic algorithms, genetic programming, Coevolution, Problem decomposition, Teaming, Classification, SVM
%X In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a
   significant departure from models where each individual constructs a direct input-outcome map, for example, from the set of exemplars to the set of class labels as is
   typical under the classification domain. In contrast, the proposed model focuses on learning a bidding strategy based on the exemplar feature vectors; each individual is
   associated with a single discrete action and the individual with the maximum bid wins the right to suggest its action. Thus, the number of individuals associated with each
   action is a function of the intra-action bidding behaviour. Credit assignment is designed to reward correct but unique bidding strategies relative to the target actions. An
   advantage of the model over other teaming methods is its ability to automatically determine the number of and interaction between cooperative team members. The resulting
   model shares several traits with learning classifier systems and as such both approaches are benchmarked on nine large classification problems. Moreover, both of the
   evolutionary models are compared against the deterministic Support Vector Machine classification algorithm. Performance assessment considers the computational,
   classification, and complexity characteristics of the resulting solutions. The bid-based model is found to provide simple yet effective solutions that are robust to wide
   variations in the class representation. Support Vector Machines and classifier systems tend to perform better under balanced datasets albeit resulting in black-box
   solutions.
%8 Decemeber

%A Peter Lichodzijewski
%A Malcolm I. Heywood
%T Symbiosis, complexification and simplicity under GP
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 853--860
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%X Models of Genetic Programming (GP) frequently reflect a neo-Darwinian view to evolution in which inheritance is based on a process of gradual refinement and the resulting
   solutions take the form of single monolithic programs. Conversely, introducing an explicitly symbiotic model of inheritance makes a divide-and-conquer metaphor for problem
   decomposition central to evolution. Benchmarking gradualist versus symbiotic models of evolution under a common evolutionary framework illustrates that not only does
   symbiosis result in more accurate solutions, but the solutions are also much simpler in terms of instruction and attribute count over a wide range of classification problem
   domains.
%8 7-11  July
%Z Also known as \cite1830640 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Peter Lichodzijewski
%A Malcolm Heywood
%T The Rubik Cube and GP Temporal Sequence Learning: An Initial Study
%B Genetic Programming Theory and Practice VIII
%S Genetic and Evolutionary Computation
%E Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva
%V 8
%D 2010
%P 35--54
%I Springer
%C Ann Arbor, USA
%K genetic algorithms, genetic programming
%U http://www.springer.com/computer/ai/book/978-1-4419-7746-5
%O 3
%8 20-22  May
%Z part of \citeRiolo:2010:GPTP

%A Peter Lichodzijewski
%T A Symbiotic Bid-Based Framework for Problem Decomposition using Genetic Programming
%R Ph.D. Thesis
%D 2011
%I 
%I Dalhousie University
%C Halifax, Canada
%K genetic algorithms, genetic programming, problem decomposition, symbiosis, Coevolution, Machine Learning
%U http://dalspace.library.dal.ca/bitstream/handle/10222/13260/Lichodzijewski_Peter.pdf
%X This thesis investigates the use of symbiosis as an evolutionary metaphor for problem decomposition using Genetic Programming. It begins by drawing a connection between
   lateral problem decomposition, in which peers with similar capabilities coordinate their actions, and vertical problem decomposition, whereby solution subcomponents are
   organised into increasingly complex units of organisation. Furthermore, the two types of problem decomposition are associated respectively with context learning and layered
   learning. The thesis then proposes the Symbiotic Bid-Based framework modelled after a three-staged process of symbiosis abstracted from biological evolution. As such, it is
   argued, the approach has the capacity for both types of problem decomposition. Three principles capture the essence of the proposed framework. First, a bid-based approach
   to context learning is used to separate the issues of `what to do' and `when to do it'. Whereas the former issue refers to the problem-specific actions, e.g., class label
   predictions, the latter refers to a bidding behaviour that identifies a set of problem conditions. In this work, Genetic Programming is used to evolve the bids casting the
   method in a non-traditional role as programs no longer represent complete solutions. Second, the proposed framework relies on symbiosis as the primary mechanism of
   inheritance driving evolution, where this is in contrast to the crossover operator often encountered in Evolutionary Computation. Under this evolutionary metaphor, a set of
   symbionts, each representing a solution subcomponent in terms of a bid-action pair, is compartmentalised inside a host. Communication between symbionts is realised through
   their collective bidding behaviour, thus, their cooperation is directly supported by the bid-based approach to context learning. Third, assuming that challenging tasks
   where problem decomposition is likely to play a key role will often involve large state spaces, the proposed framework includes a dynamic evaluation function that
   explicitly models the interaction between candidate solutions and training cases. As such, the computational overhead incurred during training under the proposed framework
   does not depend on the size of the problem state space. An approach to model building, the Symbiotic Bid-Based framework is first evaluated on a set of real-world
   classification problems which include problems with multi-class labels, unbalanced distributions, and large attribute counts. The evaluation includes a comparison against
   Support Vector Machines and AdaBoost. Under temporal sequence learning, the proposed framework is evaluated on the truck reversal and Rubik's Cube tasks, and in the former
   case, it is compared with the Neuroevolution of Augmenting Topologies algorithm. Under both problems, it is demonstrated that the increased capacity for problem
   decomposition under the proposed approach results in improved performance, with solutions employing vertical problem decomposition under temporal sequence learning proving
   to be especially effective.
%8 22  February
%Z http://www.cs.dal.ca/news/presentations/2011-02-22-symbiotic-bid-based-framework-problem-decomposition-using-genetic-prog

%A Thomas Liddle
%A Mark Johnston
%A Mengjie Zhang
%T Multi-Objective Genetic Programming for object detection
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X In object detection, the goals of successfully discriminating between different kinds of objects (object classification) and accurately identifying the positions of all
   objects of interest in a large image (object localisation) are potentially in conflict. We propose a Multi-Objective Genetic Programming (MOGP) approach to the task of
   providing a decision-maker with a diverse set of alternative object detection programs that balance between high detection rate and low false-alarm rate. Experiments on two
   datasets, simple shapes and photographs of coins, show that it is difficult for a Single-Objective GP (SOGP) system (which weights the multiple objectives a priori) to
   evolve effective object detectors, but that an MOGP system is able to evolve a range of effective object detectors more efficiently.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586072

%A Dudy Lim
%A Yew-Soon Ong
%A Yaochu Jin
%A Bernhard Sendhoff
%A Bu Sung Lee
%T Inverse multi-objective robust evolutionary design
%J Genetic Programming and Evolvable Machines
%V 7
%N 4
%D 2006
%P 383--404
%I 
%K Evolutionary algorithms, Robust design optimisation, Design optimisation in the presence of uncertainty
%X we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the
   uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimisation search. To reduce the high computational
   costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the
   objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments
   (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions
   having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances
   efficiently.
%8 Decemeber
%Z p390 'one dimensional Michalewicz 2 function'

%A Ik Soo Lim
%A Daniel Thalmann
%T Indexed Memory as a Generic Protocol for Handling Vectors of Data in Genetic Programming
%B Fifth International Conference on Parallel Problem Solving from Nature
%S LNCS
%E Agoston E. Eiben and Thomas Back and Marc Schoenauer and Hans-Paul Schwefel
%V 1498
%D 1998
%P 325--334
%I Springer-Verlag Berlin
%C Amsterdam
%K genetic algorithms, genetic programming
%X Indexed memory is used as a generic protocol for handling vectors of data in genetic programming. Using this simple method, a single program can generate many outputs. It
   eliminates the complexity of maintaining different trees for each desired parameter and avoid problem-specific function calls for handling the vectors. This allows a single
   set of programming language primitives applicable to wider range of problems. For a test case, the technique is appliedto evolution of behavioural control programs for a
   simulated 2d vehicle in a corridor following problem.
%8 27-30  September
%Z PPSN-V
%@ 3-540-65078-4

%A Ik Soo Lim
%A Daniel Thalmann
%T How Not to Be a Black-Box: Evolution and Genetic-Engineering of High-Level Behaviours
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1329--1335
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-001.pdf
%X In spite of many success stories in various domains, Genetic Algorithm and Genetic Programming still suffer from some significant pitfalls. Those evolved programs often
   lack of some important properties such as robustness, comprehensibility, transparency, modifiability and usability of domain knowledge easily available. We attempt to
   resolve these problems, at least in evolving high-level behaviours, by adopting a technique of conditions-and-behaviours originally used for minimizing the learning space
   in reinforcement learning. We experimentally validate the approach on a foraging task.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99). computer
   animations, foraging task, non-transparency
%@ 1-55860-611-4

%A Soo Ling Lim
%A Peter J. Bentley
%T Evolving Relationships between Social Networks and Stakeholder Involvement in Software Projects
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1899--1906
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, cartesian genetic programming, SBSE, Social network analysis, search-based software engineering
%U http://soolinglim.files.wordpress.com/2010/07/fp551-lim.pdf
%X Software projects often fail because stakeholder communication and involvement are inadequate. This paper proposes a novel method to understand project social networks and
   their corresponding stakeholder involvement. The method uses five types of model social network, which represent various types of stakeholder activity in a project. It
   exploits evolutionary computation to correlate the social network of a real software project against each model. Experiments show that the real project most resembles the
   rational model where stakeholders who are more highly connected in the social network are more involved in the project.
%8 12-16  July
%Z Also known as \cite2001831 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Sungsoo Lim
%A Kyoung-Min Kim
%A Jin-Hyuk Hong
%A Sung-Bae Cho
%T Interactive Genetic Programming for the Sentence Generation of Dialog-Based Travel Planning System
%B Proceedings of The Second Asian-Pacific Workshop on Genetic Programming
%E R I Mckay and Sung-Bae Cho
%D 2004
%I 
%C Cairns, Australia
%K genetic algorithms, genetic programming
%U sclab.yonsei.ac.kr/publications/Papers/IC/ASPGP2004.pdf
%X As dialogue systems have been widely investigated, the research on natural language generation in dialogue has aroused interest. Contrary to conventional dialogue systems
   that reply to the user with a set of predefined answers, a newly developed dialogue system generates them dynamically and trains answers to support more flexible and
   customised dialogues with humans. The paper proposes an evolutionary method for generating sentences using genetic programming. Sentence plan trees, which stand for the
   sentence structure, are adopted as the representation of genetic programming. With interactive evolution process with the user, a set of customized sentence structures is
   obtained. The proposed method applies to a dialogue-based travel planning system and the usability test demonstrates the usefulness of the proposed method.
%8 6-7  Decemeber
%Z http://www.itee.adfa.edu.au/~rim/ASPGP/programme.html

%A Sung-Soo Lim
%A Sung-Bae Cho
%T Language Generation for Conversational Agent by Evolution of Plan Trees with Genetic Programming
%B Modeling Decisions for Artificial Intelligence, Second International Conference, MDAI 2005, Proceedings
%S Lecture Notes in Computer Science
%E Vicenc Torra and Yasuo Narukawa and Sadaaki Miyamoto
%V 3558
%D 2005
%P 305--315
%I Springer
%C Tsukuba, Japan
%K genetic algorithms, genetic programming, Dialogue system, Natural language generation, Interactive genetic programming, Sentence plan tree
%X As dialogue systems are widely demanded, the research on natural language generation in dialogue has raised interest. Contrary to conventional dialogue systems that reply
   to the user with a set of predefined answers, a newly developed dialogue system generates them dynamically and trains answers to support more flexible and customised
   dialogues with humans. The paper proposes an evolutionary method for generating sentences using interactive genetic programming. Sentence plan trees, which stand for the
   sentence structure, are adopted as the representation of genetic programming. With interactive evolution process with the user, a set of customized sentence structures is
   obtained. The proposed method applies to a dialogue-based travel planning system and the usability test demonstrates the usefulness of the proposed method
%8 July  25-27
%@ 3-540-27871-0

%A Yow Tzu Lim
%A Pau Chen Cheng
%A John Andrew Clark
%A Pankaj Rohatgi
%T Policy Evolution with Genetic Programming: A Comparison of Three Approaches
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%P 1792--1800
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming
%X In the early days a policy was a set of simple rules with a clear intuitive motivation that could be formalised to good effect. However the world is now much more complex.
   Subtle risk decisions may often need to be made and people are not always adept at expressing rationale for what they do. Previous research has demonstrated that Genetic
   Programming can be used to infer statements of policies from examples of decisions made [1]. This allows a policy that may not formally have been documented to be
   discovered automatically, or an underlying set of requirements to be extracted by interpreting user decisions to posed ``what if'' scenarios. This study compares the
   performance of three different approaches in using Genetic Programming to infer security policies from decision examples made, namely symbolic regression, IF-THEN rules
   inference and fuzzy membership functions inference. The fuzzy membership functions inference approach is found to have the best performance in terms of accuracy. Also, the
   fuzzification and de-fuzzification methods are found to be strongly correlated; incompatibility between them can have strong negative impact to the performance.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Yow Tzu Lim
%A Pau Chen Cheng
%A Pankaj Rohatgi
%A John Andrew Clark
%T MLS security policy evolution with genetic programming
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1571--1578
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, MLS, policy inference, security policy, Real-World application
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.6020
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389395

%A Yow Tzu Lim
%A Pau-Chen Cheng
%A John Andrew Clark
%A Pankaj Rohatgi
%T Policy Evolution with Grammatical Evolution
%B Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)
%S Lecture Notes in Computer Science
%E Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb
   and Kay Chen Tan and J\"urgen Branke and Yuhui Shi
%V 5361
%D 2008
%P 71--80
%I Springer
%C Melbourne, Australia
%K genetic algorithms, genetic programming, Grammatical Evolution
%X Security policies are becoming more sophisticated. Operational forces will often be faced with making tricky risk decisions and policies must be flexible enough to allow
   appropriate actions to be facilitated. Access requests are no longer simple subject access object matters. There is often a great deal of context to be taken into account.
   Most security work is couched in terms of risk management, but the benefits of actions will need to be taken into account too. In some cases it may not be clear what the
   policy should be. People are often better at dealing with specific examples than producing general rules. In this paper we investigate the use of Grammatical Evolution (GE)
   to attempt to infer Fuzzy MLS policy from decision examples. This approach couches policy inference as a search for a policy that is most consistent with the supplied
   examples set. The results show this approach is promising.
%8 Decemeber  7-10

%A Cheng-Han Lin
%T Application of Genetic Programming to Fuzzy Modeling and the Schedule of Direct Load Control
%R M.S. Thesis ???
%I 
%I Electrical Engineering, National Taipei University of Technology
%C Taiwan
%K genetic algorithms, genetic programming
%U http://etds.ncl.edu.tw/theabs/english_site/search_result_eng.jsp?hot_query=Cheng+Han+Lin&field=AU#
%X Based on a good searching ability of structure, Genetic Programming is designed to search the structural solution and to use crossover mechanism of tree structure to get
   the better structure. In this thesis, we will use the performance of Genetic Programming to solve two optimal questions. (1).Application of Genetic Programming to fuzzy
   modeling: This study is concerned with a general methodology of identification of fuzzy models. Unlike other numeric models, fuzzy models operate at a level of information
   granules (fuzzy sets), and this aspect brings up an important requirement of design on about the transparency of the model. (2).Application of Genetic Programming to the
   schedule of direct load control: Based on the searching ability of Genetic Programming, we can find an optimal control strategy to reduce the peak load.

%A Jung-Yi Lin
%A Been-Chian Chien
%A Tzung-Pei Hong
%T A Function-Based Classifier Learning Scheme Using Genetic Programming
%B Advances in Knowledge Discovery and Data Mining : 6th Pacific-Asia Conference, PAKDD 2002
%S Lecture Notes in Computer Science
%E M.-S. Chen and P. S. Yu and B. Liu
%V 2336
%D 2002
%P 92--103
%I Springer-Verlag Heidelberg
%C Taipel, Taiwan
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2336/23360092.pdf", acknowledgement = ack-nhfb
%X Classification is an important research topic in knowledge discovery and data mining. Many different classifiers have been motivated and developed of late years. In this
   paper, we propose an effective scheme for learning multi-category classifiers based on genetic programming. For a $k$-class classification problem, a training strategy
   called adaptive incremental learning strategy and a new fitness function are used to generate $k$ discriminant functions. We urge the discriminant functions to map the
   domains of training data into a specified interval, and thus data will be assigned into one of the classes by the values of functions. Furthermore, a $Z$-value measure is
   developed for resolving the conflicts. The experimental results show that the proposed GP-based classification learning approach is effective and performs a high accuracy
   of classification.
%8 6-8  May
%Z http://arbor.ee.ntu.edu.tw/pakdd02/ chinese version http://www.bohr.idv.tw/chinese/pdf/B013.pdf

%A Jung-Yi Lin
%A Hao-Ren Ke
%A Been-Chian Chien
%A Wei-Pang Yang
%T Classifier design with feature selection and feature extraction using layered genetic programming
%J Expert Systems with Applications
%V 34
%N 2
%D 2007
%P 1384--1393
%I 
%K genetic algorithms, genetic programming, Feature generation, Feature selection, Pattern classification, Multi-population genetic programming, Layered genetic programming
%X This paper proposes a novel method called FLGP to construct a classifier device of capability in feature selection and feature extraction. FLGP is developed with layered
   genetic programming that is a kind of the multiple-population genetic programming. Populations advance to an optimal discriminant function to divide data into two classes.
   Two methods of feature selection are proposed. New features extracted by certain layer are used to be the training set of next layer's populations. Experiments on several
   well-known datasets are made to demonstrate performance of FLGP.
%8 February
%Z a Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Road, HsinChu 300, Taiwan b Library and Institute of Information Management, National Chiao
   Tung University, Taiwan c Department of Computer Science and Information Engineering, National University of Tainan, Taiwan d Department of Information Management, National
   Dong Hwa University, Taiwan

%A Jung-Yi Lin
%A Hao-Ren Ke
%A Been-Chian Chien
%A Wei-Pang Yang
%T Designing a classifier by a layered multi-population genetic programming approach
%J Pattern Recognition
%V 40
%N 8
%D 2007
%P 2211--2225
%I 
%K genetic algorithms, genetic programming, Classification, Evolutionary computation, Multi-population genetic programming
%U http://www.sciencedirect.com/science/article/B6V14-4MVVSM4-5/2/2085e138e1b34ae21d5e76438ae3fc70
%X This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer
   architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions
   transform the training set to construct a new training set. The successive layer uses the new training set to obtain better discriminant functions. Moreover, because the
   functions generated by each layer will be composed to a long discriminant function, which is the result of LAGEP, every layer can evolve with short individuals. For each
   population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations. Several experiments are
   conducted with different settings of LAGEP and several real-world medical problems. Experiment results show that LAGEP achieves comparable accuracy to single population GP
   in much less time.
%O Part Special Issue on Visual Information Processing

%A Jung-Yi Lin
%T Layered Multi-Population Genetic Programming And Its Applications
%R Ph.D. Thesis
%D 2007
%I 
%I Computer Science, National Chiao Tung University (NCTU)
%C HsinChu, Taiwan
%K genetic algorithms, genetic programming
%U http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/ccd=CYyGVt/result#result
%Z Supervisor: Dr. Wei-Pang Yang, Dr. Been-Chian Chien

%A Jung-Yi Lin
%T Cancer classification using microarray and layered architecture genetic programming
%B GECCO-2009 Late-Breaking Papers
%E Anna I. Esparcia and Ying-ping Chen and Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and Hans-Georg Beyer and Nikolaus Hansen and Steffen Finck and
   Raymond Ros and Darrell Whitley and Garnett Wilson and Simon Harding and W. B. Langdon and Man Leung Wong and Laurence D. Merkle and Frank W. Moore and Sevan G. Ficici and
   William Rand and Rick Riolo and Nawwaf Kharma and William R. Buckley and Julian Miller and Kenneth Stanley and Jaume Bacardit and Will Browne and Jan Drugowitsch and Nicola
   Beume and Mike Preuss and Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and Alexandru Floares and Aaron Baughman and Steven Gustafson and Maarten Keijzer and Arthur
   Kordon and Clare Bates Congdon and Laurence D. Merkle and Frank W. Moore
%D 2009
%P 2085--2090
%I ACM New York, NY, USA
%I SigEvo
%C Montreal
%K genetic algorithms, genetic programming
%X An important problem of cancer diagnosis and treatment is to distinguish tumors from malignant or benign. Classifying tumors correctly leads us to target specific therapies
   properly to maximizing efficiency and reducing toxicity. Through the microarray technology, it is possible that monitoring expression in cells for numerous of genes
   simultaneously. Therefore we are allowed to use potential information hidden in the gene expression data to build a more accurate and more reliable classification model on
   tumor samples. In this paper we intend to investigate a new approach for cancer classification using genetic programming and microarray gene expression profiles. The
   layered architecture genetic programming (LAGEP) is applied to build the classification model. Some typical cancer gene expression datasets are validated to demonstrate the
   classification accuracy of the proposed model.
%8 8-12  July
%Z Distributed on CD-ROM at GECCO-2009. ACM Order Number 910092.

%A Jung Yi Lin
%T Fitness enhancement of layered architecture genetic programming
%B 2010 International Computer Symposium (ICS)
%D 2010
%P 700--704
%I 
%K genetic algorithms, genetic programming, classification problems, fitness enhancement, high dimensional gene expression dataset, layered architecture genetic programming,
   pattern classification
%X Layered architecture genetic programming (LAGEP) has been applied on variety classification problems. It organises populations as layers. Populations in different layers
   evolve with different training sets. Individuals produced by populations of layer Li transform training instances into new ones. Populations in Li+1 then evolve with the
   new training set instead of evolve with the original given training set. Each population in Li produces one feature for the new training instances. New training instances
   could have fewer features and are easier to be classified. Such mechanism makes consecutive layer gain better fitness value than preceding layers do. At this paper, we
   intend to analyse the enhancement of fitness value over all layers. We conduct experiments with a high-dimensional gene expression dataset to show the fitness enhancement.
%8 16-18  Decemeber
%Z Also known as \cite5685423

%A Ping-Chen Lin
%A Jiah-Shing Chen
%T FuzzyTree crossover for multi-valued stock valuation
%J Information Sciences
%V 177
%N 5
%D 2007
%P 1193--1203
%I 
%K genetic algorithms, genetic programming, Multi-valued stock valuation, Intrinsic value, Fuzzy number
%X Stock valuation is very important for fundamental investors in order to select undervalued stocks so as to earn excess profits. However, it may be difficult to use stock
   valuation results, because different models generate different estimates for the same stock. This suggests that the value of a stock should be multi-valued rather than
   single-valued. We therefore develop a multi-valued stock valuation model based on fuzzy genetic programming (GP). In our fuzzy GP model the value of a stock is represented
   as a fuzzy expression tree whose terminal nodes are allowed to be fuzzy numbers. There is scant literature available on the crossover operator for our fuzzy trees, except
   for the vanilla subtree crossover. This study generalises the subtree crossover in order to design a new crossover operator for the fuzzy trees. Since the stock value is
   estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued. In addition, the resulting fuzzy stock value induces a
   natural trading strategy which can readily be executed and evaluated. These experimental results indicate that the fuzzy tree (FuzzyTree) crossover is more effective than a
   subtree (SubTree) crossover in terms of expression tree complexity and run time. Secondly, shorter training periods produce a better return of investment (ROI), indicating
   that long-term financial statements may distort the intrinsic value of a stock. Finally, the return of a multi-valued fuzzy trading strategy is better than that of
   single-valued and buy-and-hold strategies.
%O Including: The 3rd International Workshop on Computational Intelligence in Economics and Finance (CIEF'2003)
%8 1  March

%A Wen-Yang Lin
%T Improving Parallel Ordering of Sparse Matrices using Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1790
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications, poster papers
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-776.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Yingqiang Lin
%A Bir Bhanu
%T Object Detection via Feature Synthesis Using MDL-Based Genetic Programming
%J IEEE Transactions on Systems, Man and Cybernetics, Part B
%V 35
%N 3
%D 2005
%P 538--547
%I 
%K genetic algorithms, genetic programming, Feature learning, minimum description length (MDL), primitive feature image, primitive operator, synthetic aperture radar (SAR)
   image
%U http://ieeexplore.ieee.org/iel5/3477/30862/01430837.pdf
%X we use genetic programming (GP) to synthesise composite operators and composite features from combinations of primitive operations and primitive features for object
   detection. The motivation for using GP is to overcome the human experts' limitations of focusing only on conventional combinations of primitive image processing operations
   in the feature synthesis. GP attempts many unconventional combinations that in some cases yield exceptionally good results. To improve the efficiency of GP and prevent its
   well-known code bloat problem without imposing severe restriction on the GP search, we design a new fitness function based on minimum description length principle to
   incorporate both the pixel labelling error and the size of a composite operator into the fitness evaluation process. To further improve the efficiency of GP, smart
   crossover, smart mutation and a public library ideas are incorporated to identify and keep the effective components of composite operators. Our experiments, which are
   performed on selected training regions of a training image to reduce the training time, show that compared to normal GP, our GP algorithm finds effective composite
   operators more quickly and the learned composite operators can be applied to the whole training image and other similar testing images. Also, compared to a traditional
   region-of-interest extraction algorithm, the composite operators learned by GP are more effective and efficient for object detection.
%8 June

%A Yingqiang Lin
%A Bir Bhanu
%T Evolutionary feature synthesis for object recognition
%J IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews
%V 35
%N 2
%D 2005
%P 156--171
%I 
%K genetic algorithms, genetic programming, feature extraction, object recognition, radar imaging, synthetic aperture radar, SAR images, coevolutionary genetic programming
   approach, domain-independent primitive operator, evolutionary feature synthesis, human experts, object recognition, real synthetic aperture radar, vehicle recognition
%X Features represent the characteristics of objects and selecting or synthesising effective composite features are the key to the performance of object recognition. In this
   paper, we propose a coevolutionary genetic programming (CGP) approach to learn composite features for object recognition. The knowledge about the problem domain is
   incorporated in primitive features that are used in the synthesis of composite features by CGP using domain-independent primitive operators. The motivation for using CGP is
   to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. CGP, on the other hand,
   can try a very large number of unconventional combinations and these unconventional combinations yield exceptionally good results in some cases. Our experimental results
   with real synthetic aperture radar (SAR) images show that CGP can discover good composite features to distinguish objects from clutter and to distinguish among objects
   belonging to several classes. The comparison with other classical classification algorithms is favourable to the CGP-based approach proposed in this paper.
%8 May

%A Yi-Shen Lin
%A Xiao-Ting Liang
%T Gene expression programming with parallel hybrid model
%B 2010 International Conference on Machine Learning and Cybernetics (ICMLC)
%V 5
%D 2010
%P 2406--2409
%I 
%K genetic algorithms, genetic programming, gene expression programming, GEP algorithm, distributed model, island-model, parallel hybrid model, evolutionary computation,
   mathematical programming, parallel processing
%X In this paper we discussed a hybrid parallel and distributed model and their relationships of diversity phenomenon. We study the synchronous and asynchronous version of the
   island-model in GEP algorithm. The experiments that we have performed have allowed us to find an interesting link between the subpopulations and the parameters setting to
   GEP.
%8 July
%Z Coll. of Inf., South China Agric. Univ., Guangzhou, China Also known as \cite5580717

%A D. Linaro
%A M. Storace
%T A method based on a genetic algorithm to find PWL approximations of multivariate nonlinear functions
%B Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on
%D 2008
%P 336--339
%I 
%K genetic algorithms, Hodgkin-Huxley neuron model, circuit, multivariate continuous nonlinear functions, piecewise-linear approximations, nonlinear network analysis,
   piecewise linear techniques
%8 May
%Z Not on GP

%A Fredrik Lindblad
%A Peter Nordin
%A Krister Wolff
%T Evolving 3D model interpretation of images using graphics hardware
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 225--230
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming, machine vision, GPU
%U http://fy.chalmers.se/~wolff/LNW_wcci02.pdf
%X We present a novel approach for 3d-scene interpretation with numerous applications, for instance in robotics. The models are rendered using 3d graphics hardware and
   DirectX. Both artificial and real images were used to test the system. More than one target image can be used, allowing stereoscopic vision. These experiments present
   results of interesting generalization.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Ricardo Linden
%A Amit Bhaya
%T Evolving fuzzy rules to model gene expression
%J Biosystems
%V 88
%N 1-2
%D 2007
%P 76--91
%I 
%K genetic algorithms, genetic programming, Fuzzy logic, Microarrays, Reverse engineering, Gene regulatory network
%X This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic.
   Reverse polish notation is used (RPN) to describe the rules and to facilitate the GP approach. The algorithm also allows for the insertion of prior knowledge, making it
   possible to find sets of rules that include the relationships between genes already known. The algorithm proposed is applied to problems arising in the construction of gene
   regulatory networks, using two different sets of real data from biological experiments on the Arabidopsis thaliana cold response and the rat central nervous system,
   respectively. The results show that the proposed technique can fit data to a pre-defined precision even in situations where the data set has thousands of features but only
   a limited number of points in time are available, a situation in which traditional statistical alternatives encounter difficulties, due to the scarcity of time points.
%8 March

%A Gwenda Lindhorst
%T Relational Genetic Algorithms: With application to Surface Mount Technology Placement Machines
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 543--550
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A J. M. Link
%A P. M. Yager
%A J. C. Anjos
%A I. Bediaga
%A C. Castromonte
%A C. Gobel
%A A. A. Machado
%A J. Magnin
%A A. Massafferri
%A J. M. {de Miranda}
%A I. M. Pepe
%A E. Polycarpo
%A A. C. {dos Reis}
%A S. Carrillo
%A E. Casimiro
%A E. Cuautle
%A A. Sanchez-Hernandez
%A C. Uribe
%A F. Vazquez
%A L. Agostino
%A L. Cinquini
%A J. P. Cumalat
%A B. O'Reilly
%A I. Segoni
%A K. Stenson
%A J. N. Butler
%A H. W. K. Cheung
%A G. Chiodini
%A I. Gaines
%A P. H. Garbincius
%A L. A. Garren
%A E. Gottschalk
%A P. H. Kasper
%A A. E. Kreymer
%A R. Kutschke
%A M. Wang
%A L. Benussi
%A M. Bertani
%A S. Bianco
%A F. L. Fabbri
%A S. Pacetti
%A A. Zallo
%A M. Reyes
%A C. Cawlfield
%A D. Y. Kim
%A A. Rahimi
%A J. Wiss
%A R. Gardner
%A A. Kryemadhi
%A Y. S. Chung
%A J. S. Kang
%A B. R. Ko
%A J. W. Kwak
%A K. B. Lee
%A K. Cho
%A H. Park
%A G. Alimonti
%A S. Barberis
%A M. Boschini
%A A. Cerutti
%A P. D'Angelo
%A M. DiCorato
%A P. Dini
%A L. Edera
%A S. Erba
%A P. Inzani
%A F. Leveraro
%A S. Malvezzi
%A D. Menasce
%A M. Mezzadri
%A L. Moroni
%A D. Pedrini
%A C. Pontoglio
%A F. Prelz
%A M. Rovere
%A S. Sala
%A T. F. {Davenport III}
%A V. Arena
%A G. Boca
%A G. Bonomi
%A G. Gianini
%A G. Liguori
%A D. {Lopes Pegna}
%A M. M. Merlo
%A D. Pantea
%A S. P. Ratti
%A C. Riccardi
%A P. Vitulo
%A H. Hernandez
%A A. M. Lopez
%A H. Mendez
%A A. Paris
%A J. Quinones
%A J. E. Ramirez
%A Y. Zhang
%A J. R. Wilson
%A T. Handler
%A R. Mitchell
%A D. Engh
%A M. Hosack
%A W. E. Johns
%A E. Luiggi
%A J. E. Moore
%A M. Nehring
%A P. D. Sheldon
%A E. W. Vaandering
%A M. Webster
%A M. Sheaff
%T Application of Genetic Programming to High Energy Physics Event Selection
%J Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
%V A551
%N 2-3
%D 2005
%P 504--527
%I 
%K genetic algorithms, genetic programming, Event selection, Classification
%U http://arxiv.org/abs/hep-ex/0503007
%X We review genetic programming principles, their application to FOCUS data samples, and use the method to study the doubly Cabibbo suppressed decay D+ -> K+ pi+ pi- relative
   to its Cabibbo favoured counterpart, D+ -> K- pi+ pi+. We find that this technique is able to improve upon more traditional analysis methods. To our knowledge, this is the
   first application of the genetic programming technique to High Energy Physics data.
%O The FOCUS Collaboration
%8 11  October
%Z lilgp, PACS: 02.50.Sk, 07.05.Kf, 13.25.Ft, Journal-ref: Nucl.Instrum.Meth. A551 (2005) 504-527 see \citeLink:2005ym

%A J. M. Link
%A P. M. Yager
%A J. C. Anjos
%A I. Bediaga
%A C. Castromonte
%A A. A. Machado
%A J. Magnin
%A A. Massafferri
%A J. M. {de Miranda}
%A I. M. Pepe
%A E. Polycarpo
%A A. C. {dos Reis}
%A S. Carrillo
%A E. Casimiro
%A E. Cuautle
%A A. Sanchez-Hernandez
%A C. Uribe
%A F. Vazquez
%A L. Agostino
%A L. Cinquini
%A J. P. Cumalat
%A B. O'Reilly
%A I. Segoni
%A K. Stenson
%A J. N. Butler
%A H. W. K. Cheung
%A G. Chiodini
%A I. Gaines
%A P. H. Garbincius
%A L. A. Garren
%A E. Gottschalk
%A P. H. Kasper
%A A. E. Kreymer
%A R. Kutschke
%A M. Wang
%A L. Benussi
%A M. Bertani
%A S. Bianco
%A F. L. Fabbri
%A S. Pacetti
%A A. Zallo
%A M. Reyes
%A C. Cawlfield
%A D. Y. Kim
%A A. Rahimi
%A J. Wiss
%A R. Gardner
%A A. Kryemadhi
%A Y. S. Chung
%A J. S. Kang
%A B. R. Ko
%A J. W. Kwak
%A K. B. Lee
%A K. Cho
%A H. Park
%A G. Alimonti
%A S. Barberis
%A M. Boschini
%A A. Cerutti
%A P. D'Angelo
%A M. DiCorato
%A P. Dini
%A L. Edera
%A S. Erba
%A P. Inzani
%A F. Leveraro
%A S. Malvezzi
%A D. Menasce
%A M. Mezzadri
%A L. Moroni
%A D. Pedrini
%A C. Pontoglio
%A F. Prelz
%A M. Rovere
%A S. Sala
%A T. F. {Davenport III}
%A V. Arena
%A G. Boca
%A G. Bonomi
%A G. Gianini
%A G. Liguori
%A D. {Lopes Pegna}
%A M. M. Merlo
%A D. Pantea
%A S. P. Ratti
%A C. Riccardi
%A P. Vitulo
%A C. Gobel
%A H. Hernandez
%A A. M. Lopez
%A H. Mendez
%A A. Paris
%A J. Quinones
%A J. E. Ramirez
%A Y. Zhang
%A J. R. Wilson
%A T. Handler
%A R. Mitchell
%A D. Engh
%A M. Hosack
%A W. E. Johns
%A E. Luiggi
%A J. E. Moore
%A M. Nehring
%A P. D. Sheldon
%A E. W. Vaandering
%A M. Webster
%A M. Sheaff
%T Search for Lambda/c+ --> p K+ pi- and D/s+ --> K+ K+ pi- using genetic programming event selection
%J Physics Letters B
%V B624
%N 3-4
%D 2005
%P 166--172
%I 
%K genetic algorithms, genetic programming
%U http://arxiv.org/pdf/hep-ex/0507103
%X We apply a genetic programming technique to search for the doubly Cabibbo suppressed decays \lambda +c to pK+p- and D+s to K+K+p-. We normalise these decays to their
   Cabibbo favoured partners and find BR(\lambda to pK+p-)/BR(\lambda to pK-p+) = (0.05 \pm 0.26 \pm 0.02)% and BR(D+s to K+K+p-)/BR(D to K-K+p+) = (0.52 \pm 0.17 \pm 0.11)
   percent where the first errors are statistical and the second are systematic. Expressed as 90 percent confidence levels (CL), we find < 0.46 percent and < 0.78 percent
   respectively. This is the first successful use of genetic programming in a high energy physics data analysis.
%8 29  September
%Z http://www-focus.fnal.gov/authors.html for additional author information. PACS: 13.25.Ft; 13.30.Eg see \citeoai:arXiv.org:hep-ex/0503007

%A Shie-Yui Liong
%A Tirtha Raj Gautam
%A Soon Thiam Khu
%A Vladan Babovic
%A Maarten Keijzer
%A Nitin Muttil
%T Genetic Programming: A New Paradigm in Rainfall Runoff Modeling
%J Journal of American Water Resources Association
%V 38
%N 3
%D 2002
%P 705--718
%I 
%K genetic algorithms, genetic programming, Rainfall-runoff relationships, Runoff forecasting, Rainfall-runoff models, Algorithms, Singapore, Upper Bukit Timah catchment
%U http://www.awra.org/jawra/papers/J00146.html
%X Genetic Programming (GP) is a domain-independent evolutionary programming technique that evolves computer programs to solve, or approximately solve, problems. To verify
   GP's capability, a simple example with known relation in the area of symbolic regression, is considered first. GP is then used as a flow forecasting tool. A catchment in
   Singapore with a drainage area of about 6 km2 is considered in this study. Six storms of different intensities and durations are used to train GP and then verify the
   trained GP. Analysis of the GP induced rainfall and runoff relationship shows that the cause and effect relationship between rainfall and runoff is consistent with the
   hydrologic process. The result shows that the runoff prediction accuracy of symbolic regression based models, measured in terms of root mean square error and correlation
   coefficient, is reasonably high. Thus, GP induced rainfall runoff relationships can be a viable alternative to traditional rainfall runoff models.
%8 June
%Z AWRA Paper Number 00146

%A Hod Lipson
%A Jordan B. Pollack
%T Automatic design and manufacture of robotic lifeforms
%J Nature
%N 406
%D 2000
%P 974--978
%I 
%K genetic algorithms, genetic programming, evolutionary programming, evolutionstrategies
%U http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v406/n6799/full/406974a0_fs.html&content_filetype=pdf
%X Biological life is in control of its own means of reproduction, which generally involves complex, autocatalysing chemical reactions. But this autonomy of design and
   manufacture has not yet been realized artificially. Robots are still laboriously designed and constructed by teams of human engineers, usually at considerable expense. Few
   robots are available because these costs must be absorbed through mass production, which is justified only for toys, weapons and industrial systems such as automatic teller
   machines. Here we report the results of a combined computational and experimental approach in which simple electromechanical systems are evolved through simulations from
   basic building blocks (bars, actuators and artificial neurons); the 'fittest' machines (defined by their locomotive ability) are then fabricated robotically using rapid
   manufacturing technology. We thus achieve autonomy of design and construction using evolution in a 'limited universe' physical simulation coupled to automatic fabrication.
%8 31  August
%Z Note I have filed as GP even though the authors state they are not using GP (their genetic search uses only mutation) however (as far as I can tell) the representation of
   the genome is variable length. Nice mpeg videos online at www.nature.com

%A Hod Lipson
%T How to Draw a Straight Line Using a GP: Benchmarking Evolutionary Design Against 19th Century Kinematic Synthesis
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP063.pdf
%X This paper discusses the application of genetic programming to the synthesis of compound 2D kinematic mechanisms, and benchmarks the results against one of the classical
   kinematic challenges of 19th century mechanical design. Considerations for selecting a representation for mechanism design are presented, and a number of human-competitive
   inventions are shown.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Hod Lipson
%T Evolutionary synthesis of kinematic mechanisms
%J Artificial Intelligence for Engineering Design, Analysis and Manufacturing
%V 22
%N 3
%D 2008
%P 195--205
%I 
%K genetic algorithms, genetic programming
%X This paper discusses the application of genetic programming to the synthesis of compound two-dimensional kinematic mechanisms, and benchmarks the results against one of the
   classical kinematic challenges of 19th century mechanical design. Considerations for selecting a representation for mechanism design are presented, and a number of
   human-competitive inventions are shown.

%A Michael L. Littman
%T Memoryless Policies: Theoretical limitations and practical results
%B Simulation of Adaptive Behaviour (SAB-94)
%D 1994
%P 238--245
%I 
%I Brown University / Bellcore
%K genetic algorithms
%Z Discusses designing agents to solve completely known problems. Agents 1) are entriely reactive or 2) finite state machines (1 bit memory). Determinstic. Proof given:
   satisfactory determinsistic memory less agent is NP complete problem. So too is design of optimal agent. Presents method which _may_ be able to find optimal solution in
   polynomial time. Shown producing optimal or near optimal agents in almost all runs on three differnt problems.

%A V. I. Litvinenko
%A P. I. Bidyuk
%A J. N. Bardachov
%A V. G. Sherstjuk
%A A. A. Fefelov
%T Combining Clonal Selection Algorithm and Gene Expression Programming for Time Series Prediction
%B Proceedings of the Third Workshop 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications
%D 2005
%P 133--138
%I IEEE
%C Sofia, Bulgaria
%K genetic algorithms, genetic programming, Gene Expression Programming, artificial immune systems, clonal selection algorithm, time series
%X Dynamic system identification algorithm is developed using the basic mechanisms of clonal selection and idea of a new evolutionary computing paradigm - gene expression
   programming. On the basis of the algorithm developed a computer based system is proposed for making decisions relevant to forecasting of single variable and multivariate
   time series. The results of computing experiments achieved with the system developed show high quality of short and medium period forecasts.
%8 5-7  September

%A David Liu
%T Development of Game-Playing Strategies in a Darwinistic Game Using Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 261--268
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z part of \citekoza:2000:gagp

%A Fang Liu
%A Antoaneta Serguieva
%A Paresh Date
%T A mixed-game and co-evolutionary genetic programming agent-based model of financial contagion
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X Over the past two decades, financial market crises with similar features have occurred in different regions of the world. Unstable cross-market linkages during financial
   crises are referred to as financial contagion. We simulate the transmission of financial crises in the context of a model of market participants adopting various
   strategies; this allows testing for financial contagion under alternative scenarios. Using a comprehensive approach, we develop an agent-based multinational model and
   investigate the reasons for contagion. Our model comprises four types of traders: noise, herd, game, and technical traders respectively. Different types of traders use
   different computational strategies to make buy, sell, or hold decisions. Although contagion has been extensively investigated in the financial literature, it has not yet
   been studied through computational intelligence techniques. Our simulations shed light on parameter values and characteristics which can be exploited to detect contagion at
   an earlier stage, hence recognising financial crises with the potential to destabilise cross-market linkages. In the real world, such information would be extremely
   valuable to develop appropriate risk management strategies.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586243

%A Guiquan Liu
%A Xiufang Jiang
%A Lingyun Wen
%T A Clustering System for Gene Expression Data Based upon Genetic Programming and the HS-Model
%B Third International Joint Conference on Computational Science and Optimization (CSO)
%V 1
%D 2010
%P 238--241
%I 
%K genetic algorithms, genetic programming, hierarchical statistical
%X Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods
   existed at present usually need manual operations or pre-determined parameters, which are difficult for gene data. Besides, gene data possess their own characteristics,
   such as large scale, high-dimension, and noise. Therefore, a systematic clustering algorithm should be proposed to effectively deal with gene data. In this paper, a novel
   genetic programming (GP) clustering system for gene data based on hierarchical statistical model (HS-model) is proposed. And an appropriate fitness function is also
   proposed in this system. This clustering system can largely eliminate the infection of data scale and dimension. The proposed GP clustering system is applied to cluster the
   whole intact yeast gene data without dimensionality reduction. The experimental results indicate that the algorithm is highly efficient and can effectively deal with
   missing values in gene dataset.
%8 28-31  May
%Z Key Laboratory of Software in Computing and Communication, Anhui Province School of Computer Science and Technology University of Science and Technology of China, Hefei,
   Anhui 230027, China Also known as \cite5532998

%A Heng Liu
%A Julian F. Miller
%A Andy M. Tyrrell
%T Intrinsic Evolvable Hardware Implementation of a Robust Biological Development Model for Digital Systems
%B Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
%E Jason Lohn and David Gwaltney and Gregory Hornby and Ricardo Zebulum and Didier Keymeulen and Adrian Stoica
%D 2005
%P 87--92
%I IEEE Press IEEE Service Center 445 Hoes Lane Asia P.O. Box 1331 Piscataway, NJ 08855-1331
%I NASA, DoD
%C Washington, DC, USA
%K genetic algorithms, genetic programming, EHW
%X An intrinsic evolvable hardware platform was realized to accelerate the evolutionary search process of a biologically inspired developmental model targeted at off the shelf
   FPGA implementation. The model has the capability of exhibiting very large transient fault-tolerance. The evolved circuits make up a digital "organism" from identical cells
   which only differ in internal states. Organisms implementing a 2-bit multiplier were evolved that can "recover" from almost any kinds of transient faults. This paper
   focuses on the design concerns and details of the evolvable hardware system, including the digital organism/cell and the intrinsic FPGA-based evolvable hardware platform.
%8 29  June -1  July
%Z EH2005 IEEE Computer Society Order Number P2399
%@ 0-7695-2399-4

%A Hongwei Liu
%A Hitoshi Iba
%T Multi-agent Learning of Heterogeneous Robots by Evolutionary Subsumption
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1715--1728
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming
%U http://www.iba.k.u-tokyo.ac.jp/papers/2003/lhwGECCO2003.pdf
%X Many multi-robot systems are heterogeneous cooperative systems, systems consisting of different species of robots cooperating with each other to achieve a common goal. This
   paper presents the emergence of cooperative behaviors of heterogeneous robots by means of GP. Since directly using GP to generate a controller for complex behaviors is
   inefficient and intractable, especially in the domain of multi-robot systems, we propose an approach called Evolutionary Subsumption, which applies GP to subsumption
   architecture. We test our approach in an "eye"-"hand" cooperation problem. By comparing our approach with direct GP and artificial neural network (ANN) approaches, our
   experimental results show that ours is more efficient in emergence of complex behaviors.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Hongwei Liu
%A Hitoshi Iba
%T A Hierarchical Approach for Adaptive Humanoid Robot Control
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 1546--1553
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Real-world applications, Evolutionary design \& evolvable hardware, CBR
%U http://www.iba.k.u-tokyo.ac.jp/papers/2004/lhwCEC2004.pdf
%X The key idea in our approach is to extract control rules with GP in simplified simulation and get a prototype of the control program then interpret and interpolate it with
   CBR in the real world environments. Accordingly, our proposed approach consists of two stages: the evolution stage and the adaptation stage. In the first stage, the
   prototype of the control program is evolved based on abstract primitive behaviors in a highly simplified simulation. In the second stage, the best control program is
   applied to a physical robot thereby adapting it to the real world environments by using CBR.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Hongwei Liu
%A Hitoshi Iba
%T An Evolution-Adapation Approach of Genetic Programming for Programming Humanoid Robot
%B Proceedings of The Second Asian-Pacific Workshop on Genetic Programming
%E R I Mckay and Sung-Bae Cho
%D 2004
%I 
%C Cairns, Australia
%K genetic algorithms, genetic programming
%8 6-7  Decemeber
%Z http://www.itee.adfa.edu.au/~rim/ASPGP/programme.html

%A Hongwei Liu
%A Hitoshi Iba
%T Humanoid Robot Programming Based on CBR Augmented GP
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 708--709
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming, Poster
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030708.htm
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)
%@ 3-540-22343-6

%A Heng Liu
%A Julian F. Miller
%A Andy M. Tyrrell
%T An Intrinsic Robust Transient Fault-Tolerant Developmental Model for Digital Systems
%B GECCO 2004 Workshop Proceedings
%E R. Poli and S. Cagnoni and M. Keijzer and E. Costa and F. Pereira and G. Raidl and S. C. Upton and D. Goldberg and H. Lipson and E. de Jong and J. Koza and H. Suzuki and H.
   Sawai and I. Parmee and M. Pelikan and K. Sastry and D. Thierens and W. Stolzmann and P. L. Lanzi and S. W. Wilson and M. O'Neill and C. Ryan and T. Yu and J. F. Miller and
   I. Garibay and G. Holifield and A. S. Wu and T. Riopka and M. M. Meysenburg and A. W. Wright and N. Richter and J. H. Moore and M. D. Ritchie and L. Davis and R. Roy and M.
   Jakiela
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, cartesian genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WWOR005.pdf
%X A biologically inspired developmental model targeted at hardware implementation (off-shelf FPGA) is proposed which exhibits extremely robust transient fault-tolerant
   capability: in the software simulation of the experimental application. In a 6x6 cell French Flag, some individuals were discovered using evolution that have the ability to
   "recover" themselves from almost any kinds of transient faults, even in the worst case of only one "live" cell remaining. All cells in this model have identical genotype
   (physical structures), and only differ in internal states.
%8 26-30  June
%Z Distributed on CD-ROM at GECCO-2004

%A Heng Liu
%T Biological Development model for the design of Robust Digital System
%R Ph.D. Thesis
%D 2008
%I 
%I Electronic Engineering, York University
%C UK
%K genetic algorithms, genetic programming, Cartesian genetic programming, Fault-tolerance, Evolvable hardware, FPGA, Development principle, Multicellular organism,
   Evolutionary algorithm, French Flag, Multiplier, Digital circuit, Autonomous Robot Controller
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/HengLiu_thesis.pdf
%X This thesis presents a biologically-inspired developmental model for the design of digital circuits. Circuits have been evolved that exhibit the ability to self-repair and
   correct transient faults to recover correct functionality. The method devised gives no explicit coordinate information to the evolved cell circuits. The method presented
   has been implemented fully in electronic hardware. This allowed developmental circuits to be evolved considerably more quickly than in software simulation. The methods
   presented have been applied to produce a self-repairing two bit multiplier and an autonomous robot controller circuit. Results are presented that shows that after
   introduction of faults, both circuits can autonomously recover correct functionality.
%8 September
%Z Liu Heng created a hardware model based on my developmental 'French flag' work. He introduced an execution unit in each cell, whose code was evolved (written in CGP),
   together with developmental code (also in CGP). He was able to evolve a self-repairing 2-bit parallel multiplier and also robot controllers, which recovered autonomously
   fater damage. EO, Xilinx XCV1000, Celoxia RC1000, PLX PCI9080, multiplier, Kiki robot Supervised by Julian Francis Miller and Andy Tyrrell

%A Jie Liu
%A Yaxiong Xie
%A Ryan Cooper
%A Danica M. K. Ducharme
%A Raymond Tennant
%A Bhalchandra A. Diwan
%A Michael P. Waalkes
%T Transplacental exposure to inorganic arsenic at a hepatocarcinogenic dose induces fetal gene expression changes in mice indicative of aberrant estrogen signaling and
   disrupted steroid metabolism
%J Toxicology and Applied Pharmacology
%V 220
%N 3
%D 2007
%P 284--291
%I 
%U http://www.sciencedirect.com/science/article/B6WXH-4N0HJBH-4/2/17b4a380d5a6ecedeb7db7df525f7fb9
%Z Not on GP

%A J. Liu
%A S. Ghafari
%A W. Wang
%A F. Golnaraghi
%A F. Ismail
%T Bearing Fault Diagnostics Based on Reconstructed Features
%B IEEE Industry Applications Society Annual Meeting, IAS '08
%D 2008
%P 1--7
%I 
%K genetic algorithms, genetic programming, bearing condition monitoring, bearing fault diagnostic technique, fault diagnostic reliability, feature reconstruction, modified
   kurtosis ratio, one-scale wavelet analysis, condition monitoring, fault diagnosis, feature extraction, image reconstruction, machine bearings, wavelet transforms
%X Rolling-element bearings are widely used in various mechanical and electrical systems. A reliable bearing fault diagnostic technique is critically needed in industries to
   recognize a bearing fault at its early stage so as to prevent system's performance degradation and malfunction. In this work, a genetic programming based feature
   reconstruction approach is proposed for bearing fault diagnostics. A new fitness measure is proposed to improve the GP operations in feature formulation. The original
   features are from the modified kurtosis ratio and the one-scale wavelet analysis. Investigation results show that the proposed method is an effective feature formulation
   tool; the reconstructed features are more robust against the variations in bearing geometry and operating conditions. The corresponding fault diagnostic reliability can be
   enhanced significantly. As a result, this work provides a promising technique and tool for bearing condition monitoring for real-world applications.
%8 October
%Z Also known as \cite4658961

%A Jiming Liu
%A Shiwu Zhang
%T Multi-phase Sumo Maneuver Learning
%J Robotica
%V 22
%N 1
%D 2004
%P 61--75
%I 
%K genetic algorithms, genetic programming, Multi-phase genetic programming (MPGP), Autonomous robots, Sumo tasks, Maneuver learning, Evolutionary robotics
%X In this paper, we demonstrate a multi-phase genetic programming (MPGP) approach to an autonomous robot learning task, where a sumo wrestling robot is required to execute
   specialised pushing manoeuvres in response to different opponents' postures. The sumo robot used has a very simple, minimalist hardware configuration. This example differs
   from the earlier studies in evolutionary robotics in that the former is carried out on-line during the performance of a robot, whereas the latter is concerned with the
   evolution of a controller in a simulated environment based on extended genetic algorithms. As illustrated in several sumo maneuver learning experiments, strategic
   manoeuvres with respect to some possible changes in the shape and size of an opponent can readily emerge from the on-line MPGP learning sessions.
%Z See also \citeLIU:2004:IJPRAI Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong (P.R. of China). Department of Precision Machinery &
   Precision Instrumentation, University of Science and Technology of China (P.R. of China)

%A Jiming Liu
%A Shiwu Zhang
%T Multiphase Genetic Programming: A case Study in Sumo Maneuver Evolution
%J International Journal of Pattern Recognition and Artificial Intelligence
%V 18
%N 4
%D 2004
%P 665--684
%I 
%K genetic algorithms, genetic programming, Multiphase genetic programming (MPGP), sumo manoeuvre evolution, adaptive behaviour
%X In this paper, we describe a new evolutionary computation approach, called multiphase genetic programming (MPGP). The special features of this approach lie in its
   variable-granularity representations of chromosomes and their corresponding genetic operations. In the paper, we provide an overview of the MPGP approach as well as details
   on how the sumo manoeuvre evolution experiments are carried out and how the MPGP-based case study differs from others.
%Z IJPRAI Partial results presented in this paper have been published in Jiming Liu and Shiwu Zhang, Multi-phase sumo maneuver learning, Robotica 22 (2004) 61-75,
   \citeDBLP:journals/robotica/LiuZ04

%A Jing Liu
%A Wenlong Fu
%A Weicai Zhong
%T Hybrid Genetic Programming for Optimal Approximation of High Order and Sparse Linear Systems
%B Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)
%S Lecture Notes in Computer Science
%E Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb
   and Kay Chen Tan and J\"urgen Branke and Yuhui Shi
%V 5361
%D 2008
%P 462--472
%I Springer
%C Melbourne, Australia
%K genetic algorithms, genetic programming
%X A Hybrid Genetic Programming (HGP) algorithm is proposed for optimal approximation of high order and sparse linear systems. With the intrinsic property of linear systems in
   mind, an individual in HGP is designed as an organization that consists of two cells. The nodes of the cells include a function and a terminal. All GP operators are
   designed based on organizations. In the experiments, three kinds of linear system approximation problems, namely stable, unstable, and high order and sparse linear systems,
   are used to test the performance of HGP. The experimental results show that HGP obtained a good performance in solving high order and sparse linear systems.
%8 Decemeber  7-10
%Z Institute of Intelligent Information Processing, Xidian University Xi'an, China

%A Jing Liu2
%A Aiguo Wu
%T Modeling Gene Regulatory Network Based on Genetic Programming
%B 2010 International Conference on Electrical and Control Engineering (ICECE)
%D 2010
%P 5341--5344
%I 
%K genetic algorithms, genetic programming, gene regulatory network modelling, gene regulatory network structure reconstruction, linear differential equation model, biology,
   linear differential equations
%X The purpose to establish the gene regulatory network model is to study the interaction relationship between genes on the system level, and thus to understand the essentials
   of creatures activity. Currently, the mainstream methods for modelling either prerequire the regulatory relationship or are not able to demonstrate the dynamics of the
   regulatory network. This paper proposes a model based on differential equation, to study gene regulatory network using Genetic Programming. This method is able to adjust to
   continuously external changes; search for the regulatory models suitable for the experiment data using genetic operators; and realise the prediction for the random
   regulatory relationship between genes. Based on the experiment, and compared with linear differential equation model, the calculation result is better suitable for the
   experiment data. This method is sufficient for the gene regulatory network structure reconstruction.
%8 June
%Z In chinese. Also known as \cite5630756

%A Kun-Hong Liu
%A Chun-Gui Xu
%T A genetic programming-based approach to the classification of multiclass microarray datasets
%J Bioinformatics
%V 25
%N 3
%D 2009
%P 331--337
%I 
%K genetic algorithms, genetic programming, lung cancer
%X MOTIVATION: Feature selection approaches have been widely applied to deal with the small sample size problem in the analysis of micro-array datasets. For the multiclass
   problem, the proposed methods are based on the idea of selecting a gene subset to distinguish all classes. However, it will be more effective to solve a multiclass problem
   by splitting it into a set of two-class problems and solving each problem with a respective classification system. RESULTS: We propose a genetic programming (GP)-based
   approach to analyze multiclass microarray datasets. Unlike the traditional GP, the individual proposed in this article consists of a set of small-scale ensembles, named as
   sub-ensemble (denoted by SE). Each SE consists of a set of trees. In application, a multiclass problem is divided into a set of two-class problems, each of which is tackled
   by a SE first. The SEs tackling the respective two-class problems are combined to construct a GP individual, so each individual can deal with a multiclass problem directly.
   Effective methods are proposed to solve the problems arising in the fusion of SEs, and a greedy algorithm is designed to keep high diversity in SEs. This GP is tested in
   five datasets. The results show that the proposed method effectively implements the feature selection and classification tasks.
%Z multi-tree (cf ADF) individual, one tree per class. Supplementary data are available at Bioinformatics online. School of Software, Xiamen University, Xiamen, Fujian,
   361005, China PMID: 19088122 [PubMed - indexed for MEDLINE]

%A Long Liu
%A Jun Sun
%A Miao Wang
%A Guocheng Du
%A Jian Chen
%T Modeling and optimization of mixing performance for enhanced hyaluronic acid production by Streptococcus zooepidemicus using genetic programming coupling quantum-behaved
   particle swarm optimization algorithm
%J Journal of Bioscience and Bioengineering
%V 108
%N Supplement 1
%D 2009
%P S126--S126
%I 
%K genetic algorithms, genetic programming, GP-QPSO
%U http://www.sciencedirect.com/science/article/B6VSD-4XHM1DM-DH/2/c3e7c20090de04d58b8f66e53d63b264
%O APBioChEC2009

%A Mengwei Liu
%A Xia Li
%A Tao Liu
%A Dan Li
%A Zheng Lin
%T A gene expression programming algorithm for multiobjective site-search problem
%B Sixth International Conference on Natural Computation (ICNC 2010)
%V 1
%D 2010
%P 14--18
%I 
%K genetic algorithms, genetic programming, gene expression programming, bohachevsky function, MOP2 function, pareto-front, shubert function, expression trees, geographical
   information system, linear coding method, multiobjective site-search problem, simple strings coding strategy, spatial analysis problem, pareto optimisation, genetic
   algorithms, geographic information systems, trees (mathematics)
%X Multiobjective site selection is a class complicated spatial analysis problem which can hardly be solved with traditional methods of Geographical Information System (GIS).
   In this paper we described an approach based on the gene expression programming (GEP) algorithm, with which the multiobjective site-search problems can be resolved. The
   validity of this method is verified by using MOP2 function, Bohachevsky function and Shubert function. By the comparison with genetic algorithms, it is concluded that the
   proposed GEP method using the expression trees/simple strings coding strategy can generate more approximate Pareto-front than the GAs using the linear coding method. This
   proposed model is finally applied to facilities optimal location search in Guangzhou.
%8 10-12  August
%Z Also known as \cite5582975

%A Peixun Liu
%A Wei Long
%T Current Mathematical Methods Used in QSAR/QSPR Studies
%J International Journal of Molecular Sciences
%D 2009
%I Molecular Diversity Preservation International
%K genetic algorithms, genetic programming, QSAR, QSPR, Mathematical methods, Regression, Algorithm
%U http://www.mdpi.com/1422-0067/10/5/1978/; http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=14220067\&date=2009\&volume=10\&issue=5\&spage=1978
%X This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the
   mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit
   Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR),
   Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new
   and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in
   QASR/QSPR studies in the future.

%A Qinghua Liu
%A Anthony G. Frutos
%A Liman Wang
%A Andrew J. Thiel
%A Susan D. Gillmor
%A Todd Strother
%A Anne E. Condon
%A Robert M. Corn
%A Max G. Lagally
%A Lloyd M. Smith
%T Progress Toward Demonstration of a Surface Based DNA Computation: a One Word Approach to Solve a Model Satisfiability Problem
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 709--717
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K DNA Computing
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Richard Liu
%T Solving the Rubik's Cube Using Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 68--73
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Ruochen Liu
%A Qifeng Lei
%A Jing Liu
%A Licheng Jiao
%T A Population Diversity-Oriented Gene Expression Programming for Function Finding
%B 8th International Conference on Simulated Evolution and Learning (SEAL 2010)
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Arnab Bhattacharya and Nirupam Chakraborti and Partha Chakroborty and Swagatam Das and Joydeep Dutta and Santosh K. Gupta and Ashu Jain and Varun Aggarwal
   and J\"urgen Branke and Sushil J. Louis and Kay Chen Tan
%V 6457
%D 2010
%P 215--219
%I Springer
%C Kanpur, India
%K genetic algorithms, genetic programming, gene expression programming
%8 Decemeber  1-4

%A Shu-an Liu
%A Qing Wang
%A Shuai Lv
%T Application of Genetic Programming in credit scoring
%B Chinese Control and Decision Conference, CCDC 2008
%D 2008
%P 1106--1110
%I 
%K genetic algorithms, genetic programming, coding structure, combinatorial optimization problem, credit scoring, genetic programming algorithm, human-computer interactions,
   information value, combinatorial mathematics, finance
%X Derived characteristics are usually regarded as important index in credit scoring, however, only some derived characteristics in common sense can be obtained with
   analytical methods. In this paper, the selection of derived characteristics is considered as a combinatorial optimization problem of mathematical symbols and original
   characteristics. To solve the problem, a Genetic Programming algorithm is proposed, where the coding structure is in a tree form and the objective is expressed by
   Information Value (IV). A procedure of human-computer interactions are designed to choose the derived characteristics with practical significance from the better results
   obtained by the Genetic Programming algorithm. Furthermore, an improved model is proposed based on linear discriminate analysis, where derived characteristics are included
   The simulation experiments show that the results is satisfactory, the proposed models are of competitive discrimination.
%8 July
%Z Also known as \cite4597485

%A Taiyang Liu
%A Shicheng Wang
%A Zhiguo Liu
%A Haibo Min
%A Renbing Li
%T Multi-kernel SVM based star pattern recognition for Celestial Navigation
%B 9th IEEE International Conference on Cognitive Informatics (ICCI 2010)
%D 2010
%P 748--753
%I 
%K genetic algorithms, genetic programming, SVM training realisation, celestial navigation, feature vector definition, indexing scheme, intelligent learning algorithm,
   multikernel SVM algorithm, multikernel function generation, star pattern recognition character, support vector machine, astronomical image processing, image matching, image
   recognition, indexing, learning (artificial intelligence), navigation, support vector machines
%X This paper presents a combination of intelligent learning algorithm, the Support Vector Machine, and the recognition of star pattern in Celestial Navigation. Considering
   the star pattern recognition's character, noticing the advantages of SVM in learning competence, the paper proposes a solution to star pattern recognition with multi-kernel
   SVM. A multi-kernel algorithm bases on Genetic Programming is designed. Topics of multi-kernel function generation are cited in detail, and a star pattern recognition
   routine including an Indexing + recognition scheme, feature vector definition and generation, SVM training realisation are designed and realised.
%8 7-9  July
%Z Also known as \cite5599814

%A Weiguo Liu
%A Bertil Schmidt
%T A Case Study on Pattern-Based Systems for High Performance Computational Biology
%B 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 7
%D 2005
%P 197b
%I IEEE Computer Society
%K genetic algorithms, genetic programming
%U http://www.hicomb.org/papers/HICOMB2005-04.pdf
%X Computational biology research is now faced with the burgeoning number of genome data. The rigorous postprocessing of this data requires an increased role for high
   performance computing (HPC). Because the development of HPC applications for computational biology problems is much more complex than the corresponding sequential
   applications, existing traditional programming techniques have demonstrated their inadequacy. Many high level programming techniques, such as skeleton and pattern based
   programming, have therefore been designed to provide users new ways to get HPC applications without much effort. However, most of them remain absent from the mainstream
   practice for computational biology. In this paper, we present a new parallel pattern-based system prototype for computational biology. The underlying programming techniques
   are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the system to be built in a generic way at
   application level and thus provides good extensibility and flexibility. We show how this system can be used to develop HPC applications for popular computational biology
   algorithms and lead to significant runtime savings on distributed memory architectures.

%A Weiguo Liu
%T Parallel and distributed algorithms for computational biology
%R Ph.D. Thesis
%D 2007
%I 
%I School of Computer Engineering, Nanyang Technological University
%C Singapore
%K genetic algorithms, genetic programming
%U http://hdl.handle.net/10356/2474
%X Computational biology research is now faced with the burgeoning number of genome data. The rigorous postprocessing of this data requires an increased role for high
   performance computing (HPC). Because the development of HPC applications for computational biology problems is much more complex than the corresponding sequential
   applications, existing traditional programming techniques have demonstrated their inadequacy. Many high level programming techniques, such as skeleton and pattern-based
   programming, have therefore been designed to provide users new ways to get HPC applications without much effort. However, most of them remain absent from the mainstream
   practice for computational biology. In this paper, we present a new parallel pattern-based system prototype for computational biology. The underlying programming techniques
   are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the system to be built in a generic way at
   application level and, thus, provides good extensibility and flexibility. We show how this system can be used to develop HPC applications for popular computational biology
   algorithms and lead to significant run time savings on distributed memory architectures.
%Z Supervisor: Bertil Schmidt

%A Weiguo Liu
%A Bertil Schmidt
%T Mapping of Hierarchical Parallel Genetic Algorithms for Protein Folding onto Computational Grids
%J IEICE Transactions on Information and Systems
%V E89-D
%N 2
%D 2006
%P 589--596
%I 
%K genetic algorithms, genetic programming, protein folding, HP lattice models, hierarchical parallel genetic algorithms, computational grids, generic programming
%X Genetic algorithms are a general problem-solving technique that has been widely used in computational biology. In this paper, we present a framework to map hierarchical
   parallel genetic algorithms for protein folding problems onto computational grids. By using this framework, the two level communication parts of hierarchical parallel
   genetic algorithms are separated. Thus both parts of the algorithm can evolve independently. This permits users to experiment with alternative communication models on
   different levels conveniently. The underlying programming techniques are based on generic programming, a programming technique suited for the generic representation of
   abstract concepts. This allows the framework to be built in a generic way at application level and thus provides good extensibility and flexibility. Experiments show that
   it can lead to significant runtime savings on PC clusters and computational grids.
%Z Special Section on Parallel/Distributed Computing and Networking -- Papers -- Grid Computing Copyright 2005 IEICE

%A Weixin Liu
%A Masahiro Murakawa
%A Tetsuya Higuchi
%T Evolvable Hardware for On-line Adaptive Traffic Control in ATM Networks
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 504--509
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Evolvable Hardware
%8 13-16  July
%Z GP-97

%A Xiyu Liu
%A Yinghong Ma
%A Hong Liu
%A Jianping Zhang
%T A differential evolutionary architecture for artificial neural trees with applications to medical data mining
%B IEEE International Symposium on IT in Medicine and Education, ITME 2008
%D 2008
%P 761--767
%I 
%K genetic algorithms, genetic programming, artificial neural tree structure, differential evolutionary architecture, medical data mining, numbering scheme, partial
   ordering-based multidimensional fitness function, data mining, evolutionary computation, medical computing, neural nets, trees (mathematics)
%X This paper presents an evolutionary structure for neural trees by differential evolution. For a neural tree a structure tree and weights tree are defined. A partial
   ordering based multi-dimensional fitness function is applied to measure the energy of the system. Different to traditional evolution method of genetic programming, a new
   evolution technique based on differential evolution is proposed. A numbering scheme is designed for basic operations of tree structure. Finally we present an experimental
   framework for the tree evolution. Application frameworks are given in medical data mining and analysis.
%8 Decemeber
%Z Popolation of ANN with single root output. Also known as \cite4743969

%A Yanchao Liu
%A John English
%A Edward A. Pohl
%T Application of Gene Expression Programming in the Reliability of Consecutive-k-out-of-n: F Systems with Identical Component Reliabilities
%B Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, Third International Conference on Intelligent
   Computing, ICIC 2007
%S Communications in Computer and Information Science
%E De-Shuang Huang and Laurent Heutte and Marco Loog
%V 2
%D 2007
%P 217--224
%I Springer
%C Qingdao, China
%K genetic algorithms, genetic programming, gene expression programming
%X This paper presents a GEP-based simulation - data mining approach for obtaining closed-form reliability formulas of consecutive-k-out-of-n: F systems with identical
   component reliabilities. This work proves to be GEP's first exploration into the reliability realm and also provides a new perspective for the reliability community to
   solve for complex reliability formulas. Experimentation has shown the feasibility and effectiveness of the proposed framework, although further revisions and developments
   must be made to the model in order to solve larger scale problems.
%8 August  21-24
%Z Roller Conveyor Systems

%A Yang Liu
%A Gianluca Tempesti
%A James A. Walker
%A Jon Timmis
%A Andrew M. Tyrrell
%A Paul Bremner
%T A Self-Scaling Instruction Generator Using Cartesian Genetic Programming
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 298--309
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming, cartesian genetic programming: poster
%X In the past decades, a number of genetic programming techniques have been developed to evolve machine instructions. However, these approaches typically suffer from a lack
   of scalability that seriously impairs their applicability to real-world scenarios. In this paper, a novel self-scaling instruction generation method is introduced, which
   tries to overcome the scalability issue by using Cartesian Genetic Programming. In the proposed method, a dual-layer network architecture is created: one layer is used to
   evolve a series of instructions while the other is dedicated to the generation of loop control parameters.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Yi Liu
%A Taghi M. Khoshgoftaar
%T Genetic programming model for software quality classification
%B Sixth IEEE International Symposium on High Assurance Systems Engineering, HASE'01
%D 2001
%P 127--136
%I IEEE
%C Boco Raton, FL, USA
%K genetic algorithms, genetic programming, classification, evolutionary computation, software qualitygenetic programming, quality classification, software engineering,
   software metrics, software quality, SBSE
%X We apply genetic programming techniques to build a software quality classification model based on the metrics of software modules. The model we built attempts to
   distinguish the fault-prone modules from non-fault-prone modules using genetic programming (GP). These GP experiments were conducted with a random subset selection for GP
   in order to avoid overfitting. We then use the whole fit data set as the validation data set to select the best model. We demonstrate through two case studies that the GP
   technique can achieve good results. Also, we compared GP modeling with logistic regression modeling to verify the usefulness of GP
%8 October  22-24
%Z Also known as \cite966814 INSPEC Accession Number:7107475 p126 "VLWA" C++ "over 27.5 million lines of code". Logistic Regression LRM
%@ 0-7695-1275-5

%A Yi Liu
%A Taghi M. Khoshgoftaar
%T Building Decision Tree Software Quality Classification Models Using Genetic Programming
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1808--1809
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming, poster
%X Predicting the quality of software modules prior to testing or system operations allows a focused software quality improvement endeavor. Decision trees are very attractive
   for classification problems, because of their comprehensibility and white box modeling features. However, optimizing the classification accuracy and the tree size is a
   difficult problem, and to our knowledge very few studies have addressed the issue. This paper presents an automated and simplified genetic programming (GP) based decision
   tree modeling technique for calibrating software quality classification models. The proposed technique is based on multi-objective optimization using strongly typed GP. Two
   fitness functions are used to optimize the classification accuracy and tree size of the classification models calibrated for a real-world high-assurance software system.
   The performances of the classification models are compared with those obtained by standard GP. It is shown that the GP-based decision tree technique yielded better
   classification models.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Yi Liu
%T Software Reliability Engineering with Genetic Programming
%R Ph.D. Thesis
%D 2003
%I 
%I Computer Science, Florida Atlantic University
%K genetic algorithms, genetic programming
%U http://digitalcommons.fau.edu/dissertations/AAI3095028/
%X Software reliability engineering plays a vital role in managing and controlling software quality. As an important method of software reliability engineering, software
   quality estimation modelling is useful in defining a cost-effective strategy to achieve a reliable software system. By predicting the faults in a software system, the
   software quality models can identify high-risk modules, and thus, these high-risk modules can be targeted for reliability enhancements. Strictly speaking, software quality
   modeling not only aims at lowering the misclassification rate, but also takes into account the costs of different misclassifications and the available resources of a
   project. As a new search-based algorithm, Genetic Programming (GP) can build a model without assuming the size, shape, or structure of a model. It can flexibly tailor the
   fitness functions to the objectives chosen by the customers. Moreover, it can optimise several objectives simultaneously in the modelling process, and thus, a set of
   multi-objective optimisation solutions can be obtained. This research focuses on building software quality estimation models using GP. Several GP-based models of predicting
   the class membership of each software module and ranking the modules by a quality factor were proposed. The first model of categorising the modules into fault-prone or not
   fault-prone was proposed by considering the distinguished features of the software quality classification task and GP. The second model provided quality-based ranking
   information for fault-prone modules. A decision tree-based software classification model was also proposed by considering accuracy and simplicity simultaneously. This new
   technique provides a new multi-objective optimization algorithm to build decision trees for real-world engineering problems, in which several trade-off objectives usually
   have to be taken into account at the same time. The fourth model was built to find multi-objective optimisation solutions by considering both the expected cost of
   misclassification and available resources. Also, a new goal-oriented technique of building module-order models was proposed by directly optimizing several goals chosen by
   project analysts. The issues of <smcap>GP </smcap>, bloating and overfitting, were also addressed in our research. Data were collected from three industrial projects, and
   applied to validate the performance of the models. Results indicate that our proposed methods can achieve useful performance results. Moreover, some proposed methods can
   simultaneously optimize several different objectives of a software project management team.
%8 August
%Z www.fau.edu/dsr/researchnews0903.pdf page 6 Major Professor: Taghi M. Khoshgoftaar

%A Yi Liu
%A Taghi Khoshgoftaar
%T Reducing overfitting in genetic programming models for software quality classification
%B Proceedings of the Eighth IEEE Symposium on International High Assurance Systems Engineering
%D 2004
%P 56--65
%I 
%C Tampa, Florida, USA
%K genetic algorithms, genetic programming
%X A high-assurance system is largely dependent on the quality of its underlying software. Software quality models can provide timely estimations of software quality, allowing
   the detection and correction of faults prior to operations. A software metrics-based quality prediction model may depict overfitting, which occurs when a prediction model
   has good accuracy on the training data but relatively poor accuracy on the test data. In this paper, we present an approach to address the overfitting problem in the
   context of software quality classification models based on genetic programming (GP). The overfitting problem has not been addressed in depth for GP-based models. The
   general aim of classifying software modules as fault-prone (fp) and not fault-prone (nfp) is to aid software management in expending its limited resources toward improving
   only the fp modules. The presence of overfitting in such a software quality model affects its practical usefulness, because management is interested in good performance of
   the model when applied to unseen data, i.e., generalisation performance. In the process of building GP-based software quality classification models for a high-assurance
   telecommunications system, we observed that the GP models were prone to overfitting. We use a random sampling technique to reduce overfitting in our GP models. The approach
   has been found by many researchers as an effective method for reducing the time of a GP run. However, in our study we use random sampling to reduce overfitting with the aim
   of improving the generalization capability of our GP models. A case study of an industrial high-assurance software system is used to demonstrate the effectiveness of the
   random sampling technique.
%8 25-26  March
%Z HASE 2004

%A Yi Liu
%A Taghi M. Khoshgoftaar
%A Jenq-Foung Yao
%T Developing an effective validation strategy for genetic programming models based on multiple datasets
%B 2006 IEEE International Conference on Information Reuse and Integration
%D 2006
%P 232--237
%I IEEE
%C Waikoloa Village, HI, USA
%K genetic algorithms, genetic programming
%X Genetic programming (GP) is a parallel searching technique where many solutions can be obtained simultaneously in the searching process. However, when applied to real-world
   classification tasks, some of the obtained solutions may have poor predictive performances. One of the reasons is that these solutions only match the shape of the training
   dataset, failing to learn and generalise the patterns hidden in the dataset. Therefore, unexpected poor results are obtained when the solutions are applied to the test
   dataset. This paper addresses how to remove the solutions which will have unacceptable performances on the test dataset. The proposed method in this paper applies a
   multi-dataset validation phase as a filter in GP-based classification tasks. By comparing our proposed method with a standard GP classifier based on the datasets from seven
   different NASA software projects, we demonstrate that the multi-dataset validation is effective, and can significantly improve the performance of GP-based software quality
   classification models
%8 September
%Z http://ieeexplore.ieee.org/servlet/opac?punumber=4018442

%A Yi (Cathy) Liu
%A Taghi M. Khoshgoftaar
%A Naeem Seliya
%T Evolutionary Optimization of Software Quality Modeling with Multiple Repositories
%J IEEE Transactions on Software Engineering
%V 36
%N 6
%D 2010
%P 852--864
%I 
%K genetic algorithms, genetic programming, sbse, baseline classifier, evolutionary optimisation, machine learner, multiple software project repository, robust software
   quality model, search-based software quality model, software data set, software measurement data repository, software metrics, software quality modelling, validation
   classifier, validation-and-voting classifier, software management, software metrics, software quality
%X A novel search-based approach to software quality modelling with multiple software project repositories is presented. Training a software quality model with only one
   software measurement and defect data set may not effectively encapsulate quality trends of the development organisation. The inclusion of additional software projects
   during the training process can provide a cross-project perspective on software quality modelling and prediction. The genetic-programming-based approach includes three
   strategies for modeling with multiple software projects: Baseline Classifier, Validation Classifier, and Validation-and-Voting Classifier. The latter is shown to provide
   better generalisation and more robust software quality models. This is based on a case study of software metrics and defect data from seven real-world systems. A second
   case study considers 17 different (nonevolutionary) machine learners for modelling with multiple software data sets. Both case studies use a similar majority-voting
   approach for predicting fault-proneness class of program modules. It is shown that the total cost of misclassification of the search-based software quality models is
   consistently lower than those of the non-search-based models. This study provides clear guidance to practitioners interested in exploiting their organization's software
   measurement data repositories for improved software quality modelling.
%8 November /  Decemeber
%Z Also known as \cite5467094

%A Paul C. K. Lo
%T Genetically-Evolved Mastermind Strategy: A Self-Simplifying Symbolic Approach to Reinforcement Learning
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 74--83
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Lawrence K. Lo
%T A Genetic Algorithm to Solve the 2-D Bin Packing Problem
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 122--130
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Leung-Yau Lo
%A Tak-Ming Chan
%A Kin-Hong Lee
%A Kwong-Sak Leung
%T Challenges rising from learning motif evaluation functions using genetic programming
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 171--178
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming, Bioinformatics, computational, systems and synthetic biology
%X Motif discovery is an important Bioinformatics problem for deciphering gene regulation. Numerous sequence-based approaches have been proposed employing human specialist
   motif models (evaluation functions), but performance is so unsatisfactory on benchmarks that the underlying information seems to have already been exploited and have
   doomed. However, we have found that even a simple modified representation still achieves considerably high performance on a challenging benchmark, implying potential for
   sequence-based motif discovery. Thus we raise the problem of learning motif evaluation functions. We employ Genetic programming (GP) which has the potential to evolve human
   competitive models. We take advantage of the terminal set containing specialist-model-like components and have tried three fitness functions. Results exhibit both great
   challenges and potentials. No models learnt can perform universally well on the challenging benchmark, where one reason may be the data appropriateness for sequence-based
   motif discovery. However, when applied on different widely-tested datasets, the same models achieve comparable performance to existing approaches based on specialist
   models. The study calls for further novel GP to learn different levels of effective evaluation models from strict to loose ones on exploiting sequence information for motif
   discovery, namely quantitative functions, cardinal rankings, and learning feasibility classifications.
%8 7-11  July
%Z Also known as \cite1830515 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Fernando G. Lobo
%A Kalyanmoy Deb
%A David E. Goldberg
%A Georges R. Harik
%A Liwei Wang
%T Compressed Introns in a Linkage Learning Genetic Algorithm
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 551--558
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z SGA-98

%A Fernando Lobo
%T Solving Problems of Bounded Difficulty Using Genetic Algorithms
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms
%8 22-25  July
%Z GP-98LB

%A Kenneth N. Lodding
%T Hitchhiker's Guide to Biomorphic Software
%J ACM Queue
%V 2
%N 4
%D 2004
%P 66--75
%I 
%K genetic algorithms, genetic programming
%U http://www.acmqueue.org/modules.php?name=Content&pa=printer_friendly&pid=166&page=1
%8 June
%Z yet another useless new name:-( cites why biology. Boids (cf eg \citeReynolds:1994:sab), ants (Marco Dorigo), particle swarm (PSO). Notes scale up problem. p74 "evolve" the
   program.

%T The Second NASA/DoD Workshop on Evolvable Hardware
%E Jason Lohn and Adrian Stoica and Didier Keymeulen
%D 2000
%I IEEE Computer Society 1730 Massachusetts Avenue, N.W., Washington, DC, 20036-1992, USA
%I Jet Propulsion Laboratory, California Institute of Technology
%C Palo Alto, California
%K genetic algorithms, evolvable hardware
%U http://ic-www.arc.nasa.gov/ic/eh2000/
%8 13-15  July
%Z http://csdl.computer.org/comp/proceedings/eh/2000/0762/00/0762toc.htm
%@ 0-7695-0762-X

%A Jason Lohn
%A James Crawford
%A Al Globus
%A Gregory Hornby
%A William Kraus
%A Gregory Larchev
%A Anna Pryor
%A and Deepak Srivastava
%T Evolvable Systems for Space Applications
%B International Conference on Space Mission Challenges for Information Technology (SMC-IT)
%D 2003
%I 
%C Pasadena, CA, USA
%K genetic algorithms
%U http://people.nas.nasa.gov/~globus/home.html
%8 July

%A Jason Lohn
%A Gregory Hornby
%A Derek Linden
%T An Evolved Antenna for Deployment on Nasa's Space Technology 5 Mission
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 301--315
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, evolvable hardware, design, computational design, antenna, wire antenna, spacecraft, evolutionary computation
%X We present an evolved X-band antenna design and flight prototype currently on schedule to be deployed on NASAùs Space Technology 5 (ST5) spacecraft. Current methods of
   designing and optimising antennas by hand are time and labour intensive, limit complexity, and require significant expertise and experience. Evolutionary design techniques
   can overcome these limitations by searching the design space and automatically finding effective solutions that would ordinarily not be found. The ST5 antenna was evolved
   to meet a challenging set of mission requirements, most notably the combination of wide beam width for a circularly-polarised wave and wide bandwidth. Two evolutionary
   algorithms were used: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style
   tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both
   yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we
   consider them examples of human-competitive performance by evolutionary algorithms. As of this writing, one of our evolved antenna prototypes is undergoing flight
   qualification testing. If successful, the resulting antenna would represent the first evolved hardware in space, and the first deployed evolved antenna.
%O 18
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A Jason D. Lohn
%A Gregory S. Hornby
%A Derek S. Linden
%T Rapid Re-evolution of an X-Band Antenna for NASA's Space Technology 5 Mission
%B Genetic Programming Theory and Practice III
%S Genetic Programming
%E Tina Yu and Rick L. Riolo and Bill Worzel
%V 9
%D 2005
%P 65--78
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, design, computational design, evolutionary design, antenna, spacecraft
%X One of the challenges in engineering design is adapting a set of created designs to a change in requirements. Previously we presented two four-arm, symmetric, evolved
   antennas for NASA's Space Technology 5 mission. However, the mission's orbital vehicle was changed, putting it into a much lower earth orbit, changing the specifications
   for the mission. With minimal changes to our evolutionary system, mostly in the fitness function, we were able to evolve antennas for the new mission requirements and,
   within one month of this change, two new antennas were designed and prototyped. Both antennas were tested and both had acceptable performance compared with the new
   specifications. This rapid response shows that evolutionary design processes are able to accommodate new requirements quickly and with minimal human effort.
%O 5
%8 12-14  May
%Z part of \citeyu:2005:GPTP Published Jan 2006 after the workshop
%@ 0-387-28110-X

%A Jason D. Lohn
%A Gregory S. Hornby
%T Evolvable Hardware Using Evolutionary Computation to Design and Optimize Hardware Systems
%J IEEE Computational Intelligence Magazine
%V 1
%N 1
%D 2006
%P 19--27
%I 
%K genetic algorithms, genetic programming, EHW
%8 February
%Z Mention of several applications of GP to EHW

%A Jason D. Lohn
%A Gregory Hornby
%A Derek S. Linden
%T Human-competitive evolved antennas
%J Artificial Intelligence for Engineering Design, Analysis and Manufacturing
%V 22
%N 3
%D 2008
%P 235--247
%I 
%K genetic algorithms, genetic programming, Antenna, Computational Design Design, Evolutionary Computation, Spacecraft, Wire Antenna
%X We present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas
   in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically
   designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most
   notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. Two evolutionary algorithms were used in the development process: one used a
   genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that
   allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both
   antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive
   performance by evolutionary algorithms. Our design was approved for flight, and three copies of it were successfully flown on NASA's Space Technology 5 mission between
   March 22 and June 30, 2006. These evolved antennas represent the first evolved hardware in space and the first evolved antennas to be deployed.
%Z * better coverage *  significantly higher efficiency *  fewer parts: lower cost, increased reliability, easier manufacture *  naturally matched to 50 Ohms *  faster design
   time *  rapid redesign accomplished at a small cost and in a short time frame

%A Jason D. Lohn
%A Jonathan M. Becker
%A Derek S. Linden
%T An evolved anti-jamming adaptive beamforming network
%J Genetic Programming and Evolvable Machines
%V 12
%N 3
%D 2011
%P 217--234
%I 
%K genetic algorithms, evolvable hardware, Antenna, Beamforming, Anti-jamming
%X Interference in wireless networks is undesirable, whether it is due to unintentional or malicious causes. Adaptive beamforming is a spatial filtering technique that can
   prevent jammers from disrupting wireless networks. This paper presents an evolvable hardware (EH) application in which an evolutionary algorithm (EA) is used to configure
   an adaptive beamformer to achieve two goals: (1) steering nulls towards jamming signals and (2) directing gain in the direction of the desired signal. This is the first
   demonstration of an EA-configured adaptive beamformer to counter a jamming system. Simulation results show that the EA is able to thwart up to three jamming signals. The
   results suggest that EH is a promising approach towards wireless network security.
%O Special Issue Title: Evolvable Hardware Challenges
%8 September

%A Dome Lohpetch
%A David Corne
%T Discovering effective technical trading rules with genetic programming: towards robustly outperforming buy-and-hold
%B World Congress on Nature Biologically Inspired Computing, NaBIC 2009
%D 2009
%P 439--444
%I 
%K genetic algorithms, genetic programming, effective trading rules, financial applications, fitness function, profitable rules, research tool, stocks, technical trading
   rules, financial management, profitability, stock markets
%X Genetic programming is now a common research tool in financial applications. One classic line of exploration is their use to find effective trading rules for individual
   stocks or for groups of stocks (such as an index). The classic work in this area (Allen amp; Karjaleinen, 99) found profitable rules, but which did not outperform a
   straightforward buy and hold strategy. Several later works report similar outcomes, while a small number of works achieve out-performance of buy and hold, but prove
   difficult to replicate. We focus here on indicating clearly how the performance in one such study (Becker amp; Seshadri, 03) was replicated, and we carry out additional
   investigations which point towards guidelines for generating results that robustly outperform buy-and-hold. These guidelines relate to strategies for organizing the
   training dataset, and aspects of the fitness function.
%8 Decemeber
%Z Also known as \cite5393324

%A Dome Lohpetch
%A David Corne
%T Outperforming Buy-and-Hold with Evolved Technical Trading Rules: Daily, Weekly and Monthly Trading
%B EvoFIN
%S LNCS
%E Cecilia Di Chio and Anthony Brabazon and Gianni A. Di Caro and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and
   Michael O'Neill and Ernesto Tarantino and Neil Urquhart
%V 6025
%D 2010
%P 171--181
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X Genetic programming (GP) is increasingly popular as a research tool for applications in finance and economics. One thread in this area is the use of GP to discover
   effective technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find rules that were profitable, but were nevertheless outperformed by the
   simple buy and hold trading strategy. Many succeeding attempts have reported similar findings. There are a small handful of cases in which such work has managed to find
   rules that outperform buy-and-hold, but these have tended to be difficult to replicate. Recently, however, Lohpetch & Corne (2009) investigated work by Becker & Seshadri
   (2003), which showed out performance of buy-and-hold. In turn, Becker & Seshadri's work had made several modifications to Allen & Karjalainen's work, including the adoption
   of monthly rather than daily trading. Lohpetch et al (2009) provided a replicable account of this, and also showed how further modifications enabled fairly reliable out
   performance of buy-and-hold. It remained unclear, however, whether adoption of monthly trading is necessary to achieve robust out performance of buy-and-hold. Here we
   investigate and compare each of daily, weekly and monthly trading; we find that outperformance of buy-and-hold can be achieved even for daily trading, but as we move from
   monthly to daily trading the performance of evolved rules becomes increasingly dependent on prevailing market conditions.
%8 7-9  April
%Z EvoFIN'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Dome Lohpetch
%A David Corne
%T Multiobjective Algorithms for Financial Trading Multiobjective Out-trades Single-Objective
%B Proceedings of the 2011 IEEE Congress on Evolutionary Computation
%E Alice E. Smith
%D 2011
%P 192--199
%I IEEE Press
%I IEEE Computational Intelligence Society
%C New Orleans, USA
%K genetic algorithms, genetic programming, Finance and economics, Multiobjective optimization
%X Genetic programming (GP) is increasingly investigated in finance and economics. One area of study is its use to discover effective rules for technical trading in the
   context of a portfolio of equities (or an index). Early work used GP to find rules that were profitable, but were outperformed by the simple buy and hold strategy. Attempts
   since then report similar findings, except a handful of cases where GP has been found to outperform BH. Recent work has clarified that robust out performance of BH depends
   on, mainly, the adoption of a relatively infrequent trading strategy (e.g. monthly), as well as a range of other factors. Here we add a comprehensive study of
   multiobjective approaches to this investigation, and find that multiobjective strategies provide even more robustness in outperforming BH, even in the context of more
   frequent (e.g. weekly) trading decisions.
%8 5-8  June
%Z CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.
%@ 0-7803-8515-2

%A Steve Lohr
%T The Programming Gene
%J PC Magazine
%D 2002
%I 
%K genetic algorithms, genetic programming
%U http://www.pcmag.com/article2/0,4149,429437,00.asp
%X As part of a series on future technologies, surveys genetic programming and related research.
%8 3  September

%A A. Loizides
%A M. Slater
%A W. B. Langdon
%T Measuring Facial Emotional Expressions Using Genetic Programming
%B Soft Computing and Industry Recent Applications
%E Rajkumar Roy and Mario K\"oppen and Seppo Ovaska and Takeshi Furuhashi and Frank Hoffmann
%D 2001
%P 545--554
%I Springer-Verlag
%K genetic algorithms, genetic programming, data visualisation, symbolic regression
%U http://citeseer.ist.psu.edu/474911.html
%X Genetic Programming techniques can be used to produce regression equations that quantify emotional expressions on a facial model. The formulae give emotional scores based
   on the position of 25 automatically generated "landmarks" on the face. The method shown here is an integrated part of a system that maps multidimensional data sets to
   naturalistic visual structures such as a face
%O Published 2002
%8 10--24  September
%Z WSC6 Out of print? http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394
%@ 1-85233-539-4

%A Shreenivas N. Londhe
%T Development Of Wave Buoy Network Using Soft Computing Techniques
%B OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean
%D 2008
%P 1--8
%I 
%C Kobe, Japan
%K genetic algorithms, genetic programming, AD 2002 to 2004, Artificial Neural Networks, Australia, Canada, Germany, Gulf of Mexico, India, UK, USA, buoy programs, ocean wave
   buoys network development, ocean wave data measurements, soft computing techniques, stochastic techniques, geophysics computing, neural nets, ocean waves, oceanographic
   techniques, stochastic processes
%X Wave buoys are perhaps the only reliable source measuring waves continuously for years. This is perhaps the most vital reason for establishment of data buoy programs by
   various countries like USA (NDBC), Australia, Canada, UK, Germany, India (NDBP) etc. The wave data measurements not only provide real time wave information for Coastal and
   Ocean related activities but also form wave data base useful for predicting future events using statistical or stochastic techniques. However some times these wave buoys
   stop functioning either due to malfunctioning instruments or maintenance-related reasons resulting into loss of data. This paper presents use of soft computing techniques
   like Artificial Neural Networks (ANN) and Genetic Programming (GP) to retrieve this lost data by forming a network of wave buoys in a region. For developing the buoy
   network common data of hourly significant wave heights at six buoys in the Gulf of Mexico namely 42001, 42003, 42007, 42036, 42039 and 42040 for the years 2002 and 2004 is
   used. A separate network for each buoy is developed as the 'target buoy' with other 5 buoys as 'input buoys' which can be operated to retrieve lost data at a location. The
   testing results of both approaches when compared showed superiority of Genetic Programming over Artificial Neural Network as evident by higher correlation coefficient
   between observed and predicted wave heights in all cases. The wave height plots also pointed out that GP estimates wave heights in extreme events (peaks) more accurately
   than ANN.
%8 8-11  April
%Z Also known as \cite4530913

%A S. N. Londhe
%T Soft computing approach for real-time estimation of missing wave heights
%J Ocean Engineering
%V 35
%N 11-12
%D 2008
%P 1080--1089
%I 
%K genetic algorithms, genetic programming, Water waves, Buoy systems, Soft computing, Artificial Neural Network, Missing data
%U http://www.sciencedirect.com/science/article/B6V4F-4SK633V-1/2/22702929635b97a45da2f5fbba866111
%X This paper presents soft computing approach for estimation of missing wave heights at a particular location on a real-time basis using wave heights at other locations. Six
   such buoy networks are developed in Eastern Gulf of Mexico using soft computing techniques of Artificial Neural Networks (ANN) and Genetic Programming (GP). Wave heights at
   five stations are used to estimate wave height at the sixth station. Though ANN is now an established tool in time series analysis, use of GP in the field of time series
   forecasting/analysis particularly in the area of Ocean Engineering is relatively new and needs to be explored further. Both ANN and GP approach perform well in terms of
   accuracy of estimation as evident from values of various statistical parameters employed. The GP models work better in case of extreme events. Results of both approaches
   are also compared with the performance of large-scale continuous wave modeling/forecasting system WAVEWATCH III. The models are also applied on real time basis for 3 months
   in the year 2007. A software is developed using evolved GP codes (C++) as back end with Visual Basic as the Front End tool for real-time application of wave estimation
   model.
%Z See also \citeAlavi20101239

%A Shreenivas Londhe
%A Shrikant Charhate
%T Comparison of data-driven modelling techniques for river flow forecasting
%J Hydrological Sciences Journal
%V 55
%N 7
%D 2010
%P 1163--1174
%I 
%K genetic algorithms, genetic programming, streamflow, data-driven modelling, artificial neural networks, genetic programming, M5 model trees
%X Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore,
   researchers try to devise alternative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models
   are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river
   flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of
   prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only
   marginally in terms of prediction accuracy in normal and extreme events.
%Z Department of Civil Engineering, Vishwakarma Institute of Information Technology, Survey no. 2/3/4, Kondhwa (Bk), Pune, MH, 411048, India Department of Civil Engineering,
   Datta Meghe College of Engineering, Airoli, Navi Mumbai, MH, 400708, India p1172 'Rajghat and Mandaleshwar in the Narmada basin in India. The GP models performed better
   compared to ANN and MT models, though marginally.' Comparaison de techniques de modelisation conditionnee par les donnees pour la prevision des debits fluviaux

%A Michael A. Lones
%A Andy M. Tyrrell
%T Enzyme Genetic Programming
%B Proceedings of the 2001 Congress on Evolutionary Computation, CEC 2001
%D 2001
%P 1183--1190
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, biomimetic representations, Metabolic Pathways, Evolutionary Electronics
%U http://folk.ntnu.no/lones/lones-cec2001.pdf
%X The work reported in this paper follows from the hypothesis that better performance in artificial evolution can be achieved by adhering more closely to the features that
   make natural evolution effective within biological systems. An important issue in evolutionary computation is the choice of solution representation. Genetic programming,
   whilst borrowing from biology in the evolutionary axis of behaviour, remains firmly rooted in the artificial domain with its use of a parse tree representation. Following
   concerns that this approach does not encourage solution evolvability, this paper presents an alternative method modelled upon representations used by biology. Early results
   are encouraging; demonstrating that the method is competitive when applied to problems in the area of combinatorial circuit design. Whilst too early to gauge its
   suitability to a more general domain of programming, these results do indicate that the concept of bringing ideas from biological representations to genetic programming is
   a promising one.
%8 27--30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =
%@ 0-7803-6657-3

%A Michael A. Lones
%A Andy M. Tyrrell
%T Biomimetic Representation in Genetic Programming
%B Computation in Gene Expression
%E Hillol Kargupta
%D 2001
%P 199--204
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming
%U http://folk.ntnu.no/lones/lones-gecco2001a.pdf
%X Biological representations underly biological evolution. Moreover, they are also a product of evolution and consequently well adapted for their purpose. The argument
   presented in this paper is that the representations of biology are also suitable for representing artificial executable systems in genetic programming and, furthermore,
   that biomimetic representations could improve both the adaptability and evolvability of GP. To this end a biomimetic approach to GP, enzyme genetic programming, is
   introduced and its behaviour is analysed when applied to the domain of combinational circuit design.
%8 7  July
%Z GECCO-2001WKS Part of heckendorn:2001:GECCOWKS

%A Michael A. Lones
%A Andy M. Tyrrell
%T Pathways into Genetic Programming
%B Graduate Student Workshop
%E Conor Ryan
%D 2001
%P 425--428
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming
%U http://folk.ntnu.no/lones/lones-gecco2001b.pdf
%X Biochemical pathways are the fundamental structures of biological representations. Biological representations are the fundamental targets of natural evolution. Evolution is
   the fundamental principle behind genetic programming. Could biological representations be useful to genetic programming? Are biochemical pathways a suitable representation
   for programs? These are the fundamental questions addressed by this paper.
%8 7  July
%Z GECCO-2001WKS Part of heckendorn:2001:GECCOWKS

%A Michael A. Lones
%A Andy M. Tyrrell
%T Biomimetic Representation with Genetic Programming Enzyme
%J Genetic Programming and Evolvable Machines
%V 3
%N 2
%D 2002
%P 193--217
%I 
%K genetic algorithms, genetic programming, biomimetic representation
%U http://www.kluweronline.com/issn/1389-2576/current
%X The standard parse tree representation of genetic programming, while a good choice from a generative viewpoint, does not capture the variational demands of evolution. This
   paper addresses the issue of whether representations in genetic programming might be improved by mimicry of biological behaviors, particularly those thought to be important
   in the evolution of metabolic pathways, computational structures of the cell. This issue is broached through a presentation of enzyme genetic programming, a form of genetic
   programming which uses a biomimetic representation. Evaluation upon problems in combinational logic design does not show any significant performance advantage over other
   approaches, though does demonstrate a number of interesting behaviors including the preclusion of bloat.
%8 June
%Z Special issue on Gene Expression \citeKargupta:2002:GPEM Title of paper should be "Biomimetic Representation with Enzyme Genetic Programming" Also see paper in WCCI 2002.
   This article subsumes \citeLonTyr01, \citelones:2001:brgp and \citelones:2001:pgp Article ID: 408588 cf. Genetic Programming and Evolvable Machines, 3, 315, 2002 Erratum
   The Publisher apologizes for a misprint that appeared in Genetic Programming and Evolvable Machines, volume 3, number 2.The correct title of the article by Michael A. Lones
   and Andy M. Tyrrell, pages 193-217, is 'Biomimetic Representation with Enzyme Genetic Programming'.

%A Michael Lones
%A Andy Tyrrell
%T Crossover and Bloat in the Functionality Model of Enzyme Genetic Programming
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 986--991
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/534034.html
%X The functionality model is a new approach in enzyme genetic programming which enables the evolution of variable length solutions whilst preserving local context. This paper
   introduces the model and presents an analysis of crossover and the evolution of program size.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Michael A. Lones
%A Andy M. Tyrrell
%T Modelling Biological Evolvability: Implicit Context and Variation Filtering in Enzyme Genetic Programming
%B Proceedings of the Fifth International Workshop on Information Processing in Cells and Tissues (IPCAT2003)
%E D. Mange and C. Teuscher and M. Holcombe and R. Paton and A. Stauffer and G. Tempesti
%D 2003
%I 
%K genetic algorithms, genetic programming, evolvability, representation, self-organisation
%U http://folk.ntnu.no/lones/lones-ipcat2003.pdf
%X This paper describes recent insights into the role of implicit context within the representations of evolving artifacts and specifically within the program representation
   used by enzyme genetic programming. Implicit context occurs within self-organising systems where a component's connectivity is both determined implicitly by its own
   definition and is specified in terms of the behavioural context of other components. This paper argues that implicit context is an important source of evolvability and
   presents experimental evidence that supports this assertion. In particular, it introduces the notion of variation filtering, suggesting that the use of implicit context
   within representations leads to meaningful variation filtering whereby inappropriate change is ignored and meaningful change is encouraged during evolution.
%8 September
%Z To appear in BioSystems.

%A Michael A. Lones
%T Enzyme Genetic Programming: Modelling Biological Evolvability in Genetic Programming
%R Ph.D. Thesis
%D 2003
%I 
%I The University of York
%C Heslington, York, YO10 5DD, UK
%K genetic algorithms, genetic programming, evolvability, representation, self-organisation, biological modelling
%U http://www-users.york.ac.uk/~mal503/common/thesis/main.html
%X This thesis introduces a new approach to program representation in genetic programming in which interactions between program components are expressed in terms of a
   component's behaviour rather through its relative position within a representation or through other non-behavioural systems of reference. This approach has the advantage
   that a component's behaviour is expressed in a way that is independent of any particular program it finds itself within; and thereby overcomes the problem when using
   conventional program representations whereby program components lose their behavioural context following recombination. More generally, this implicit context representation
   leads to a process of meaningful variation filtering; whereby inappropriate change induced by variation operators can be wholly or partially ignored. This occurs as a
   consequence of program behaviours emerging from the self-organisation of program components, ignoring those components which do not fit the contexts declared by the other
   components within the program. This process results in gradual change within the behaviour of a program during evolution. This thesis also presents results which show that
   implicit context representation leads to better size evolution characteristics than conventional genetic programming; and that functional redundancy and Lamarckian
   reinforcement learning both improve evolutionary search, agreeing with previous research by other authors.
%8 September
%Z small section on 'homologous crossovers' http://folk.ntnu.no/lones/thesis/c7.html#tth_sEc7.2 These choose crossover points non-randomly according to recognition of genetic
   homology (bits that look the same).

%A Michael A. Lones
%A Andy M. Tyrrell
%T Modelling biological evolvability: Implicit context and variation filtering in enzyme genetic programming
%J BioSystems
%V 76
%N 1--3
%D 2004
%P 229--238
%I 
%K genetic algorithms, genetic programming, Evolvability, Implicit context, Variation filtering
%U http://www.sciencedirect.com/science/article/B6T2K-4D09KY2-8/2/cf043c46f0d2f5a9997b5b62067c1f20
%X We describe recent insights into the role of implicit context within the representations of evolving artefacts and specifically within the program representation used by
   enzyme genetic programming. Implicit context occurs within self-organising systems where a component's connectivity is both determined implicitly by its own definition and
   is specified in terms of the behavioural context of other components. This paper argues that implicit context is an important source of evolvability and presents
   experimental evidence that supports this assertion. In particular, it introduces the notion of variation filtering, suggesting that the use of implicit context within
   representations leads to meaningful variation filtering whereby inappropriate change is ignored and meaningful change is encouraged during evolution.
%8 August --  October
%Z Papers presented at the Fifth International Workshop on Information Processing in Cells and Tissues

%A Michael A. Lones
%A Andy M. Tyrrell
%T Enzyme Genetic Programming
%B Cellular Computing
%S Series in Systems Biology
%E Martyn Amos
%D 2004
%P 19--42
%I Oxford University Press
%K genetic algorithms, genetic programming
%U http://www.oup.com/us/catalog/general/subject/LifeSciences/GenomicsBioinformatics/?view=usa&sf=toc&ci=9780195155396
%O 3
%@ 0-19-515539-4

%A Michael A. Lones
%A Andy M. Tyrrell
%T A Co-Evolutionary Framework for Regulatory Motif Discovery
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 3894--3901
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming, biology computing, evolutionary computation, genetics, pattern classification, Boolean classification rules, co-evolutionary
   framework, co-expressed genes, regulatory motif discovery, sequence classifiers
%X In previous work, we have shown how an evolutionary algorithm with a clustered population can be used to concurrently discover multiple regulatory motifs present within the
   promoter sequences of co-expressed genes. In this paper, we extend the algorithm by co-evolving a population of Boolean classification rules in parallel with the motif
   population. Results using synthetic data suggest that this approach allows poorly conserved motifs to be identified in promoter sequences an order of magnitude longer than
   using population clustering alone, whilst results using muscle-specific promoter data show the algorithm is able to evolve meaningful sequence classifiers in parallel with
   motifs' suggesting that co-evolution provides a suitable framework for composite motif discovery within eukaryotic sequences.
%8 25-28  September
%Z also known as \cite4424978. Page numbers from IEEE Xplore 2009. 3896 GP-like, binary tree. CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog
   Number: 07TH8963C
%@ 1-4244-1340-0

%A Michael Lones
%A Andy Tyrrell
%A Susan Stepney
%A Leo Caves
%T Controlling Complex Dynamics with Artificial Biochemical Networks
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 159--170
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X Artificial biochemical networks (ABNs) are computational models inspired by the biochemical networks which underlie the cellular activities of biological organisms. This
   paper shows how evolved ABNs may be used to control chaotic dynamics in both discrete and continuous dynamical systems, illustrating that ABNs can be used to represent
   complex computational behaviours within evolutionary algorithms. Our results also show that performance is sensitive to model choice, and suggest that conservation laws
   play an important role in guiding search.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Michael A. Lones
%A Stephen L. Smith
%A Andrew T. Harris
%A Alec S. High
%A Sheila E. Fisher
%A D. Alastair Smith
%A Jennifer Kirkham
%T Discriminating Normal and Cancerous Thyroid Cell Lines using Implicit Context Representation Cartesian Genetic Programming
%B 2010 IEEE World Congress on Computational Intelligence
%E Pilar Sobrevilla
%D 2010
%P 1945--1950
%I IEEE
%I IEEE Computational Intelligence Society
%C Barcelona
%K genetic algorithms, genetic programming, cartesian genetic programming
%U http://www-users.york.ac.uk/~mal503/common/papers/lones-wcci2010.pdf
%X In this paper, we describe a method for discriminating between thyroid cell lines. Five commercial thyroid cell lines were obtained, ranging from non-cancerous to cancerous
   varieties. Raman spectroscopy was used to interrogate native cell biochemistry. Following suitable normalisation of the data, implicit context representation Cartesian
   genetic programming was then used to search for classifiers capable of distinguishing between the spectral fingerprints of the different cell lines. The results are
   promising, producing comprehensible classifiers whose output values correlate with biological aggressiveness.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586494

%A Michael Lones
%T Sean Luke: essentials of metaheuristics
%J Genetic Programming and Evolvable Machines
%V 12
%N 3
%D 2011
%P 333--334
%I 
%K genetic algorithms, genetic programming
%O Book review
%8 September
%Z Review of \citeLuke2009Metaheuristics

%A Tom Longshaw
%T Evolutionary learning of large Grammars
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 445
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K evolutionary programming and evolution strategies
%8 13-16  July
%Z GP-97

%A Moshe Looks
%A Ben Goertzel
%A Cassio Pennachin
%T Learning Computer Programs with the Bayesian Optimization Algorithm
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 1
%D 2005
%P 747--748
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, BOA, Estimation of Distribution Algorithms, Poster, design, empirical study, representations
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p747.pdf
%X We describe an extension of the Bayesian Optimisation Algorithm (BOA), a probabilistic model building genetic algorithm, to the domain of program tree evolution. The new
   system, BOA programming (BOAP), improves significantly on previous probabilistic model building genetic programming (PMBGP) systems in terms of the articulacy and
   open-ended flexibility of the models learnt, and hence control over the distribution of instances generated. Innovations include a novel tree representation and a
   generalised program evaluation scheme.
%8 25-29  June
%Z Sunspot time series perdiction. GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic
   programming conference (GP-2005). ACM Order Number 910052
%@ 1-59593-010-8

%A Moshe Looks
%T Competent Program Evolution
%R Ph.D. Thesis Doctor of Science
%D 2006
%I 
%I Washington University
%C St. Louis, USA
%K genetic algorithms, genetic programming
%U http://metacog.org/main.pdf
%X Heuristic optimization methods are adaptive when they sample problem solutions based on knowledge of the search space gathered from past sampling. Recently, competent
   evolutionary optimization methods have been developed that adapt via probabilistic modeling of the search space. However, their effectiveness requires the existence of a
   compact problem decomposition in terms of prespecified solution parameters. How can we use these techniques to effectively and reliably solve program learning problems,
   given that program spaces will rarely have compact decompositions? One method is to manually build a problem-specific representation that is more tractable than the general
   space. But can this process be automated? My thesis is that the properties of programs and program spaces can be leveraged as inductive bias to reduce the burden of manual
   representation-building, leading to competent program evolution. The central contributions of this dissertation are a synthesis of the requirements for competent program
   evolution, and the design of a procedure, meta-optimizing semantic evolutionary search (MOSES), that meets these requirements. In support of my thesis, experimental results
   are provided to analyze and verify the effectiveness of MOSES, demonstrating scalability and real-world applicability.
%8 11  Decemeber
%Z http://metacog.org/doc.html

%A Moshe Looks
%T Scalable estimation-of-distribution program evolution
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 1
%D 2007
%P 539--546
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Estimation of Distribution Algorithms, empirical Study, heuristics, optimisation, representation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p539.pdf
%X I present a new estimation-of-distribution approach to program evolution where distributions are not estimated over the entire space of programs. Rather, a novel
   representation-building procedure that exploits domain knowledge is used to dynamically select program subspaces for estimation over. This leads to a system of demes
   consisting of alternative representations (i.e. program subspaces) that are maintained simultaneously and managed by the overall system. Meta-optimising semantic
   evolutionary search (MOSES), a program evolution system based on this approach, is described, and its representation-building subcomponent is analysed in depth.
   Experimental results are also provided for the overall MOSES procedure that demonstrate good scalability.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071 New initialisation scheme but disappointingly no big performance boost. Parity (AND OR NOT) Mux6 Mux-11. Semantic sampling. C++. Holman Elegant normal
   form (cf. http://www.patterncraft.com/) ENF Catalan lil-gp.

%A Moshe Looks
%T On the behavioral diversity of random programs
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1636--1642
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, empirical Study, heuristics, optimisation, representation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1636.pdf
%X Generating a random sampling of program trees with specified function and terminal sets is the initial step of many program evolution systems. I present a theoretical and
   experimental analysis of the expected distribution of uniformly sampled programs, guided by algorithmic information theory. This analysis demonstrates that increasing the
   sample size is often an inefficient means of increasing the overall diversity of program behaviours (outputs). A novel sampling scheme (semantic sampling) is proposed that
   exploits semantics to heuristically increase behavioral diversity. An important property of the scheme is that no calls of the problem-specific fitness function are
   required. Its effectiveness at increasing behavioural diversity is demonstrated empirically for Boolean formulae. Furthermore, it is found to lead to statistically
   significant improvements in performance for genetic programming on parity and multiplexer problems.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Heitor S. Lopes
%A Mario S. Coutinho
%A Walter C. Lima
%T An evolutionary approach to simulate cognitive feedback learning in medical domain
%B Genetic Algorithms and Fuzzy Logic Systems
%S Advances in Fuzzy Systems - Applications and Theory
%E E. Sanchez and T. Shibata and L. A. Zadeh
%V 7
%D 1997
%P 193--207
%I World Scientific Publishing
%K genetic algorithms, medical diagnosis
%U http://www.cpgei.cefetpr.br/~hslopes/publicacoes/1997/book97.zip
%@ 981-02-2423-0

%A Heitor S. Lopes
%T A medical diagnostic system optimization using parallel genetic algorithms
%B Computational Intelligence and Applications
%S Studies in fuzziness and soft computing
%E Piotr S. Szczepaniak
%V 23
%D 1999
%P 222--227
%I Physica-Verlag
%K genetic algorithms, parallel, medical diagnosis
%U http://www.amazon.com/exec/obidos/ASIN/3790811610/qid=/103-1848228-1466238
%@ 3-7908-1161-0

%A Heitor S. Lopes
%A Wagner R. Weinert
%T EGIPSYS: an Enhanced Gene Expression Programming Approach for Symbolic Regression Problems
%J International Journal of Applied Mathematics and Computer Science
%V 14
%N 3
%D 2004
%I 
%K genetic algorithms, genetic programming, gene expression programming, evolutionary computation, symbolic regression, mathematical modeling, systems identification
%U http://matwbn.icm.edu.pl/ksiazki/amc/amc14/amc1434.pdf
%X This enhanced system, called EGIPSYS, has features specially suited to deal with symbolic regression problems. Amongst the new features implemented in EGIPSYS are: new
   selection methods, chromosomes of variable length, a new approach to manipulating constants, new genetic operators and an adaptable fitness function. All the proposed
   improvements were tested separately, and proved to be advantageous over the basic GEP. EGIPSYS was also applied to four difficult identification problems and its
   performance was compared with a traditional implementation of genetic programming (LilGP). Overall, EGIPSYS was able to obtain consistently better results than the system
   using genetic programming, finding less complex solutions with less computational effort. The success obtained suggests the adaptation and extension of the system to other
   classes of problems.
%O Special Issue: Evolutionary Computation
%Z AMCS Centro Federal de Educacao Tecnologica do Parana / CPGEI Av. 7 de setembro, 3165, 80230-901 Curitiba (PR), Brazil

%A Heitor S. Lopes
%T Genetic programming for epileptic pattern recognition in electroencephalographic signals
%J Applied Soft Computing
%V 7
%N 1
%D 2007
%P 343--352
%I 
%K genetic algorithms, genetic programming, Pattern recognition, Epilepsy, EEG
%X the genetic programming paradigm, in conjunction with pattern recognition principles, can be used to evolve classifiers capable of recognising epileptic patterns in human
   electroencephalographic signals. The procedure for feature extraction from the raw signal is detailed, as well as the genetic programming system that properly selects the
   features and evolves the classifiers. Based on the data sets used, two different epileptic patterns were detected: 3 Hz spike-and-slow-wave-complex (SASWC) and
   spike-or-sharp-wave (SOSW). After training, classifiers for both patterns were tested with unseen instances, and achieved sensibility = 1.00 and specificity = 0.93 for
   SASWC patterns, and sensibility = 0.94 and specificity = 0.89 for SOSW patterns. Results are very promising and suggest that the methodology presented can be applied to
   other pattern recognition tasks in complex signals.
%8 January

%A Rui Lopes
%A Ernesto Costa
%T ReNCoDe: A Regulatory Network Computational Device
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 142--153
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X In recent years, our biologic understanding was increased with the comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of
   inheritance and of development, and some researchers advocate the need to explore computationally this new understanding. One of the outcomes was the Artificial Gene
   Regulatory (ARN) model, first proposed by Wolfgang Banzhaf. In this paper, we use this model as representation for a computational device and introduce new variation
   operators, showing experimentally that it is effective in solving a set of benchmark problems.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Rui L. Lopes
%A Ernesto Costa
%T Using feedback in a regulatory network computational device
%B GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 1499--1506
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, Generative and developmental systems
%X The relationship between the genotype and the phenotype in Evolutionary Algorithms (EA) is a recurrent issue among researchers. Based on our current understanding of the
   multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, some researchers start exploring
   computationally this new insight, including those mechanism in the EA. The Artificial Gene Regulatory (ARN) model, proposed by Wolfgang Banzhaf was one of the first
   tentatives. Following his seminal work some variants were proposed with increased capabilities. In this paper, we present another modification of this model, consisting in
   the use the regulatory network as a computational device where feedback edges are used. Using two classical benchmarks, the n-bit parity and the Fibonacci sequence
   problems, we show experimentally the effectiveness of the proposal.
%8 12-16  July
%Z Also known as \cite2001778 GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic
   programming conference (GP-2011)

%A Rui L. Lopes
%A Ernesto Costa
%T The Regulatory Network Computational Device
%J Genetic Programming and Evolvable Machines
%I 
%K genetic algorithms, genetic programming, evolvable hardware
%X Evolutionary Algorithms (EA) approach the genotype-phenotype relationship differently than does nature, and this discrepancy is a recurrent issue among researchers.
   Moreover, in spite of some performance improvements, it is a fact that biological knowledge has advanced faster than our ability to incorporate novel biological ideas into
   EAs. Recently, some researchers have started exploring computationally new comprehension of the multitude of the regulatory mechanisms that are fundamental in both
   processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial
   Gene Regulatory Network (ARN) model, by Wolfgang Banzhaf. Soon after some variants of the ARN were tested. we describe one of those, the Regulatory Network Computational
   Device, demonstrating experimentally its capabilities. The efficacy and efficiency of this alternative is tested experimentally using typical benchmark problems for Genetic
   Programming (GP) systems. We devise a modified factorial problem to investigate the use of feedback connections and the scalability of the approach. In order to gain a
   better understanding about the reasons for the improved quality of the results, we undertake a preliminary study about the role of neutral mutations during the evolutionary
   process.
%O Online first
%Z N-bit parity, Fibonacci, squares, modified factorial, ReNCoDe symbolic regression, artificial ant \citelangdon:1998:antspace, cart centering, neutrality analysis, ARN

%A Antonio M. Lopez
%A Hilario Lopez
%A Luciano Sanchez
%T GA-P based search of structures and parameters of dynamical process models
%B Advances in Soft Computing - Engineering, Design and Manufacturing
%E Jose Benitez and Oscar Cordon and Frank Hoffmann and Rajkumar Roy
%D 2003
%P 371--380
%I Springer
%C London
%K genetic algorithms, genetic programming, GA-P algorithms, System Identification, Hierarchical models
%U http://www.di.uniovi.es/~luciano/articulos/alopez_wsc7.pdf
%X The most effective approaches for evolutionary identifying dynamical processes depend on iterative trial-error searches in a hierarchical fashion: a new structure is
   proposed first; then, its set of parameters is numerically determined, and the process is repeated until a model accurate enough is found. Canonical Genetic Programming has
   been used to automate this search; but its output can be diffcult to interpret. Because of this reason, the use of hierarchical learning methods, that combine GP search of
   structures with deterministic optimisation algorithms, has been proposed. We will show in this paper that the output of such methods can be further improved with non
   hierarchical algorithms. In particular, we will show that the use of GA-P improves the interpretability of the models and does a better model search than previous
   approaches.
%O on line
%8 September  23 -  October  4
%Z WSC7 http://wsc7.ugr.es Workshop in 2002 but published by Springer in October 2003. Fig. 2. SMOG evolution. Canonical GP is used for structural search and Hooke-Jeeves
   method is used for parameter tuning. Modeling direct electrical current motor.

%A Edgar {Galvan Lopez}
%A Riccardo Poli
%A Carlos A. {Coello Coello}
%T Reusing Code in Genetic Programming
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 359--368
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming, cartesian genetic programming, PSO, code reuse, logic circuit design, evolvable hardware
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=359
%X We propose an approach to Genetic Programming based on reuse of code and we test our algorithm in the design of combinational logic circuits at the gate-level. The proposed
   algorithm is validated using examples taken from the evolvable hardware literature, and is compared against circuits produced by human designers, by Particle Swarm
   Optimization, by an n-cardinality GA and by Cartesian Genetic Programming.
%8 5-7  April
%Z p-node Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Edgar {Galvan Lopez}
%A Katya {Rodriguez Vazquez}
%A Riccardo Poli
%T Beneficial Aspects of Neutrality in GP
%B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2005)
%E Franz Rothlauf
%D 2005
%I 
%C Washington, D.C., USA
%K genetic algorithms, genetic programming, EHW
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/45-lopez.pdf
%X We propose a new approach, called Multiple Outputs in a Single Tree (MOST), to Genetic Programming. The idea of this approach is to specify explicitly Neutrality and study
   how this improves the evolutionary process. For this sake, we have used several evolvable hardware problems of different complexity taken from the literature. Our results
   indicate that our approach has a better overall performance in terms of consistency to reach feasible solutions
%8 25-29  June
%Z Distributed on CD-ROM at GECCO-2005

%A Edgar Galvan-Lopez
%A Katya Rodriguez-Vazquez
%T The Importance of Neutral Mutations in GP
%B Parallel Problem Solving from Nature - PPSN IX
%S LNCS
%E Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao
%V 4193
%D 2006
%P 870--879
%I Springer-Verlag Berlin
%C Reykjavik, Iceland
%K genetic algorithms, genetic programming
%U http://ppsn2006.raunvis.hi.is/proceedings/208.pdf
%X Understanding how neutrality works in EC systems has drawn increasing attention. However, some researchers have found neutrality to be beneficial for the evolutionary
   process while others have found it either useless or worse. We believe there are various reasons for these contradictory results: (a) many studies have based their
   conclusions using crossover and mutation as main operators rather than using only mutation (Kimura's studies were done analysing only mutations) and, (b) studies often
   consider problems and representation with larger complexity. The aim of this paper is to analyse how neutral mutations tend to behave in GP and establish how important they
   are. For this purpose we introduce an approach which has two advantages: (a) it allows us to specify neutrality and, (b) this makes possible to understand how neutrality
   affects the evolutionary search process.
%8 9-13  September
%Z PPSN-IX
%@ 3-540-38990-3

%A Edgar Galv\'an-L\'opez
%A Katya Rodriguez-V\'azquez
%T Multiple Interactive Outputs in a Single Tree: An Empirical Investigation
%B Proceedings of the 10th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar
%V 4445
%D 2007
%P 341--350
%I Springer
%C Valencia, Spain
%K genetic algorithms, genetic programming
%X This paper describes Multiple Interactive Outputs in a Single Tree (MIOST), a new form of Genetic Programming (GP). Our approach is based on two ideas. Firstly, we have
   taken inspiration from graph-GP representations. With this idea we decided to explore the possibility of representing programs as graphs with oriented links. Secondly, our
   individuals could have more than one output. This idea was inspired on the divide and conquer principle, a program is decomposed in subprograms, and so, we are expecting to
   make the original problem easier by breaking down a problem into two or more sub-problems. To verify the effectiveness of our approach, we have used several evolvable
   hardware problems of different complexity taken from the literature. Our results indicate that our approach has a better overall performance in terms of consistency to
   reach feasible solutions.
%8 11-13  April
%Z Part of \citeebner:2007:GP EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007
%@ 3-540-71602-5

%A Edgar Galvan-Lopez
%A Stephen Dignum
%A Riccardo Poli
%T The Effects of Constant Neutrality on Performance and Problem Hardness in GP
%B Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008
%S Lecture Notes in Computer Science
%E Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel Esparcia Alcazar and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino
%V 4971
%D 2008
%P 312--324
%I Springer
%C Naples
%K genetic algorithms, genetic programming
%8 26-28  March
%Z Also known as \citeconf/eurogp/LopezDP08 Part of \citeconf/eurogp/2008 EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008

%A Edgar Galvan
%T An Analysis of the Effects of Neutrality on Problem Hardness for Evolutionary Algorithms
%R Ph.D. Thesis
%D 2009
%I 
%I School of Computer Science and Electronic Engineering, University of Essex
%C United Kingdom
%K genetic algorithms, genetic programming
%U http://www.essex.ac.uk/csee/department/news/newsletter/08_12_08.aspx
%Z http://www.essex.ac.uk/csee/department/news/newsletter/08_12_08.aspx

%A Edgar Galvan-Lopez
%A Michael O'Neill
%A Anthony Brabazon
%T Towards Understanding the Effects of Locality in GP
%B Eighth Mexican International Conference on Artificial Intelligence, MICAI 2009
%E Arturo Hernandez Aguirre and Raul Monroy and Carlos Alberto Reyes Garcia
%D 2009
%P 9--14
%I 
%C Guanajuato, Mexico
%K genetic algorithms, genetic programming
%X Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been defined as a key element in Evolutionary Computation systems to explore and
   exploit the search space. Locality has been studied empirically using the typical Genetic Algorithms (GAs) representation (i.e., bitstrings),and it has been argued that
   locality plays an important role in the performance of evolution. To our knowledge, there are no studies of locality using the typical Genetic Programming
   (GP)representation (i.e., tree-like structures). The aim of this paper is to shed some light on this matter by using GP. To do so, we use three different types of mutation
   taken from the specialised literature. We then perform extensive experiments by comparing the difference of distances at the genotype level between parent and offspring and
   their corresponding fitnesses. Our findings indicate that there is low-locality in GP when using these forms of mutation on a multimodal-deceptive landscape.
%8 9-13  November
%Z Also known as \cite5372725

%A Edgar Galvan-Lopez
%A John Mark Swafford
%A Michael O'Neill
%A Anthony Brabazon
%T Evolving a Ms. PacMan Controller Using Grammatical Evolution
%B EvoGAMES
%S LNCS
%E Cecilia Di Chio and Stefano Cagnoni and Carlos Cotta and Marc Ebner and Aniko Ekart and Anna I. Esparcia-Alcazar and Chi-Keong Goh and Juan J. Merelo and Ferrante Neri and
   Mike Preuss and Julian Togelius and Georgios N. Yannakakis
%V 6024
%D 2010
%P 161--170
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming, grammatical evolution
%X In this paper we propose an evolutionary approach capable of successfully combining rules to play the popular video game, Ms. Pac-Man. In particular we focus our attention
   on the benefits of using Grammatical Evolution to combine rules in the form of if <condition> then perform <action>. We defined a set of high-level functions that we think
   are necessary to successfully manoeuvre Ms. Pac-Man through a maze while trying to get the highest possible score. For comparison purposes, we used four Ms. Pac-Man agents,
   including a hand-coded agent, and tested them against three different ghosts teams. Our approach shows that the evolved controller achieved the highest score among all the
   other tested controllers, regardless of the ghost team used.
%8 7-9  April
%Z EvoGAMES'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Edgar Galvan-Lopez
%A James McDermott
%A Michael O'Neill
%A Anthony Brabazon
%T Towards an understanding of locality in genetic programming
%B GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
%E Juergen Branke and Martin Pelikan and Enrique Alba and Dirk V. Arnold and Josh Bongard and Anthony Brabazon and Juergen Branke and Martin V. Butz and Jeff Clune and Myra
   Cohen and Kalyanmoy Deb and Andries P Engelbrecht and Natalio Krasnogor and Julian F. Miller and Michael O'Neill and Kumara Sastry and Dirk Thierens and Jano van Hemert and
   Leonardo Vanneschi and Carsten Witt
%D 2010
%P 901--908
%I ACM New York, NY, USA
%I SIGEVO
%C Portland, Oregon, USA
%K genetic algorithms, genetic programming
%X Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been defined as a key element affecting how Evolutionary Computation systems explore
   and exploit the search space. Locality has been studied empirically using the typical Genetic Algorithm (GA) representation (i.e., bitstrings), and it has been argued that
   locality plays an important role in EC performance. To our knowledge, there are few explicit studies of locality using the typical Genetic Programming (GP) representation
   (i.e., tree-like structures). The aim of this paper is to address this important research gap. We extend the genotype-phenotype definition of locality to GP by studying the
   relationship between genotypes and fitness. We consider a mutation-based GP system applied to two problems which are highly difficult to solve by GP (a multimodal deceptive
   landscape and a highly neutral landscape). To analyse in detail the locality in these instances, we adopt three popular mutation operators. We analyse the operators'
   genotypic step sizes in terms of three distance measures taken from the specialised literature and in terms of corresponding fitness values. We also analyse the frequencies
   of different sizes of fitness change.
%8 7-11  July
%Z Also known as \cite1830646 GECCO-2010 A joint meeting of the nineteenth international conference on genetic algorithms (ICGA-2010) and the fifteenth annual genetic
   programming conference (GP-2010)

%A Edgar Galvan-Lopez
%A David Fagan
%A Eoin Murphy
%A John Mark Swafford
%A Alexandros Agapitos
%A Michael O'Neill
%A Anthony Brabazon
%T Comparing the Performance of the Evolvable PiGrammatical Evolution Genotype-Phenotype Map to Grammatical Evolution in the Dynamic Ms. Pac-Man Environment
%B 2010 IEEE World Congress on Computational Intelligence
%D 2010
%P 1587--1594
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Barcelona, Spain
%K genetic algorithms, genetic programming, grammatical evolution
%X In this work, we examine the capabilities of two forms of mappings by means of Grammatical Evolution (GE) to successfully generate controllers by combining high-level
   functions in a dynamic environment. In this work we adopted the Ms. Pac-Man game as a benchmark test bed. We show that the standard GE mapping and Position Independent GE
   (piGE) mapping achieve similar performance in terms of maximising the score. We also show that the controllers produced by both approaches have an overall better
   performance in terms of maximising the score compared to a hand-coded agent. There are, however, significant differences in the controllers produced by these two
   approaches: standard GE produces more controllers with invalid code, whereas the opposite is seen with piGE.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586508

%A Edgar Galvan-Lopez
%A James McDermott
%A Michael O'Neill
%A Anthony Brabazon
%T Defining Locality in Genetic Programming to Predict Performance
%B 2010 IEEE World Congress on Computational Intelligence
%D 2010
%P 1828--1835
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Barcelona, Spain
%K genetic algorithms, genetic programming
%X A key indicator of problem difficulty in evolutionary computation problems is the landscape's locality, that is whether the genotype-phenotype mapping preserves
   neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotypefitness mapping is of interest. In this paper we extend
   the original standard quantitative definition of locality to cover the genotype-fitness case, considering three possible definitions. By relating the values given by these
   definitions with the results of evolutionary runs, we investigate which definition is the most useful as a predictor of performance.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586095

%A Edgar Galvan-Lopez
%A James McDermott
%A Michael O'Neill
%A Anthony Brabazon
%T Defining locality as a problem difficulty measure in genetic programming
%J Genetic Programming and Evolvable Machines
%V 12
%N 4
%D 2012
%P 365--401
%I 
%K genetic algorithms, genetic programming
%X A mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to
   neighbouring phenotypes. A representation has high locality if most genotypic neighbours are mapped to phenotypic neighbours. Locality is seen as a key element in
   performing effective evolutionary search. It is believed that a representation that has high locality will perform better in evolutionary search and the contrary is true
   for a representation that has low locality. When locality was introduced, it was the genotype-phenotype mapping in bit string based Genetic Algorithms which was of
   interest; more recently, it has also been used to study the same mapping in Grammatical Evolution. To our knowledge, there are few explicit studies of locality in Genetic
   Programming (GP). The goal of this paper is to shed some light on locality in GP and use it as an indicator of problem difficulty. Strictly speaking, in GP the genotype and
   the phenotype are not distinct. We attempt to extend the standard quantitative definition of genotype-phenotype locality to the genotype-fitness mapping by considering
   three possible definitions. We consider the effects of these definitions in both continuous- and discrete-valued fitness functions. We compare three different GP
   representations (two of them induced by using different function sets and the other using a slightly different GP encoding) and six different mutation operators. Results
   indicate that one definition of locality is better in predicting performance.
%8 Decemeber
%Z Rothlauf. Mutation only (no crossover). Sum of surplus distances. Even-3-parity, even-4-parity (two function sets) Artificial ant \citelangdon:1998:antspace (two function
   sets) two symbolic regression. Uniform GP. Ant negative correlation (r=-.74). Normalised compression distance (Kolmogorov complexity). Size fair, size fair range crossovers
   \citelangdon:2000:fairxo Hoist, one point, subtree, _permutation_ mutations. p388 'mutations involving the discontinuous protected division operator' p390 'always a counter
   example'.

%A Oscar Javier {Romero Lopez}
%A Angelica {de Antonio}
%T Hybrid Behaviour Orchestration in a Multilayered Cognitive Architecture Using an Evolutionary Approach
%B 2008 IEEE World Congress on Computational Intelligence
%E Jun Wang
%D 2008
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Hong Kong
%K genetic algorithms, genetic programming, gene expression programming
%X Managing and arbitrating behaviours, processes and components in multilayered cognitive architectures when a huge amount of environmental variables are changing
   continuously with increasing complexity, ensue in a very comprehensive task. The presented framework proposes an hybrid cognitive architecture that relies on subsumption
   theory and includes some important extensions. These extensions can be condensed in inclusion of learning capabilities through bioinspired reinforcement machine learning
   systems, an evolutionary mechanism based on gene expression programming to self-configure the behaviour arbitration between layers, a co-evolutionary mechanism to evolve
   behaviour repertories in a parallel fashion and finally, an aggregation mechanism to combine the learning algorithms outputs to improve the learning quality and increase
   the robustness and fault tolerance ability of the cognitive agent. The proposed architecture was proved in an animat environment using a multi-agent platform where several
   learning capabilities and emergent properties for selfconfiguring internal agent's architecture arise.
%8 1-6  June
%Z WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.

%A Oscar Javier {Romero Lopez}
%T Self-Organized and Evolvable Cognitive Architecture for Intelligent Agents and Multi-agent Systems
%B Second International Conference on Computer Engineering and Applications (ICCEA, 2010)
%V 1
%D 2010
%P 417--421
%I 
%C Bali Island
%K genetic algorithms, genetic programming, aggregation mechanism, evolutionary mechanism, evolvable cognitive architecture, gene expression programming, intelligent agents,
   multi-agent system, multilayered cognitive architectures, reinforcement machine learning systems, self-organised evolvable architecture, subsumption theory, cognitive
   systems, learning (artificial intelligence), multi-agent systems, software architecture
%X Managing and arbitrating behaviours, processes and components in multilayered cognitive architectures when a huge amount of environmental variables are changing
   continuously with increasing complexity, ensue in a very comprehensive task. The presented framework proposes an hybrid cognitive architecture that relies on subsumption
   theory and includes some important extensions. These extensions can be condensed in inclusion of learning capabilities through bio-inspired reinforcement machine learning
   systems, an evolutionary mechanism based on gene expression programming to self-configure the behaviour arbitration between layers, a co-evolutionary mechanism to evolve
   behaviour repertories in a parallel fashion and finally, an aggregation mechanism to combine the learning algorithms outputs to improve the learning quality and increase
   the robustness and fault tolerance ability of the cognitive agent. The proposed architecture was proved in an animate environment using a multi-agent platform where several
   learning capabilities and emergent properties for self-configuring internal agent's architecture arise.
%8 19-21  March
%Z Software Engineering Department Fundacion Universitaria Konrad Lorenz, Cra 9 bis No. 62-43 Bogota, Colombia. Also known as \cite5445796

%A E. Consuelo Lopez-Diez
%A Giorgio Bianchi
%A Royston Goodacre
%T Rapid Quantitative Assessment of the Adulteration of Virgin Olive Oils with Hazelnut Oils Using Raman Spectroscopy and Chemometrics
%J Journal of Agricultural and Food Chemistry
%V 51
%N 21
%D 2003
%P 6145--6150
%I 
%K genetic algorithms, genetic programming, Raman spectroscopy, olive oil, hazelnut oil, adulteration, quantification, principal component analysis, partial least-squares
   regression
%X The authentication of extra virgin olive oil and its adulteration with lower-priced oils are serious problems in the olive oil industry. In addition to the obvious effect
   on producer profits, adulteration can also cause severe health and safety problems. A number of techniques, including chromatographic and spectroscopic methods, have
   recently been employed to assess the purity of olive oils. In this study Raman spectroscopy together with multivariate and evolutionary computational-based methods have
   been employed to assess the ability of Raman spectroscopy to discriminate between chemically very closely related oils. Additionally, the levels of hazelnut oils used to
   adulterate extra virgin olive oil were successfully quantified using partial least squares and genetic programming.

%A A. G. Lopez-Herrera
%A E. Herrera-Viedma
%A F. Herrera
%T A Multiobjective Evolutionary Algorithm for Spam E-mail Filtering
%B 3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008
%V 1
%D 2008
%P 366--371
%I 
%K genetic algorithms, genetic programming, NSGA-II, multiobjective evolutionary algorithm, spam e-mail filtering, unsolicited commercial email, e-mail filters, unsolicited
   e-mail
%X Unsolicited commercial email, also known as spam, has been a major problem on the Internet. In this paper a well known multiobjective evolutionary Algorithm, NSGA-II, is
   first time used for spam e-mail filtering. NSGA-II is adapted to use Genetic Programming components to achieve a set of filtering rules with different profiles.
%8 November
%Z Also known as \cite4730957

%A A. G. Lopez-Herrera
%A E. Herrera-Viedma
%A F. Herrera
%T Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems
%J Fuzzy Sets and Systems
%V 160
%N 15
%D 2009
%P 2192--2205
%I 
%K genetic algorithms, genetic programming, MOGP, Information retrieval systems, Inductive query by example, Multi-objective evolutionary algorithms, Query learning
%U http://www.sciencedirect.com/science/article/B6V05-4VPM59B-4/2/21a5a32bf1a659a371ce5c4d320da182
%X The performance of information retrieval systems (IRSs) is usually measured using two different criteria, precision and recall. Precision is the ratio of the relevant
   documents retrieved by the IRS in response to a user's query to the total number of documents retrieved, whilst recall is the ratio of the number of relevant documents
   retrieved to the total number of relevant documents for the user's query that exist in the documentary database. In fuzzy ordinal linguistic IRSs (FOLIRSs), where extended
   Boolean queries are used, defining the user's queries in a manual way is usually a complex task. In this contribution, our interest is focused on the automatic learning of
   extended Boolean queries in FOLIRSs by means of multi-objective evolutionary algorithms considering both mentioned performance criteria. We present an analysis of two
   well-known general-purpose multi-objective evolutionary algorithms to learn extended Boolean queries in FOLIRSs. These evolutionary algorithms are the non-dominated sorting
   genetic algorithm (NSGA-II) and the strength Pareto evolutionary algorithm (SPEA2).
%O Special Issue: The Application of Fuzzy Logic and Soft Computing in Information Management

%A Peter J. Lorenzen
%T Comparing the Evaluation of Antiderivatives of Complex Functions with Cartesian versus Polar Representations via Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 84--93
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Christopher G. Lott
%T Terrain Flattening by Autonomous Robot: A Genetic Programming Application
%B Genetic Algorithms at Stanford 1994
%E John R. Koza
%D 1994
%P 99--109
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming, agents
%8 Decemeber
%Z "Successfully used GP to ... robot control programs which can transform any random 12 x 12 grid into basically a flat plane." "rudimentary cooperation between robots in
   acheiving the same goal". This volume contains 20 papers written and submitted by students describing their term projects for the course "Genetic Algorithms and Genetic
   Programming" (Computer Science 426) at Stanford University offered during the fall quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs426.html
%@ 0-18-187263-3

%A Marco Lotz
%A Sara Silva
%T Application of Genetic Programming Classification in an Industrial Process Resulting in Greenhouse Gas Emission Reductions
%B EvoENVIRONMENT
%S LNCS
%E Cecilia Di Chio and Anthony Brabazon and Gianni A. Di Caro and Marc Ebner and Muddassar Farooq and Andreas Fink and Jorn Grahl and Gary Greenfield and Penousal Machado and
   Michael O'Neill and Ernesto Tarantino and Neil Urquhart
%V 6025
%D 2010
%P 131--140
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X This paper compares Genetic Programming and the Classification and Regression Trees algorithm as data driven modelling techniques on a case study in the ferrous metals and
   steel industry in South Africa. These industries are responsible for vast amounts of greenhouse gas production, and greenhouse gas emission reduction incentives exist that
   can fund these abatement technologies. Genetic Programming is used to derive pure classification rule sets, and to derive a regression model used for classification, and
   both these results are compared to the results obtained by decision trees, regarding accuracy and human interpretability. Considering the overall simplicity of the rule set
   obtained by Genetic Programming, and the fact that its accuracy was not surpassed by any of the other methods, we consider it to be the best approach, and highlight the
   advantages of using a rule based classification system. We conclude that Genetic Programming can potentially be used as a process model that reduces greenhouse gas
   production.
%8 7-9  April
%Z EvoENVIRONMENT'2010 held in conjunction with EuroGP'2010 EvoCOP2010 EvoBIO2010

%A Jean Louchet
%T An evolutionary algorithm for physical motion analysis
%B British Machine Vision Conference
%E Edwin R. Hancock
%V 2
%D 1994
%P 701--710
%I BMVA Press
%C York, UK
%K genetic algorithms, genetic programming
%U http://www.bmva.org/bmvc/1994/bmvc-94-069.pdf
%8 13-16  September
%Z "cornerstone" paper. BMVC Press http://www.bmva.ac.uk/bmvc/index.html

%A Jean Louchet
%A Xavier Provot
%A David Crochemore
%T Evolutionary identification of cloth animation models
%B Computer Animation and Simulation '95
%S LNCS
%E Dimitri Terzolpoulos and Daniel Thalmann
%D 1995
%P 44--54
%I Springer-Verlag
%C Maastricht, Netherlands
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/louchet95evolutionary.html
%X This paper presents an application of evolutionary genetic techniques to the identification of internal parameters of a mass-spring physically-based animation model. A
   physical model of fabrics is first presented. It uses a mass-spring mesh and an inverse dynamics procedure in order to model the non-linear elasticity of fabrics. A method
   to identify the internal parameters of the model from geometric data is then presented. It is based on a cost function which measures the difference in...
%O Proceedings of the Eurographics Workshop
%8 2-3  September
%Z possible problem with citeseer PDF

%A Jean Louchet
%A Michael Boccara
%A David Crochemore
%A Xavier Provot
%T Building new tools for synthetic image animation using evolutionary techniques
%B Artificial Evolution AE'95
%S LNCS
%E Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald and Marc Schoenauer and Dominique Snyers
%V 1063
%D 1995
%P 273--286
%I Springer Verlag
%C Brest, France
%K genetic algorithms, genetic programming
%X Particle-based models and articulated models are increasingly used in synthetic image animation applications. This paper aims at showing examples of how Evolutionary
   Algorithms can be used as tools to build realistic physical models for image animation. First, a method to detect regions with rigid 2D motion in image sequences, without
   solving explicitly the Optical Flow equation, is presented. It is based on the resolution of an equation involving rotation descriptors and first-order image derivatives.
   An evolutionary technique is used to obtain a raw segmentation based on motion; the result of segmentation is then refined by an accumulation technique in order to
   determine more accurate rotation centres and deduce articulation points. Second, an evolutionary algorithm designed to identify internal parameters of a mass spring
   animation model from kinematic data (Physics from Motion) is presented through its application to cloth animation modelling.
%8 4-6  September
%@ 3-540-61108-8

%A Jean Louchet
%A Li Jiang
%T An identification tool to build physical models for virtual reality
%B Proceedings of the 3rd International Workshop on Image and Signal Processing IWISP96
%E B. G. Mertzios and P. Liatsis
%D 1996
%P 669--672
%I Elsevier
%I UMIST
%C Manchester, UK
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/74697.html
%X This paper focuses on the second step. We propose an original method to automatically identify physical models built through using local masses and generalised
   springs.<F1.116e+04> 1 The Physical Model.
%8 4-7  November
%Z "cornerstone" paper. Proceedings IWISP '96 http://www.elsevier.com/inca/publications/store/6/0/0/2/3/2/

%A Jean Louchet
%A Lionel Castillon
%A Jean-Marie Rocchisant
%T Evolving flies for stereovision and 3-D reconstruction in medical imaging
%B Second International Conference on Advanced concepts for intelligent vision systems ACIVS2000
%E Jacques Blanc-Talon and Dan Popescu
%D 2000
%I 
%C Baden-Baden, Germany
%K genetic algorithms, genetic programming
%8 3-4  August
%Z ACIVS 2000 http://www.etca.fr/CTA/Events/Conf/acivs00.html

%A Jean Louchet
%T Stereo analysis using individual evolution strategy
%B 15th International conference on pattern recognition
%V 1
%D 2000
%P 908--911
%I 
%C Barcelona
%K genetic algorithms, genetic programming
%X This paper presents an individual evolutionary strategy devised for image analysis applications. The example problem chosen is obstacle detection using a pair of cameras.
   The algorithm evolves a population of three-dimensional points ('flies') in the cameras fields of view, using a low complexity fitness function giving highest values to
   flies likely to be on the surfaces of 3-D obstacles. The algorithm uses classical sharing, mutation and crossover operators. The result is a fraction of the population
   rather than a single individual. Some test results are presented and potential extensions to real-time image sequence processing and mobile robotics are discussed.
%8 September

%A Jean Louchet
%T Using an Individual Evolution Strategy for Stereovision
%J Genetic Programming and Evolvable Machines
%V 2
%N 2
%D 2001
%P 101--109
%I 
%K genetic algorithms, genetic programming, artificial evolution, individual evolution strategy, flies, computer vision, image processing, stereovision, software engineering
%X The fly algorithm is an individual evolution strategy developed for parameter space exploration in computer vision applications. In the application described, each
   individual represents a geometrical point in the scene and the population itself is used as a three-dimensional model of the scene. A fitness function containing all
   pixel-level calculations is introduced to exploit simple optical and geometrical properties and evaluate the relevance of each individual as taking part to the scene
   representation. Classical evolutionary operators (sharing, mutation, crossover) are used. The combined individual approach and low complexity fitness function allow fast
   processing. Test results and extensions to real-time image sequence processing, mobile objects tracking and mobile robotics are presented.
%8 June
%Z Article ID: 335709

%A Jean Louchet
%A Maud Guyon
%A Marie-Jeanne Lesot
%A Amine Boumaza
%T L'algorithme des mouches: apprendre une forme par evolution artificielle, application en vision robotique
%B Extraction des Connaissances et Apprentissage
%E Claude Lattaud
%D 2002
%I Hermes
%K genetic algorithms, genetic programming
%X To guide a robot by artificial evolution in real time
%O in French
%8 January
%Z Langue : FRANCAIS, http://www.lavoisier.fr/notice/fr2746203600.html See also \citelouchet:2002:ECAeng,

%A Jean Louchet
%A Maud Guyon
%A Marie-Jeanne Lesot
%A Amine Boumaza
%T Dynamic flies: a new pattern recognition tool applied to stereo sequence processing
%J Pattern Recognition Letters
%V 23
%N 1-3
%D 2002
%P 335--345
%I 
%K genetic algorithms, genetic programming, Artificial evolution, Pattern recognition, Computer vision, Image processing, Parameter space exploration
%X The "fly algorithm" is a fast artificial evolution-based technique devised for the exploration of parameter space in pattern recognition applications. In the application
   described, we evolve a population which constitutes a particle-based three-dimensional representation of the scene. Each individual represents a three-dimensional point in
   the scene and may be fitted with optional velocity parameters. Evolution is controlled by a fitness function which contains all pixel-level calculations, and uses classical
   evolutionary operators (sharing, mutation, crossover). The combined individual approach and low complexity fitness function allow fast processing. Test results and an
   application to mobile robotics are presented.
%8 January
%Z Francais voir \citelouchet:2002:ECA

%A Daniel H. Loughlin
%A S. Ranji Ranjithan
%T Chance-Constrained Genetic Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 369--376
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-381.ps
%X latin square, latin hypercube sampling
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Roisin Loughran
%T Music Instrument Identification with Feature Selection Using Evolutionary Methods
%R Ph.D. Thesis
%D 2009
%I 
%I University of Limerick
%C Ireland
%K genetic algorithms, genetic programming, feature selection
%X Musical instruments may be identified using machine learning methods, but it is not clear which aspects of the sound or features are best used in such methods.
   Classification experiments using Principal Component Analysis (PCA) and Multi-Layered Perceptrons (MLP) in this thesis and that the addition of extra features may not
   necessarily be beneficial - optimisation of the features is required. This optimisation is implemented using Evolutionary Computation methods as they have yet to be
   extensively applied in musical sound analysis. A Genetic Algorithm (GA) with a new instrument-clustering fitness function based on PCA is applied to optimise a set of 95
   features for classification with an MLP. With this method, the number of features used to classify an instrument is reduced from 95 to as low as 22 with a classification
   accuracy reduction of less than 0.3percent. This method is tested against another evolutionary method that has not yet been applied to instrument identification - Genetic
   Programming (GP). GP is used to evolve a classifier program that can identify unseen samples with an accuracy of 94.3percent using just 14 of the 95 original features.
   Though not as high as the MLP or the GA-MLP, it is found that the GP is more consistent with its choice of features, offering a possible insight into timbre and the nature
   of sound recognition. In both EC methods it is found that the first principal component of the envelope of the centroid, a new measure of this feature, is the most
   important among all 95 features. It is also seen that each classification method performs significantly better when tested with a general set of samples, than with a
   one-octave sample set common to each instrument. The classifiers are compared to a set of human listening tests on particularly troublesome samples. It is seen that
   although the GA and GP are accurate at identifying general unseen samples, the human ear performs significantly better than both methods at identifying these difficult
   samples.
%8 October

%A Roisin Loughran
%A Jacqueline Walker
%A Michael O'Neill
%A James McDermott
%T Genetic Programming for Musical Sound Analysis
%B Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012
%S LNCS
%E Penousal Machado and Juan Romero and Adrian Carballal
%V 7247
%D 2012
%P 176--186
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Musical Information Retrieval, timbre
%X This study uses Genetic Programming (GP) in developing a classifier to distinguish between five musical instruments. Using only simple arithmetic and Boolean operators with
   95 features as terminals, a program is developed that can classify 300 unseen samples with an accuracy of 94percent. The experiment is then run again using only 14 of the
   most often chosen features. Limiting the features in this way raised the best classification to 94.3percent and the average accuracy from 68.2percent to 75.67percent. This
   demonstrates that not only can GP be used to create a classifier but it can be used to determine the best features to choose for accurate musical instrument classification,
   giving an insight into timbre.
%8 11-13  April
%Z Part of \citeMachado:2012:EvoMusArt EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012

%A Sushil J. Louis
%A Yongmian Zhang
%T A Sequential Similarity Metric for Case Injected Genetic Algorithms applied to TSPs
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 377--384
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-379.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Sushil J. Louis
%A Rilun Tang
%T Interactive Genetic Algorithms for the Traveling Salesman Problem
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 385--392
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-385.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Thomas Loveard
%A Victor Ciesielski
%T Representing Classification Problems in Genetic Programming
%B Proceedings of the Congress on Evolutionary Computation
%V 2
%D 2001
%P 1070--1077
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, Classification
%U http://goanna.cs.rmit.edu.au/~toml/cec2001.ps
%X In this paper five alternative methods are proposed to perform multi-class classification tasks using genetic programming. These methods are: Binary decomposition, in which
   the problem is decomposed into a set of binary problems and standard genetic programming methods are applied; Static range selection, where the set of real values returned
   by a genetic program is divided into class boundaries using arbitrarily chosen division points; Dynamic range selection in which a subset of training examples are used to
   determine where, over the set of reals, class boundaries lie; Class enumeration which constructs programs similar in syntactic structure to a decision tree; and evidence
   accumulation which allows separate branches of the program to add to the certainty of any given class. Results showed that the dynamic range selection method was well
   suited to the task of multi-class classification and was capable of producing classifiers more accurate than the other methods tried when comparable training times were
   allowed. Accuracy of the generated classifiers was comparable to alternative approaches over several datasets.
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number = . Tested on
   UCI machine learning testsets. STGP. 5 approaches to multiclass classifications: binary decomposition, static range, dynamic range, class enumeration (additional data type
   "ClassType" (cf C4.5), evidence accumulation cf "AddToClass", cf Teller
%@ 0-7803-6657-3

%A Thomas Loveard
%A Vic Ciesielski
%T Employing Nominal Attributes in Classification Using Genetic Programming
%B Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02)
%E Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and Jong-Hwan Kim and Xin Yao
%D 2002
%P 487--491
%I IEEE
%C Orchid Country Club, Singapore
%K genetic algorithms, genetic programming
%U http://goanna.cs.rmit.edu.au/~vc/papers/seal02-loveard.pdf
%X In this paper methods for performing classification using Genetic Programming (GP) on datasets with nominal attributes are developed and evaluated. The two methods
   developed included the splitting of GP program execution based upon the value of a nominal attribute (execution branching), and the conversion of a nominal attribute to a
   continuous or binary attribute (numeric conversion). These two methods of using nominal attributes are tested against six datasets containing either nominal and continuous
   attributes or nominal only attributes. Results show that the use of the methods developed in this paper allow classifiers trained with GP to perform accurate classification
   of datasets containing nominal attributes. When compared to other well-known methods of classification the GP method is capable of classifying one of six datasets more
   accurately than any of the conventional methods tested, and accuracy close to the best achieved method on 3 other datasets.
%8 18-22  November
%Z SEAL 2002
%@ 981-04-7522-5

%A Thomas Loveard
%A Vic Ciesielski
%T Genetic Programming for Classification: An Analysis of Convergence Behaviour
%B AI 2002: Advances in Artificial Intelligence : 15th Australian Joint Conference on Artificial Intelligence
%S Lecture Notes in Computer Science
%E Bob McKay and John Slaney
%V 2557
%D 2002
%P 309--320
%I Springer
%C Canberra, Australia
%K genetic algorithms, genetic programming
%U http://link.springer.de/link/service/series/0558/papers/2557/25570309.pdf
%X This paper investigates the unexpected convergence behaviour of genetic Programming (GP) for classification problems. Firstly the paper investigates the relationship
   between computational effort and attainable classification accuracy. Secondly we attempt to understand why GP classifiers sometimes fail to reach satisfactory levels of
   accuracy for certain problems regardless of computational effort. The investigation uses an artificially generated dataset for which certain properties are known in advance
   for the exploration of these areas. Results from this artificial problem show that by increasing computational effort, in the form of larger population sizes and more
   generations, the probability of success for a run does improve, but that the computational cost far outweighs the rate of this success. Also, some runs, even with very
   large populations running for many generations, became stagnant and were unable to find an acceptable solution. These results are also reflected in real world
   classification problems. From analysis of sub-tree components making up successful and unsuccessful programs it was noted that a small number of particular components were
   almost always present in successful programs, and that these components were often absent from unsuccessful programs. Also a variety of components appeared in unsuccessful
   programs that were never present in successful ones. Evidence from runs suggests that these components represent paths leading to optimal and sub-optimal branches in the
   evolutionary search space. Additionally, results suggest that if sub-optimal components (which mirror the concept of deception in genetic algorithms) are relatively greater
   in number than the optimal components for the problem, then the chances of GP finding a successful solution are reduced.
%8 2-6  Decemeber
%@ 3-540-00197-2

%A Thomas Loveard
%T Genetic Programming With Meta-Search: Searching For a Successful Population Within The Classification Domain
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 119--129
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=119
%X The genetic programming (GP) search method can often vary greatly in the quality of solution derived from one run to the next. As a result, it is often the case that a
   number of runs must be performed to ensure that an effective solution is found. This paper introduces several methods which attempt to better use the computational
   resources spent on performing a number of independent GP runs. Termed meta-search strategies, these methods seek to search the space of evolving GP populations in an
   attempt to focus computational resources on those populations which are most likely to yield competitive solutions. Two meta-search strategies are introduced and evaluated
   over a set of classification problems. The meta-search strategies are termed a pyramid search strategy and a population beam search strategy. Additional to these methods, a
   combined approach using properties of both the pyramid and population beam search methods is evaluated.Over a set of five classification problems, results show that
   meta-search strategies can substantially improve the accuracy of solutions over those derived by a set of independent GP runs. In particular the combined approach is
   demonstrated to give more accurate classification performance whilst requiring less time to train than a set of independent GP runs, making this method a promising approach
   for problems for which multiple GP runs must be performed to ensure a quality solution.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Thomas Loveard
%T Genetic Programming Methods for Classification Problems
%R Ph.D. Thesis
%D 2003
%I 
%I Department of Computer Science, RMIT
%K genetic algorithms, genetic programming
%U http://goanna.cs.rmit.edu.au/~vc/papers/loveard-phd.pdf
%8 20  January

%A J{\"o}rn Loviscach
%A Jennis Meyer-Spradow
%T Genetic Programming of Vertex Shaders
%B Proceedings of EuroMedia 2003
%E M. Chover and H. Hagen and D. Tost
%D 2003
%P 29--31
%I 
%C University of Plymouth, Plymouth, United Kingdom
%K genetic algorithms, genetic programming, GPU
%X Modern consumer 3-D graphics chips can synthesise procedural textures at a speed comparable to or even better than typical CPUs. We propose genetic programming of vertex
   shader assembly code for the real-time display and interactive design of procedural video textures and for the approximation and artistic abstraction of given static
   textures by compact vertex shaders.
%8 April  14-16
%Z cited by \citeeurogp:EbnerRA05 http://viscg.uni-muenster.de/publications/2003/LM03 http://www.eurosis.org/cms/index.php?q=taxonomy/term/58
%@ 90-77381-01-5

%A Joern Loviscach
%T Graphical Control of a Parametric Equalizer
%B AES
%D 2008
%I 
%C Amsterdam
%K genetic algorithms, genetic programming
%U http://www.aes.org/e-lib/browse.cfm?elib=14567
%X Graphic equalisers allow the user to define a filter's magnitude response virtually free of restrictions. Parametric equalisers are much more limited. However, they offer
   some vital advantages over graphic equalizers, such as consuming less computational power and operating minimally invasively with naturally soft magnitude and phase
   responses. This work aims at combining the best of both worlds: It presents a range of methods to control a digital parametric equaliser graphically through a curve or a
   collection of anchor points. While the user is editing the graphical input, an optimisation process runs in the background and adjusts the equaliser's parameters to reflect
   the input. In addition, the number of bands and their type (shelving/peak) can be adjusted automatically to produce a simple solution.
%8 17-20  May
%Z Paper Number: 7437 AES Convention: 124 (May 2008)

%A David Lowsky
%T Using a Cooperative Fitness Function to Coevolve Optimal Strategies in the Iterated Prisoner's Dilemma Game
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 131--139
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Manuel Lozano
%A Francisco Herrera
%A Jose Ramon Cano
%T Replacement strategies to preserve useful diversity in steady-state genetic algorithms
%J Information Sciences
%V 178
%N 23
%D 2008
%P 4421--4433
%I 
%K genetic algorithms
%X In this paper, we propose a replacement strategy for steady-state genetic algorithms that considers two features of the candidate chromosome to be included into the
   population: a measure of the contribution of diversity to the population and the fitness function. In particular, the proposal tries to replace an individual in the
   population with worse values for these two features. In this way, the diversity of the population becomes increased and the quality of the solutions gets better, thus
   preserving high levels of useful diversity. Experimental results show the proposed replacement strategy achieved significant performance for problems with different
   difficulties, with regards to other replacement strategies presented in the literature. Replacement strategies to preserve useful diversity in steady-state genetic
   algorithms
%O Special Section: Genetic and Evolutionary Computing
%8 1  Decemeber
%Z not on GP

%A Hui-Ling Lu
%T Search the Model Parameters of the Articulatory Singing Voice Synthesizer via Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 1998
%E John R. Koza
%D 1998
%P 94--100
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 17  March
%Z part of \citekoza:1998:GAGPs
%@ 0-18-212568-8

%A Ji Lu
%A Tao Li
%T Computation Process Evolution
%B 2006 IEEE International Conference on Engineering of Intelligent Systems
%D 2006
%P 1--6
%I IEEE
%I Islamabad
%K genetic algorithms, genetic programming, gene expression programming
%X Unlike other genetic methods which are devoted to optimise the input data, this paper proposes an approach, CPE, aiming at finding the computation process of any problem by
   only using a few input and output data, consisting of the cases needed to be satisfied and those needed to be avoided. It first encodes the antibody using the method
   similar to that of gene expression programming (GEP), a new efficient technique of genetic programming (GP) with linear representation. Through the gradual evolution, the
   affinity between antibody and the non-selves become more and more intense. At the same time, every time after the chromosomes are mutated, the chromosomes should be checked
   to determine whether the antibody chromosome would match the selves, which are the conditions that should be satisfied. Two kind of experiment are examined in order to test
   the performance of the approach. The results show that CPE evolves out the data-processing processes which are exactly the same as those from which the experimental input
   data were generated, and compared with GP and GEP which is currently one of the most efficient genetic methods, CPE experiences shorter evolution process. Most importantly,
   unlike previous evolutionary methods that only consider increasing fitness, this approach takes into account both the goal (fitness) and the constraints of actual problems,
   which makes it possible to solve complex real problems using evolutionary computation
%Z INSPEC Accession Number: 9133110 Dept. of Comput. Sci., Sichuan Univ., Chengdu;
%@ 1-4244-0456-8

%A Jian-Jun Lu
%A Yun-Ling Liu
%A Shozo Tokinaga
%T Nonlinear Modeling for Time Series Based on the Genetic Programming and its Applications
%B International Conference on Machine Learning and Cybernetics
%D 2006
%P 2097--2102
%I IEEE
%C Dalian
%K genetic algorithms, genetic programming
%X This paper deals with clustering of segments of stock prices by using nonlinear modelling system for time series based on the Genetic Programming (GP). We apply the GP
   procedure in learning phase of the system where we improve the nonlinear functional forms to approximate the models used to generate time series. The variation of the
   individuals with relatively high capability in the pool can cope with clustering for various kinds of time series which belong to the same cluster similar to the classifier
   systems. As an application, we show clustering of artificially generated time series obtained by expanding or shrinking by transformation functions. Then, we apply the
   system to clustering of 8 kinds of segments of real stock prices.
%8 August
%Z Graduate School of Economics, Kyushu University, Fukuoka 812-8581, Japan
%@ 1-4244-0061-9

%A Jianjun Lu
%A Yunling Liu
%A Shozo Tokinaga
%T Feature Description Systems for Clusters by Using Logical Rule Generations Based on the Genetic Programming and Its Applications to Data Mining
%B Proceedings of the 7th International Conference on Computational Science, ICCS 2007, Part IV
%S Lecture Notes in Computer Science
%E Yong Shi and G. Dick van Albada and Jack Dongarra and Peter M. A. Sloot
%V 4490
%D 2007
%P 162--165
%I Springer
%C Beijing, China
%K genetic algorithms, genetic programming
%X This paper deals with the realization of retrieval and feature description systems for clusters by using logical rule generations based on the Genetic Programming (GP). At
   first, whole data is divided into several clusters and the rules are improved based the GP. The fitness of individuals is defined in proportion to the hits of corresponding
   logical expression to the samples in targeted cluster c, but also in inversely proportion to the hits outside the cluster c. The GP method is applied to various real world
   data by showing effective performance compared to conventional methods.
%8 May  27-30
%Z China Agricultural University, Beijing Graduate School of Economics, Kyushu University, 812-8581, Japan

%A Shichang Lu
%A Zhiwei Fan
%T The Timing Correlation Dimension Study on Regional Economic Growth
%B 2010 International Conference on E-Business and E-Government (ICEE)
%D 2010
%P 5339--5342
%I 
%K Liaoning Province, experimental data, fractal extraction dimension method, regional economic growth, time series, timing correlation dimension study, correlation methods,
   economic forecasting, time series
%X Based on the relevance of fractal theory, taking Liaoning Province as an example to research the time series of regional economic growth, applied GP algorithm to calculate
   the correlation dimension and observe whether it has fractal characteristics, correlation dimension is a relatively simple extraction fractal dimension method through
   experimental data by calculating fractal dimension, has a certain operational, the method lays the foundation for predicting the regional economic growth and judging the
   economic situation.
%8 May
%Z Not GP but Grassberger and Procaccia. Fac. of Bus., Liaoning Tech. Univ., Huludao, China . Also known as \cite5592299

%A Wei Lu
%A Issa Traore
%T Detecting new forms of network intrusion using genetic programming
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 2165--2172
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%U http://www.isot.ece.uvic.ca/publications/journals/coi-2004.pdf
%X How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution
   approach based on Genetic Programming (GP) for detecting novel attacks on network is presented and four genetic operators namely reproduction, mutation, crossover and
   dropping condition operators are used to evolve new rules. New rules are used to detect novel or known network attacks. A training and testing dataset proposed by DARPA is
   used to evolve and evaluate these new rules. The proof of concept implementation shows that the rule generated by GP has a low false positive rate (FPR), a low false
   negative rate (FNR) and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate (DR) with low false alarm rate
   (FAR).
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Yueh-Chun Lu
%A Ming-Hung Chang
%A Te-Jen Su
%T Wiener Model Identification using Genetic Programming
%B Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2008
%V II
%D 2008
%P 1261--1265
%I 
%C Hong Kong
%K genetic algorithms, genetic programming, Wiener model, system identification, Akaike information criterion (AIC)
%X A Wiener model consists of a dynamic linear transfer function in series with a static nonlinear function. We can through the essences of GP, like robustness, domain
   independence and ability to search for satisfying solutions in solving complicated nonlinear problems, this study hoped that the evolved GP models could have a better
   applicability and accuracy of evaluations, and easily obtain the correct structure and parameters of the nonlinear function, and number of zeros and poles of the linear
   transfer function. GP is applied to the determine nonlinearity and unknown parameters in the nonlinear function and linear dynamic system model are estimated by a least
   square algorithm. The results of numerical studies indicate the usefulness of proposed approach to Wiener model identification.
%8 19-21  March

%A Peter B. Lubell-Doughtie
%T Using Genetic Programming to Evolve a General Purpose Sorting Network for Comparable Data Sets
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 128--132
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%U http://www.genetic-programming.org/sp2003/LubellDoughtie.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A Simon Lucas
%T Evolving spring-mass models: a test-bed for graph encoding schemes
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 1952--1957
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%U http://algoval.essex.ac.uk/rep/springs/cec2002.pdf
%X For many interesting design problems the solution is most naturally represented as a type of graph. This paper proposes that the problem of evolving spring-mass models for
   a set of design challenges makes an excellent test-bed for evaluating the performance of various graph encoding schemes. We describe how the problem is set up, and
   intro-duce a planar graph coding scheme. Results demonstrate that the planar graph encoding scheme significantly out-performs a simple direct encoding scheme on a
   height-challenge design problem.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Simon M. Lucas
%T Evolving Finite State Transducers: Some Initial Explorations
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 130--141
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=130
%X Finite state transducers (FSTs) are finite state machines that map strings in a source domain into strings in a target domain. While there are many reports in the
   literature of evolving general finite state machines, there has been much less work on evolving FSTs. In particular, the fitness functions required for evolving FSTs are
   generally different to those used for FSMs. This paper considers three string-distance based fitness functions. We compute their fitness distance correlations, and present
   results on using two of these (Strict and Hamming) to evolve FSTs. We can control the difficulty of the problem by the presence of short strings in the training set, which
   make the learning problem easier. In the case of the harder problem, the Hamming measure performs best, while the Strict measure performs best on the easier problem.
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Simon Lucas
%T Exploiting Reflection in Object Oriented Genetic Programming
%B Genetic Programming 7th European Conference, EuroGP 2004, Proceedings
%S LNCS
%E Maarten Keijzer and Una-May O'Reilly and Simon M. Lucas and Ernesto Costa and Terence Soule
%V 3003
%D 2004
%P 369--378
%I Springer-Verlag Berlin
%I EvoNet
%C Coimbra, Portugal
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=369
%X Most programs currently written by humans are object-oriented ones. Two of the greatest benefits of object oriented programming are the separation of interface from
   implementation, and the notion that an object may have state. This paper describes a simple system that enables object-oriented programs to be evolved. The system exploits
   reflection to automatically discover features about the environment (the existing classes and objects) in which it is to operate. This enables us to evolve object-oriented
   programs for the given problem domain with the minimum of effort. Currently, we are only evolving method implementations. Future work will explore how we can also evolve
   interfaces and classes, which should be beneficial to the automatic generation of structured solutions to complex problems. We demonstrate the system with the aid of an
   evolutionary art example.
%8 5-7  April
%Z Part of \citekeijzer:2004:GP EuroGP'2004 held in conjunction with EvoCOP2004 and EvoWorkshops2004
%@ 3-540-21346-5

%A Socrates A. Lucas-Gonzalez
%A Hugo Terashima-Marin
%T Generating Programs for Solving Vector and Matrix Problems using Genetic Programming
%B 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers
%E Erik D. Goodman
%D 2001
%P 260--266
%I 
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, GP-BNF
%8 9-11  July
%Z GECCO-2001LB. Implementation based on \citehorner-class GP Kernel. GP-BNF uses C. Element on an array, dot product, adding two matrices, inverse of matrix. iteration (loop)
   6 test cases. popsize=100. No details of grammar.

%A Alexey Luchko
%T Genetic Programming Application to One-way Quantum Finite State Automata Generation
%B Proceedings of International Workshop on Quantum Computation and Learning
%E Richard Bonner and Rusins Freivalds
%D 2002
%I 
%C Riga, Latvia
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/523944.html
%X In this paper I would like to introduce genetic programming application to one-way quantum finite state automata (QFA) generation.
%O The Pennsylvania State University CiteSeer Archives
%8 25-26  May

%A Bradley J. Lucier
%A Sudhakar Mamillapalli
%A Jens Palsberg
%T Program Optimization for Faster Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 202--207
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://www.cs.ucla.edu/~palsberg/paper/gp98.pdf
%8 22-25  July
%Z GP-98
%@ 1-55860-548-7

%A Simone A. Ludwig
%T Prediction of breast cancer biopsy outcomes using a distributed genetic programming approach
%B Proceedings of the 1st ACM International Health Informatics Symposium
%E Tiffany C. Veinot and \"Umit V. \c Cataly\"urek and Gang Luo and Henrique Andrade and Neil R. Smalheiser
%D 2010
%P 694--699
%I ACM
%C Arlington, Virginia, USA
%K genetic algorithms, genetic programming, benign, cancer recurrence, classification, malignant
%X Worldwide, breast cancer is the second most common type of cancer after lung cancer and the fifth most common cause of cancer death accounting for 519,000 deaths worldwide
   in 2004. The most effective method for breast cancer screening today is mammography. However, presently predictions of breast biopsies resulting from mammogram
   interpretation lead to approximately 70percent biopsies with benign outcomes, which are preventable. Therefore, an automatic method is necessary to aid physicians in the
   prognosis of mammography interpretations. The data set used for this investigation is based on BI-RADS findings. Previous work has achieved good results using a decision
   tree, an artificial neural networks and a case-based reasoning approach to develop predictive classifiers. This paper uses a distributed genetic programming approach to
   predict the outcomes of the mammography achieving even better prediction results.

%A Simone A. Ludwig
%A Stefanie Roos
%T Prognosis of Breast Cancer Using Genetic Programming
%B 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2010), Part IV
%S Lecture Notes in Computer Science
%E Rossitza Setchi and Ivan Jordanov and Robert J. Howlett and Lakhmi C. Jain
%V 6279
%D 2010
%P 536--545
%I Springer
%C Cardiff, UK
%K genetic algorithms, genetic programming
%X Worldwide, breast cancer is the second most common type of cancer after lung cancer and the fifth most common cause of cancer death. In 2004, breast cancer caused 519,000
   deaths worldwide. In order to reduce the cancer deaths and thereby increasing the survival rates an automatic approach is necessary to aid physicians in the prognosis of
   breast cancer. This paper investigates the prognosis of breast cancer using a machine learning approach, in particular genetic programming, whereas earlier work has
   approached the prognosis using linear programming. The genetic programming method takes a digitized image of a patient and automatically generates the prediction of the
   time to recur as well as the disease-free survival time. The breast cancer dataset from the University of California Irvine Machine Learning Repository was used for this
   study. The evaluation shows that the genetic programming approach outperforms the linear programming approach by 33 percent.
%8 September  8-10

%A Simone A. Ludwig
%A Stefanie Roos
%A Monique Frize
%A and Nicole Yu
%T Medical Outcome Prediction for Intensive Care Unit Patients
%J International Journal of Computational Models and Algorithms in Medicine (IJCMAM)
%V 1
%N 4
%D 2010
%P 19--30
%I 
%K genetic algorithms, genetic programming, Intelligent Technologies
%U http://www.irma-international.org/article/medical-outcome-prediction-intensive-care/51668/
%X The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug
   events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken
   patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical
   decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: hours of ventilation and the
   mortality rate in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic
   programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected
%Z Simone A. Ludwig (North Dakota State University, USA), Stefanie Roos (Darmstadt University, Germany), Monique Frize (Carleton University, Canada), and Nicole Yu (Carleton
   University, Canada)

%A Martin H. Luerssen
%T Graph Grammar Encoding and Evolution of Automata Networks
%B Twenty-Eighth Australasian Computer Science Conference (ACSC2005)
%S CRPIT
%E Vladimir Estivill-Castro
%V 38
%D 2005
%P 229--238
%I ACS
%C Newcastle, Australia
%K genetic algorithms, genetic programming, graph grammars, neural networks
%U http://crpit.com/confpapers/CRPITV38Luerssen.pdf
%X The global dynamics of automata networks (such as neural networks) are a function of their topology and the choice of automata used. Evolutionary methods can be applied to
   the optimisation of these parameters, but their computational cost is prohibitive unless they operate on a compact representation. Graph grammars provide such a
   representation by allowing network regularities to be efficiently captured and reused. We present a system for encoding and evolving automata networks as collective
   hypergraph grammars, and demonstrate its efficacy on the classical problems of symbolic regression and the design of neural network architectures.

%A Martin Holger Luerssen
%T Experimental Investigations into Graph Grammar Evolution
%R Ph.D. Thesis PhD
%D 2006
%I 
%I School of Informatics and Engineering, The Flinders University of South Australia
%C Adelaide, Australia
%K genetic algorithms, genetic programming, embryogeny
%U http://theses.flinders.edu.au/public/adt-SFU20110328.120915/index.html
%X Artificial and natural instances of networks are ubiquitous, and many problems of practical interest may be formulated as questions about networks. Determining the optimal
   topology of a network is pertinent to many domains. Evolutionary algorithms constitute a well-established optimisation method, but they scale poorly if applied to the
   combinatorial explosion of possible network topologies. Generative representation schemes aim to overcome this by facilitating the discovery and reuse of design
   dependencies and allowing for adaptable exploration strategies. Biological embryogenesis is a strong inspiration for many such schemes, but the associated complexities of
   modelling lead to impractical simulation times and poor conceptual understanding. Existing research also predominantly focuses on specific design domains such as neural
   networks. This thesis seeks to define a simple yet universally applicable and scalable method for evolving graphs and networks. A number of contributions are made in this
   regard. We establish the notion of directly evolving a graph grammar from which a population of networks can be derived. Compact cellular productions that form a hypergraph
   grammar are optimised by a novel multi-objective evolutionary design system called G/GRADE. A series of empirical investigations are then carried out to gain a better
   understanding of graph grammar evolution. G/GRADE is applied to four domains: symbolic regression, circuit design, neural networks, and telecommunications. We compare
   different strategies for composing graphs from randomly mutated productions and examine the relationship between graph grammar diversity and fitness, presenting both the
   use of phenotypic diversity objectives and an island model to improve this. Additionally, we address the issue of bloat and demonstrate how concepts from swarm intelligence
   can be applied to production selection and mutation to improve grammatical convergence. The results of this thesis are relevant to evolutionary research into networks and
   grammars, and the wide applicability and potential of graph grammar evolution is expected to inspire further study.
%8 May  29
%Z http://csem.flinders.edu.au/research/papers/bibtex.html http://www.flinders.edu.au/science_engineering/csem/publications/phd-theses.cfm

%A Martin H. Luerssen
%A David M. W. Powers
%T Graph Design by Graph Grammar Evolution
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 386--393
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Determining the optimal topology of a graph is pertinent to many domains, as graphs can be used to model a variety of systems. Evolutionary algorithms constitute a popular
   optimisation method, but scalability is a concern with larger graph designs. Generative representation schemes, often inspired by biological development, seek to address
   this by facilitating the discovery and reuse of design dependencies and allowing for adaptable exploration strategies. We present a novel developmental method for
   optimising graphs that is based on the notion of directly evolving a hypergraph grammar from which a population of graphs can be derived. A multi-objective design system is
   established and evaluated on problems from three domains: symbolic regression, circuit design, and neural control. The observed performance compares favourably with
   existing methods, and extensive reuse of subgraphs contributes to the efficient representation of solutions. Constraints can also be placed on the type of explored graph
   spaces, ranging from tree to pseudograph. We show that more compact solutions are attainable in less constrained spaces, although convergence typically improves with more
   constrained designs.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C is it a GP? Evolution of executable grammar?
%@ 1-4244-1340-0

%A Martin H. Luerssen
%A David M. W. Powers
%T Evolvability and Redundancy in Shared Grammar Evolution
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 370--377
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Shared grammar evolution (SGE) is a novel scheme for representing and evolving a population of variable length programs as a shared set of grammatical productions.
   Productions that fail to contribute to selected solutions can be retained for several generations beyond their last use. The ensuing redundancy and its effects are assessed
   in this paper on two circuit design tasks associated with random number generation: finding a recurrent circuit with maximum period, and reproducing a De Bruijn counter
   from a set of seed/output pairs. In both instances, increasing redundancy leads to significantly higher success rates, outperforming comparable increases in population
   size. The results support previous studies that have shown that representational redundancy can be beneficial to evolutionary search. However, redundancy promotes an
   increase in further redundancy by encouraging the creation of large offspring, the evaluation of which is computationally costly. This observation should generalise to any
   unconstrained variablelength representation and therefore represents a notable drawback of redundancy in evolution.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Martin H. Luerssen
%A David M. W. Powers
%T Evolving encapsulated programs as shared grammars
%J Genetic Programming and Evolvable Machines
%V 9
%N 3
%D 2008
%P 203--228
%I 
%K genetic algorithms, genetic programming, Shared grammars, Developmental systems, Encapsulation, Modularity, Memoization
%X Facilitating the discovery and reuse of modular building blocks is generally regarded as the key to achieving better scalability in genetic programming (GP). A precedent
   for this exists in biology, where complex designs are the product of developmental processes that can also be abstractly modelled as generative grammars. We introduce
   shared grammar evolution (SGE), which aligns grammatical development with the common application of grammars in GP as a means of establishing declarative bias. Programs are
   derived from and represented by a global context-free grammar that is transformed and extended according to another, user-defined grammar. Grammatical productions and the
   subroutines they encapsulate are shared between programs, which enables their reuse without reevaluation and can significantly reduce total evaluation time for large
   programs and populations. Several variants of SGE employing different strategies for controlling solution size and diversity are tested on classic GP problems. Results
   compare favourably against GP and newer techniques, with the best results obtained by promoting diversity between derived programs.
%8 September

%A Martin H. Luerssen
%T Experimental Investigations into Graph Grammar Evolution: A Novel Approach to Evolutionary Design
%D 2009
%I Verlag Dr. Mueller
%C Saaarbruecken, Germany
%K genetic algorithms, genetic programming
%U http://www.amazon.com/Experimental-Investigations-Graph-Grammar-Evolution/dp/363912328X
%X Artificial and natural instances of networks are ubiquitous, and the problem of determining the optimal topology of a network is of practical value to many domains.
   Evolutionary algorithms constitute a well-established optimisation method, but they scale poorly if applied to the combinatorial explosion of possible network topologies.
   Generative representation schemes aim to overcome this problem by facilitating the discovery and reuse of design dependencies and allowing for adaptable exploration
   strategies. This book seeks to define a simple yet universally applicable and scalable method for evolving graphs and networks. A number of contributions are made in this
   regard. We establish the notion of directly evolving a graph grammar from which a population of networks can be derived. Compact cellular productions that form a hypergraph
   grammar are optimised by a novel multi-objective evolutionary design system. A series of empirical investigations are then carried out to gain a better understanding of
   graph grammar evolution.
%8 February  22
%Z See also \citeLuerssen2006
%@ 363912328X

%A Gerald Luiz
%T Sufficient Parameters for Population Dynamics Simulations with Adaptation
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 91--98
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%8 June
%Z This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford
   University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Sean Luke
%A Lee Spector
%T Evolving Teamwork and Coordination with Genetic Programming
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 150--156
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262611279
%X Some problems can be solved only by multi-agent teams. In using genetic programming to produce such teams, one faces several design decisions. First, there are questions of
   team diversity and of breeding strategy. In one commonly used scheme, teams consist of clones of single individuals; these individuals breed in the normal way and are
   cloned to form teams during fitness evaluation. In contrast, teams could also consist of distinct individuals. In this case one can either allow free interbreeding between
   members of different teams, or one can restrict interbreeding in various ways. A second design decision concerns the types of coordination-facilitating mechanisms provided
   to individual team members; these range from sensors of various sorts to complex communication systems. This paper examines three breeding strategies (clones, free, and
   restricted) and three coordination mechanisms (none, deictic sensing, and name-based sensing) for evolving teams of agents in the Serengeti world, a simple predator/prey
   environment. Among the conclusions are the fact that a simple form of restricted interbreeding outperforms free interbreeding in all teams with distinct individuals, and
   the fact that name-based sensing consistently outperforms deictic sensing.
%8 28--31  July
%Z GP-96

%A Sean Luke
%A Lee Spector
%T Evolving Graphs and Networks with Edge Encoding: Preliminary Report
%B Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996
%E John R. Koza
%D 1996
%P 117--124
%I Stanford Bookstore Stanford University, Stanford, California 94305-3079, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/412757.html
%X We present an alternative to the cellular encoding technique [Gruau 1992] for evolving graph and network structures via genetic programming. The new technique, called edge
   encoding, uses edge operators rather than the node operators of cellular encoding. While both cellular encoding and edge encoding can produce all possible graphs, the two
   encodings bias the genetic search process in different ways; each may therefore be most useful for a different set of problems. The problems for which these techniques may
   be used, and for which we think edge encoding may be particularly useful, include the evolution of recurrent neural networks, finite automata, and graph-based queries to
   symbolic knowledge bases. In this preliminary report we present a technical description of edge encoding and an initial comparison to cellular encoding. Experimental
   investigation of the relative merits of these encoding schemes is currently in progress.
%8 28--31  July
%Z GP-96LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
%@ 0-18-201031-7

%A Sean Luke
%A Lee Spector
%T A Comparison of Crossover and Mutation in Genetic Programming
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 240--248
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming
%U http://www.cs.gmu.edu/~sean/papers/comparison/comparison.ps.gz
%X This paper presents a large and systematic body of data on the relative effectiveness of mutation, crossover, and combinations of mutation and crossover in genetic
   programming (GP). The literature of traditional genetic algorithms contains related studies, but mutation and crossover in GP differ from their traditional counterparts in
   significant ways. In this paper we present the results from a very large experimental data set, the equivalent of approximately 12,000 typical runs of a GP system,
   systematically exploring a range of parameter settings. The resulting data may be useful not only for practitioners seeking to optimize parameters for GP runs, but also for
   theorists exploring issues such as the role of "building blocks" in GP.
%8 13-16  July
%Z GP-97. 6-mux, lawn mower, symbolic regression, Santa Fe trail artificial ant. See alse \citeluke:1998:rcxmGP. The Gzipped PostScript version (.ps.gz) does not come with
   figures; to get the figures for the PostScript version, use the figures URLs below

%A Sean Luke
%A Charles Hohn
%A Jonathan Farris
%A Gary Jackson
%A James Hendler
%T Co-evolving Soccer Softbot Team Coordination with Genetic Programming
%B Proceedings of the First International Workshop on RoboCup, at the International Joint Conference on Artificial Intelligence
%D 1997
%I 
%C Nagoya, Japan
%K genetic algorithms, genetic programming
%U http://www.cs.gmu.edu/~sean/papers/robocupc.ps.gz
%X Genetic Programming is a promising new method for automatically generating functions and algorithms through natural selection. In contrast to other learning methods,
   Genetic Programming's automatic programming makes it a natural approach for developing algorithmic robot behaviors. In this paper we present an overview of how we apply
   Genetic Programming to behavior-based team coordination in the RoboCup Soccer Server domain. The result is not just a hand-coded soccer algorithm, but a team of softbots
   which have learned on their own how to play a reasonable game of soccer.
%Z IJCAI-97 Given the acknowledged challenges of applying Genetic Programming to robot soccer, we were happy to just show up at Nagoya with an entry in the RoboCup simulation
   track. However, Maryland's Genetic Programming entry in in fact beat its first two competitors (5-2 against U British Columbia, Canada and 17-0 over Toyohashi University of
   Science and Technology, Japan) before losing to University of Tokyo (last year's champion, 6-1) and subsequently Tokyo Institute of Technology (16-4) in the
   single-elimination round. For its research achievement in demonstrating the feasibility of evolutionary computation in a very difficult domain, Maryland's entry also won
   the RoboCup Scientific Challenge Award. http://ci.etl.go.jp/~noda/soccer/RoboCup97/result.html Part of Email from John Koza Fri, 29 Aug 1997 21:37:50 PDT to
   genetic-programming@cs.stanford.edu "The Maryland entry competed against various hand-written robot controllers (all of which are very good examples of clever human
   programming) and its success demonstrated, I think, that GP is precisely the right way to create programmers when the task really gets difficult. " Too short to give full
   technical details: STGP, 50ish problem dependant functions. team composed of 2-3 squads of identical players. Each squad 2 trees (used for possetion and non-possetion of
   ball. 6 or 12 trees per GP indivdual. Co-evolution. lil-gp. Stepped evolution (like seeding?) build squad from good players, team from good squads.

%A Sean Luke
%A Lee Spector
%T A Revised Comparison of Crossover and Mutation in Genetic Programming
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 208--213
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://www.cs.gmu.edu/~sean/papers/revisedgp98.ps.gz
%X In [Luke and Spector 1997] we presented a comprehensive suite of data comparing GP crossover and point mutation over four domains and a wide range of parameter settings.
   Unfortunately, the results were marred by statistical flaws. This revision of the study eliminates these flaws, with three times as much the data as the original
   experiments had. Our results again show that crossover does have some advantage over mutation given the right parameter settings (primarily larger population sizes), though
   the difference between the two surprisingly small. Further, the results are complex, suggesting that the big picture is more complicated than is commonly believed.
%8 22-25  July
%Z GP-98 This paper is a revision of a previous paper \citeluke:1997:ccmGP, with statistical correction and a considerable new set of data. However, the original also has some
   data that does not appear here, so you may want to consider getting both. Also: Figures 1 through 4 are separated from the rest of the paper in the Gzipped PostScript
   version (not the PDF version). The figures are listed in the figure URLs below. Finally: if you downloaded a copy of this paper prior to May 20, 1998, its graphs were
   wrong; get the revised revised version. :-
%@ 1-55860-548-7

%A Sean Luke
%T Genetic Programming Produced Competitive Soccer Softbot Teams for RoboCup97
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 214--222
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://www.cs.gmu.edu/~sean/papers/robocupgp98.ps.gz
%X At RoboCup, teams of autonomous robots or software softbots compete in simulated soccer matches to demonstrate cooperative robotics techniques in a very difficult,
   real-time, noisy environment. At the IJCAI/RoboCup97 softbot competition, all entries but ours used human-crafted cooperative decision-making behaviors. We instead entered
   a softbot team whose high-level decision making behaviors had been entirely evolved using genetic programming. Our team won its first two games against human-crafted
   opponent teams, and received the RoboCup Scientific Challenge Award. This report discusses the issues we faced and the approach we took to use GP to evolve our robot soccer
   team for this difficult environment.
%8 22-25  July
%Z GP-98 This paper is similar to an earlier workshop paper \citeluke:1997:csstcGP. The key difference being that the workshop paper, which was not for a Genetic Programming
   audience, is short on experimental details and long on introductions to how GP works. There also exists a short invited paper \citeluke:1998:sretro detailing how this
   experiment could have been improved. Also available is a short sidebar for an AI Magazine article.
%@ 1-55860-548-7

%A Sean Luke
%A Shugo Hamahashi
%A Koji Kyoda
%A Hiroki Ueda
%T Biology: See It Again -- for the First Time
%J IEEE Intelligent Systems
%V 13
%N 5
%D 1998
%I 
%K genetic algorithms, genetic programming, biological modelling, DNA
%U http://www.cs.gmu.edu/~sean/papers/biology.ps.gz
%X Computer science owes a huge debt to biological systems. The field itself came about largely as an attempt to understand and replicate the function and abilities of the
   brain, a complex biological creation. From this early lineage has sprung many subfields derived largely from biological metaphors: computer vision, neural networks,
   evolutionary computation, robotics, multi-agent studies, and much of artificial intelligence. In some areas, the computer has bested its biological counterparts in
   efficiency and simplicity. But for many domains, even after decades of hard work, the biological "real thing" is still superior to the artificial algorithms inspired by it.
%8 September /  October
%Z Invited Article. Argues for a revisitation of the biological roots behind artificial intelligence and evolutionary computation

%A Sean Luke
%T Evolving SoccerBots: A Retrospective
%B Proceedings of the 12th Annual Conference of the Japanese Society for Artificial Intelligence
%D 1998
%I 
%K genetic algorithms, genetic programming
%U http://www.cs.gmu.edu/~sean/papers/robocupShort.ps.gz
%X In the RoboCup97 robot soccer tournament, we entered a team of softbot programs whose player strategies had been entirely learned by computer. Our team beat other
   human-coded competitors and received the RoboCup97 Scientific Challenge award. This paper discusses our approach, and details various ways that, in retrospect, it could
   have been improved.
%Z Invited Article. This short invited paper was meant to complement the more complete GP98 and RoboCup97 papers, and an AI Magazine sidebar, by discussing things that could
   have been improved from our previous attempt.

%A Sean Luke
%A Shugo Hamahashi
%A Hiroaki Kitano
%T ``Genetic'' Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1098--1105
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming
%U http://www.cs.gmu.edu/~sean/papers/gene-gecco99.ps.gz
%X Much of evolutionary computation was inspired by Mendelian genetics. But modern genetics has since advanced considerably, revealing that genes are not simply parameter
   settings, but interactive cogs in a complex chemical machine. At the same time, an increasing number of evolutionary computation domains are evolving non-parameterized
   mechanisms such as neural networks or symbolic computer programs. As such, we think modern biological genetics offers much in helping us understand how to evolve such
   things. In this paper, we present a gene regulation model for Drosophila melanogaster. We then apply gene regulation to evolve deterministic finite-state automata, and show
   that our approach does well compared to past examples from the literature.
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Sean Luke
%T Two Fast Tree-Creation Algorithms for Genetic Programming
%J IEEE Transactions on Evolutionary Computation
%V 4
%N 3
%D 2000
%P 274--283
%I 
%K genetic algorithms, genetic programming, Population Initialization, Tree Creation, Subtree Mutation, Tree Growth, Introns, Bloat
%U http://citeseer.ist.psu.edu/409667.html
%X Genetic programming is an evolutionary optimization method that produces functional programs to solve a given task. These programs commonly take the form of trees
   representing LISP s-expressions, and a typical evolutionary run produces a great many of these trees. For this reason, a good tree generation algorithm is very important to
   genetic programming. This paper presents two new tree-generation algorithms for genetic programming and for strongly-typed genetic programming, a common variant. These
   algorithms are fast, allow the user to request specific tree sizes, and guarantee probabilities of certain nodes appearing in trees. The paper analyzes these two algorithms
   and compares them with traditional and recently proposed approaches.
%8 September

%A Sean Luke
%T Code Growth is Not Caused by Introns
%B Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference
%E Darrell Whitley
%D 2000
%P 228--235
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, bloat, introns, ineffective code
%U http://citeseer.ist.psu.edu/300709.html
%X Genetic programming trees have a strong tendency to grow rapidly and relatively independent of fitness, a serious flaw which has received considerable attention in the
   genetic programming literature. Much of this literature has implicated introns, subtree structures with no effect on the an individual's fitness assessment. The propagation
   of inviable code, a certain kind of intron, has been especially linked to tree growth. However this paper presents evidence which shows that denying inviable code the
   opportunity to propagate actually increases tree growth. The paper argues that rather than causing tree growth, a rise in inviable code is in fact an expected result of
   tree growth. Lastly, this paper proposes a more general theory of growth for which introns are merely a symptom.
%8 8  July
%Z Part of \citewhitley:2000:GECCOlb

%A Sean Luke
%T Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat
%R Ph.D. Thesis
%D 2000
%I 
%I Department of Computer Science, University of Maryland
%C A. V. Williams Building, University of Maryland, College Park, MD 20742 USA
%K genetic algorithms, genetic programming
%U http://www.cs.gmu.edu/~sean/papers/thesis2p.ps.gz
%X Genetic Programming is an evolutionary computation technique which searches for those computer programs that best solve a given problem. As genetic programming is applied
   to increasingly difficult problems, its effectiveness is hampered by the tendency of candidate program solutions to grow in size independent of any corresponding increases
   in quality. This bloat in solutions slows the search process, interferes with genetic programming's searching, and ultimately consumes all available memory. The challenge
   for scaling up genetic programming is to find the best solutions possible before bloat puts a stop to evolution. This can be tackled either by finding better solutions more
   rapidly, or by taking measures to delay bloat as long as possible. This thesis discusses issues both in speeding the search process and in delaying bloat in order to scale
   genetic programming to tackle harder problems. It describes evolutionary computation and genetic programming, and details the application of genetic programming to
   cooperative robot soccer and to language induction. The thesis then compares genetic programming breeding strategies, showing the conditions under which each strategy
   produces better individuals with less bloating. It then analyzes the tree growth properties of the standard tree generation algorithms used, and proposes new, fast
   algorithms which give the user better control over tree size. Lastly, it presents evidence which directly contradicts existing bloat theories, and gives a more general
   theory of code growth, showing that the issue is more complicated than it first appears.
%Z errata 1. In Algorithm 2 (p. 6), the line P<-P\\[q] should read P<-P\\[s]. 2. Figures 5.2 through 5.5 (p. 38-39) are not in proper evolutionary-time order. The proper order
   is 5.4, 5.5, 5.2, 5.3.

%A Sean Luke
%A Liviu Panait
%T A Survey and Comparison of Tree Generation Algorithms
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 81--88
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, tree generation algorithms, initalization
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Sean Luke
%T When Short Runs Beat Long Runs
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 74--80
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, schedules, restarts, run length, critical points
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Sean Luke
%A Liviu Panait
%T Lexicographic Parsimony Pressure
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 829--836
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, bloat, parsimony pressure
%U http://citeseer.ist.psu.edu/535375.html
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Sean Luke
%A Liviu Panait
%T Is The Perfect The Enemy Of The Good?
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 820--828
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming, computational effort, cumulative probability of success
%U http://citeseer.ist.psu.edu/532114.html
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
   Nominated for best at GECCO award
%@ 1-55860-878-8

%A Sean Luke
%A Liviu Panait
%T Fighting Bloat with Nonparametric Parsimony Pressure
%B Parallel Problem Solving from Nature - PPSN VII
%S Lecture Notes in Computer Science, LNCS
%E Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel
%N 2439
%D 2002
%P 411--421
%I Springer-Verlag
%C Granada, Spain
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=411
%O Available from http://link.springer.de/link/service/series/0558/papers/2439/243900411.pdf
%8 7-11  September
%@ 3-540-44139-5

%A Sean Luke
%T Modification Point Depth and Genome Growth in Genetic Programming
%J Evolutionary Computation
%V 11
%N 1
%D 2003
%P 67--106
%I 
%K genetic algorithms, genetic programming, Introns, Inviable Code, Code Bloat, Crossover Point
%X The evolutionary computation community has shown increasing interest in arbitrary-length representations, particularly in the field of genetic programming. A serious
   stumbling block to the scalability of such representations has been bloat: uncontrolled genome growth during an evolutionary run. Bloat appears across the evolutionary
   computation spectrum, but genetic programming has given it by far the most attention. Most genetic programming models explain this phenomenon as a result of the growth of
   introns, areas in an individual which serve no functional purpose. This paper presents evidence which directly contradicts intron theories as applied to tree-based genetic
   programming. The paper then uses data drawn from this evidence to propose a new model of genome growth. In this model, bloat in genetic programming is a function of the
   mean depth of the modification (crossover or mutation) point. Points far from the root are correspondingly less likely to hurt the child's survivability in the next
   generation. The modication point is in turn strongly correlated to average parent tree size and to removed subtree size, both of which are directly linked to the size of
   the resulting child.
%8 Spring

%A Sean Luke
%A Gabriel Catalin Balan
%A Liviu Panait
%T Population Implosion in Genetic Programming.
%B Genetic and Evolutionary Computation -- GECCO-2003
%S LNCS
%E E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J.
   Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller
%V 2724
%D 2003
%P 1729--1739
%I Springer-Verlag Berlin
%C Chicago
%K genetic algorithms, genetic programming
%U http://cs.gmu.edu/~lpanait/papers/luke03population.pdf
%X With the exception of a small body of adaptive-parameter literature, evolutionary computation has traditionally favored keeping the population size constant through the
   course of the run. Unfortunately, genetic programming has an aging problem: for various reasons, late in the run the technique become less effective at optimization. Given
   a fixed number of evaluations, allocating many of them late in the run may thus not be a good strategy. In this paper we experiment with gradually decreasing the population
   size throughout a genetic programming run, in order to reallocate more evaluations to early generations. Our results show that over four problem domains and three different
   numbers of evaluations, decreasing the population size is always as good as, and frequently better than, various fixed-sized population strategies.
%8 12-16  July
%Z GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)
%@ 3-540-40603-4

%A Sean Luke
%T Evolutionary computation and the c-value paradox
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 1
%D 2005
%P 91--97
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Artificial Life, Evolutionary Robotics, Adaptive Behaviour, code bloat, code growth, c-value paradox, evolutionary genetics,
   experimentation, theoretical biology
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p91.pdf
%X The C-value Paradox is the name given in biology to the wide variance in and often very large amount of DNA in eukaryotic genomes and the poor correlation between DNA
   length and perceived organism complexity. Several hypotheses exist which purport to explain the Paradox. Surprisingly there is a related phenomenon in evolutionary
   computation, known as code bloat, for which a different set of hypotheses has arisen. This paper describes a new hypothesis for the Cvalue Paradox derived from models of
   code bloat. The new explanation is that there is a selective bias in preference of genetic events which increase DNA material over those which decrease it. The paper
   suggests one possible concrete mechanism by which this may occur: deleting strands of DNA is more likely to damage genomic material than migrating or copying strands. The
   paper also discusses other hypotheses in biology and in evolutionary computation, and provides a simulation example as a proof of concept.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Sean Luke
%A Liviu Panait
%T A Comparison of Bloat Control Methods for Genetic Programming
%J Evolutionary Computation
%V 14
%N 3
%D 2006
%P 309--344
%I 
%K genetic algorithms, genetic programming
%X Genetic programming has highlighted the problem of bloat, the uncontrolled growth of the average size of an individual in the population. The most common approach to
   dealing with bloat in tree-based genetic programming individuals is to limit their maximal allowed depth. An alternative to depth limiting is to punish individuals in some
   way based on excess size, and our experiments have shown that the combination of depth limiting with such a punitive method is generally more effective than either alone.
   Which such combinations are most effective at reducing bloat? In this article we augment depth limiting with nine bloat control methods and compare them with one another.
   These methods are chosen from past literature and from techniques of our own devising. testing with four genetic programming problems, we identify where each bloat control
   method performs well on a per-problem basis, and under what settings various methods are effective independent of problem. We report on the results of these tests, and
   discover an unexpected winner in the cross-platform category.
%8 Fall

%A Sean Luke
%T Essentials of Metaheuristics
%D 2009
%I lulu.com
%K genetic algorithms, genetic programming
%U http://www.lulu.com/product/paperback/essentials-of-metaheuristics/14303559
%X Gradient Ascent/Descent, Newton's Method, Hill-Climbing, Random Search, Hill Climbing with Random Restarts, Steepest Ascent Hill-Climbing (with and without Replacement),
   (1+1), (1+lambda), and (1, lambda), Simulated Annealing, Tabu Search, Iterated Local Search, Evolution Strategies and Evolutionary Programming, The Genetic Algorithm,
   Elitism, Steady State GAs, The Tree-Style GP Pipeline, Hybrid Evolutionary and Hill-Climbing ("Memetic") Algorithms, Scatter Search, Differential Evolution, Particle Swarm
   Optimization, Island Models, Master-Slave Fitness Assessment, Spatially-Embedded Models, 1-Population, 2-Population, and N-Population Coevolution Methods, Explicit and
   Implicit Fitness Sharing, Crowding and Deterministic Crowding, Naieve Multiobjective Optimization, Non-Dominated Sorting (NSGA-II), Pareto Strength Methods (SPEA2),
   Optimization with Hard Constraints, GRASP, Ant Colony Optimization (AS, ACS), Guided Local Search, Model Fitting by Classification (LEM), Estimation of Distribution
   Algorithms (PBIL, UMDA, cGA, BOA), Policy Optimization (Q-Learning, SAMUEL, ZCS, XCS), Representation Issues:, Vectors, Direct Encoded Graphs, Trees, Genetic Programming,
   Strongly-Typed Genetic Programming, Cellular Encoding, Lists, Machine-code Genetic Programming, Grammatical Evolution, Rulesets: State-Action Rules, Production Rules,
   Bloat, Experimental Methodolgy, Sample Text Problems, Resources, Example Course Syllabi
%O Available at http://cs.gmu.edu/$\sim$sean/books/metaheuristics/
%Z Published by Lulu 14 Dec 2010, Reviewed by \citeLones:2011:GPEM

%A Sean Luke
%T The ECJ Owner's Manual -- A User Manual for the ECJ Evolutionary Computation Library
%D 2010
%I 
%I Department of Computer Science, George Mason University
%K genetic algorithms, genetic programming
%U http://www.cs.gmu.edu/~eclab/projects/ecj/docs/manual/manual.pdf
%X The purpose of this manual is to describe practically every feature of ECJ, an evolutionary computation toolkit. It's not a good choice to learn the system. It's very
   terse, boring, and long, and not organised as a tutorial but rather as an encyclopedia. Instead, I refer you to ECJ's four tutorials and various other documentation that
   comes with the system. But when you need to know about some particular feature that ECJ has available, this manual is where to look.
%8 October

%A Eduard Lukschandl
%A Magus Holmlund
%A Eirk Moden
%T Automatic Evolution of Java Bytecode: First experience with the Java virtual machine
%B Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming
%E Riccardo Poli and W. B. Langdon and Marc Schoenauer and Terry Fogarty and Wolfgang Banzhaf
%D 1998
%P 14--16
%I CSRP-98-10, The University of Birmingham, UK School of Computer Science
%C Paris, France
%K genetic algorithms, genetic programming
%U ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-10.ps.gz
%8 14-15  April
%Z EuroGP'98LB part of \citePoli:1998:egplb

%A Eduard Lukschandl
%A Magnus Holmlund
%A Eric Moden
%A Mats Nordahl
%A Peter Nordin
%T Induction of Java Bytecode with Genetic Programming
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%P 135--142
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, Machine Code GP, Java, Java Bytecode
%8 22-25  July
%Z GP-98LB

%A Eduard Lukschandl
%A Henrik Borgvall
%A Lars Nohle
%A Mats Nordahl
%A Peter Nordin
%T Evolving Routing Algorithms with the JBGP-System
%B Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99
%S LNCS
%E Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty
%V 1596
%D 1999
%P 193--202
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%X This paper describes work in progress where we apply genetic programming to the problem of finding routing algorithms in telecommunications networks, using a network
   simulator and the Java Bytecode Genetic Programming System being developed at EHPT lab.
%8 28-29  May
%Z EvoIASP99'99 and EuroEcTel'99
%@ 3-540-65837-8

%A Eduard Lukschandl
%T Evolving the Behavior of Collaborating Entities Using Genetic Programming
%B GECCO-99 Student Workshop
%E Una-May O'Reilly
%D 1999
%P 377--378
%I 
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, agents, java, telecommunications
%U http://www.ai.mit.edu/people/unamay/phd-final/GECCO-99-Student.html
%8 13  July
%Z GECCO-99WKS Part of wu:1999:GECCOWKS

%A Eduard Lukschandl
%A Henrik Borgvall
%A Lars Nohle
%A Mats Nordahl
%A Peter Nordin
%T Distributed Java Bytecode Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 316--325
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=316
%X This paper describes a method for evolutionary program induction of binary Java bytecode. Like many other machine code based methods it uses a linear genome. The genetic
   operators are adapted to the stack architecture and preserve stack depth during crossover. In this work we have extended a previous system to run in a distributed manner on
   several different physical machines. We call our new system Distributed Java Bytecode Genetic Programming (DJBGP). We use the Voyager package for migration of Java
   individuals. The system's feasibility is demonstrated on a telecom routing problem.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Eduard Lukschandl
%A Peter Nordin
%A Mats Nordahl
%T Using the Java Method Evolver for Load Balancing in Communication Networks
%B Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference
%E Darrell Whitley
%D 2000
%P 236--239
%I 
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming
%8 8  July
%Z Part of \citewhitley:2000:GECCOlb

%A Ron {Luman II}
%T Dynamic Keystroke Analysis via Genetic Algorithms
%B Genetic Algorithms and Genetic Programming at Stanford 2002
%E John R. Koza
%D 2002
%P 129--138
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2002/Luman.pdf
%8 June
%Z part of \citekoza:2002:gagp

%A Jose Maria Luna
%A Jose Raul Romero
%A Sebastian Ventura
%T G3PARM: A Grammar Guided Genetic Programming algorithm for mining association rules
%B IEEE Congress on Evolutionary Computation (CEC 2010)
%D 2010
%I IEEE Press
%C Barcelona, Spain
%K genetic algorithms, genetic programming, NSGA-II
%X This paper presents the G3PARM algorithm for mining representative association rules. G3PARM is an evolutionary algorithm that uses G3P (Grammar Guided Genetic Programming)
   and an auxiliary population made up of its best individuals who will then act as parents for the next generation. Due to the nature of G3P, the G3PARM algorithm allows us
   to obtain valid individuals by defining them through a context-free grammar and, furthermore, this algorithm is generic with respect to data type. We compare our algorithm
   to two multiobjective algorithms frequently used in literature and known as NSGA2 (Non dominated Sort Genetic Algorithm) and SPEA2 (Strength Pareto Evolutionary Algorithm)
   and demonstrate the efficiency of our algorithm in terms of running-time, coverage and average support, providing the user with high representative rules.
%8 18-23  July
%Z WCCI 2010. Also known as \cite5586504

%A Jose Maria Luna
%A Aurora Ramirez
%A Jose Raul Romero
%A Sebastian Ventura
%T An intruder detection approach based on infrequent rating pattern mining
%B 10th International Conference on Intelligent Systems Design and Applications (ISDA 2010)
%D 2010
%P 682--688
%I 
%K genetic algorithms, genetic programming, G3PARM algorithm, association rule mining, collaborative recommender system, context free grammar, evolutionary algorithm, grammar
   guided genetic programming, incremental intruder detection, infrequent rating pattern mining, context-free grammars, data mining, recommender systems, security of data
%X This work presents a novel proposal for incremental intruder detection in collaborative recommender systems. We explore the use of rare association rule mining to reveal
   the existence of a suspected raid of attackers that would alter the normal behaviour of a rating-based system. In this position paper we have extended our previous G3PARM
   algorithm, which has already proven to serve as a solid method for extracting frequent association rules. G3PARM is an evolutionary algorithm that uses G3P (Grammar Guided
   Genetic Programming), which provides expressiveness and flexibility enough to adapt and apply the base context-free grammar to each specific problem or domain. We fully
   outline, moreover, the complete exploration and detection model, which includes some further post-analysis steps. Finally, as a proof of concept, we validate the
   scalability, efficiency and accuracy of our proposal showing the results obtained when different malicious intruders want to attack an on line recommender system.
%8 November  29-  Decemeber  1
%Z Also known as \cite5687184

%A Borje Lundberg
%T Elvis ror pa sig
%J Expressen
%D 1991
%P 17
%I 
%K genetic algorithms, genetic programming
%U http://www.expressen.se/article.asp?id=21927
%O Largest circulation swedish newspaper
%8 21  August
%Z Peter Nordin and Mats Nordahl, Chalmers Unversity of Technology humanoid robot Elvis

%A Torbjorn Lundh
%T Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Applications, and Analysis Authors: Andreas Deutsch and Sabine Dormann, Birkhauser, 2005,
   XXVI, 334 p., 131 illus., Hardcover. ISBN:0-8176-4281-1, List Price: \$89.95
%J Genetic Programming and Evolvable Machines
%V 8
%N 1
%D 2007
%P 105--106
%I 
%K genetic algorithms, genetic programming
%8 March
%Z Book Review

%A Xiao Luo
%A Malcolm I. Heywood
%A A. Nur Zincir-Heywood
%T Evolving recurrent models using linear GP
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1787--1788
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Poster, recurrent architectures, experimentation, languages, linear genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1787.pdf
%X Turing complete Genetic Programming (GP) models introduce the concept of internal state, and therefore have the capacity for identifying interesting temporal properties.
   Surprisingly, there is little evidence of the application of such models to problems for prediction. An empirical evaluation is made of a simple recurrent linear GP model
   over standard prediction problems.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052 Even Parity problem, sun spot
%@ 1-59593-010-8

%A Xiao Luo
%A A. Nur Zincir-Heywood
%T Evolving Recurrent Linear-GP for Document Classification and Word Tracking
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 8605--8612
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X we propose a novel document classification system where the recurrent linear Genetic Programming is employed to classify the documents that are represented in encoded word
   sequences. During this process, word sequences of documents are tracked, frequent patterns are detected and document is classified. We describe the word encoding model and
   the recurrent linear Genetic Programming based classification mechanism. The performance results on benchmark data set Reuters 21578 show that this system can analyse the
   temporal sequence patterns of a document and get competitive performance on classification. We expect that it can be easily applied to other application
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Maria Luque
%A Oscar Cordon
%A Enrique Herrera-Viedma
%T A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments
%B Multi-Objective Machine Learning
%S Studies in Computational Intelligence
%E Yaochu Jin
%V 16
%D 2006
%P 601--627
%I Springer
%K genetic algorithms, genetic programming
%X Persistent queries are a specific kind of queries used in information retrieval systems to represent a user's long-term standing information need. These queries can present
   many different structures, being the bag of words that most commonly used. They can be sometimes formulated by the user, although this task is usually difficult for him and
   the persistent query is then automatically derived from a set of sample documents he provides.

%A Leslie Luthi
%A Marco Tomassini
%A Mario Giacobini
%A William B. Langdon
%T The Genetic Programming Collaboration Network and its Communities
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1643--1650
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, human factors
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1643.pdf
%X Useful information about scientific collaboration structures and patterns can be inferred from computer databases of published papers. The genetic programming bibliography
   is the most complete reference of papers on GP. In addition to locating publications, it contains coauthor and coeditor relationships from which a more complete picture of
   the field emerges. We treat these relationships as undirected small world graphs whose study reveals the community structure of the GP collaborative social network.
   Automatic analysis discovers new communities and highlights new facets of them. The investigation reveals many similarities between GP and coauthorship networks in other
   scientific fields but also some subtle differences such as a smaller central network component and a high clustering.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071 demo http://www.cs.bham.ac.uk/~wbl/biblio/gp-coauthors/ Also known as 1277284

%A Evelyne Lutton
%A Jacques Levy-Vehel
%A Guillaume Cretin
%A Philippe Glevarec
%A Cedric Roll
%T Mixed IFS: Resolution of the Inverse Problem Using Genetic Programming
%R Research Report No 2631
%D 1995
%I 
%I Inria
%K genetic algorithms, genetic programming
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.286
%X We address here the resolution of the so-called inverse problem for IFS. This problem has already been widely considered, and some studies have been performed for affine
   IFS, using deterministic or stochastic methods (Simulated Annealing or Genetic Algorithm). When dealing with non affine IFS, the usual techniques do not perform well,
   except if some <i> a priori</I> hypotheses on the structure of the IFS (number and type functions) are made. In this work, a Genetic Programming method is investigated to
   solve the &quot;general&quot; inverse problem, which permits to perform at the same time a numeric and a symbolic optimization. The use of &quot;mixed IFS&quot;, as we call
   them, may enlarge the scope of some applications, as for example image compression, because they allow to code a wider range of shapes.
%Z Mainly in english, abstract also en francaise Use distance masks for deciding how close GP is to target image (part of fitness function). Says "The distance images are very
   efficient" [page 12]. Mutation of constants by +/-10% and variables to constants. Notes constants "disappear" from the population. popsize 20 to 50 and 1000 to 2000
   generations [page 10]. GP functions "does not resemble the one [used to create] the target images" [page 12]. "GP algorith, which seems to perform a more efficient search
   in a large space." [page 16]

%A Evelyne Lutton
%A Jacques Levy-Vehel
%A Guillaume Cretin
%A Philippe Glevarec
%A Cidric Roll
%T Mixed IFS: Resolution of the Inverse Problem Using Genetic Programming
%J Complex Systems
%V 9
%N 5
%D 1995
%P 375--398
%I 
%K genetic algorithms, genetic programming, fractals
%U http://www.complex-systems.com/pdf/09-5-3.pdf
%X We address here the resolution of the so-called inverse problem for the iterated functions system (IFS). This problem has already been widely considered, and some studies
   have been performed for the affine IFS, using deterministic or stochastic methods (simulated annealing or genetic algorithm). In dealing with the nonaffine IFS, the usual
   techniques do not perform well unless some a priori hypotheses on the structure of the IFS (number and type of functions) are made. In this work, a genetic programming
   method is investigated to solve the ``general'' inverse problem, which allows the simultaneous performance of a numeric and a symbolic optimization. The use of a ``mixed
   IFS'' may enlarge the scope of some applications, for example, image compression, because it allows a wider range of shapes to be coded.
%Z see also \citeCretin:al:EA95 http://www-syntim.inria.fr/fractales/fractales-eng.html

%A Evelyne Lutton
%A Pierre Collet
%A Jean Louchet
%T EASEA Comparisons on Test Functions: GALib versus EO
%B Artificial Evolution : 5th International Conference, Evolution Artificielle, EA 2001
%S LNCS
%E P. Collet and C. Fonlupt and J.-K. Hao and E. Lutton and M. Schoenauer
%V 2310
%D 2001
%P 219--230
%I Springer-Verlag
%C Le Creusot, France
%K genetic algorithms, genetic programming
%U http://link.springer-ny.com/link/service/series/0558/papers/2310/23100219.pdf", acknowledgement = ack-nhfb
%X The EASEA1 language (EAsy Specification of Evolutionary Algorithms) was created in order to allow scientists to concentrate on evolutionary algorithm design rather than
   implementation. EASEA currently supports two C++ libraries (GALib and EO) and a JAVA library for the DREAM. The aim of this paper is to assess the quality of
   EASEA-generated code through an extensive test procedure comparing the implementation for EO and GALib of the same test functions.
%8 October  29-31

%T Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%I Springer-Verlag Berlin
%I EvoNet
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%8 3-5  April
%Z EuroGP'2002
%@ 3-540-43378-3

%A Michelle Lyman
%A Gary Lewandowski
%T Genetic programming for association rules on card sorting data
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1551--1552
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Poster, card sorts, data mining, experimentation
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1551.pdf
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Collin Lynch
%A Kevin D. Ashley
%A Niels Pinkwart
%A Vincent Aleven
%T Argument graph classification with Genetic Programming and C4.5
%B The 1st International Conference on Educational Data Mining 2008
%E Ryan Shaun Joazeiro de Baker and Tiffany Barnes and Joseph E. Beck
%D 2008
%P 137--146
%I www.educationaldatamining.org
%C Montreal, Quebec, Canada
%K genetic algorithms, genetic programming
%U http://www.cs.cmu.edu/~aleven/Papers/2008/Lynch_ea_EDM2008.pdf
%X In well-defined domains there exist well-accepted criteria for detecting good and bad student solutions. Many ITS implement these criteria characterize solutions and to
   give immediate feedback. While this has been shown to promote learning, it is not always possible in ill-defined domains that typically lack well-accepted criteria. In this
   paper we report on the induction of classification rules for student solutions in an ill-defined domain. We compare the viability of classifications using statistical
   measures with classification trees induced via C4.5 and Genetic Programming.
%8 June  20-21

%A Chao Y Ma
%A Frances V Buontempo
%A Xue Z Wang
%T Inductive data mining: Automatic generation of decision trees from data for QSAR modelling and process historical data analysis
%B 18th European Symposium on Computer Aided Process Engineering
%S Computer Aided Chemical Engineering
%E Bertrand Braunschweig and Xavier Joulia
%V 25
%D 2008
%P 581--586
%I Elsevier
%K genetic algorithms, genetic programming, inductive data mining, decision trees, QSAR, process historical data analysis
%U http://www.sciencedirect.com/science/article/B8G5G-4TK2DGX-3M/2/2d0cbf83807000db928a8f08986360cf
%X A new inductive data mining method for automatic generation of decision trees from data (GPTree) is presented. Compared with other decision tree induction techniques that
   are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node therefore will necessarily miss regions of the
   search space, GPTree can overcome the problem. In addition, the approach is extended to a new method (YAdapt) that models the original continuous endpoint by adaptively
   finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretization prior to tree induction and allowing the ordinal
   nature of the endpoint to be taken into account in the models built. A strategy for further improving the predictive performance for previously unseen data is investigated
   that uses multiple decisions trees, i.e., a decision forest, and a majority voting strategy to give a prediction (GPForest). The methods were applied to QSAR (quantitative
   structure--activity relationships) modeling for eco-toxicity prediction of chemicals and to the analysis of a historical database for a wastewater treatment plant.
%Z See \citeMa20091602

%A Chao Y. Ma
%A Xue Z. Wang
%T Inductive data mining based on genetic programming: Automatic generation of decision trees from data for process historical data analysis
%J Computers \& Chemical Engineering
%V 33
%N 10
%D 2009
%P 1602--1616
%I 
%K genetic algorithms, genetic programming, Process historical data analysis, Decision trees, Decision forest, Wastewater treatment plant, Inductive data mining
%U http://www.sciencedirect.com/science/article/B6TFT-4W7420M-3/2/7984765c8dbd5fb91cfbad06b2673cd3
%X An inductive data mining algorithm based on genetic programming, GPForest, is introduced for automatic construction of decision trees and applied to the analysis of process
   historical data. GPForest not only outperforms traditional decision tree generation methods that are based on a greedy search strategy therefore necessarily miss regions of
   the search space, but more importantly generates multiple trees in each experimental run. In addition, by varying the initial values of parameters, more decision trees can
   be generated in new experiments. From the multiple decision trees generated, those with high fitness values are selected to form a decision forest. For predictive purpose,
   the decision forest instead of a single tree is used and a voting strategy is employed which allows the combination of the predictions of all decision trees in the forest
   in order to generate the final prediction. It was demonstrated that in comparison with decision tree methods in the literature, GPForest gives much improved performance.
%O Selected Papers from the 18th European Symposium on Computer Aided Process Engineering (ESCAPE-18)
%Z See \citeMa2008581

%A Chao Y. Ma
%A Frances V. Buontempo
%A Xue Z. Wang
%T Inductive data mining: automatic generation of decision trees from data for QSAR modelling and process historical data analysis
%J International Journal of Modelling, Identification and Control
%V 12
%N 1/2
%D 2011
%P 101--106
%I Inderscience Publishers
%K genetic algorithms, genetic programming, inductive data mining, decision trees, quantitative structure activity relationships, QSAR, process historical data analysis;
   wastewater treatment, modelling, eco-toxicity prediction
%U http://www.inderscience.com/link.php?id=37837
%X A new inductive data mining method for automatic generation of decision trees from data (GPTree) is presented. Compared with other decision tree induction techniques that
   are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node therefore will necessarily miss regions of the
   search space, GPTree can overcome the problem. In addition, the approach is extended to a new method (YAdapt) that models the original continuous endpoint by adaptively
   finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretisation prior to tree induction and allowing the ordinal
   nature of the endpoint to be taken into account in the models built. A strategy for further improving the predictive performance for previously unseen data is investigated
   that uses multiple decision trees, i.e., a decision forest, and a majority voting strategy to give predictions (GPForest). The methods were applied to QSAR (quantitative
   structure -- activity relationships) modelling for eco-toxicity prediction of chemicals and to the analysis of a historical database for a wastewater treatment plant.

%A Irwin Ma
%A Tony Wong
%A Thiagas Sankar
%T Volatility forecasting using time series data mining and evolutionary computation techniques
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 2262--2262
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Real-World Applications: Poster, data mining, economics, financial volatility, forecasting, S&P 100
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2262.pdf
%X Traditional parametric methods have limited success in estimating and forecasting the volatility of financial securities. Recent advance in evolutionary computation has
   provided additional tools to conduct data mining effectively. The current work applies the genetic programming in a Time Series Data Mining framework to characterise the
   S&P100 high frequency data in order to forecast the one step ahead integrated volatility. Results of the experiment have shown to be superior to those derived by the
   traditional methods.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Xiaoli Ma
%A Guifa Teng
%A Mengjie Zhang
%T Carbon Potential Using Genetic Programming
%J Control and Automation
%V 23
%N 9
%D 2007
%P 239--241
%I 
%K genetic algorithms, genetic programming, carbon potential, symbolic regression, Correlation forecasting
%U doggy http://c.wanfangdata.com.cn/periodical/wjsjxx/2007-9.aspx
%X This article describes the principle and technology of genetic programming. We propose a new approach to the use of genetic programming for Carbon potential problems. This
   approach does not rely on the problem domain, and does not need data preprocessing either. It can be used as a general method of solving related problems. This approach is
   of high-accuracy, and low-cost, and is suitable for online testing and controlling of Carbon Potential. In addition, it can produce visible function expressions, and deal
   with complex nonlinear problems.
%Z College of Information Science and Technology Agricultural University of Hebei, 071001, China; School of Mathematics, Statistics and Computer Science, Victoria University
   of Wellington, New Zealand

%A Shingo Mabu
%A Kotaro Hirasawa
%A Jinglu Hu
%A Junichi Murata
%T Online learning of Genetic Network Programming (GNP)
%B Proceedings of the 2002 Congress on Evolutionary Computation CEC2002
%E David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton
%D 2002
%P 321--326
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)
%K genetic algorithms, genetic programming
%X A new evolutionary computation method named Genetic Network Programming (GNP) was proposed recently. In this paper, an online learning method for GNP is proposed. This
   method uses Q learning to improve its state transition rules so that it can make GNP adapt to the dynamic environments efficiently.
%8 12-17  May
%Z CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI
   2002)
%@ 0-7803-7278-6

%A Shingo Mabu
%A Kotaro Hirasawa
%A Jinglu Hu
%T Genetic network programming with learning and evolution for adapting to dynamical environments
%B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
%E Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon
%D 2003
%P 69--76
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C Canberra
%K genetic algorithms, genetic programming
%8 8-12  Decemeber
%Z CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.
%@ 0-7803-7804-0

%A Shingo Mabu
%A Kotaro Hirasawa
%A Jinglu Hu
%T Genetic Network Programming with Reinforcement Learning and its Performance Evaluation
%B Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference
%E Maarten Keijzer
%D 2004
%I 
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, GNP
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP036.pdf
%X A new graph-based evolutionary algorithm named 'Genetic Network Programming, GNP' has been proposed. GNP represents its solutions as graph structures, which can improve the
   expression ability and performance. Since GA, GP and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we
   propose GNP with Reinforcement Learning (GNP with RL) in this paper in order to search solutions quickly. Evolutionary algorithm of GNP makes very compact graph structure
   which contributes to reducing the size of the Q-table and saving memory. Reinforcement Learning of GNP improves search speed for solutions because it can use the
   information obtained during task execution.
%8 26  July
%Z Part of \citekeijzer:2004:GECCO:lbp

%A Shingo Mabu
%A Hiroyuki Hatakeyama
%A Kotaro Hirasawa
%A Jinglu Hu
%T Genetic Network Programming with Reinforcement Learning Using Sarsa Algorithm
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 1570--1576
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X A new graph-based evolutionary algorithm called Genetic Network Programming (GNP) has been proposed. The solutions of GNP are represented as graph structures, which can
   improve the expression ability and performance. In addition, GNP with Reinforcement Learning (GNP-RL) has been proposed to search for solutions efficiently. GNP-RL can use
   current information and change its programs during task execution, i.e., online learning. Thus, it has an advantage over evolution-based algorithms in case much information
   can be obtained during task execution. GNP-RL has a special state-action space and it contributes to reducing the size of the Qtable and learning efficiently. The proposed
   method is applied to the controller of Khepera simulator and its performance is evaluated.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Shingo Mabu
%A Yan Chen
%A Kotaro Hirasawa
%A Jinglu Hu
%T Genetic network programming with actor-critic and its application to stock trading model
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 2263--2263
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, Real-World Applications: Poster, reinforcement learning, stock trading model, technical index
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2263.pdf
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Shingo Mabu
%A Yan Chen
%A Etsushi Ohkawa
%A Kotaro Hirasawa
%T Stock trading strategies by genetic network programming with flag nodes
%B GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel
   Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara
   Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener
%D 2008
%P 1709--1710
%I ACM New York, NY, USA
%C Atlanta, GA, USA
%K genetic algorithms, genetic programming, decision making, stock trading model, technical analysis, Real-World application: Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1709.pdf
%8 12-16  July
%Z GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).
   ACM Order Number 910081. Also known as \cite1389421

%A Shingo Mabu
%A Yan Chen
%A Kotaro Hirasawa
%A Jinglu Hu
%T Stock Trading Rules Using Genetic Network Programming with Actor-Critic
%B 2007 IEEE Congress on Evolutionary Computation
%E Dipti Srinivasan and Lipo Wang
%D 2007
%P 508--515
%I IEEE Press
%I IEEE Computational Intelligence Society
%C Singapore
%K genetic algorithms, genetic programming
%X Genetic Network Programming (GNP) is an evolutionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has
   an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In this paper, GNP is applied to creating a stock trading model.
   The first important point is to combine GNP with Actor-Critic which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after
   task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created
   efficiently. The second important point is that GNP with Actor-Critic (GNP-AC) can select appropriate technical indexes to judge the buying and selling timing of stocks
   using Importance Index especially designed for stock trading decision making. In the simulations, the trading model is trained using the stock prices of 20 brands in 2001,
   2002 and 2003. Then the generalisation ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of GNP-AC
   obtain higher profits than Buy and Hold method.
%8 25-28  September
%Z CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C
%@ 1-4244-1340-0

%A Shingo Mabu
%A Kotaro Hirasawa
%T Evolving plural programs by genetic network programming with multi-start nodes
%B IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
%D 2009
%P 1382--1387
%I 
%C San Antonio, TX, USA
%K genetic algorithms, genetic programming, automatic program generation, directed graph structures, even-n-parity problem, evolutionary computation, genetic network
   programming, graph-based evolutionary algorithm, mirror symmetry, multistart nodes, performance evaluation, plural programs, automatic programming, directed graphs
%X Automatic program generation is one of the applicable fields of evolutionary computation, and genetic programming (GP) is the typical method for this field. On the other
   hand, genetic network programming (GNP) has been proposed as an extended algorithm of GP in terms of gene structures. GNP is a graph-based evolutionary algorithm and
   applied to automatic program generation in this paper. GNP has directed graph structures which have some features inherently such as re-usability of nodes and the fixed
   number of nodes. These features contribute to creating complicated programs with compact program structures. In this paper, the extended algorithm of GNP is proposed, which
   can create plural programs simultaneously in one individual by using multi-start nodes. In addition, GNP can evolve the programs in one individual considering the fitness
   and also its standard deviation in order to evolve the plural programs efficiently. In the simulations, even-n-parity problem and mirror symmetry problem are used for the
   performance evaluation, and the results show that the proposed method outperforms the original GNP.
%8 October
%Z INSPEC Accession Number: 11004402 Also known as \cite5346275

%A Shingo Mabu
%A Ci Chen
%A Nannan Lu
%A Kaoru Shimada
%A Kotaro Hirasawa
%T An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming
%J IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
%V 41
%N 1
%D 2011
%P 130--139
%I 
%K genetic algorithms, genetic programming, directed graph structures, fuzzy class-association-rule mining, fuzzy set theory, genetic network programming, intrusion-detection
   model, data mining, directed graphs, fuzzy set theory, security of data
%X As the Internet services spread all over the world, many kinds and a large number of security threats are increasing. Therefore, intrusion detection systems, which can
   effectively detect intrusion accesses, have attracted attention. This paper describes a novel fuzzy class-association-rule mining method based on genetic network
   programming (GNP) for detecting network intrusions. GNP is an evolutionary optimisation technique, which uses directed graph structures instead of strings in genetic
   algorithm or trees in genetic programming, which leads to enhancing the representation ability with compact programs derived from the reusability of nodes in a graph
   structure. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database that contains both discrete and continuous attributes and also
   extract many important class-association rules that contribute to enhancing detection ability. Therefore, the proposed method can be flexibly applied to both misuse and
   anomaly detection in network-intrusion-detection problems. Experimental results with KDD99Cup and DARPA98 databases from MIT Lincoln Laboratory show that the proposed
   method provides competitively high detection rates compared with other machine-learning techniques and GNP with crisp data mining.
%8 January
%Z Also known as \cite5499108

%A Shingo Mabu
%A Kotaro Hirasawa
%T Efficient program generation by evolving graph structures with multi-start nodes
%J Applied Soft Computing
%V 11
%N 4
%D 2011
%P 3618--3624
%I 
%K genetic algorithms, genetic programming, Evolutionary computation, Program generation, Graph structure, Even-n-Parity problem, Mirror Symmetry problem
%U http://www.sciencedirect.com/science/article/B6W86-5230PMW-2/2/83938061ebc19cc5a8ad1b3aa41d96c3
%X Automatic program generation is one of the applicable fields of evolutionary computation, and Genetic Programming (GP) is the typical method for this field. On the other
   hand, Genetic Network Programming (GNP) has been proposed as an extended algorithm of GP in terms of gene structures. GNP is a graph-based evolutionary algorithm and
   applied to automatic program generation in this paper. GNP has directed graph structures which have some features inherently, for example, re-usability of nodes and the
   small number of nodes. These features contribute to creating complicated programs with compact structures and never cause bloat. In this paper, the extended algorithm of
   GNP is proposed, which can create plural programs simultaneously in one individual by using multi-start nodes. In addition, GNP can evolve the programs in one individual
   considering the fitness and also its standard deviation in order to evolve the plural programs efficiently. In the simulations, Even-n-Parity problem and Mirror Symmetry
   problem are used for the performance evaluation, and the results show that the proposed method outperforms the standard GNP with single start node.

%A Robert M. MacCallum
%T Evolving Perl code for protein secondary structure prediction
%R Technical Report
%D 2002
%I 
%I Stockholm Bioinformatics Center, Stockholm University
%K genetic algorithms, genetic programming, grammar, regular expressions, scan, strict typing
%U http://www.sbc.su.se/~maccallr/publications/maccallum_ppsn02_talk.pdf
%O Presented at the PPSN 2002 workshop entitled: Evolutionary and Neural Computation in the BioSciences
%Z Extended abstract: http://www.sbc.su.se/~maccallr/publications/maccallum_ppsn02_workshop.pdf ; Workshop presentation :
   http://www.sbc.su.se/~maccallr/publications/maccallum_ppsn02_talk.pdf mimicking homologous crossover. Soft-max penalty on tree size and execution time, prosite, pop=2000,
   60hours

%A Robert M. MacCallum
%T Introducing a Perl Genetic Programming System: and Can Meta-evolution Solve the Bloat Problem?
%B Genetic Programming, Proceedings of EuroGP'2003
%S LNCS
%E Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa
%V 2610
%D 2003
%P 364--373
%I Springer-Verlag Berlin
%I EvoNet
%C Essex
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=364
%X An open source Perl package for genetic programming, called PerlGP, is presented. The supplied algorithm is strongly typed tree-based GP with homologous crossover.
   User-defined grammars allow any valid Perl to be evolved, including object oriented code and parameters of the PerlGP system itself. Time trials indicate that PerlGP is
   around 10 times slower than a C based system on a numerical problem, but this is compensated by the speed and ease of implementing new problems, particularly string-based
   ones. The effect of per-node, fixed and self-adapting crossover and mutation rates on code growth and fitness is studied. On a pi estimation problem, self-adapting rates
   give both optimal and compact solutions. The source code and manual can be found at http://perlgp.org. See \citemaccallum:2003:perlgp
%8 14-16  April
%Z EuroGP'2003 held in conjunction with EvoWorkshops 2003
%@ 3-540-00971-X

%A Robert M. MacCallum
%T PerlGP - The Manual
%D 2003
%I 
%I Stockholm Bioinformatics Center, Stockholm University
%C 106 91 Stockholm, Sweden
%K genetic algorithms, genetic programming, perl
%U http://perlgp.org/docs/manual/manual/manual.html
%O never came out in print
%8 3  February
%Z see \citemaccallum03

%A Robert M. MacCallum
%T Striped sheets and protein contact prediction
%J Bioinformatics
%V 20
%N Suppl 1
%D 2004
%P I224--I231
%I 
%K genetic algorithms, genetic programming, SOM
%U http://bioinformatics.oxfordjournals.org/cgi/reprint/20/suppl_1/i224.pdf
%X MOTIVATION: Current approaches to contact map prediction in proteins have focused on amino acid conservation and patterns of mutation at sequentially distant positions.
   This sequence information is poorly understood and very little progress has been made in this area during recent years. RESULTS: In this study, an observation of 'striped'
   sequence patterns across beta-sheets prompted the development of a new type of contact map predictor. Computer program code was evolved with an evolutionary algorithm
   (genetic programming) to select residues and residue pairs likely to make contacts based solely on local sequence patterns extracted with the help of self-organising maps.
   The mean prediction accuracy is 27percent on a validation set of 156 domains up to 400 residues in length, where contacts are separated by at least 8 residues and length/10
   pairs are predicted. The retrospective accuracy on a set of 15 CASP5 targets is 27percent and 14percent for length/10 and length/2 predicted pairs, respectively (both using
   a minimum residue separation of 24). This compares favourably to the equivalent 21percent and 13percent obtained for the best automated contact prediction methods at CASP5.
   The results suggest that protein architectures impose regularities in local sequence environments. Other sources of information, such as correlated/compensatory mutations,
   may further improve accuracy. AVAILABILITY: A web-based prediction service is available at http://www.sbc.su.se/~maccallr/contactmaps
%8 August  4
%Z PMID: 15262803 [PubMed - in process] cited by \citeLatek:2008:BMCsb

%A Penousal Machado
%A Francisco B. Pereira
%A Amilcar Cardoso
%A Ernesto Costa
%T Busy Beaver -- the Influence of Representation
%B Genetic Programming, Proceedings of EuroGP'99
%S LNCS
%E Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty
%V 1598
%D 1999
%P 29--38
%I Springer-Verlag Berlin
%I EvoNet
%C Goteborg, Sweden
%K genetic algorithms, genetic programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=29
%X The Busy Beaver is an interesting theoretical problem proposed by Rado in 1962. In this paper we propose an evolutionary approach to this problem. We will focus on the
   representational issues, proposing alternative ways of codifying and interpreting Turing Machines. These alternative representations take advantage of the existence of
   equivalent Turing machine sets. The experimental results show that the proposed representations provide improvement over the standard genetic codification.
%8 26-27  May
%Z EuroGP'99, part of \citepoli:1999:GP Penousal Machado won special jury prize. Busy beaver = Turing machine which generates longest pattern of 1s and terminates. Solutions
   only known for very small Turing machines.
%@ 3-540-65899-8

%A Penousal Machado
%A Andre Dias
%A Amilcar Cardoso
%T GenCo: A project report
%B ISAS 2001 -- International Symposium on Adaptive Systems -- Evolutionary Computation and Probabilistic Graphical Models
%E Alberto Ochoa Rodriguez
%D 2002
%I 
%C Havana, Cuba
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/510392.html
%X Genetic Programming involves the evolution of computer programs, which are usually represented by trees composed by functions and terminals. In order to assign fitness, one
   must evaluate the programs, which is the most time demanding step of GP. In nowadays standard approaches, the evaluation involves an interpretation step. To avoid this
   step, which significantly slows the algorithm, some researchers evolve, directly, machine code programs. An alternative approach is to build a Genome Compiler, i.e. a
   system that transforms the individual's trees in machine-code programs and executes this code. Both techniques can bring huge speed improvements. However, these approaches
   have some shortcomings. In this paper we present GenCo: a research project whose main goal is development of a Genetic Programming Genome Compiler system, that overcomes
   some of the drawbacks of current approaches, enabling high speed improvements in a wider range of domains. We will also present experimental results in a programmatic
   compression task, in which GenCo was, on average, 80 times faster than a standard C based GP system.
%O The Pennsylvania State University CiteSeer Archives
%8 19-23  March
%Z context of the International Conference CIMAF 2001. Not verified LilGP \citezonger:1996:lilgp interpretation step replaced by a compilation step. Lena image compression.
   Claims in the region of 100000 to 1 million individuals evaluated per second.

%A Penousal Machado
%A Andre Dias
%A Nuno Duarte
%A Amilcar Cardoso
%T Giving Colour to Images
%B AI and Creativity in Arts and Science
%E Amilcar Cardoso and Geraint Wiggins
%D 2002
%I 
%I the Society for the Study of Artificial Intelligence and the Simulation of Behaviour
%C Imperial College, United Kingdom
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/508435.html
%X This paper is about the colouring of greyscale images. More specifically, we address the problem of learning to colour greyscale images from a set of examples of true
   colour ones. We employ Genetic Programming to evolve computer programs that take as input the Lightness channel of the training images and output the Hue channel. The best
   programs evolved can then be used to give colour to greyscale images. Due to the computational complexity of the learning task, we use a genome compiler system, GenCo,
   specially suited to image processing tasks.
%O The Pennsylvania State University CiteSeer Archives
%O A symposium as part of AISB'02
%8 2-5  April
%Z http://comma.doc.ic.ac.uk/aisb2002/ http://www.soi.city.ac.uk/~geraint/aisb02/programme.htm

%A Penousal Machado
%A Amilcar Cardoso
%T Speeding up Genetic Programming
%B Proceedings of the Second International Symposium on Artificial Intelligence, Adaptive Systems (CIMAF - 99)
%D 1999
%I 
%C Havana, Cuba
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/336117.html
%X One of the major drawbacks of Evolutionary Computation is the need for great computational power. The set of problems that can be solved, in practice, by evolutionary
   approaches is highly connected with the efficiency of the algorithm. In most Genetic Programming applications the majority of time is spent on the evaluation of the
   individuals. Accordingly, it is desirable to optimise this step of the process. In this paper we present two approaches through which significant speed improvements can be
   achieved. The first approach, T-functions, is effective in tasks, such as symbolic regression, that require repeated evaluation of the individuals. The second approach,
   caching, resorts to the storage of the execution results of individuals' sub-trees, thus avoiding the recalculation of these sub-programs. Caching finds its application
   when the function set includes complex, time-consuming functions.
%O The Pennsylvania State University CiteSeer Archives
%8 March  22-26

%A Penousal Machado
%A Francisco B. Pereira
%A Jorge Tavares
%A Ernesto Costa
%A Amilcar Cardoso
%T Evolutionary Turing machines: The quest for busy beavers
%B Recent Developments in Biologically Inspired Computing
%E Leandro N. de Castro and Fernando J. Von Zuben
%D 2004
%I Idea Group Publishing
%K genetic algorithms, genetic programming
%U http://www.cisuc.uc.pt/acg/dlfile.php?fn=792_pub_Beaver-chapter-2004.pdf
%X In this chapter we study the feasibility of using Turing Machines as a model for the evolution of computer programs. To assess this idea we select, as test problem, the
   Busy Beaver - a well-known theoretical problem of undisputed interest and difficulty proposed by Tibor Rado in 1962. We focus our research on representational issues and on
   the development of specific genetic operators, proposing alternative ways of encoding and manipulating Turing Machines. The results attained on a comprehensive set of
   experiments show that the proposed techniques bring significant performance improvements. Moreover, the use of a graph based crossover operator, in conjunction with new
   representation techniques, allowed us to establish new best candidates for the 6, 7, and 8 states instances of the 4 tuple Busy Beaver problem.
%O 2
%Z http://www.idea-group.com/books/details.asp?id=4376
%@ 1-59140-312-X

%A Penousal Machado
%A Juan Romero
%A Antonino Santos
%A Amilcar Cardoso
%A Alejandro Pazos
%T On the development of evolutionary artificial artists
%J Computers \& Graphics
%V 31
%N 6
%D 2007
%P 818--826
%I 
%K genetic algorithms, genetic programming, Artificial art, Evolutionary computation, Artificial intelligence, Digital art, NEvAr
%U http://www.sciencedirect.com/science/article/B6TYG-4PTMXVB-1/2/0c81ca71ea76186b393615d17177d4de
%X The creation and the evaluation of aesthetic artifacts are tasks related to design, music and art, which are highly interesting from the computational point of view.
   Nowadays, Artificial Intelligence systems face the challenge of performing tasks that are typically human, highly subjective, and eventually social. The present paper
   introduces an architecture which is capable of evaluating aesthetic characteristics of artifacts and of creating artifacts that obey certain aesthetic properties. The
   development methodology and motivation, as well as the results achieved by the various components of the architecture, are described. The potential contributions of this
   type of systems in the context of digital art are also considered.

%A Penousal Machado
%A Ant\'{o}nio Leit\~{a}o
%T Evolving Fitness Functions for Mating Selection
%B Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011
%S LNCS
%E Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado
%V 6621
%D 2011
%P 227--238
%I Springer Verlag
%I EvoStar
%C Turin, Italy
%K genetic algorithms, genetic programming
%X The tailoring of an evolutionary algorithm to a specific problem is typically a time-consuming and complex process. Over the years, several approaches have been proposed
   for the automatic adaptation of parameters and components of evolutionary algorithms. We focus on the evolution of mating selection fitness functions and use as case study
   the Circle Packing in Squares problem. Each individual encodes a potential solution for the circle packing problem and a fitness function, which is used to assess the
   suitability of its potential mating partners. The experimental results show that by evolving mating selection functions it is possible to surpass the results attained with
   hardcoded fitness functions. Moreover, they also indicate that genetic programming was able to discover mating selection functions that: use the information regarding
   potential mates in novel and unforeseen ways; outperform the class of mating functions considered by the authors.
%8 27-29  April
%Z Part of \citeSilva:2011:GP EuroGP'2011 held in conjunction with EvoCOP2011 EvoBIO2011 and EvoApplications2011

%A Penousal Machado
%A Joao Correia
%A Juan Romero
%T Improving Face Detection
%B Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012
%S LNCS
%E Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta
%V 7244
%D 2012
%P 73--84
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Face detection, Haar cascade
%X A novel Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is presented. The approach
   relies on the ability of the Genetic Programming engine to identify and exploit shortcomings of classifier systems, and generate instances that are misclassified by them.
   The addition of these instances to the training set has the potential to improve classifier's performance. The experimental results attained with face detection classifiers
   are presented and discussed. Overall they indicate the success of the approach.
%8 11-13  April
%Z Part of \citeMoraglio:2012:GP EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012

%A Penousal Machado
%A Joao Correia
%A Juan Romero
%T Expression-Based Evolution of Faces
%B Proceedings of the 1st International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012
%S LNCS
%E Penousal Machado and Juan Romero and Adrian Carballal
%V 7247
%D 2012
%P 187--198
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Evolutionary Art, Automatic Fitness Assignment, Face Detection
%X The combination of a classifier system with an evolutionary image generation engine is explored. The framework is instantiated using an off-the-shelf face detection system
   and a general purpose, expression-based, genetic programming engine. By default, the classifier returns a binary output, which is inadequate to guide evolution. By
   retrieving information provided by intermediate results of the classification task, it became possible to develop a suitable fitness function. The experimental results show
   the ability of the system to evolve images that are classified as faces. A subjective analysis also reveals the unexpected nature and artistic potential of the evolved
   images.
%8 11-13  April
%Z Part of \citeMachado:2012:EvoMusArt EvoMUSART'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBIO2012 and EvoApplications2012

%A Kenneth J. Mackin
%A Eiichiro Tazaki
%T Unsupervised training of Multiobjective Agent Communication using Genetic Programming
%B Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technology
%V 2
%D 2000
%P 738--741
%I IEEE
%C Brighton, UK
%K genetic algorithms, genetic programming
%U http://www.lania.mx/~ccoello/EMOO/mackin00.pdf.gz
%X Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete distributed tasks concurrently under autonomous control.
   Agent communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most research on training or evolving group behavior in
   multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large-scale systems. The problem is
   further complicated in a multiobjective scenario. In order to solve this problem, in our previous research we had proposed a method applying genetic programming techniques,
   in particular automatically defined function genetic programming (ADF-GP), to allow agents to autonomously learn effective agent communication messaging. For this research
   we take this approach further and combine multiobjective genetic programming in order to adapt the system to a multiobjective environment. In the proposed method separate
   agent communication protocols are trained for each objective. A software simulation of a multiagent transaction system is used to observe the effectiveness of the proposed
   method in multiobjective environments
%8 30  August -1  September

%A Kenneth J. Mackin
%A Eiichiro Tazaki
%T Multiagent communication combining genetic programming and pheromone communication
%J Kybernetes
%V 31
%N 6
%D 2002
%P 827--843
%I 
%K genetic algorithms, genetic programming, Cybernetics; Programming, Communications, Electronic Commence
%X Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control.
   Agent Communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most researches on training or evolving group behavior in
   multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large-scale systems. In order to
   solve this problem, in this paper we propose a new application of existing training methods. By applying Genetic Programming techniques, namely Automatically Defined
   Function Genetic Programming (ADF-GP), in combination with pheromone communication features, we allowed the agent system to autonomously learn effective agent communication
   messaging for coordinated group behavior. A software simulation of a multiagent transaction system aiming at e-commerce usage will be used to observe the effectiveness of
   the proposed method in the targeted environment. Using the proposed method, automatic training of a compact and efficient agent communication protocol for the multiagent
   system was observed.

%A Duncan MacLean
%A Eric A. Wollesen
%A Bill Worzel
%T Listening to Data: Tuning a Genetic Programming System
%B Genetic Programming Theory and Practice II
%E Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel
%D 2004
%P 245--262
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming, classifier, molecular biology, cancer, microarray, genetics
%X Genetic Programming (GP) may be used to model complex data but it must be tuned to get the best results. This process of tuning often gives insights into the data itself.
   This is discussed using examples from classification problems in molecular biology and the results and rules of thumb developed to tune the GP system are reviewed in light
   of current GP theory.
%O 15
%8 13-15  May
%Z part of \citeoreilly:2004:GPTP2
%@ 0-387-23253-2

%A Janos Madar
%A Janos Abonyi
%A and Ferenc Szeifert
%T Genetic Programming for the Identification of Nonlinear Input-Output Models
%J Industrial and Engineering Chemistry Research
%V 44
%N 9
%D 2005
%P 3178--3186
%I 
%K genetic algorithms, genetic programming
%U http://www.fmt.vein.hu/softcomp/gp/ie049626e.pdf
%X Linear-in-parameters models are quite widespread in process engineering, e.g., nonlinear additive autoregressive models, polynomial ARMA models, etc. This paper proposes a
   new method for the structure selection of these models. The method uses genetic programming to generate nonlinear input-output models of dynamical systems that are
   represented in a tree structure. The main idea of the paper is to apply the orthogonal least squares (OLS) algorithm to estimate the contribution of the branches of the
   tree to the accuracy of the model. This method results in more robust and interpretable models. The proposed approach has been implemented as a freely available MATLAB
   Toolbox, www.fmt.veim.hu/softcomp. The simulation results show that the developed tool provides an efficient and fast method for determining the order and structure for
   nonlinear input-output models.
%Z http://pubs.acs.org/journals/iecred/index.html S0888-5885(04)09626-5 American Chemical Society Department of Process Engineering, University of Veszprem, P.O. Box 158,
   Veszprem 8201, Hungary

%A Janos Madar
%T Application of A Priori Knowledge in Chemical Engineering
%R Ph.D. Thesis
%D 2005
%I 
%I School of Chemical Engineering, University of Veszprem
%C Hungary
%K genetic algorithms, genetic programming
%U http://konyvtar.uni-pannon.hu/doktori/2005/Madar_Janos_theses_en.pdf
%Z Although 7 page summary available in English, most of Madar_Janos_theses_hu.pdf is also in english. Supervised by Dr. Janos Abonyi

%A Payam Madjidi
%T Genetic Programming and analysis of high frequency financial data
%R TRITA-PDC Report ISRN KTH/PDC/R--96/3--SE
%D 1996
%I 
%I Center for Parallel Computers, Royal Institute of Technology
%C Stockholm, Sweden
%K genetic algorithms, genetic programming
%U http://www.pdc.kth.se/~payam/pub/gp.ps
%Z Also covers implementation in C? In gp.ps pages in reverse order. May have been presented at 2NWGA 1996 ga96NWGA But not mentioned in ftp://ftp.uwasa.fi/cs/2NWGA/main.ps.Z
   or ftp://garbo.uwasa.fi/cs/2NWGA/2NWGA.bib

%A Ken-ichi Maeda
%A Chiaki Sakama
%T Identifying Cellular Automata Rules
%J Journal of Cellular Automata
%V 2
%N 1
%D 2007
%P 1--20
%I 
%K genetic algorithms, genetic programming, Cellular automata, identification problem, decision tree
%U http://www.sys.wakayama-u.ac.jp/~sakama/papers/jca07.pdf
%X This paper studies a method for identifying cellular automata rules (CA rules). Given a sequence of CA configurations, we first seek an appropriate neighbourhood of a cell
   and collect cellular changes of states as evidences. The collected evidences are then classified using a decision tree, which is used for constructing CA transition rules.
   Conditions for classifying evidences in a decision tree are computed using genetic programming. We perform experiments using several types of CAs and verify that the
   proposed method successfully identifies correct CA rules.

%A Yoichiro Maeda
%A Satomi Kawaguchi
%T Redundant Node Pruning and Adaptive Search Method for Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)
%E Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer
%D 2000
%P 535
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Las Vegas, Nevada, USA
%K genetic algorithms, genetic programming, Poster
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP102.ps
%8 10-12  July
%Z A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of
   \citewhitley:2000:GECCO
%@ 1-55860-708-0

%A Tadao Maekawa
%A Osamu Ueno
%A Norie Kawai
%A Emi Nishina
%A Manabu Honda
%A Tsutomu Oohashi
%T Evolutionary acquisition of genetic program for death
%B Advances in Artificial Life, ECAL 2011: Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems
%E Tom Lenaerts and Mario Giacobini and Hugues Bersini and Paul Bourgine and Marco Dorigo and Rene Doursat
%D 2011
%P 481--486
%I MIT Press
%I International Society of Artificial Life (ISAL)
%C Paris
%K genetic algorithms
%U http://mitpress.mit.edu/books/chapters/0262297140chap74.pdf
%X As part of our research on <programmed self-decomposition>, we formed the hypothesis that originally immortal terrestrial organisms evolve into ones that are programmed for
   autonomous death. We then conducted simulation experiments in which we examined this hypothesis using an artificial ecosystem that we designed to refer to a terrestrial
   ecosystem endowed with Artificial Chemistry (AChem). Our findings suggest that, in the case of a mortal organism appearing among a population of immortal organisms as a
   mutant which evolutionarily acquires a genetic program for death by means of self-decomposition, this organism and its surviving offspring surpass immortal organisms and
   eventually prosper with adaptive divergence under various environmental conditions within a certain probability.
%8 8-12  August
%Z Appears to be on artificial chemistry rather than genetic programming. http://www.ecal11.org/ Complete Proceedings e-Book Available at:
   http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12760

%A Tetsuya Maeshiro
%A Masayuki Kimura
%T Genetic Code as an Evolving Organism
%B Genetic Programming 1997: Proceedings of the Second Annual Conference
%E John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo
%D 1997
%P 413
%I Morgan Kaufmann San Francisco, CA, USA
%C Stanford University, CA, USA
%K Artifical life and evolutionary robotics
%8 13-16  July
%Z GP-97

%A Anjali Mahajan
%A M S Ali
%T Superblock scheduling using genetic programming for embedded systems
%B 7th IEEE International Conference on Cognitive Informatics, ICCI 2008
%D 2008
%P 261--266
%I 
%K genetic algorithms, genetic programming, NP-complete problem, embedded system, instruction scheduling, optimally scheduling instruction, optimized compiler, processor
   architecture, superblock scheduling, embedded systems, optimising compilers, scheduling
%X Instruction scheduling is an important issue in the compiler optimization for embedded systems. The instruction scheduling problem is mainly solved heuristically since
   finding an optimal solution requires significant computational resources and, in general, the problem of optimally scheduling instructions is known to be NP-Complete. The
   development of processors with pipelines and multiple functional units has increased the demands on compiler writers to write complex instruction scheduling algorithms.
   These algorithms are required to ensure that the most efficient use of resources, i.e. the functional units and pipelines of the processor, is made due to the increased
   complexity of processor architectures. In this paper, the specific problem of automatically creating instruction scheduling heuristics is addressed.
%8 August
%Z Also known as \cite4639177

%A Kiarash Mahdavi
%A Mark Harman
%T Book Review: Automatic Re-Engineering of Software Using Genetic Programming
%J Genetic Programming and Evolvable Machines
%V 3
%N 2
%D 2002
%P 219--221
%I 
%K genetic algorithms, genetic programming
%8 June
%Z Review of \citeryan:book Cites: M.D.Ernst, Jake Cockrell, William G. Griswold and David Notkin, Dynamically discovering likely program invariants to support program
   evolution, IEEE Transactions on Software Engineering, Vol. 27, No. 2, pp. 1-25, 2001. Kenneth Peter Williams, Evolutionary algorithms for automatic parallelization, PhD
   Thesis, University of Reading, UK, Department of Computer Science, September 1998. Article ID: 408589

%A S. J. {Seyyed Mahdavi}
%A K. Mohammadi
%T Reliability enhancement of digital combinational circuits based on evolutionary approach
%J Microelectronics Reliability
%V 50
%N 3
%D 2010
%P 415--423
%I 
%K genetic algorithms, EHW
%U http://www.sciencedirect.com/science/article/B6V47-4Y1W8TW-1/2/e36a1a110cf2e893fd2c978d4cb0c0f8
%X Reliability has become an integral part of the system design process, especially for those systems with life-critical applications such as aircraft and spacecraft flight
   control. The recent rapid growth in demand for highly reliable digital circuits has focused attention on tools and techniques we might use to enhance the reliability of the
   circuit. In this paper, we present an algorithm to improve the reliability of digital combinational circuits based on evolutionary approach. This method generates a global
   VHDL file for the selected initial set of components based on inserting multiplexers at the gate inputs of the circuit which helps to perform the simulations in only one
   session. This simulation framework is combined with single-pass reliability analysis approach to implement the evolutionary algorithm. The search space of the genetic
   algorithm is limited by the idea of slicing the initial set of components and also circuit partitioning could be used to further overcome the scalability limitations. The
   framework is applied to a subset of combinational benchmark circuits and our experiments demonstrate that higher reliabilities can be achieved while other factors such as
   power, speed and area overhead will remain admissible.
%8 March
%Z Fixed sized chromosome. GA evolved contents and connectivity?.VHDL

%A S{\'e}bastien Mahler
%A Denis Robilliard
%A Cyril Fonlupt
%T Tarpeian Bloat Control and Generalization Accuracy
%B Proceedings of the 8th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Maarten Keijzer and Andrea Tettamanzi and Pierre Collet and Jano I. van Hemert and Marco Tomassini
%V 3447
%D 2005
%P 203--214
%I Springer
%I EvoNet
%C Lausanne, Switzerland
%K genetic algorithms, genetic programming
%U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=203
%X In this paper we will focus on machine-learning issues solved with Genetic Programming (GP). Excessive code growth or bloat often happens in GP , greatly slowing down the
   evolution process. Poli proposed the Tarpeian Control method to reduce bloat, but possible side-effects of this method on the generalisation accuracy of GP hypotheses
   remained to be tested. In particular, since Tarpeian Control puts a brake on code growth, it could behave as a kind of Occam's razor, promoting shorter hypotheses more able
   to extend their knowledge to cases apart from any learning steps. To answer this question, we experiment Tarpeian Control with symbolic regression. The results are
   contrasted, showing that it can either increase or reduce the generalization power of GP hypotheses, depending on the problem at hand. This suggest that a blind use of TC
   is not safe, but also that a careful parameter setup may be profitable in some cases.
%8 30  March  - 1  April
%Z Part of \citekeijzer:2005:GP EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005 \citepoli03
%@ 3-540-25436-6

%A Tobias Mahlmann
%A Julian Togelius
%A Georgios N. Yannakakis
%T Modelling and evaluation of complex scenarios with the Strategy Game Description Language
%B Proceedings of the 2011 IEEE Conference on Computational Intelligence and Games
%D 2011
%P 174--181
%I IEEE
%C Seoul, South Korea
%K genetic algorithms, genetic programming
%U http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper45.pdf
%X The Strategy Game Description Game Language (SGDL) is intended to become a complete description of all aspects of strategy games, including rules, parameters, scenarios,
   maps, and unit types. Our aim is to be able to model a wide variety of strategy games, simple ones as well as complex commercially available titles. In our previous work
   [1] we introduced the basic concepts of modelling game rules in a tree structure and evaluating them through simulated playthrough. In this paper we present some additions
   to the language and discuss and compare three methods to evaluate the quality of a set of game rules in two different scenarios. We find that the proposed evaluation
   measures are complementary, and depend on the artificial agent used.
%8 31  August  - 3  September

%A Muhammad Tariq Mahmood
%A Abdul Majid
%A Tae-Sun Choi
%T Optimal depth estimation by combining focus measures using genetic programming
%J Information Sciences
%V 181
%N 7
%D 2011
%P 1249--1263
%I 
%K genetic algorithms, genetic programming, 3D shape recovery, Focus measure, Shape From Focus, Combining focus measures
%U http://www.sciencedirect.com/science/article/B6V0C-51N22D2-3/2/463bf41cb8ecb1292e814f690c94cf70
%X Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape From Focus (SFF) is one of the passive optical methods for 3D
   shape recovery that uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of
   each pixel in the image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To
   address this problem, we develop Optimal Composite Depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is constructed by
   optimally combining the primary information extracted using one/or more focus measures. The genetically developed composite function is then used to compute the optimal
   depth map of objects. The performance of the developed nonlinear function is investigated using both the synthetic and the real world image sequences. Experimental results
   demonstrate that the proposed estimator is more useful in computing accurate depth maps as compared to the existing SFF methods. Moreover, it is found that the
   heterogeneous function is more effective than homogeneous function.

%A Ogier Maitre
%A Laurent A. Baumes
%A Nicolas Lachiche
%A Avelino Corma
%A Pierre Collet
%T Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA
%B GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
%E Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and
   Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van
   Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique
   Alba
%D 2009
%P 1403--1410
%I ACM New York, NY, USA
%I SIGEVO
%C Montreal, Qu\'ebec, Canada
%K genetic algorithms, GPU, Numerical Analysis Optimisation, Performance, Parallelisation, evolutionary computation, GPGPU, Graphic Processing Unit, EASEA
%X This paper presents a straightforward implementation of a standard evolutionary algorithm that evaluates its population in parallel on a GPGPU card. Tests done on a
   benchmark and a real world problem using an old nVidia 8800GTX card and a newer but not top of the range GTX260 card show a roughly 30x (resp. 100x) speedup for the whole
   algorithm compared to the same algorithm running on a standard 3.6GHz PC. Knowing that much faster hardware is already available, this opens new horizons to evolutionary
   computation, as search spaces can now be explored 2 or 3 orders of magnitude faster, depending on the number of used GPGPU cards. Since these cards remains very difficult
   to program, the knowhow has been integrated into the old EASEA language, that can now output code for GPGPU (-cuda option).
%8 8-12  July
%Z Not on GP but mentions it GECCO-2009 A joint meeting of the eighteenth international conference on genetic algorithms (ICGA-2009) and the fourteenth annual genetic
   programming conference (GP-2009). ACM Order Number 910092.

%A Ogier Maitre
%A Pierre Collet
%A Nicolas Lachiche
%T Fast Evaluation of GP Trees on GPGPU by Optimizing Hardware Scheduling
%B Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010
%S LNCS
%E Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar
%V 6021
%D 2010
%P 301--312
%I Springer
%I EvoStar
%C Istanbul
%K genetic algorithms, genetic programming
%X This paper shows that it is possible to use General Purpose Graphic Processing Unit cards for a fast evaluation of different Genetic Programming trees on as few as 32
   fitness cases by using the hardware scheduling of NVIDIA cards. Depending on the function set, observed speedup ranges between x50 and x250 on one half of an NVidia GTX295
   GPGPU card, vs a single core of an Intel Quad core Q8200.
%8 7-9  April
%Z Part of \citeEsparcia-Alcazar:2010:GP EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010

%A Ogier Maitre
%A Stephane Querry
%A Nicolas Lachiche
%A Pierre Collet
%T EASEA Parallelization of Tree-Based Genetic Programming
%B 2010 IEEE World Congress on Computational Intelligence
%E Pilar Sobrevilla
%D 2010
%P 1997--2004
%I IEEE
%I IEEE Computational Intelligence Society
%C Barcelona
%K genetic algorithms, genetic programming, GPU
%X This paper introduces the implementation of Koza-style tree-based Genetic Programming on General Purpose Graphic Processing Units (GPGPU) using the EASEA language, and
   shows how a GP algorithm can be easily implemented using EASEA and CUDA. Performance is first discussed on a classical toy problem taken from one of Koza's books and then
   on a real world problem inspired from aeronautics, that extends the results to difficult problems with large data sets.
%8 18-23  July
%Z tree GP. nVidia 295 GTX CUDA 2.3 Trigonometric regression (RMS error fitness disabled) cos(2x) Population 128 to 65536. TGPNode. Lyshevski F3A radio-controlled aerobatic
   aircraft model competitions. 4 control dimensions quaternion. Population 40960, 100 generations, 51000 training values GP operation per second not given. WCCI 2010 - A
   joint meeting of the IEEE, the INNS, the EPS and the IET. Also known as \cite5586258

%A Hammad Majeed
%A Conor Ryan
%A R. Muhammad Atif Azad
%T Evaluating GP schema in context
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1773--1774
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, Poster, module acquisition, schema theory, tree semantics
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1773.pdf
%X We propose a methodology to look at the fitness contributions (semantics) of different schemata in Genetic Programming (GP). We hypothesise that the significance of a
   schema can be evaluated by calculating its fitness contribution to the total fitness of the trees that contain it, and use our methodology to test this hypothesis. It is
   shown that this method can also be used to identify schemata that are important in terms of both individual runs and individual problems (that is, schema that will be
   important across many runs on a particular problem). The usefulness of this study to existing schema theories and its effective use in the detection of introns, in the
   identification of potentially useful modular functions are also discussed.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052 See also \citemajeed:gecco05ws. Quartic polynomial.
%@ 1-59593-010-8

%A Hammad Majeed
%T A New Approach to Evaluate GP Schema in Context
%B Genetic and Evolutionary Computation Conference (GECCO2005) workshop program
%E Franz Rothlauf and Misty Blowers and J\"urgen Branke and Stefano Cagnoni and Ivan I. Garibay and Ozlem Garibay and J\"orn Grahl and Gregory Hornby and Edwin D. de Jong and
   Tim Kovacs and Sanjeev Kumar and Claudio F. Lima and Xavier Llor\`a and Fernando Lobo and Laurence D. Merkle and Julian Miller and Jason H. Moore and Michael O'Neill and
   Martin Pelikan and Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and Stephen L. Smith and Hal Stringer and Keiki Takadama and Marc Toussaint and Stephen C. Upton
   and Alden H. Wright
%D 2005
%P 378--381
%I ACM Press
%C Washington, D.C., USA
%K genetic algorithms, genetic programming
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0378.pdf
%X Evaluating GP schema in context is considered to be a complex,and, at times impossible, task. The tightly linked nodes of a GP tree is the main reason behind its
   complexity. We present a new approach to evaluate GP schema in context. It is simple in its implementation with a potential to address well-known GP problems, such as
   identification of significant schema, dead code (introns) and module acquisition to name a few. It is based on the principle that the contribution of a schema can be
   evaluated by neutralising the effect of the schema in the tree containing it (container-tree) and then checking its effect on the container-tree's fitness. Its usefulness
   is empirically demonstrated along with its limitation.
%8 25-29  June
%Z Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006

%A Hammad Majeed
%A Conor Ryan
%T A Less Destructive, Context-aware Crossover Operator for GP
%B Proceedings of the 9th European Conference on Genetic Programming
%S Lecture Notes in Computer Science
%E Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art
%V 3905
%D 2006
%P 36--48
%I Springer
%I EvoNet
%C Budapest, Hungary
%K genetic algorithms, genetic programming, steepest ascent crossover hill climbing
%U http://link.springer.de/link/service/series/0558/papers/3905/39050036.pdf
%X Standard GP crossover is widely accepted as being a largely \em destructive operator, creating many poor offspring in the search for better ones. One of the major reasons
   for its destructiveness is its disrespect for the context of swapped subtrees in their respective parent trees when creating offspring. At times, this hampers GP's
   performance considerably, and results in populations with \em low average fitness values. Many attempts have been made to make it a more constructive crossover, mostly by
   preserving the context of the selected subtree in the offspring. Although successful at preserving context, none of these methods provide the opportunity to discover new
   and better contexts for exchanged subtrees. We introduce a context-aware crossover operator which operates by identifying all possible contexts for a subtree, and
   evaluating each of them. The context that produces the highest fitness is used to create a child which is then passed into the next generation. We have tested its
   performance on many benchmark problems. It has shown better results than the standard GP crossover operator, using either the same number or fewer individual evaluations.
   Furthermore, the average fitness of populations using this scheme improves considerably, and programs produced in this way are much smaller than those produced using
   standard crossover.
%8 10 - 12  April
%Z Part of \citecollet:2006:GP EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006. Variation in operator frequencies from beginning to end of GP run.
%@ 3-540-33143-3

%A Hammad Majeed
%A Conor Ryan
%T A re-examination of a real world blood flow modeling problem using context-aware crossover
%B Genetic Programming Theory and Practice IV
%S Genetic and Evolutionary Computation
%E Rick L. Riolo and Terence Soule and Bill Worzel
%V 5
%D 2006
%P -
%I Springer
%C Ann Arbor
%K genetic algorithms, genetic programming
%X context-aware crossover. This is an improved crossover technique for GP which always swaps subtrees into their best possible context in a parent. We show that this style of
   crossover is considerably more constructive than the standard method, and present several experiments to demonstrate how it operates, and how well it performs, before
   applying the technique to a real world application, the Blood Flow Modelling Problem.
%O 14
%8 11-13  May
%Z part of \citeRiolo:2006:GPTP Published Jan 2007 after the workshop
%@ 0-387-33375-4

%A Hammad Majeed
%A Conor Ryan
%T Using context-aware crossover to improve the performance of GP
%B GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation
%E Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta
   and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and
   Conor Ryan and Dirk Thierens
%V 1
%D 2006
%P 847--854
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Seattle, Washington, USA
%K genetic algorithms, genetic programming, context, context aware crossover, destructive effects, one point crossover, standard crossover, tree context
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p847.pdf
%8 8-12  July
%Z GECCO-2006 A joint meeting of the fifteenth international conference on genetic algorithms (ICGA-2006) and the eleventh annual genetic programming conference (GP-2006). ACM
   Order Number 910060
%@ 1-59593-186-4

%A Hammad Majeed
%A Conor Ryan
%T Context-aware mutation: a modular, context aware mutation operator for genetic programming
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1651--1658
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, building blocks, cache, constructive, context, context aware crossover, crossover, fitness, modules
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1651.pdf
%X This paper introduces a new type of mutation, Context-Aware Mutation, which is inspired by the recently introduced context-aware crossover. Context-Aware mutation operates
   by replacing existing sub-trees with modules from a previously constructed repository of possibly useful subtrees. We describe an algorithmic way to produce the repository
   from an initial, exploratory run and test various GP set ups for producing the repository. The results show that when the exploratory run uses context-aware crossover and
   the main run uses context-aware mutation, not only is the final result significantly better, the overall cost of the runs in terms of individuals evaluated is significantly
   lower.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A Hammad Majeed
%A Conor Ryan
%T On the constructiveness of context-aware crossover
%B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
%E Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and
   Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and
   Thomas Stutzle and Richard A Watson and Ingo Wegener
%V 2
%D 2007
%P 1659--1666
%I ACM Press New York, NY, USA
%I ACM SIGEVO (formerly ISGEC)
%C London
%K genetic algorithms, genetic programming, cache, constructive, context, context aware crossover, crossover, fitness
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1659.pdf
%X Crossover in Genetic Programming is mostly a destructive operator, generally producing children worse than the parents and occasionally producing those who are better. A
   recently introduced operator, Context-Aware Crossover, which implicitly discovers the best possible crossover site for a subtree has been shown to consistently attain
   higher fitnesses while processing fewer individuals. It has been observed that context-aware crossover is similar to Brood Crossover in that multiple children are produced
   during each crossover event. This paper performs a thorough analysis of these crossover operators and compares the performance of the two and demonstrates that, although
   they do work similarly, context-aware crossover performs a far better sampling of the search space and thus performs much better. We also demonstrate that context-aware
   crossover benefits from a speed up of almost an order of magnitude when using a simple and very small cache, which is over two orders of magnitude smaller than caches
   typically used.
%8 7-11  July
%Z GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM
   Order Number 910071

%A H. Majeed
%A C. Ryan
%T A New Approach to Calculate the Best Context of a Tree and its Application in Defining a Constructive, Context Aware Crossover for GP
%B Proceedings of the 2007 International Conference Frontiers in the Convergence of Bioscience and Information Technologies (FBIT 2007)
%D 2007
%P 765--768
%I IEEE Press
%C Jeju Island, Korea
%K genetic algorithms, genetic programming
%X Genetic programming (GP) is an evolutionary algorithm that evolves computer programs. Its main recombination operator is standard one point crossover which is generally
   accepted to be one of GP's weak points, due to its ignorance of the context into which genetic material is placed. This work introduces a new context aware recombination
   operator called context-aware crossover. It implicitly calculates the best possible context of the subtree-to- be-exchanged in the other parent and places it there. It is
   tested on a wide range of problems and found quite constructive in general and quite effective on hard problems, in particular. It has also shown the ability to generate
   quite smaller trees than standard GP without effecting the fitness of a population adversely.
%8 October  11-13
%Z Comput. Sci. & Inf. Syst., Limerick Univ., Limerick

%A Hammad Majeed
%T The Importance of semantic context in tree based GP and its application in defining a less destructive, context aware crossover for GP
%R Ph.D. Thesis
%D 2007
%I 
%I University of Limerick
%C Ireland
%K genetic algorithms, genetic programming, context aware crossover, destructive crossover,
%X This thesis gives an empirical proof of the existence of competitive building blocks in Grammatical Evolution (GE), a grammar based program evolving algorithm. It shows
   that in GE, rooted and non-rooted building blocks exist and over the period of time rooted building blocks compete with each other to grow in size, while non-rooted
   building blocks help them to accomplish that. This is an offline study and done in a retrospective manner. We also present a comprehensive study of the importance of
   semantic context of a sub-tree in tree based systems and introduce a novel context aware evaluation technique for evaluating sub-trees in context. The usefulness of this
   technique is demonstrated on a benchmark problem. In this work, we introduce a new constructive and context aware crossover for GP, Context-Aware crossover, which works by
   placing the selected sub- trees in their best possible context in any tree. This is a greedy approach and results in an improved performance. It is tested on a wide range
   of problems and showed better performance on all the problems except the Uni-Variate and Bi-Variate Polynomial Symbolic Regression problems. Furthermore, the results show
   that it generates very compact form of trees without adversely affecting their fitness. Finally, we show the usefulness of the context aware evaluation technique in
   encapsulating useful trees at the end of a run and using them to create a module repository. This repository is later used to improve the performance of the second cascaded
   run. For the second run, a variant of context-aware crossover is introduced, Context-Aware mutation which works on module repository. The effectiveness of this setup is
   demonstrated by re-examining a real world blood flow problem and improving the previously published results.

%A Abdul Majid
%A Asifullah Khan
%A Anwar M. Mirza
%T Gender classification using discrete cosine transformation: a comparison of different classifiers
%B Proceedings of the 7th International Multi Topic Conference, INMIC 2003
%D 2003
%P 59--64
%I IEEE
%C Islamabad
%K AUROC
%X We have investigated the problem of gender classification using a library of four hundred standard frontal facial images employing five classifiers, namely k-means,
   k-nearest neighbours, linear discriminant analysis (LDA), Mahalanobis distance based (MDB) classifiers and our modified KNN classifier. The image data independent discrete
   cosine transformation (DCT) basis is used for facial feature extraction. Areas under the convex hull (AUCH) of the classifiers are measured by varying the values of
   threshold for each feature subset in the receiver operating characteristics (ROC) curve. The scalar values of AUCH of the ROC curve increases with increasing number of
   features. More features yield a better representation of the gender facial image. The overall performance of classifiers is compared with different values of AUCH versus
   features under different conditions. It has been observed that when the number of features is increased beyond 5, AUCH starts to saturate. Our experimental results
   demonstrate that modified-KNN performs better than the rest of the conventional classifiers under all conditions. The LDA classifier did not perform well in the DCT domain;
   however, it gradually improved its performance with increasing number of features.
%8 8-9  Decemeber

%A Abdul Majid
%A Asifullah Khan
%A Anwar M. Mirza
%T Improving Performance of Nearest Neighborhood Classifier Using Genetic Programming
%B The Third International Conference on Machine Learning and Applications (ICMLA-04)
%D 2004
%P 469--476
%I 
%I IEEE/ACM
%C Louisville, KY, USA
%K genetic algorithms, genetic programming
%8 16-18  Decemeber
%Z http://www.cs.csubak.edu/~icmla/icmla04/ also known as \cite1383552

%A Abdul Majid
%A Asifullah Khan
%A Anwar M. Mirza
%T Intelligent Combination of Kernels Information for Improved Classification
%B Fourth International Conference on Machine Learning and Applications (ICMLA'05)
%D 2005
%P 16--21
%I IEEE Computer Society Los Alamitos, CA, USA
%C Los Angeles
%K genetic algorithms, genetic programming
%X we are proposing a combination scheme of kernels information of Support Vector Machines (SVMs) for improved classification task using Genetic Programming. In the scheme,
   first, the predicted information is extracted by SVM through the learning of different kernel functions. GP is then used to develop an Optimal Composite Classifier (OCC)
   having better performance than individual SVM classifiers. The experimental results demonstrate that OCC is more effective, generalised and robust. Specifically, it attains
   high margin of improvement at small features. Another side advantage of our GP based intelligent combination scheme is that it automatically incorporates the issues of
   optimal kernel and model selection to achieve a higher performance prediction model.
%Z http://ieeexplore.ieee.org/servlet/opac?punumber=10693
%@ 0-7695-2495-8

%A Abdul Majid
%A Asifullah Khan
%A Anwar M. Mirza
%T Combination of Nearest Neighborhood Classifiers Using Genetic Programming
%B 9th International Multitopic Conference (INMIC 2005)
%D 2005
%P 1--6
%I Pakistan Section IEEE
%C Karachi, Pakistan
%K genetic algorithms, genetic programming, pattern classification, GP-based intelligent scheme, decision space, nearest neighborhood classifiers, optimal composite
   classifier, optimal model selection
%X In this paper, GP based intelligent scheme has been used to develop an optimal composite classifier (OCC) from individual nearest neighbor (NN) classifiers. In the
   combining scheme, first, the predicted information is extracted from the component classifiers. Then, GP is used to develop OCC having better performance than individual NN
   classifiers. The experimental results demonstrate that the combined decision space of OCC is more effective. Further, we observed that heterogeneous combination of
   classifiers has more promising results than their homogenous one. Another side advantage of our GP based intelligent combination scheme is that it automatically
   incorporates the issues of optimal model selection of NN classifiers to achieve a higher performance prediction model
%8 23-25  Decemeber  2005
%Z Also known as \cite4133501
%@ 0-7803-9429-1

%A Abdul Majid
%A Asifullah Khan
%A Anwar M. Mirza
%T Combination of support vector machines using genetic programming
%J International Journal of Hybrid Intelligent Systems
%V 3
%N 2
%D 2006
%P 109--125
%I 
%K genetic algorithms, genetic programming, Support vector machines, optimal composite classifiers, receiver operating characteristics curves, Area Under the Convex Hull
   (AUCH), AUROC
%U http://iospress.metapress.com/link.asp?id=d5u73e9lpf6493nb
%X the combination of support vector machine (SVM) classifiers using Genetic Programming (GP) for gender classification problem. In our scheme, individual SVM classifiers are
   constructed through the learning of different SVM kernel functions. The predictions of SVM classifiers are then combined using GP to develop Optimal Composite Classifier
   (OCC). In this way, the combined decision space is more informative and discriminant. OCC has shown improved performance than that of optimised individual SVM classifiers
   using grid search. Another advantage of our GP combination scheme is that it automatically incorporates the issues of optimal kernel function and model selection to achieve
   high performance classification model. The classification performance is reported by using Receiver Operating Characteristics (ROC) Curve. Experiments are conducted under
   various feature sets to show that OCC is more informative and robust as compared to their individual SVM classifiers. Specifically, it attains high margin of improvement
   for small feature sets.
%8 June

%A Abdul Majid
%T Optimization and Combination of Classifiers Using Genetic Programming
%R Ph.D. Thesis
%D 2006
%I 
%I Ghulam Ishaq Khan Institute of Engineering Sciences \& Technology
%C Topi, Swabi, NWFP, Pakistan
%K genetic algorithms, genetic programming
%U http://prr.hec.gov.pk/Thesis/349S.pdf
%X The success of pattern classification system depends on the improvement of its classification stage. The work of thesis has investigated the potential of Genetic
   Programming (GP) search space to optimise the performance of various classification models. In this thesis, two GP approaches are proposed. In the first approach, GP is
   used to optimize the performance of individual classifiers. The performance of linear classifiers and nearest neighbour classifiers is improved during GP evolution to
   develop a high performance numeric classifier. In second approach, component classifiers are trained on the input data and their predictions are extracted. GP search space
   is then used to combine the predictions of component classifiers to develop an optimal composite classifier (OCC). This composite classifier extracts useful information
   from its component classifiers during evolution process. In this way, the decision space of composite classifier is more informative and discriminant. Effectiveness of GP
   combination technique is investigated for four different types of classification models including linear classifiers, support vector machines (SVMs) classifiers,
   statistical classifiers and instance based nearest neighbour classifiers. The successfulness of such composite classifiers is demonstrated by performing various
   experiments, while using Receiver Operating Characteristics (ROC) curve as the performance measure. It is evident from the experimental results that OCC outperforms its
   component classifiers. It attains high margin of improvement at small feature sets. Further, it is concluded that classification models developed by heterogeneous
   combination of classifiers have more promising results than their homogeneous combination. GP optimisation technique automatically caters the selection of suitable
   component classifiers and model selection. Two main objectives are achieved, while using GP optimisation. First, objective achieved is the development of more optimal
   classification models. The second one is the enhancement in the GP search strategy itself.
%8 May
%Z Item Type: Thesis (PhD) ID Code: 2511 Deposited By: Ch Abdulla fayyaz Chattha Last Modified: 28 Jul 2009 21:16

%A Abdul Majid
%A Muhammad Tariq Mahmood
%A Tae-Sun Choi
%T A novel noise-free pixels based impulse noise filtering
%B 17th IEEE International Conference on Image Processing (ICIP 2010)
%D 2010
%P 125--128
%I 
%K genetic algorithms, genetic programming, GP based estimator, impulse noise density, impulse noise filtering, iterative impulse noise removal, iterative process, noise-free
   pixel, filtering theory, image denoising, impulse noise, iterative methods
%X Generally, impulse noise filtering schemes consider all pixels within a large neighbourhood. However, the estimate from all pixels within the neighborhood may not be
   accurate. Moreover, large window may remove edges and fine details. In contrast to this approach, we propose iterative impulse noise removal scheme that emphasises on few
   noise-free pixels within a small neighbourhood. This iterative process continues until all noisy pixels are replaced with the estimated values. To estimate the optimal
   value of noisy pixel, we developed genetic programming (GP) based estimator using noise-free pixels. The estimator is constituent of useful local pixels information.
   Experimental results show that the proposed scheme is capable of removing impulse noise effectively while preserving the fine details. Especially, our approach has shown
   effectiveness against high impulse noise density.
%8 26-29  September
%Z Sch. of Inf. & Mechatron., Gwangju Inst. of Sci. & Technol., Gwangju, South Korea. Also known as \cite5651975

%A William H. Majoros
%T Methods for Computational Gene Prediction
%D 2007
%P 346--348?
%I Cambridge University Press
%K genetic algorithms, genetic programming
%O 10.12
%Z http://www.cup.cam.ac.uk/catalogue/catalogue.asp?isbn=9780521877510&ss=fro

%A Adetokunbo Makanju
%A A. Nur Zincir-Heywood
%A Evangelos E. Milios
%T Adaptabilty of a GP Based IDS on Wireless Networks
%B Third International Conference on Availability, Reliability and Security, ARES 08
%D 2008
%P 310--318
%I 
%K genetic algorithms, genetic programming, GP based IDS, Kismet, Snort-Wireless, WiFi networks, data link layer, intrusion detection system, machine learning, wireless
   networks, learning (artificial intelligence), security of data, wireless LAN
%X Security and Intrusion detection in WiFi networks is currently an active area of research where WiFi specific Data Link layer attacks are an area of focus; particularly
   recent work has focused on producing machine learning based IDSs for these WiFi specific attacks. These proposed machine learning based IDSs come in addition to the already
   deployed signatures which are already in use in conventional intrusion detection systems like Snort-Wireless and Kismet. In this paper, we compare the detection capability
   of Snort-Wireless and a Genetic Programming (GP) based intrusion detector, based on the ability to adapt to modified attacks, ability to adapt to similar unknown attacks
   and infrastructure independent detection. Our results show that the GP based detection system is much more robust against modified attacks compared to Snort-Wireless.
   Moreover, by focusing on the method(s) used in feature preprocessing for presentation to learning algorithms, GP based IDSs can achieve infrastructure independent detection
   and can adapt to similar unknown attacks too. On the other hand, even though Snort-Wireless is an infrastructure independent detector, it cannot adapt to unknown attacks
   even if they are similar to others for which it has signatures on.
%8 March
%Z Also known as \cite4529352

%A Dmitrii E. Makarov
%A Horia Metiu
%T Fitting potential-energy surfaces: A search in the function space by directed genettic programming
%J Journal of Chemical Physics
%V 108
%N 2
%D 1998
%P 590--598
%I 
%K genetic algorithms, genetic programming
%U http://ojps.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&amp;id=JCPSA6000108000002000590000001&amp;jsessionid=1455991010357837219
%X We propose new procedures by which genetic programming can be used to find the best functional form and the best set of parameters to fit the energies and the energy
   derivatives provided by ab initio calculations. Our main contribution is a new procedure, which we call a directed genetic search, which is more efficient and more stable
   than a "traditional" genetic program.
%8 8  January
%Z See also \citemakarov:2000:JPCA

%A Dmitrii E. Makarov
%A Horia Metiu
%T Using Genetic Programming To Solve the Schrodinger Equation
%J Journal of Physical Chemistry A
%V 104
%D 2000
%P 8540--8545
%I 
%K genetic algorithms, genetic programming, DGP, mathematica
%X In a recent paper [Makarov, D. E.; Metiu, H. J. Chem. Phys. 1998, 108, 590], \citemakarov:1999:fpes:sfsdGP we developed a directed genetic programming approach for finding
   the best functional form that fits the energies provided by ab initio calculations. In this paper, we use this approach to find the analytic solutions of the
   time-independent Schrodinger equation. This is achieved by inverting the Schrodinger equation such that the potential is a functional depending on the wave function and the
   energy. A genetic search is then performed for the values of the energy and the analytic form of the wave function that provide the best fit of the given potential on a
   chosen grid. A procedure for finding excited states is discussed. We test our method for a one-dimensional anharmonic well, a double well, and a two-dimensional anharmonic
   oscillator.
%Z http://pubs.acs.org/journals/jpcafh/index.html directed genetic programming (DGP), monte Carlo, "straightforward GP...leads to poor results" p8451 DGP adds form of
   solution? Fset=+,-<*,/ pop=100 G<=250. Ekart well best(?) -1.5576eV, most within 0.5 percent. Even better with Bessel function. Also tried Gaussian. NB "proper choice of
   the grid is important" p852. Asymptotic region dominates tunnelling. "we believe that...more readily find the solution that has the simplest functional form" p8542 (ie the
   lowest energy eigenstate). Excited states. Harmonic oscillator creation operator. problems with second excited state? Hartree approximation (p8544) , separate x and y
   dimensions, use same bell curve for both x and y. x,y back together? Seeded run? Fset now also includes exp First excited state E=2.534.

%A A. Makkeasoyrn
%A Ni-Bin Chang
%A Xiaobing Zhou
%T Short-term Streamflow Forecasting with Global Climate Change Implications - A Comparative Study between Genetic Programming and Neural Network Models
%J Journal of Hydrology
%V 352
%N 3-4
%D 2008
%P 336--354
%I 
%K genetic algorithms, genetic programming, ANN, Streamflow forecasting, Neural network, Global climate change, NEXRAD, Sea surface temperature
%U http://www.sciencedirect.com/science/article/B6V6C-4RRFNK3-2/2/26f7ea5d045a8c5457038f4c4d0b73e5
%X Summary Sustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may
   also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time
   for flood warning, the issues have led us to seek advanced techniques for improving stream flow forecasting on a short-term basis. This study emphasizes the inclusion of
   sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather
   stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial
   intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the
   validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both
   SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day
   forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the stream-flow forecasts. The most forward looking GP-derived models
   can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.

%A Ammarin Makkeasorn
%A Ni-Bin Chang
%A Jiahong Li
%T Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed
%J Journal of Environmental Management
%V 90
%N 2
%D 2009
%P 1069--1080
%I 
%K genetic algorithms, genetic programming, Riparian classification, Soil moisture, RADARSAT-1, LANDSAT, Vegetation index, Ecohydrology
%U http://www.sciencedirect.com/science/article/B6WJ7-4SNGRR7-1/2/952c6978ecce3d3e3e5a40f16f9ad11b
%X Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve
   classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method - the RIparian Classification
   Algorithm (RICAL) - to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort
   to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil
   moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses
   and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These
   spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP
   practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land
   in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum
   likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the
   RICAL algorithm may yield around 90percent accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final
   quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies for the handling of non-point
   source pollution, bird habitat monitoring, and grazing and live stock management in the future.

%A C. C. Maley
%T Four Steps Toward Open-Ended Evolution
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1336--1343
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-049.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Abhinav Malhotra
%A Varun Aggarwal
%T HIER-HEIR: an evolutionary system with hierarchical representation \&\#38; contextual operators applied to fashion design
%B GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
%E Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike
   Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and
   Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and
   Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger
%D 2011
%P 215--216
%I ACM New York, NY, USA
%I SIGEVO
%C Dublin, Ireland
%K genetic algorithms, genetic programming, Real world applications: Poster
%X There has been considerable interest in using evolutionary algorithms based techniques to design creative systems. However, these techniques are either too 'creative' and
   violate design constraints of the domain, or, those catering to a limited search space, but operating within design constraints. Our new evolutionary system 'HIER-HEIR', is
   not only creative(searches a large space effectively), but creates only such designs which are valid with respect to the design domain. Inspired by human design
   methodology, the representation is a hierarchy of components and the variation is contextual acting at all levels of the hierarchy intelligently, facilitating effective
   search in the design space with explicit control over exploitation and exploration. We have explained our technique with the metaphor of automatic design of a fashion dress
   in this paper. The experimental results validate our hypotheses with regard to the system. With regard to previous work, our technique is new both with regard to previously
   published hierarchical systems and those designed for evolving fashion designs.
%8 12-16  July
%Z Also known as \cite2001980 Distributed on CD-ROM at GECCO-2011. ACM Order Number 910112.

%A Muhammad Afzaal Malik
%A Saheeb Ahmed Kayani
%T Verifying experiment for automated design of mechatronic systems using Bond-Graph modelling and simulation and genetic programming
%J International Journal of Computer Applications in Technology
%V 32
%N 3
%D 2008
%P 173--180
%I 
%K genetic algorithms, genetic programming, automated design, unified design, bond graphs, object-oriented modelling, simulation, mechatronics, multi-domain systems,
   mechatronic system design
%X All modern dynamic engineering systems can be characterised as mechatronic systems. The multi-domain nature of a mechatronic system makes it difficult to model using a
   single modelling technique over the whole system as varying sets of system variables are required. Bond-Graphs offer an advanced object-oriented modelling and simulation
   technique. They are domain independent allowing straight forward and efficient model composition, classification and analysis. Bond-Graph model of the mechatronic system
   can be directly simulated on a digital computer using simulation software such as 20-Sim and Modelica graphically or manipulated mathematically to yield state equations
   using a simplified set of power and energy variables. The simulation scheme can be augmented to synthesise designs for mechatronic systems using genetic programming as a
   tool for open-ended search. This research paper presents results of an experiment conducted to verify a unified approach developed by combining Bond-Graph modelling and
   simulation with genetic programming for automated mechatronic system design. A comprehensive review of various aspects of the physical modelling paradigm along with the
   concept and development of automated design and the methodology is also included.
%Z IJCAT

%A Sergey Malinchik
%A Belinda Orme
%A Joseph Rothermich
%A Eric Bonabeau
%T Interactive Exploratory Data Analysis
%B Proceedings of the 2004 IEEE Congress on Evolutionary Computation
%D 2004
%P 1098--1104
%I IEEE Press
%C Portland, Oregon
%K genetic algorithms, genetic programming, Real-world applications
%X We illustrate with two simple examples how Interactive Evolutionary Computation (IEC) can be applied to Exploratory Data Analysis (EDA). IEC is valuable in an EDA context
   because the objective function is by definition either unknown a priori or difficult to formalize. In the first example IEC is used to evolve the "true" metric of attribute
   space. The goal here is to evolve the attribute space distance function until "interesting" features of the data are revealed when a clustering algorithm is applied. In a
   second example, we show how a user can interactively evolve an auditory display of cluster data. In this example, we use IEC with Genetic Programming to evolve a mapping of
   data to sound for sonifying qualities of data clusters.
%8 20-23  June
%Z CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.
%@ 0-7803-8515-2

%A Devayan Mallick
%A Vincent C. S. Lee
%A Yew Soon Ong
%T An empirical study of Genetic Programming generated trading rules in computerized stock trading service system
%B International Conference on Service Systems and Service Management
%D 2008
%P 1--6
%I 
%C Melbourne, Australia
%K genetic algorithms, genetic programming, computerized stock trading service system, financial market, genetic programming based trading rules, statistical analysis, stock
   market, electronic trading, statistical analysis, stock markets
%X Technical analysis is aimed at devising trading rules capable of exploiting short-term fluctuations on the financial markets. The application of genetic programming (GP) as
   a means to automatically generate such trading rules on the stock markets has been studied. Computational results, based on historical pricing and transaction volume data,
   are reported for the thirty component stocks of the Dow Jones Industrial Average index. Statistical evidence shows that for the stocks that were studied, the use of GP
   based trading rules ensures a positive dollar return in all market scenarios. The performance of the GP based trading rules was also evaluated against the performance of
   the popularly used MACD technical indicator. In general, GP based trading rules offer greater returns over the simple buy and hold approach than the MACD trading signal.
%8 30  June  2008-2  July  2008
%Z Also known as \cite4598507

%A Hugh Mallinson
%A Peter Bentley
%T Evolving Fuzzy Rules for Pattern Classification
%B Computational Integration for Modelling, Control and Automation '99
%S Concurrent Systems Engineering Series
%E Masoud Mohammadian
%V 55
%D 1999
%P 184--191
%I IOS Press Amsterdam, The Netherlands
%C Hotel Marriott, Vienna, Austria
%K genetic algorithms, genetic programming, fuzzy classification, control, modelling
%U http://iospress.nl.master.com/texis/master/redir/?u=http%3A//www.iospress.nl/html/9789051994742.php
%X This paper describes the use of a Hybrid Fuzzy-Genetic Programming system to discover patterns in large databases. It does this by evolving a series of variablelength fuzzy
   rules which generalise from a training set of labelled classes. Numerous novel techniques, including the use of genotypes in Genetic Programming, two new genetic crossover
   operators, and the processes of Modal Evolution, Modal Reevolution and Nested Evolutionary Search are described. Experimental results show that the system is able to
   classify data from the Wisconsin Breast Cancer database correctly 95% of the time.
%8 17-19  February
%Z CIMCA'99 http://www.gscit.monash.edu.au/conferences/cimca99/ UCI Wisconsin Breast Cancer
%@ 90-5199-473-7

%A Daniel Malmer
%T Hive: Development of a Language Among Artificial Life Forms
%B Artificial Life at Stanford 1994
%E John R. Koza
%D 1994
%P 99--107
%I Stanford Bookstore
%I Stanford University
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, Finite State Machine, Agents, Communication
%8 June
%Z Bees, Genesys This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at
   Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html
%@ 0-18-182105-2

%A Pekka Malo
%A Pyry-Antti Siitari
%A Ankur Sinha
%T Automated Query Learning with Wikipedia and Genetic Programming
%J CoRR
%V abs/1012.0841
%D 2010
%I 
%K genetic algorithms, genetic programming, Wikipedia, Information retrieval, Genetic programming, Query learning, Automatic indexing, Concept recognition SVM,
   C4.5,coevolution
%U http://arxiv.org/abs/1012.0841
%X Most of the existing information retrieval systems are based on bag of words model and are not equipped with common world knowledge. Work has been done towards improving
   the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the direction of incorporating
   human-and-society level knowledge in the queries. This paper is one of the first attempts where such information is incorporated into the search queries using Wikipedia
   semantics. The paper presents an essential shift from conventional token based queries to concept based queries, leading to an enhanced efficiency of information retrieval
   systems. To efficiently handle the automated query learning problem, we propose Wikipedia-based Evolutionary Semantics (Wiki-ES) framework where concept based queries are
   learnt using a co-evolving evolutionary procedure. Learning concept based queries using an intelligent evolutionary procedure yields significant improvement in performance
   which is shown through an extensive study using Reuters newswire documents. Comparison of the proposed framework is performed with other information retrieval systems.
   Concept based approach has also been implemented on other information retrieval systems to justify the effectiveness of a transition from token based queries to concept
   based queries.
%O arXiv
%Z Inductive Query By Example (IQBE),TREC-11 dataset with Reuters RCV1 corpus, Wiki as alternative to Cyc, ngrams wikifier and a named-entity recognizer (NER).Conditional
   Random Fields (CRF)-based classifier using several individuals, JGAP, java weka, best on "F-score"

%A Andrzej Malolepszy
%A Edward Kacki
%A T Dogdanik
%T Application of genetic programming for the differential diagnosis of acid-base and anion gap disorders
%B Medical Infobahn for Europe
%E Arie Hasman
%D 2000
%I IOS Press
%K genetic algorithms, genetic programming
%Z Also referenced as: Studies in Health Technol Inform. 2000, 77, 388-92. From Book News, Inc. This (Medical Infobahn for Europe) huge collection of more than 250 papers
   represents the 2000 Medical Informatics Europe Congress, held in Hanover, Germany, and hosted by the German Association for Medical Informatics, Biometry, and Epidemiology.
   Medical informatics is an interdisciplinary field of applied research that also involves the use of information technologies in medical research, health economics, and
   health system sciences. A few of the subject areas covered are better and faster documentation, health information systems, modeling and simulation, electronic prescribing,
   workflow, security, robotics, and telemedicine. Indexed by author but not by subject.Book News, Inc.ź, Portland, OR
%@ 1-58603-063-9

%T Bio-Inspired Computing Machines
%E Daniel Mange and Marco Tomassini
%D 1998
%I Presses Polytechniques et Universitaires Romandes
%U http://lslwww.epfl.ch/pages/publications/books/1998_1/contents.html
%X This volume, written by experts in the field, gives a modern, rigorous and unified presentation of the application of biological concepts to the design of novel computing
   machines and algorithms. While science has as its fundamental goal the understanding of Nature, the engineering disciplines attempt to use this knowledge to the ultimate
   benefit of Mankind. Over the past few decades this gap has narrowed to some extent. A growing group of scientists has begun engineering artificial worlds to test and probe
   their theories, while engineers have turned to Nature, seeking inspiration in its workings to construct novel systems. The organization of living beings is a powerful
   source of ideas for computer scientists and engineers. This book studies the construction of machines and algorithms based on natural processes: biological evolution, which
   gives rise to genetic algorithms, cellular development, which leads to self-replicating and self-repairing machines, and the nervous system in living beings, which serves
   as the underlying motivation for artificial learning systems, such as neural networks.
%Z Contents An Introduction to Bio-Inspired Machines - An Introduction to Digital Systems - An Introduction to Cellular Automata - Evolutionary Algorithms and their
   Applications - Programming Cellular Machines by Cellular Programming - Multiplexer-Based Cells - Demultiplexer-Based Cells - Binary Decision Machine-Based Cells -
   Self-Repairing Molecules and Cells - L-hardware: Modeling and Implementing Cellular Development - Using L-systems - Artificial Neural Networks: Algorithms and Hardware
   Implementation - Evolution and Learning in Autonomous Robotic Agents - Bibliography - Index. Reviewed in \citegreenwood:2001:bicm
%@ 2-88074-371-0

%A Jandhyala Seetha Manognya
%A A/P Lipo Wang
%T Gene expression programming for induction of finite transducer
%B 7th International Conference on Information, Communications and Signal Processing, ICICS 2009
%D 2009
%I 
%K genetic algorithms, genetic programming, Gene expression programming, Mealy machine, finite transducer induction, gene expression programming, roulette-wheel sampling,
   finite automata
%X This paper presents an alternative method for solving the problem of finite transducers using gene expression programming (GEP). Each individual in the GEP system
   represents a Mealy machine with outputs for each state. Be means of roulette-wheel sampling, individuals are chosen for the next generation and are put through a series of
   genetic operators which seek to change the mark-up of the individual to better fit the selection environment/ fitness sets. The system was tested with five problems to show
   its effectiveness and success at solving all of those problems.
%8 Decemeber
%Z Also known as \cite5397573

%A Steven Manos
%A Peter J. Bentley
%T Evolving Microstructured Optical Fibres
%B Evolutionary Computation in Practice
%S Studies in Computational Intelligence
%E Tina Yu and David Davis and Cem Baydar and Rajkumar Roy
%V 88
%D 2008
%P 87--124
%I Springer
%K genetic algorithms, genetic programming, embryogeny
%O 5
%Z Part of \citeTinaYu:2008:book

%A Daniel Manrique
%A Fernando Marquez
%A Juan Rios
%A Alfonso Rodriguez-Paton
%T Grammar Based Crossover Operator in Genetic Programming
%B Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial
   Computation, IWINAC 2005, Proceedings, Part II
%S Lecture Notes in Computer Science
%E Jos\'e Mira and Jos\'e R. \'Alvarez
%V 3562
%D 2005
%P 252--261
%I Springer
%C Las Palmas, Canary Islands, Spain
%K genetic algorithms, genetic programming
%X This paper introduces a new crossover operator for the genetic programming (GP)paradigm, the grammar-based crossover (GBX). This operator works with any grammar-guided
   genetic programming system. GBX has three important features: it prevents the growth of tree-based GP individuals (a phenomenon known as code bloat), it provides a
   satisfactory trade-off between the search space exploration and the exploitation capabilities by preserving the context in which subtrees appear in the parent trees and,
   finally, it takes advantage of the main feature of ambiguous grammars, namely, that there is more than one derivation tree for some sentences (solutions). These features
   give GBX a high convergence speed and low probability of getting trapped in local optima, as shown throughout the comparison of the results achieved by GBX with other
   relevant crossover operators in two experiments: a laboratory problem and a real-world task: breast cancer prognosis.
%8 June  15-18
%Z http://www.iwinac.uned.es/iwinac2005/
%@ 3-540-26319-5

%A Daniel Manrique
%A Juan Rios
%A Alfonso Rodriguez-Paton
%T Grammar-Guided Genetic Programming
%B Encyclopedia of Artificial Intelligence
%E Juan R. Rabu\~nal and Julian Dorado and Alejandro Pazos
%D 2009
%P 767--773
%I IGI Global
%K genetic algorithms, genetic programming
%U http://www.igi-global.com/bookstore/titledetails.aspx?TitleId=343
%O 114
%Z 3 Volumes. Inteligencia Artificial, Facultad de Informatica, UPM, Spain

%A Johanna Mansilla
%A Shingo Mabu
%A Lu Yu
%A Kotaro Hirasawa
%T Adaptive controller for double-deck elevator system using genetic network programming
%B ICCAS-SICE, 2009
%D 2009
%P 3870--3873
%I 
%K genetic algorithms, genetic programming, adaptive controller, double-deck elevator system, genetic network programming, transportation capacity, adaptive control, lifts
%U http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5332931
%X In this paper, an improved approach is proposed based on an updating strategy using Genetic Network Programming (GNP) for the controller of Double-Deck Elevator System
   (DDES). Since the elevator controller has to deal with constant changes of its environment, our approach is proposed to deal with better the environment changes, resulting
   in a reduction of the waiting time and increasing the transportation capacity. This updating looks forward to contributing to the efficient adjustment of the system
   periodically according to the gained system information. The performance of the proposed method is evaluated by comparison with the conventional GNP method, which does not
   update the controller. By this evaluation, the enhancement of our model is confirmed.
%8 August
%Z Also known as \cite5332931

%A Steven M. Manson
%T Agent-based modeling and genetic programming for modeling land change in the Southern Yucatan Peninsular Region of Mexico
%J Agriculture, Ecosystems and Environment
%V 111
%N 1-4
%D 2005
%P 47--62
%I 
%K genetic algorithms, genetic programming, Agent-based model, Genetic program, Land-use and land-cover change, Multicriteria evaluation, Symbolic regression
%X Land-use and land-cover change research increasingly takes the form of integrated land-change science, the explicit joining of ecological, social and information sciences.
   Traditional interdisciplinary methods are buttressed by new ones stemming from computational intelligence research and the complexity sciences. Several of these genetic
   programming, cellular modelling and agent-based modeling are applied to land change in the Southern Yucatan Peninsular Region (SYPR) of Mexico through the SYPR Integrated
   Assessment (SYPRIA). This work illustrates how computational intelligence techniques, such as genetic programming, can be used to model decision making in the context of
   human environment relationships. This application also contributes to methodological innovations in multicriteria evaluation and modeling of coupled human environment
   systems. This effort also demonstrates the importance of considering both social and environmental drivers of land change, particularly with respect to the decision making
   of change agents within the context of key socioeconomic and political drivers, particularly as channelled through market institutions and land tenure, and ecological
   factors, especially characteristics of land-use and land-cover such as state, history and fragmentation. SYPRIA demonstrates the utility of modelling methods based in
   computational intelligence and the complexity sciences in helping understand the decision making of land-change agents as a function of both social and environment drivers.
%8 1  Decemeber

%A Steven Manson
%T Land use in the southern Yucatan peninsular region of Mexico: Scenarios of population and institutional change
%J Computers, Environment and Urban Systems
%V 30
%N 3
%D 2006
%P 230--253
%I 
%K genetic algorithms, genetic programming, Agent-based model, Genetic program, Land-use and land-cover change, Multicriteria evaluation, Symbolic regression
%X Land-use and land-cover change, human activity that results in altered land-use systems and surface features, defines the environmental and socioeconomic sustainability of
   communities around the globe. It is a key response to global environmental change in addition to being both a key cause and medium of this change. This article examines an
   application of the Southern Yucatan Peninsular Region Integrated Assessment (SYPRIA), a scenario-based spatially explicit model designed to examine and project land use in
   Mexico. SYPRIA combines Geographic Information Systems (GIS) with agent-based modelling, cellular modeling, and genetic programming. The application examined here explores
   the effects on land-use and land-cover projections of scenarios that rely on varying assumptions pertaining to population growth, land-use trends, role of agrarian
   technology, and effects of resource institutions. This work also highlights the importance of understanding the many factors influencing land use, particularly population,
   different production systems, and the contextual nature of resource institutions in determining the nature of land use.
%8 May

%A F. Marcelloni
%A M. Vecchio
%T A Multi-objective Evolutionary Approach to Data Compression in Wireless Sensor Networks
%B Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on
%D 2009
%P 402--407
%I 
%K genetic algorithms, data compression, data reception, data transmission, differential pulse code modulation scheme, energy efficiency, multiobjective evolutionary
   algorithm, radio communication, sensor nodes, wireless sensor networks, data communication, data compression, pulse code modulation, wireless sensor networks
%8 30 2009-  Decemeber  2
%Z Not on GP. Also known as \cite5364892

%A Bruno Marchesi
%A Alvaro Luiz Stelle
%A Heitor Silverio Lopes
%T Detection of Epileptic Events using Genetic Programming
%B Proceedings of the 19th Annual International Conference of the IEEE and Engineering in Medicine and Biology Society
%V 3
%D 1997
%P 1198--1201
%I 
%I IEEE
%C Chicago, IL. USA
%K genetic algorithms, genetic programming, signal processing, EEG, 3 Hz, Darwinian survival and reproduction, EEG signals, automatic detection, complexes recognition,
   epileptic events detection, genetic algorithm, ictal period, pattern recognition, spike-and-slow-wave complexes, training features, typical absences, visually classified
   frames, electroencephalography, evolutionary computation, learning (artificial intelligence), medical expert systems, medical signal processing, pattern classification
%X This paper presents a method using genetic programming for automatic detection of 3 Hz spike-and-slow- wave complexes, that are a characteristic of typical absences, in
   electroencephalogram (EEG) signals. Training features are extracted from 1s EEG frames, randomly chosen from pre-recorded files. The frames are visually classified as
   spike-and-slow-wave complexes (SASWC) or non-spike- and-slow-wave complexes (NSASWC). Genetic programming techniques are then applied to these data to build a program
   capable of recognising such complexes.
%8 October  30 -  November  2
%@ 0-7803-4262-3

%A Elena Marchiori
%A Claudio Rossi
%T A Flipping Genetic Algorithm for Hard 3-SAT Problems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 393--400
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms and classifier systems
%U http://www.cs.vu.nl/~elena/ga-858.ps.gz
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jamie Marconi
%A James A. Foster
%T A Hard Problem for Genetic Algorithms: Finding Cliques in Keller Graphs
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%D 1998
%P 650--655
%I IEEE Press
%C Anchorage, Alaska, USA
%K genetic algorithms, genetic programming, Keller conjecture, Keller graphs, maximum clique, hardness, complexity
%X We present evidence that finding the maximum clique in Keller graphs is an example of a family of problems which are both natural and inherently difficult for genetic
   algorithms. Specifically, we employ a hybrid genetic algorithm to find the largest clique in Keller graphs. We present theoretical reasons why this problem is likely to be
   particularly hard for this family of graphs. Our results confirm this suspicion. We then discuss several characteristics of this graph family which confound genetic
   algorithms: its uniformity, edge density and small diameter.
%8 5-9  May
%Z ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
%@ 0-7803-4869-9

%A Andrew J. Marek
%A William D. Smart
%A Martin C. Martin
%T Learning Visual Feature Detectors for Obstacle Avoidance Using Genetic Programming
%B Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002)
%E Erick Cant\'u-Paz
%D 2002
%P 330--336
%I AAAI 445 Burgess Drive, Menlo Park, CA 94025
%C New York, NY
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/563994.html
%8 July
%Z Late Breaking Papers, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming
   Conference (GP-2002) part of cantu-paz:2002:GECCO:lbp Suggests negative impact of seed in initial population p335

%A Andrew J. Marek
%T Learning Feature Detectors Using Genetic Programming With Multiple Sensors
%R M.S. Thesis Master of Science
%N WUCSE-2004-22
%D 2004
%I 
%I Sever Institute, Dept. of Computer Science and Engineering, Washington University in St. Louis
%C Saint Louis, Missouri, USA
%K genetic algorithms, genetic programming
%U http://cse.wustl.edu/Research/Lists/Technical%20Reports/Attachments/594/345_thesis-main.pdf
%X In this thesis, we describe the use of Genetic Programming (GP) to learn obstacle detectors to be used for obstacle avoidance on a mobile robot. The first group of
   experiments focus on learning visual feature detectors for this task. We provide experimental results across a number of different environments, each with different
   characteristics, and draw conclusions about the performance of the learned feature detector and the training data used to learn such detectors. We also explore the utility
   of seeding the initial population with previously evolved individuals and subtrees, and discuss the performance of the resulting individuals. We then include sensory data
   from a laser range-finder and a camera and discuss the performance of resulting individuals as we use just laser data, just image data, and both in combination.
%8 May
%Z Drew. Robot computer vision, Laser Range-finder, Open Beagle

%A Peter Marenbach
%A Kurt Dirk Bettenhausen
%A Bernd Cuno
%T Selbstorganisierende Generierung strukturierter Prozemodelle
%J at -- Automatisierungstechnik
%V 43
%N 6
%D 1995
%P 277--288
%I 
%K genetic algorithms, genetic programming, Selbstorganisierende Modellbildung
%Z In German

%A Peter Marenbach
%T Status und Perspektiven der strukturierten Modellbildung mit Hilfe Genetischer Algorithmen
%R Technical Report
%D 1995
%I 
%I FG Regelsystemtheorie \& Robotik, TH Darmstadt
%C Landgraf-Georg-Str.~4, D-64283 Darmstadt, Germany
%K genetic algorithms, genetic programming, Selbstorganisierende Modellbildung
%U http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/trsmog9501.ps.gz broken
%Z In German

%A Peter Marenbach
%A Kurt D. Bettenhausen
%A Stephan Freyer
%T Signal Path Oriented Approach for Generation of Dynamic Process Models
%B Genetic Programming 1996: Proceedings of the First Annual Conference
%E John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo
%D 1996
%P 327--332
%I MIT Press
%C Stanford University, CA, USA
%K genetic algorithms, genetic programming, process engineering, modelling, SMOG
%U http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_96_11.ps.gz
%X This paper discusses our tool for automatic generation of structured models for complex dynamic processes by means of genetic programming. In contrast to other techniques
   which use genetic programming to find an appropriate arithmetic expression in order to describe the input-output behaviour of a process, this tool is based on a block
   oriented approach with a transparent description of signal paths. A short survey on other techniques for computer based system identification is given and the basic concept
   of SMOG (Structured MOdel Generator) is described. Furthermore latest extensions of the system are presented in detail, including automatic defined sub-models and
   qualitative fitness criteria.
%8 28--31  July
%Z GP-96 onject oriented GP OOGP

%A P. Marenbach
%A M. Brown
%T Evolutionary versus inductive construction of neurofuzzy systems for bioprocess modelling
%B Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA
%E Ali Zalzala
%D 1997
%I Institution of Electrical Engineers Savoy Place, London WC2R 0BL, UK
%C University of Strathclyde, Glasgow, UK
%K genetic algorithms, genetic programming
%U http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=681045
%X The control and optimization of biotechnological processes is a complex task of industrial relevance, due to the growing importance attached to biotechnology. Therefore,
   there is an increasing use of intelligent data analysis methods for the development and optimization of bioprocess modelling and control. Since a clear understanding of the
   underlying physics does not exist, nonlinear learning systems, which can accurately model exemplar data sets and explain their behaviour to the designer, are an attractive
   approach. This paper investigates applying neurofuzzy construction algorithms to this problem and in particular compares a Genetic Programming structuring approach with a
   more conventional forwards inductive learning-type algorithm. It is shown that for simple problems, the inductive learning technique generally outperforms the Genetic
   Programming, although for large complex problems, the latter may prove beneficial.
%8 1-4  September
%Z GALESIA'97
%@ 0-85296-693-8

%A Peter Marenbach
%T Using Prior Knowledge and Obtaining Process Insight in Data Based Modelling of Bioprocesses
%J System Analysis Modelling Simulation
%V 31
%D 1998
%P 39--59
%I 
%K genetic algorithms, genetic programming, biotechnology, bioprocesses, data based modelling, SMOG
%U http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/publications.html#SAMS98
%X In biotechnology, as is many other fields of technology, the development of a appropriate process model is one of the most important engineering tasks. Data driven
   modelling becomes an attractive approach whenever analytical modelling is difficult or too time consuming. Disadvantages of the often used artificial neural networks are
   their missing transparency and the difficulty to integrate prior knowledge. The paper at hand gives an overview of several common modelling techniques with focus on their
   application to bioprocesses and presents a novel modelling technique that uses genetic programming for the construction and refinement of transparent structured models.
%O Overseas Publishers association
%Z Reprints available from P. Marenbach.

%A Peter Marenbach
%A Kurt D. Bettenhausen
%A Stephan Freyer
%A Ulrich Nieken
%A Hans Rettenmaier
%T Data-driven Structured Modelling of a Biotechnological Fed-batch Fermentation by Means of Genetic Programming
%J Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
%V 211
%N 5
%D 1997
%P 325--332
%I 
%K genetic algorithms, genetic programming, biotechnology, modelling, system identification, fermentation processes, SMOG
%U http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/publications.html#MEP97
%X This paper describes an approach for data-driven generation of structured models of complex and unknown processes by means of genetic programming. The basic approach which
   is used to generate and to modify symbolic model descriptions represented as block diagrams is introduced and an application for modelling of an industrial biotechnological
   fed-batch fermentation process is presented.
%8 1  August
%Z This article is an extended version of the conference paper presented at GALESIA '95 (see \citebettenhausen:1995:sombbffG ). It presents a view more details and is, since
   it is extended, probably easier to understand. Reprints available from P. Marenbach

%A Peter Marenbach
%A Stephan Freyer
%T Generierung von Modellen biotechnologischer Prozesse
%B Industrielle Anwendung Evolution\"arer Algorithmen
%E S. Hafner
%D 1998
%P 91--102
%I R.\ Oldenbourg Verlag
%K genetic algorithms, genetic programming, bioprocess, modelling, SMOG
%U http://www.rt.e-technik.tu-darmstadt.de/LIT
%Z In German

%A Peter Marenbach
%T Rechnergest\"utzte Methoden zur interaktiven Modellierung biotechnologischer Prozesse
%R Ph.D. Thesis Dissertation, Berichte aus der Automatisierungstechnik
%D 1999
%I Shaker Verlag
%I TU Darmstadt
%C Aachen, Germany
%K genetic algorithms, genetic programming, Automatisierungstechnik, Modellbildung, Evolutionare Algorithmen, Biotechnologie, Neuro-Fuzzy-Systeme, datengetriebene
   Modellbildung
%U http://www.shaker.de/Online-Gesamtkatalog/Details.idc?ISBN=3-8265-6574-6
%8 October
%Z In German
%@ 3-8265-6574-6

%A Steve Margetts
%A Antonia J. Jones
%T An Adaptive Mapping for Developmental Genetic Programming
%B Genetic Programming, Proceedings of EuroGP'2001
%S LNCS
%E Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon
%V 2038
%D 2001
%P 97--107
%I Springer-Verlag Berlin
%I EvoNET
%C Lake Como, Italy
%K genetic algorithms, genetic programming, Developmental Genetic Programming, Adaptive Genotype to Phenotype Mappings, MAX Problem
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=97
%X In this article we introduce a general framework for constructing an adaptive genotype-to-phenotype mapping, and apply it to developmental genetic programming. In this
   preliminary investigation, we run a series of comparative experiments on a simple test problem. Our results show that the adaptive algorithm is able to outperform its
   non-adaptive counterpart.
%8 18-20  April
%Z EuroGP'2001, part of \citemiller:2001:gp
%@ 3-540-41899-7

%A Stephen Margetts
%T Adaptive Genotype to Phenotype Mappings for Evolutionary Algorithms
%R Ph.D. Thesis
%D 2001
%I 
%I Department of Computer Science, Cardiff University
%K genetic algorithms, genetic programming
%U http://www.cs.cf.ac.uk/user/Antonia.J.Jones/Theses/SMargettsThesis.pdf
%X This thesis investigates the notion of an adaptive genotype to phenotype mapping in an evolutionary algorithm. We start by defining an abstract framework for evolutionary
   search which highlights the similarities and differences between evolutionary algorithms. Studying this framework leads us to the idea that an evolutionary algorithm can be
   specified without defining the structures which make up its genotype and phenotype. Such algorithms are useful as they can be applied to many different problem domains with
   very little modification. To compare and test this type of algorithm, we construct an abstract problem generator which can be used to create test problems for a wide
   variety of phenotypes. To help us compare different evolutionary algorithms, we also develop a number of statistics which enable us to efficiently extract useful
   information from a running evolutionary algorithm. We then use the abstract framework to identify an interesting possibility for an adaptive evolutionary algorithm: an
   algorithm which has an adaptive mapping function from genotype to phenotype. We develop a general framework for this concept which involves the co-evolution of a population
   of mappings with a population of structures to which the mappings are applied. To test this idea, we implement an adaptive genetic algorithm and an adaptive genetic
   programming system. We evaluate these adaptive algorithms by running comparative experiments against several standard evolutionary algorithms, using both artificially
   generated and real world problems. Although the improvements are perhaps are not as impressive as we might have hoped, we are able to show that our adaptive algorithms are
   at least as effective as their non-adaptive counterparts.
%8 May
%Z 1.7 Contributions of this Work This thesis makes five main contributions: 1. Showing that we can construct generic evolutionary algorithms (chapters 3 and 6). Such
   algorithms are specified without identifying the structures to be used as genotypes and phenotypes, and so can be used in a wide range of problem domains. 2. Demonstrating
   that the mapping between the genotype and phenotype affects performance of an evolutionary algorithm (chapter 6). We develop the notion of a phlegmatic genotype to
   phenotype mapping, which constrains the mapping such that changes to a genotype generate similar changes to its corresponding phenotype. This minimises the impact of the
   mapping on the performance of the algorithm. 3. Motivating and investigating evolutionary algorithms with adaptive genotype to phenotype mappings (chapters 6 and 7). We
   develop a generic model for constructing evolutionary algorithms with adaptive genotype to phenotype mappings. We demonstrate this model using genetic algorithms for
   real-valued function optimisation, and with developmental genetic programming. 4. Developing a framework for the generation of optimisation problems (chapter 4). This
   framework is easily adapted to wide variety of common representations such as lists, strings, trees and graphs. The user of the problem generator specifies the position and
   fitness of the optima in the search landscape, and so can control the difficulty of the problem. In addition, as the optima are known explicitly, a solution can be assessed
   for quality directly. 5. Developing population statistics based on near neighbour distances (chapter 5). By calculating a small number of near neighbours of an evolving
   population, we can measure how the population is distributed in the search space. This allows us to determine the diversity of a population directly, instead of using
   indirect measures based on fitness. p194 chaotic time series: The Henon Map p202 Huffman encoding p218 Future Work p219 Problem Generation for Genetic Programming p221
   Phlegmatic Mappings for Genetic Programming

%A M. H. Marghny
%A I. E. El-Semman
%T Exact Logical Classification Rules with Gene Expression Programming; Microarray Case Study
%B CGST International Conference on Artificial Intelligence and Machine Learning (AIML-05)
%E H. Elmahdy
%D 2005
%P PID: P1120535113
%I 
%C Cairo, Egypt
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.icgst.com/AIML05/papers/P1120535113.pdf
%8 19-21  Decemeber
%Z http://www.icgst.com/AIML05/conference/index.html aiml2005@icgst.com

%A M. H. Marghny
%A I. E. El-Semman
%T Exact Fuzzy Classification Rules with Gene Expression Programming
%B CGST International Conference on Artificial Intelligence and Machine Learning (AIML-05)
%E H. Elmahdy
%D 2005
%P PID: P1120535114
%I 
%C Cairo, Egypt
%K genetic algorithms, genetic programming, Gene Expression Programming
%U http://www.icgst.com/AIML05/papers/P1120535114.pdf
%8 19-21  Decemeber
%Z http://www.icgst.com/AIML05/conference/index.html aiml2005@icgst.com

%A Carlos E. Mariano
%A Eduardo Morales M.
%T MOAQ an Ant-Q Algorithm for Multiple Objective Optimization Problems
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 894--901
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K evolution strategies and evolutionary programming
%U http://dns1.mor.itesm.mx/~emorales/Papers/gecco99.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Jesus Marin
%A Ricard V. Sole
%T Evolutionary Optimization Through Extinction Dynamics
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1344--1349
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K artificial life, adaptive behavior and agents
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-036.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Simone Marini
%A Alessandra Conversi
%T Understanding zooplankton long term variability through genetic programming
%B 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012
%S LNCS
%E Mario Giacobini and Leonardo Vanneschi and William S. Bush
%V 7246
%D 2012
%P 50--61
%I Springer Verlag
%I EvoStar
%C Malaga, Spain
%K genetic algorithms, genetic programming, Ecological Modelling, Plankton Dynamics, Climate Change, Series
%X Zooplankton are considered good indicators for understanding how oceans are affected by climate change. While climate influence on zooplankton abundance variability is
   currently accepted, its mechanisms are not understood, and prediction is not yet possible. We use Genetic Programming approach to identify which environmental variables,
   and at which extent, can be used to express zooplankton abundance dynamics. The zooplankton copepod long term (since 1988) time series from the L4 station in the Western
   English Channel, has been used as test case together with local environmental parameters and large scale climate indexes. The performed simulations identify a set of
   relevant ecological drivers and highlight the non linear dynamics of the Copepod variability. These results indicate GP to be a promising approach for understanding the
   long term variability of marine populations.
%8 11-13  April
%Z Plymouth, Devon. Part of \citeGiacobini:2012:EvoBio EvoBio'2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoMusArt2012 and EvoApplications2012

%A Sheri Markose
%A Edward Tsang
%A Hakan Er
%A Abdel Salhi
%T Evolutionary Arbitrage For FTSE-100 Index Options and Futures
%B Proceedings of the 2001 Congress on Evolutionary Computation CEC2001
%D 2001
%P 275--282
%I IEEE Press 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA
%I IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)
%C COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea
%K genetic algorithms, genetic programming, FGP, Machine Discovery, Arbitrage, Options, Futures
%U http://privatewww.essex.ac.uk/~scher/eddieProj/TsangCEE2001.doc
%X The objective in this paper is to develop and implement FGP-2 (Financial Genetic Programming) on intra daily tick data for stock index options and futures arbitrage in a
   manner that is suitable for online trading when windows of profitable arbitrage opportunities exist for short periods from one to ten minutes. Our benchmark for FGP-2 is
   the textbook rule for detecting arbitrage profits. This rule has the drawback that it awaits a contemporaneous profitable signal to implement an arbitrage in the same
   direction. A novel methodology of randomised sampling is used to train FGP-2 to pick up the fundamental arbitrage patterns. Care is taken to fine tune weights in the
   fitness function to enhance performance. As arbitrage opportunities are few, missed opportunities can be as costly as wrong recommendations to trade. Unlike conventional
   genetic programs, FGP-2 has a constraint satisfaction feature supplementing the fitness function that enables the user to train the FGP to specify a minimum and a maximum
   number of profitable arbitrage opportunities that are being sought. Historical sample data on arbitrage opportunities enables the user to set these minimum and maximum
   bounds. Good FGP rules for arbitrage are found to make a 3-fold improvement in profitability over the textbook rule. This application demonstrates the success of FGP-2 in
   its interactive capacity that allows experts to channel their knowledge into machine discovery
%8 27-30  May
%Z CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. IEEE Catalog Number = 01TH8546C, Library of Congress Number =
%@ 0-7803-6658-1

%A Sheri M. Markose
%T The new evolutionary computational paradigm of complex adaptive systems. Challenges and prospects for economics and finance
%R Discussion paper series 532
%D 2001
%I 
%I Department of Economics, University of Essex
%K genetic algorithms, genetic programming
%O Forthcoming in Kluwer series on Computational Finance, Jan 2002
%8 July

%A Sheri Markose
%A Edward Tsang
%A Hakan Er
%T EDDIE for Stock Index Options and Futures Arbitrage
%B Genetic Algorithms and Genetic Programming in Computational Finance
%E Shu-Heng Chen
%D 2002
%P 281--308
%I Kluwer Academic Press
%K genetic algorithms, genetic programming
%O 14
%Z part of \citechen:2002:gagpcf Also known as: Evolutionary Decision Trees in FTSE-100 Index Options and Futures Arbitrage
%@ 0-7923-7601-3

%A Sheri M. Markose
%T The New Evolutionary Computational Paradigm of Complex Adaptive Systems: Challenges and Prospects for Economics and Finance
%B Genetic Algorithms and Genetic Programming in Computational Finance
%E Shu-Heng Chen
%D 2002
%P 443--484
%I Kluwer Academic Press
%K genetic algorithms, genetic programming
%U http://www.essex.ac.uk/economics/discussion-papers/papers-text/dp532.pdf
%O 21
%Z part of \citechen:2002:gagpcf
%@ 0-7923-7601-3

%A Robert E. Marks
%A David F. Midgley
%A Lee G. Cooper
%A G. M. Shiraz
%T Coevolution with the Genetic Algorithm: Repeated Differentiated Oligopolies
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 2
%D 1999
%P 1609--1615
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K real world applications
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-766.ps
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Robert E. Marmelstein
%A Gary B. Lamont
%T Pattern Classification using a Hybrid Genetic Program Decision Tree Approach
%B Genetic Programming 1998: Proceedings of the Third Annual Conference
%E John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and
   Rick Riolo
%D 1998
%P 223--231
%I Morgan Kaufmann San Francisco, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://en.afit.af.mil/hpc/students/rmarmels/gp98.ps.gz broken
%8 22-25  July
%Z GP-98. Pima Indians, Wisconsin Breast Cancer, SCUD missile FLIR
%@ 1-55860-548-7

%A Robert E. Marmelstein
%T GRaCCE: A Genetic Environment for Data Mining
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/103989.html
%X Data mining is the automated search for interesting and useful relationships between attributes in databases. In this regard, the rules used by classifiers are inherently
   interesting because they distinguish between similar looking data of differing class. In this paper, we introduce the Genetic Rule and Classifier Construction Environment
   (GRaCCE) as a means for extracting classification rules from data. GRaCCE uses a multi-stage, Genetic Algorithm (GA) based approach to first reduce the...
%8 22-25  July
%Z GP-98LB, GP-98PhD Student Workshop

%A Robert E. Marmelstein
%A Gary B. Lamont
%T Evolving Compact Decision Rule Sets
%B Late Breaking Papers at the Genetic Programming 1998 Conference
%E John R. Koza
%D 1998
%I Stanford University Bookstore Stanford, CA, USA
%C University of Wisconsin, Madison, Wisconsin, USA
%K genetic algorithms, genetic programming, GRaCCE
%U http://en.afit.af.mil/hpc/students/rmarmels/gp98pp.ps.gz
%8 22-25  July
%Z GP-98LB

%A Robert Evan Marmelstein
%T Evolving Compact Decision Rule Sets
%R Ph.D. Thesis
%D 1999
%I 
%I Faculty of the Graduate School of Engineering of the Air Force Institute of Technology Air University
%K genetic algorithms, genetic programming, GRaCCE, Matlab
%U ftp://math.chtf.stuba.sk/pub/vlado/thesis_Marmelstein/thesis_Marmelstein.ps.gz
%X With the increased proliferation of computing equipment, there has been a corresponding explosion in the number and size of databases. Although a great deal of time and
   effort is spent building and maintaining these databases, it is nonetheless rare that this valuable resource is exploited to its fullest. The principle reason for this
   paradox is that many organizations lack the insight and/or expertise to effectively translate this information into usable knowledge. While data mining technology holds the
   promise of automatically extracting useful patterns (such as decision rules) from data, this potential has yet to be realized. One of the major technical impediments is
   that the current generation of data mining tools produce decision rule sets that are very accurate, but extremely complex and difficult to interpret. As a result, there is
   a clear need for methods that yield decision rule sets that are both accurate and compact. The development of the Genetic Rule and Classifier Construction Environment
   (GRaCCE) is proposed as an alternative to existing decision rule induction (DRI) algorithms. GRaCCE is a multi-phase algorithm which harnesses the power of evolutionary
   search to mine classification rules from data. These rules are based on piece-wise linear estimates of the Bayes decision boundary within a winnowed subset of the data.
   Once a sufficient set of these hyper-planes are generated, a genetic algorithm (GA) based "0/1" search is performed to locate combinations of them that enclose class
   homogeneous regions of the data. It is shown that this approach enables GRaCCE to produce rule sets significantly more compact than those of other DRI methods while
   achieving a comparable level of accuracy. Since the principle of Occam's razor tells us to always prefer the simplest model that its the data, the rules found by GRaCCE are
   of greater utility than those identified by existing methods.
%8 June
%Z AFIT/DS/ENG/99-05 Approved for public release; distribution unlimited Appendix B. GRaCCE User's Guide

%A Robert E. Marmelstein
%T Evolving Compact Decision Rule Sets
%D 1999
%I Storming Media
%C USA
%K genetic algorithms, genetic programming
%U http://www.brightsurf.com/brightsurf/books/1423544757/Evolving_Compact_Decision_Rule_Sets.html
%X This is a AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON Air Force Base OH report procured by the Pentagon and made available for public release. It has been reproduced
   in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is
   A932463. The abstract provided by the Pentagon follows: While data mining technology holds the promise of automatically extracting useful patterns (such as decision rules)
   from data, this potential has yet to be realized. One of the major technical impediments is that the current generation of data mining tools produce decision rule sets that
   are very accurate, but extremely complex and difficult to interpret. As a result, there is a clear need for methods that yield decision rule sets that are both accurate and
   compact. The development of the Genetic Rule and Classifier Construction Environment (GRaCCE) is proposed as an alternative to existing decision rule induction (DRI)
   algorithms. GRaCCE is a multi-phase algorithm which harnesses the power of evolutionary search to mine classification rules from data. These rules are based on piece-wise
   linear estimates of the Bayes decision boundary within a winnowed subset of the data. Once a sufficient set of these hyper- planes are generated, a genetic algorithm (GA)
   based "0/1" search is performed to locate combinations of them that enclose class homogeneous regions of the data. It is shown that this approach enables GRaCCE to produce
   rule sets significantly more compact than those of other DRI methods while achieving a comparable level of accuracy. Since the principle of Occam's razor tells us to always
   prefer the simplest model that fits the data, the rules found by GRaCCE are of greater use than those identified by existing methods.
%Z Spiral-bound ?
%@ 1423544757

%A John Paul Marney
%A Heather F. E. Tarbert
%T Why do simulation? Towards a working epistemology for practitioners of the dark arts
%J Journal of Artificial Societies and Social Simulation
%V 3
%N 3
%D 2000
%I 
%K genetic algorithms, genetic programming, reciprocal altruism, group living, segmentation
%U http://jasss.soc.surrey.ac.uk/3/4/4.html
%X The purpose of this paper is to argue for clarity of methodology in social science simulation. Simulation is now at a stage in the social sciences where it is important to
   be clear why simulation should be used and what it is intended to achieve. The paper goes on to discuss a particularly important source of opposition to simulation in the
   social sciences which arises from perceived threats to the orthodox hard-core. This is illustrated by way of a couple of case studies. The paper then goes on to discuss
   defences to standard criticisms of simulation and the various positive reasons for using simulation in preference to other methods of theorising in particular situations.
%Z GP mentioned as an example

%A John Paul Marney
%A Heather F. E. Tarbert
%A Colin Fyfe
%T Technical Trading versus Market Efficiency-A Genetic Programming Approach
%B Computing in Economics and Finance
%D 2000
%I 
%C Universitat Pompeu Fabra, Barcelona, Spain
%K genetic algorithms, genetic programming
%X In this paper genetic programming is used to investigate a number of long time series of price data for a stock exchange quoted share, in order to discern whether there are
   any patterns in the data which could be used for technical trading purposes. This extends the work done by the authors in a previous paper (Fyfe et al. 1999) which
   suggested that, although it was possible to find a rule which did outperform simple buy and hold, there were insufficient grounds for the rejection of the efficient market
   hypothesis. The purpose of the present paper is to investigate the robustness and generalisability of the conclusion reached by Fyfe et. al.
%8 6-8  July
%Z http://enginy.upf.es/SCE/index2.html http://ideas.repec.org/p/sce/scecf0/169.html

%A John Paul Marney
%A D. Miller
%A Colin Fyfe
%A Heather F. E. Tarbert
%T Risk Adjusted Returns to Technical Trading Rules: a Genetic Programming Approach
%B 7th International Conference of Society of Computational Economics
%D 2001
%I 
%I Society for Computational Economics
%C Yale
%K genetic algorithms, genetic programming
%X Using the main six trading currencies, Neely et al. (1996, 1997) find strong evidence of economically significant out-of-sample excess returns to technical trading rules
   identified by their genetic program. In Allen and Karjaleinen (1999) a genetic algorithm is used to find technical trading rules for the S&P index. Compared to a simple
   buy-and-hold strategy, these trading rules lead to positive excess returns which are statistically and economically significant. In Fyfe et. al. (1999), a GP is used to
   discover a successful buy rule. This discovery, as such, however, was not really a refutation of the EMH, as it was really a form of timing specific buy and hold, which was
   triggered only once. Nevertheless, the return is superior to buy and hold. Using the S&P 500 index, Neely (2001) finds no evidence that technical trading rules identified
   by a GP significantly outperform buy-and-hold on a risk-adjusted basis. For the case of intraday trading on the forex market, Neely and Weller (2001) find no evidence of
   excess returns to trading rules derived from a GP and an optimised linear forecasting model. Indeed Neely (2001) observes that a number of studies have generally evaluated
   raw excess returns rather than explicitly risk-adjusted returns, leaving unclear the implications of their work for the efficient markets hypothesis' (2001, p.1). On the
   other hand, Neely et al. (1996, 1997) did calculate betas associated with foreign currency portfolio holdings, and did not find evidence of excessive risk bearing. Brown,
   Geotzman and Kumar (1998) and Bessember and Chan (1998) can also be cited in favour of the hypothesis of superior risk-adjusted returns from technical trading signals.
   Marney et al. (2000) looked again at their 1999 findings by, amongst other things, adjusting for risk. It was found that although there were other rules which apparently
   performed well by being very active in the market, the impressive returns to these rules turn out on closer inspection to be illusory, as risk adjusted returns did not
   compare well with simple buy and hold. Nevertheless, paradoxically, we did find a useful role for technical trading. It is possible to substantially improve on buy and hold
   by timing it right. Hence our argument is that it is worth analysing the market to find a good intervention point. Purpose and method of the investigation Given that very
   little work has been done on generating technical trading rules which produce excess risk-adjusted profits, and given that the empirical evidence is somewhat ambiguous,
   there is clearly considerable scope for additional work in this area. What we propose to do then is to re-examine our previous findings, this time within a more rigorous
   framework which makes use of a wider data set, more extensive use of techniques of risk adjustment, and more demanding assessment of the robustness of the result with
   respect to GP representation. 1. Hypotheses Can the GP generate technical trading rules which will generate risk-adjusted excess returns out of sample? Secondly, the is
   there any further evidence for 'timing-specific' buy and hold. Thirdly, are there any technical trading rules which generalise across data sets or time-periods? 2. Data Set
   Our data set is drawn from long time series for 5 US shares from a disparate set of industrial sectors and also the S&P 500. 3. Risk adjustment In this study we look at a
   variety of risk measures including Betas, Sharpe ratios and the X* statistic. 4. The GP - As in Marney et al. (2000) we consider how robust our conclusion is with respect
   to the GP method used.
%8 28-29  June
%Z http://www.econ.yale.edu/sce01/confpage.html 22 aug 2004 http://ideas.repec.org/p/sce/scecf1/147.html CEF 2001

%A Manuel Marques-Pita
%A Luis M. Rocha
%T Schema Redescription in Cellular Automata: Revisiting Emergence in Complex Systems
%B The 2011 IEEE Symposium on Artificial Life
%E Chrystopher Nehaniv and Terry Bossomaier and Hiroki Sayama
%D 2011
%P 233--240
%I 
%I IEEE Computational Intelligence Society
%C Paris, France
%K genetic algorithms, genetic programming, nonlinear sciences, cellular automata and lattice Gases, Artificial Intelligence, Formal Languages and Automata, Neural and
   Evolutionary Computing, Quantitative Biology, Quantitative Methods
%U http://arxiv.org/abs/1102.1691
%X We present a method to eliminate redundancy in the transition tables of Boolean automata: schema redescription with two symbols. One symbol is used to capture redundancy of
   individual input variables, and another to capture permutability in sets of input variables: fully characterising the canalisation present in Boolean functions. Two-symbol
   schemata explain aspects of the behaviour of automata networks that the characterization of their emergent patterns does not capture. We use our method to compare two
   well-known cellular automata for the density classification task: the human engineered CA GKL, and another obtained via genetic programming (GP). We show that despite
   having very different collective behaviour, these rules are very similar. Indeed, GKL is a special case of GP. Therefore, we demonstrate that it is more feasible to compare
   cellular automata via schema redescriptions of their rules, than by looking at their emergent behaviour, leading us to question the tendency in complexity research to pay
   much more attention to emergent patterns than to local interactions.
%8 April  13-15
%Z Not really on GP but does make use of GP result given by \citeandre:1996:camc http://coco.binghamton.edu/ieee-alife2011/

%A Andre C. Marta
%T Parametric Study of a Genetic Algorithm using a Aircraft Design Optimization Problem
%B Genetic Algorithms and Genetic Programming at Stanford 2003
%E John R. Koza
%D 2003
%P 133--142
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms
%U http://www.genetic-programming.org/sp2003/Marta.pdf
%8 4  Decemeber
%Z part of \citekoza:2003:gagp

%A E. Martens
%A G. Gielen
%T ANTIGONE: Top-down creation of analog-to-digital converter architectures
%J Integration, the VLSI Journal
%V 42
%N 1
%D 2009
%P 10--23
%I 
%K genetic algorithms, genetic programming, EHW, Analog systems, Analog-to-digital converters, Synthesis, Evolutionary algorithms
%U http://www.sciencedirect.com/science/article/B6V1M-4T0WJNT-1/2/78ea82c92c1ba53cc4f227f0d3e3067e
%X A new framework for high-level synthesis of analog and mixed-signal integrated systems is introduced. It focuses on the translation of a functional description into a
   behavioural model of a specific architecture with values for the parameters of its building blocks. An initial, simple, high-level solution is evolved into a more realistic
   low-level result by applying appropriate transformations of both architecture and parameters. This top-down heterogeneous optimisation algorithm deals readily with
   multifarious performance characteristics and diverse types of objectives, and integrates various sources of design knowledge and types of transformations. Furthermore, it
   creates the architecture rather than selecting it. As illustration of the methodology, a tool, ANTIGONE, has been written that allows to generate different types of A/D
   converters depending on the specifications like speed and accuracy.
%O AMF/RF CMOS Circuit design for wireless transceivers

%A Scott Martens
%T Automatic Creation of XML Document Conversion Scripts by Genetic Programming
%B Genetic Algorithms and Genetic Programming at Stanford 2000
%E John R. Koza
%D 2000
%P 269--278
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 June
%Z part of \citekoza:2000:gagp

%A Lionel Martin
%A Frederic Moal
%A Christel Vrain
%T A Relational Data Mining Tool Based on Genetic Programming
%B Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD-98)
%S Lecture Notes in Artificial Intelligence
%E Jan M. \.Zytkow and Mohamed Quafafou
%V 1510
%D 1998
%P 130--138
%I Springer-Verlag Berlin
%C Nantes, France
%K genetic algorithms, genetic programming, data mining
%8 23--26  September
%Z p134 Nice discussion of keeping data mining tree queries valid under subtree crossover

%A Lionel Martin
%A Frederic Moal
%A Christel Vrain
%T Declarative expression of biases in Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference
%E Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith
%V 1
%D 1999
%P 401--408
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C Orlando, Florida, USA
%K genetic algorithms, genetic programming, classifier systems, context free grammars
%X context free grammars, data mining application, SQL
%8 13-17  July
%Z GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)
%@ 1-55860-611-4

%A Martin C. Martin
%T Breaking Out of the Black Box: A New Approach to Robot Perception
%D 1998
%I 
%I Robotics Institute, Carnegie Mellon University
%C Pittsburgh, PA, USA
%K genetic algorithms, genetic programming
%U http://www.ri.cmu.edu/pub_files/pub2/martin_martin_c_1998_1/martin_martin_c_1998_1.ps.gz
%X Surprisingly, the state of the art in avoiding obstacles using only vision--not sonar or laser rangefinders--is roughly half an hour between collisions (at 30 cm/s, in an
   office environment). After review ing the design and failure modes of several current systems, I compare psychology's understanding of perception to current computer/robot
   perception. There are fundamental differences--which lead to fundamental limitations with current computer perception. The key difference is that robot software is built
   out of "black boxes", which have very restricted interactions with each other. In contrast, the human perceptual system is much more integrated. The claim is that a robot
   that performs any significant task, and does it as well as a person, can not be created out of "black boxes." In fact, it would probably be too interconnected to be
   designed by hand--instead, tools will be needed to create such designs. To illustrate this idea, I propose to create a visual obstacle avoidence system on the Uranus mobile
   robot. The system uses a number of visual depth cues at each pixel, as well as depth cues from neighbouring pixels and previous depth estimates. Genetic Programming is used
   to combine these into a new depth estimate. The system learns by predicting both sonar readings and the next image. The design of the system is described, and design
   decisions are rationalized.
%O Thesis proposal
%8 January
%Z see also http://citeseer.ist.psu.edu/302181.html

%A Martin C. Martin
%T Visual Obstacle Avoidance Using Genetic Programming: First Results
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 1107--1113
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming, evolutionary robotics, Obstacle Avoidance, Computer Vision
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d17.pdf
%X Genetic Programming is used to create a reactive obstacle avoidance system for an autonomous mobile robot. The evolved programs take a black and white camera image as input
   and estimate the location of the lowest nonground pixel in a given column. Traditional computer vision operators such as Sobel gradient magnitude, median filters and the
   Moravec interest operator are combined arbitrarily. Five memory locations can also be read or written to. The first evolved program is now controlling the robot.
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Martin C. Martin
%T The simulated evolution of robot perception
%R Ph.D. Thesis
%D 2001
%I 
%I Robotics Institute, Carnegie Mellon University
%C Pittsburgh, PA, USA
%K genetic algorithms, genetic programming
%U http://www.ri.cmu.edu/pub_files/pub3/martin_martin_c_2001_1/martin_martin_c_2001_1.pdf
%X This dissertation tackles the problem of using genetic programming to create the vision subsystem of a reactive obstacle avoidance algorithm for a mobile robot. To focus
   the search on computationally efficient algorithms while dealing images from a non-toy problem, the representation restricts computation to be over a window which moves
   vertically over the image. The evolved programs estimated the distance to the nearest object in various directions, given only a camera image as input. Using a typical
   supervised learning framework, images of the environment were collected from the robot's camera and the correct distance in various directions determined by hand. Evolving
   programs were evaluated on this fixed training set and compared to the hand determined answers. Once the evolution was complete, obstacle avoidance programs were written to
   use the best evolved programs, and the combined system used to control a robot. The approach can be seen as automating the iterative design process. A researcher's main
   contribution is typically at a high level -- techniques and frameworks -- yet most time is spent on an example problem, trying different instantiations until one works.
   When faced with such a problem, one can usually think of a half dozen very different approaches, and even write them out in pseudo code. The technique proposed here can be
   seen as searching the space spanned by that pseudo code. In a series of experiments, programs were evolved in three different ways for two different environments to both
   create working systems and push the limits of the approach. Even in this nascent form, the evolved programs work about as well as existing, hand written systems. They used
   a number of architectures, including a recurrent mathematical formula and a series of if statements similar to a decision tree but with non-linear relations between as many
   as five image statistics. They successfully coded around peculiarities of the imaging process and exploited regularities of the environment. Finally, when given a
   representation so general as to cause the genetic algorithm to fail, and hand constructed rough answer was used as a 'seed,' which the genetic algorithm successively
   modified to cut its error rate by a factor of 5.8. This dissertation grew out of my conviction that critiques of Artificial Intelligence can be viewed constructively, as
   intellectual lighthouses to guide us closer to the fundamental nature of thought, to the real problems at the heart of intelligence. To not address them, to work on
   techniques with fundamental flaws, would be fooling oneself no matter how impressive the demonstrations. There seems to be something fundamental about AI that we are all
   missing, and I believe these critiques bring us closer to it. This dissertation describes the experiments and their results, discusses ways to develop them further, then
   presents critiques of AI and discusses the potential of this approach to overcome those critiques.
%8 Decemeber
%Z http://www.ri.cmu.edu/pubs/pub_3875.html#text_ref

%A Martin C. Martin
%T Genetic Programming for Robot Vision
%B The Seventh International Conference on the Simulation of Adaptive Behavior (SAB'02)
%E Bridget Hallam and Dario Floreano
%D 2002
%I 
%C Edinburgh, UK
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/547772.html
%X Genetic Programming was used to create the vision subsystem of a reactive obstacle avoidance system for an autonomous mobile robot. The representation of algorithms was
   specifically chosen to capture the spirit of existing, hand written vision algorithms. Traditional computer vision operators such as Sobel gradient magnitude, median
   filters and the Moravec interest operator were combined arbitrarily. Images from an office hallway were used as training data. The evolved programs took a black and white
   camera image as input and estimated the location of the lowest non-ground pixel in a given column. The computed estimates were then given to a handwritten obstacle
   avoidance algorithm and used to control the robot in real time. Evolved programs successfully navigated in unstructured hallways, performing on par with hand-crafted
   systems.
%O The Pennsylvania State University CiteSeer Archives
%8 9-11  August
%Z http://www.isab.org.uk/sab02/program/

%A Martin C. Martin
%T Genetic programming for real world robot vision
%B IEEE/RSJ International Conference on Intelligent Robots and System
%V 1
%D 2002
%P 67--72
%I IEEE
%C EPFL, Lausanne, Switzerland
%K genetic algorithms, genetic programming, collision avoidance, computerised navigation, genetic algorithms, graph grammars, mobile robots, robot vision, autonomous mobile
   robot, median filter, navigation, obstacle avoidance algorithm, parse trees, real world robot vision, vision algorithms
%U http://citeseer.ist.psu.edu/544306.html
%X The vision subsystem of an autonomous mobile robot was created using a form of evolutionary computation known as genetic programming. In this form, individuals are
   algorithms represented as parse trees. The primitives of the representation were specifically chosen to capture the spirit of existing vision algorithms. Thus, the
   evolutionary computation can be viewed as searching roughly the same space that researchers search when developing their system using trial and error. Traditional image
   operators such as the Sobel magnitude and a median filter were combined in arbitrary ways, and images from an unmodified office environment were used as training data. A
   hand written obstacle avoidance algorithm used the output of the best vision algorithm to avoid obstacles in real time. It performed as well as the existing hand written
   combined navigation and vision systems.
%8 30  September -5  October
%Z IROS Artificial Intelligence Lab., MIT, Cambridge, MA, USA

%A Martin C. Martin
%T Evolving visual sonar: Depth from monocular images
%J Pattern Recognition Letters
%V 27
%N 11
%D 2006
%P 1174--1180
%I 
%K genetic algorithms, genetic programming, Robotics, Visual navigation, Monocular vision
%U http://martincmartin.com/papers/EvolvingVisualSonarPatternRecognitionLetters2006.pdf
%X To recover depth from images, the human visual system uses many monocular depth cues, which vision research has only begun to explore. Because a given image can have many
   possible interpretations, constraints are needed to eliminate ambiguity, and the most powerful constraints are domain specific. As an experiment in the automatic discovery
   and exploitation of constraints, genetic programming was used to find algorithms for obstacle detection. The algorithms are designed to be a replacement for sonar,
   returning the location of the nearest obstacle in a given direction. The evolved algorithms worked surprisingly well. Errors were largely transient. The algorithms
   generalised to both novel views of the office environment and to unseen obstacles. They were combined with a simple reactive wandering program originally written for sonar.
   The result exhibited good performance in an office environment, colliding only with obstacles outside the robot's field of view. Time to collision results and failure modes
   are presented. Code is available for download.
%O Evolutionary Computer Vision and Image Understanding
%8 August

%A Peter Martin
%T An Investigation into the use of Genetic Programming for Intelligent Network Service Creation
%R M.S. Thesis
%D 1998
%I 
%I Bournemouth University
%K genetic algorithms, genetic programming
%U http://www.naiadhome.com/dissertation_ps.zip
%X Service creation is crucial to the success of Intelligent Networks (IN). However, the time required to develop complex services is increasing. By reducing the elapsed time
   needed to generate the service logic and by reducing the opportunity for implementation errors to appear in the service logic, a higher quality IN service can be delivered.
   This project explores an alternative method to the existing manual service creation, by exploiting the properties of Genetic Programming (GP). Genetic Programming is a
   powerful method for evolving computer programs via the process of natural selection. [Koz92]. The use of Genetic Programming to produce service logic programs for IN is
   analysed and a number of key features identified. Principally for GP to be of benefit to IN it must be able to reduce the time to create a service and reduce the number of
   implementation errors in the resultant program. Experimental evidence is presented that shows that using Genetic Programming is a viable method for service creation in
   Intelligent Networks, and can reduce the time to create a program by several orders of magnitude compared to a human. The case is also argued that since GP needs a fitness
   function to be developed, the initial specification should be of a higher quality than one produced for a human programmer, thereby reducing the number of errors in the
   final program. To implement the experimental prototype, existing methods of evolving complex systems using GP were researched. A new method of ensuring the property of
   closure is presented that does not constrain the development of novel service logic implementations, in contrast to existing methods commonly employed in GP.

%A Peter Martin
%T Genetic Programming for Service Creation in Intelligent Networks
%B Genetic Programming, Proceedings of EuroGP'2000
%S LNCS
%E Riccardo Poli and Wolfgang Banzhaf and William B. Langdon and Julian F. Miller and Peter Nordin and Terence C. Fogarty
%V 1802
%D 2000
%P 106--120
%I Springer-Verlag Berlin
%I EvoNet
%C Edinburgh
%K genetic algorithms, genetic programming, polymorphic data, SBSE
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=106
%X Intelligent Networks are used by telephony systems to offer services to customers. The creation of these services has traditionally been performed by hand, and has required
   substantial effort, despite the advanced tools employed. An alternative to manual service creation using Genetic Programming is proposed that addresses some of the
   limitations of the manual process of service creation. The main benefit of using GP is that by focussing on what a service is required to do, as opposed to its
   implementation, it is more likely that the generated programs will be available on time and to budget, when compared to traditional software engineering techniques. The
   problem of closure is tackled by presenting a new technique for ensuring correct program syntax that maintains genetic diversity.
%8 15-16  April
%Z EuroGP'2000, part of \citepoli:2000:GP
%@ 3-540-67339-3

%A Peter Martin
%T Building a Taxonomy of Genetic Programming
%B Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)
%E Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon
   and Edmund Burke
%D 2001
%P 182
%I Morgan Kaufmann San Francisco, CA 94104, USA
%C San Francisco, California, USA
%K genetic algorithms, genetic programming: Poster, Taxonomy
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf
%8 7-11  July
%Z GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of
   \citespector:2001:GECCO
%@ 1-55860-774-9

%A Peter Martin
%T A Hardware Implementation of a Genetic Programming System Using FPGAs and Handel-C
%J Genetic Programming and Evolvable Machines
%V 2
%N 4
%D 2001
%P 317--343
%I 
%K genetic algorithms, genetic programming, evolvable hardware, FPGA, Handel-C, parallel genetic algorithm
%U http://citeseer.ist.psu.edu/568511.html
%X This paper presents an implementation of Genetic Programming using a Field Programmable Gate Array. This novel implementation uses a high level language to hardware
   compilation system, called Handel-C, to produce a Field Programmable Logic Array capable of performing all the functions required of a Genetic Programming System. Two
   simple test problems demonstrate that GP running on a Field Programmable Gate Array can outperform a software version of the same algorithm by exploiting the intrinsic
   parallelism available using hardware, and the geometric parallelisation of Genetic Programming.
%8 Decemeber
%Z Xilinx BG560 FPGA XCV2000e, Celoxica RC1000 FPGA board. See also \citemartin:2002:EuroGP Article ID: 386361

%A Peter Martin
%A Riccardo Poli
%T Analysis of the Behavior of a Hardware Implementation of GP using FPGAs and Handel-C
%R Technical Report CSM-357
%D 2002
%I 
%I Department of Computer Science, University of Essex
%C Wivenhoe Park, Colchester, CO4 3SQ UK.
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/566603.html
%X This paper analyses the behavior of a hardware implementation of Genetic Programming using Field Programmable Gate Arrays. Three crossover operators that limit the lengths
   of programs are analyzed. A truncating operator, a limiting operator that constrains the lengths of both offspring and a limiting operator that only constrains the length
   of one offspring. The latter has some interesting properties that suggest a new method of limiting code growth in the presence of fitness.
%O The Pennsylvania State University CiteSeer Archives
%8 24th  January

%A Peter Martin
%T An Analysis of Random Number Generators for a Hardware Implementation of Genetic Programming using FPGAs and Handel-C
%R Technical Report CSM-358
%D 2002
%I 
%I Department of Computer Science, University of Essex
%C Wivenhoe Park, Colchester, CO4 3SQ UK.
%K genetic algorithms, genetic programming
%U http://citeseer.ist.psu.edu/569263.html
%X This paper analyses the effect of using different random number generators (RNG) in a hardware implementation of Genetic Programming using Field Programmable Gate Arrays.
   Hardware systems have typically used RNGs based on Logical Feedback Shift Registers or Cellular Automata. Different configurations of these generators are evaluated as well
   as using a source of true random numbers and a standard multiply/add generator. We show that using a more sophisticated generator than a simple LFSR slightly improves the
   performance of the hardware GP system.
%O The Pennsylvania State University CiteSeer Archives
%8 24th  January

%A Peter Martin
%T A Pipelined Hardware Implementation of Genetic Programming using FPGAs and Handel-C
%B Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002
%S LNCS
%E James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi
%V 2278
%D 2002
%P 1--12
%I Springer-Verlag Berlin
%C Kinsale, Ireland
%K genetic algorithms, genetic programming
%X A complete Genetic Programming (GP) system implemented in a single FPGA is described in this paper. The GP system is capable of solving problems that require large
   populations and by using parallel fitness evaluations can solve problems in a much shorter time that a conventional GP system in software. A high level language to hardware
   compilation system called Handel-C is used for implementation.
%8 3-5  April
%Z EuroGP'2002, part of \citelutton:2002:GP
%@ 3-540-43378-3

%A Peter Martin
%A Riccardo Poli
%T Crossover Operators For A Hardware Implementation Of GP Using FPGAs And Handel-C
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 845--852
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Peter Martin
%T An Analysis Of Random Number Generators For A Hardware Implementation Of Genetic Programming Using FPGAs And Handel-C
%B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference
%E W. B. Langdon and E. Cant\'u-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A.
   Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska
%D 2002
%P 837--844
%I Morgan Kaufmann Publishers San Francisco, CA 94104, USA
%C New York
%K genetic algorithms, genetic programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%8 9-13  July
%Z GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
%@ 1-55860-878-8

%A Peter N. Martin
%T Genetic Programming in Hardware
%R Ph.D. Thesis
%D 2003
%I 
%I University of Essex
%C University of Essex, Wivenhoe Park, Colchester, UK
%K genetic algorithms, genetic programming
%U http://www.naiadhome.com/HardwareGeneticProgramming.pdf
%X Genetic Programming in Hardware This thesis describes a hardware implementation of a complete Genetic Programming (GP) system using a Field Programmable Gate Array, which
   is shown to speed-up GP by over 400 times when compared with a software implementation of the same algorithm. The hardware implements the creation of the initial
   population, breeding operators, parallel fitness evaluations and the output of the final result. The research was motivated by the observation that GP is usually
   implemented in software and run on general purpose computers. Although software implementations are flexible and easy to modify, they limit the performance of GP thus
   restricting the range of problems that GP can solve. The hypothesis is that implementing GP in hardware would speed up GP, allowing it to tackle problems which are
   currently too hard for software based GP. FPGAs are usually programmed using specialised hardware design languages. An alternative approach is used in this work that uses a
   high level language to hardware compilation system, called Handel-C. As part of this research, a number of general GP issues are also explored. The parameters of GP are
   described and arranged into a taxonomy of GP attributes. The taxonomy allows GP problems to be categorised with respect to their problem and GP specific attributes. The
   role that the GP algorithm plays in problem solving is shown to be part of a larger process called Meta-GP, which describes the overall process of developing a GP system
   and evolving a viable set of parameters to allow GP to solve a problem. Three crossover operators are investigated and a new operator, called single child limiting
   crossover, is presented. This operator appears to limit the tendency of GP to suffer from bloat. The economics of implementing GP in hardware are analysed and the costs and
   benefits are quantified. The thesis concludes by suggesting some applications for hardware GP.
%8 March

%A Javier {Martinez Canillas}
%A Roberto Sanchez
%A Benjamin Baran
%T Estimation Models Generation using Linear Genetic Programming
%J CLEI Electronic Journal
%V 13
%N 3
%D 2009
%P paper 4
%I 
%K genetic algorithms, genetic programming, economic indicators, time series, forecasting
%U http://www.clei.cl/cleiej/paper.php?id=172
%X he use of decision rules and estimation techniques is increasingly common for decision making. In recent years studies were conducted which applies Genetic Programming (GP)
   to obtain rules to make predictions. A new branch in the area of Evolutionary Algorithms (EA) is Linear Genetic Programming (LGP). LGP evolves instructions sequences of an
   imperative programming language. This paper proposes estimation models generation for time series forecasting using LGP. The forecasting result for the Consumer Price Index
   (CPI) and the price of soybeans per ton shows the potential of this new proposal. Spanish Abstract: El uso de reglas de decision y tecnicas de estimacion es cada vez mas
   comĂșn para la toma de decisiones. Recientemente se han hecho estudios usando programacion genetica para obtener reglas que hagan predicciones. Una area novedosa dentro de
   los algoritmos evolutivos es la programacion genetica lineal (LGP). LGP evoluciona secuencias de instrucciones de un lenguaje imperativo. Este trabajo propone generar
   modelos de estimacion para la prediccion de series de tiempo usando LGP. El resultado de la prediccion para el indice de precios al consumidor y el precio de la soja por
   tonelada muestra el potencial de esta propuesta.
%O Regular Issue and Special Issue of Best Papers presented at CLEI 2008, Santa Fe, Argentina
%8 Decemeber
%Z CLEI (Latin-american Center for Informatics Studies) http://www.clei.cl/cleiej/index.html

%A Serafin Martinez-Jaramillo
%T Artificial Financial Markets: An Agent Based Approach to Reproduce Stylized Facts and to study the Red Queen Effect
%R Ph.D. Thesis
%D 2007
%I 
%I Centre for Computational Finance and Economic Agents, University of Essex
%C UK
%K genetic algorithms, genetic programming
%U http://cswww.essex.ac.uk/Research/CSP/finance/papers/Martinez-PhD2007.pdf
%X Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world's population. Investors, governing bodies
   and the society as a whole could benefit from better understanding of the behaviour of stock markets. The traditional approach to analyze such systems is the use of
   analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions,
   such as perfect rationality and homogeneous investors, which threaten the validity of analytical results. This motivates the use of alternative methods. For those reasons,
   the study of such markets is a fertile field to use the agent-based methodology. In this work, we developed an artificial financial market and used it to study the
   behaviour of stock markets. In this market, we model technical, fundamental and noise traders. The technical traders are non-simple genetic programming based agents that
   co-evolve (by means of their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. Such traders are equipped
   with an investment strategy that we consider to be realistic and we avoid any kind of strong assumptions about the agents' rationality, utility function or risk aversion.!
   Changes in some parameters and in the agents behaviour produce different properties of the stock price series that we analyze. In this paper we investigate the different
   conditions under which the statistical properties of an artificial stock market resemble those of the real financial markets. Additionally, we modeled the pressure to beat
   the market by a behavioural constraint imposed on the agents related to the Red Queen principle in evolution. The Red Queen principle is a metaphor of a co-evolutionary
   arms race between species. We investigate the effect of such constraint on the price dynamics and the wealth distribution of the agents after several periods of trading in
   the different simulation cases. We have demonstrated how evolutionary computation plays a key role in studying stock markets.
%8 June

%A Serafin Martinez-Jaramillo
%A Edward P. K. Tsang
%T An Heterogeneous, Endogenous and Coevolutionary GP-Based Financial Market
%J IEEE Transactions on Evolutionary Computation
%V 13
%N 1
%D 2009
%P 33--55
%I 
%K genetic algorithms, genetic programming, economics, multi-agent systems, pricing, series (mathematics), statistical analysis, stock markets, Red Queen principle,
   agent-based simulation, analytical models, behavioral constraint, coevolutionary GP-based financial market, economic learning, endogenous artificial market, evolutionary
   computation, fitness function, genetic programming based agents, homogeneous investors, investment opportunity, noise traders, perfect rationality, price generation, price
   series, real financial markets, statistical property, stock markets, technical traders
%X Stock markets are very important in modern societies and their behavior has serious implications for a wide spectrum of the world's population. Investors, governing bodies,
   and society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach to analyzing such systems is the use of analytical
   models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as
   perfect rationality and homogeneous investors, which threaten the validity of their results. This motivates alternative methods. In this paper, we report an artificial
   financial market and its use in studying the behavior of stock markets. This is an endogenous market, with which we model technical, fundamental, and noise traders.
   Nevertheless, our primary focus is on the technical traders, which are sophisticated genetic programming based agents that co- evolve (by learning based on their fitness
   function) by predicting investment opportunities in the market using technical analysis as the main tool. With this endogenous artificial market, we identify the conditions
   under which the statistical properties of price series in the artificial market resemble some of the properties of real financial markets. By performing a careful
   exploration of the most important aspects of our simulation model, we determine the way in which the factors of such a model affect the endogenously generated price.
   Additionally, we model the pressure to beat the market by a behavioral constraint imposed on the agents reflecting the Red Queen principle in evolution. We have
   demonstrated how evolutionary computation could play a key role in studying stock markets, mainly as a suitable model for economic learning on an agent- based simulation.
%8 February
%Z also known as \cite4769014

%A Tshilidzi Marwala
%T Bayesian Training of Neural Networks Using Genetic Programming
%B Proceedings of the 2006 IEEE Congress on Evolutionary Computation
%E Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas
%D 2006
%P 7013--7017
%I IEEE Press
%C Vancouver
%K genetic algorithms, genetic programming
%X Bayesian neural networks trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed. It is tested and
   compared to classical MCMC method and is observed to give better results than classical approach.
%8 6-21  July
%Z WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE. IEEE Catalog Number: 06TH8846D
%@ 0-7803-9487-9

%A Tshilidzi Marwala
%T Bayesian Training of Neural Networks Using Genetic Programming
%J Pattern Recognition Letters
%V 28
%N 12
%D 2007
%P 1452--1458
%I 
%K genetic algorithms, genetic programming, Bayesian framework, Evolutionary programming, Neural networks
%U http://www.sciencedirect.com/science/article/B6V15-4NC38M7-5/2/dee1daa1b7f713474289040a57125fd4
%X Bayesian neural network trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed. The algorithm
   proposed here has the ability to learn using samples obtained from previous steps merged using concepts of natural evolution which include mutation, crossover and
   reproduction. The reproduction function is the Metropolis framework and binary mutation as well as simple crossover, are also used. The proposed algorithm is tested on
   simulated function, an artificial taster using measured data as well as condition monitoring of structures and the results are compared to those of a classical MCMC method.
   Results confirm that Bayesian neural networks trained using genetic programming offers better performance and efficiency than the classical approach.
%Z Also known as \citeMarwala20071452

%A Brij Masand
%T Optimising Confidence of Text Classification by Evolution of Symbolic Expressions
%B Advances in Genetic Programming
%E Kenneth E. Kinnear, Jr.
%D 1994
%P 445--458
%I MIT Press
%I Thinking Machines Corporation
%K genetic algorithms, genetic programming
%U http://cognet.mit.edu/library/books/view?isbn=0262111888
%O 21
%Z Classification of New Stories, Very simple formulae evolved which do better than existing human attempts at automatic coding. Automatic results comparable to human success
   rates

%A Brij Masand
%A Gregory Piatesky-Shapiro
%T Discovering Time Oriented Abstractions in Historical Data to Optimize Decision Tree Classification
%B Advances in Genetic Programming 2
%E Peter J. Angeline and K. E. Kinnear, Jr.
%D 1996
%P 489--498
%I MIT Press
%C Cambridge, MA, USA
%K genetic algorithms, genetic programming
%U http://cisnet.mit.edu/Advances-in-Genetic-Programming/506
%O 24
%@ 0-262-01158-1

%A Igor V. Maslov
%A Izidor Gertner
%T Multi-sensor fusion: an Evolutionary algorithm approach
%J Information Fusion
%V 7
%N 3
%D 2006
%P 304--330
%I 
%K genetic algorithms, genetic programming, Information fusion, Global optimization, Heuristic methods, Evolutionary algorithms, Evolution strategies, Evolutionary programming
%U http://www.sciencedirect.com/science/article/B6W76-4FBM1CY-2/2/e57f81dddd02342a16c54961518cedde
%X Modern decision-making processes rely on data coming from different sources. Intelligent integration and fusion of information from distributed multi-source, multi-sensor
   network requires an optimisation-centred approach. Traditional optimization techniques often fail to meet the demands and challenges of highly dynamic and volatile
   information flow. New methods are required, which are capable of fully automated adjustment and self-adaptation to fluctuating inputs and tasks. One such method is
   Evolutionary algorithms (EA), a generic, flexible, and versatile framework for solving complex problems of global optimisation and search in real world applications. The
   evolutionary approach provides a valuable alternative to traditional methods used in information fusion, due to its inherent parallel nature and its ability to deal with
   difficult problems. However, the application of the algorithm to a particular problem is often more an art than science. Choosing the right model and parameters requires an
   in-depth understanding of the morphological development of the algorithm, as well as its recent advances and trends. This paper attempts to give a compact overview of both
   basic and advanced concepts, models, and variants of Evolutionary algorithms in various implementations and applications particularly those in information fusion. We have
   brought together material scattered throughout numerous books, journal papers, and conference proceedings. Strong emphasis is made on the practical aspects of the EA
   implementation, including specific and detailed recommendations drawn from these various sources. However, the practical aspects are discussed from the standpoint of
   concepts and models, rather than from applications in specific problem domains, which emphasise the generality of the provided recommendations across different applications
   including information fusion.
%8 September

%A J. Todd Masonis
%T Valve Paradigm ``C'' Code Evolution
%B Genetic Algorithms and Genetic Programming at Stanford 1999
%E John R. Koza
%D 1999
%P 140--146
%I Stanford Bookstore
%C Stanford, California, 94305-3079 USA
%K genetic algorithms, genetic programming
%8 15  March
%Z part of \citekoza:1999:GAGPs

%A Paul Massey
%A John A. Clark
%A Susan Stepney
%T Evolving Quantum Circuits and Programs Through Genetic Programming
%B Genetic and Evolutionary Computation -- GECCO-2004, Part II
%S Lecture Notes in Computer Science
%E Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and
   Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell
%V 3103
%D 2004
%P 569--580
%I Springer-Verlag Heidelberg
%I ISGEC
%C Seattle, WA, USA
%K genetic algorithms, genetic programming, quantum computing
%U http://link.springer.de/link/service/series/0558/bibs/3103/31030569.htm
%X Spector et al. have shown [1],[2],[3] that genetic programming can be used to evolve quantum circuits. In this paper, we present new results in this field, introducing
   probabilistic and deterministic quantum circuits that have not been previously published. We compare our techniques with those of Spector et al, and point out some
   differences in perspective between our two approaches. Finally, we show how, by using sets of functions rather than precise quantum states as fitness cases, our basic
   technique can be extended to evolve true quantum algorithms.
%8 26-30  June
%Z GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004) See
   \citemassey:2006:EC
%@ 3-540-22343-6

%A Paul Massey
%A John A. Clark
%A Susan Stepney
%T Evolution of a human-competitive quantum fourier transform algorithm using genetic programming
%B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
%E Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and
   Kalyanmoy Deb and James A. Foster and Edwin D. de Jong and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and
   Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler
%V 2
%D 2005
%P 1657--1663
%I ACM Press New York, NY, 10286-1405, USA
%I ACM SIGEVO (formerly ISGEC)
%C Washington DC, USA
%K genetic algorithms, genetic programming, evolutionary computing, experimentation, quantum computing, quantum Fourier transform
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1657.pdf
%X In this paper, we show how genetic programming (GP) can be used to evolve system-size-independent quantum algorithms, and present a human-competitive Quantum Fourier
   Transform (QFT) algorithm evolved by GP.
%8 25-29  June
%Z GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM
   Order Number 910052
%@ 1-59593-010-8

%A Paul Massey
%A John A. Clark
%A Susan Stepney
%T Human-Competitive Evolution of Quantum Computing Artefacts by Genetic Programming
%J Evolutionary Computation
%V 14
%N 1
%D 2006
%P 21--40
%I 
%K genetic algorithms, genetic programming, quantum computing
%U http://www.mitpressjournals.org/doi/abs/10.1162/evco.2006.14.1.21
%X We show how Genetic Programming (GP) can be used to evolve useful quantum computing artefacts of increasing sophistication and usefulness: firstly specific quantum
   circuits, then quantum programs, and finally system-independent quantum algorithms. We conclude the paper by presenting a human-competitive Quantum Fourier Transform (QFT)
   algorithm evolved by GP.
%O Best of GECCO 2004 special issue
%8 Spring
%Z \citemassey:eqc:gecco2004

%A Paul S. Massey
%T Searching for Quantum Software
%R Ph.D. Thesis
%D 2006
%I 
%I Department Of Computer Science, University of York
%C UK
%K genetic algorithms, genetic programming
%U http://www.cs.york.ac.uk/ftpdir/reports/2007/YCST/11/YCST-2007-11.pdf
%X Quantum computing has the potential to bring a new class of previously intractable problems within the reach of computer science. Harnessing the quantum mechanical
   phenomena of superposition and entanglement, a quantum computer can manipulate vast amounts of information in a single computational step and perform certain operations
   exponentially faster than classical (i.e. non-quantum) computers. However, devising algorithms to harness the power of a quantum computer has proved extraordinarily
   difficult. Over twenty years after the publication of the first quantum algorithm in 1985, despite the efforts of a sizeable community of top-class researchers, only a
   handful of distinct algorithms have been discovered. This thesis makes the case that evolutionary search techniques can be used to discover quantum circuits, quantum
   programs and ultimately new quantum algorithms. It presents a number of original results, including an algorithm discovered by evolutionary search techniques which
   implements the Quantum Fourier Transform on n qubits, and an algorithm discovered by evolutionary search techniques which returns the maximum value for arbitrary
   permutation functions.
%8 August

%A Toshiyuki Masui
%T Evolutionary Learning of Graph Layout Constraints from Examples
%B Proceedings of the ACM Symposium on User Interface Software and Technology
%S Demonstrational User Interfaces
%D 1994
%P 103--108
%I ACM
%K genetic algorithms, genetic programming, Graphic object layout, Graph layout, Programming by example, Adaptive user interface
%U http://www.acm.org/pubs/articles/proceedings/uist/192426/p103-masui/p103-masui.pdf
%X We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated
   annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is. However, the
   evaluation function is usually not known beforehand, and it might vary from user to user. In our system, users show the system several pairs of good and bad layout
   examples, and the system infers the evaluation function from the examples using genetic programming technique. After the evaluation function evolves to reflect the
   preferences of the user, it is used as a general evaluation function for laying out graphs. The same technique can be used for a wide range of adaptive user interface
   systems.
%Z MRnumber = C.UIST.94.103

%A Maja Mataric
%A Dave Cliff
%T Challenges in Evolving Controllers for Physical Robots
%R Technical Report CS-95-184
%D 1995
%I 
%I Computer Science Department, Brandeis University
%K genetic algorithms, genetic programming, robots
%U http://citeseer.ist.psu.edu/mataric96challenges.html
%X Feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. Overview state of the art, main approaches, key challenges,
   unanswered problems, promising directions
%Z GP and other approaches surveyed

%A Maja J. Mataric
%A Dave Cliff
%T Challenges in Evolving Controllers for Physical Robots
%J Journal of Robotics and Autonomous Systems
%V 19
%N 1
%D 1996
%P 67--83
%I 
%K genetic algorithms, genetic progra